Methods for determining respiratory infection risk

Abstract

The present invention relates to a method for determining susceptibility to respiratory infections. In another aspect, the present invention provides a method for determining whether an individual has increased susceptibility to respiratory infections, the method comprising contacting cord blood mononuclear cells (CBMCs) from the individual with a TLR4 agonist and measuring the expression level of a biomarker in the CBMCs, wherein differential expression of the biomarker in CBMCs contacted with the TLR4 agonist compared to CBMCs not contacted with the TLR4 agonist indicates that the individual has increased susceptibility to respiratory infections.

Description

[Technical field]

[0001] The present invention relates to methods for identifying individuals who are susceptible, susceptible, predisposed or at high risk for respiratory infections, such as severe lower respiratory tract infections, at an early stage in life, which provides an opportunity for intervention in the form of pre-emptive therapy.

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority from Australian Provisional Application No. 2021 / 903424, filed on 26 October 2021, the entire contents of which are incorporated herein by reference in their entirety. [Background technology]

[0003] Severe lower respiratory tract infections (sLRI) are a leading cause of emergency room visits in infants and children and a major risk factor for the development of asthma and wheezing. Studies from a series of prospective birth cohorts have found that the association between sLRI and asthma is strongest in children with rhinovirus (RV) wheezing and early airborne allergen sensitization. However, RV can be routinely detected in asthmatic children in the absence of prominent symptoms, suggesting that RV may be necessary but not sufficient to drive the pathogenesis of sLRI. In this regard, it has been demonstrated that the presence of bacterial pathogens, including Morexella, Streptococcus, and Haemophilis species, coinciding with and / or preceding viral detection, can significantly amplify subsequent airway symptoms and increase the risk of subsequent asthma development. Conversely, exposure to microbes and their products during early childhood has also been shown to be protective against asthma, perhaps most succinctly explained through the "farm effect."

[0004] The underlying immunological mechanisms that determine why some individuals are more susceptible to sLRIs and subsequent asthma in early childhood are not well understood.

[0005] There remains a need to identify individuals with increased susceptibility to sLRIs, particularly early in life, when the immune system is not yet fully developed.

[0006] The reference to any prior art in this specification is not an admission or suggestion that this prior art forms part of the general general knowledge in any jurisdiction, or that this prior art could reasonably be expected to be understood, considered relevant, and / or combined with other prior art by a person skilled in the art. Summary of the Invention

[0007] The present invention provides a systems biology approach to characterize innate immune responses in umbilical cord blood to a panel of stimuli (LPS, Poly(I:C), Imiquimod) across multiple biological regulatory layers (transcriptome and proteome) and identify innate immune response patterns associated with risk of sLRI early in life.

[0008] In another aspect, the present invention provides a method for determining whether an individual has an increased susceptibility to a respiratory infection, the method comprising: contacting B cells and T cells from umbilical cord blood from the individual with a TLR4 agonist; - measuring the expression levels of biomarkers KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11 and PMAIP1 in CBMCs; Differential expression of the biomarkers KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11 and PMAIP1 in CBMCs exposed to a TLR4 agonist compared to CBMCs not exposed to a TLR4 agonist indicates that an individual has increased susceptibility to respiratory infections.

[0009] In any aspect, the T cells can be CD4+ and / or CD8+ T cells. In one embodiment, the CD4+ T cells are central memory cells. In one embodiment, the CD8+ T cells are central memory cells.

[0010] In another aspect, the present invention provides a method for determining whether an individual has an increased susceptibility to a respiratory infection, the method comprising: contacting cord blood mononuclear cells (CBMCs) from an individual with a TLR4 agonist; - measuring the expression levels of biomarkers KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11 and PMAIP1 in CBMCs; Differential expression of the biomarkers KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11 and PMAIP1 in CBMCs exposed to a TLR4 agonist compared to CBMCs not exposed to a TLR4 agonist indicates that an individual has increased susceptibility to respiratory infections.

[0011] In another aspect, the present invention provides a method for determining whether an individual has an increased susceptibility to a respiratory infection, the method comprising: contacting B cells and T cells from umbilical cord blood from the individual with a TLR4 agonist; - Measuring the expression levels of biomarkers KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11 and PMAIP1 in CBMCs; - comparing the expression level of a biomarker from the individual with a reference dataset, the reference dataset comprising information on the expression level of the same biomarker in CMBC contacted with a TLR4 agonist from one or more individuals with or without increased susceptibility to respiratory infections; Expression levels of the biomarkers KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11 and PMAIP1 in an individual indicate that the individual has increased susceptibility to respiratory infections compared to a reference dataset.

[0012] In another aspect, the present invention provides a method for determining whether an individual has an increased susceptibility to a respiratory infection, the method comprising: contacting cord blood mononuclear cells (CBMCs) from an individual with a TLR4 agonist; - Measuring the expression levels of biomarkers KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11 and PMAIP1 in CBMCs; - comparing the expression level of a biomarker from the individual with a reference dataset, the reference dataset comprising information on the expression level of the same biomarker in CMBC contacted with a TLR4 agonist from one or more individuals with or without increased susceptibility to respiratory infections; Expression levels of the biomarkers KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11 and PMAIP1 in an individual indicate that the individual has increased susceptibility to respiratory infections compared to a reference dataset.

[0013] In another aspect, the present invention provides a method for determining whether an individual has an increased susceptibility to a respiratory infection, the method comprising: contacting B cells and T cells from umbilical cord blood from the individual with a TLR4 agonist; - measuring the expression levels of interferon module biomarkers, Differential expression of interferon module biomarkers in B cells and T cells contacted with a TLR4 agonist compared to B cells and T cells not contacted with a TLR4 agonist indicates that an individual has increased susceptibility to respiratory infections.

[0014] In another aspect, the present invention provides a method for determining whether an individual has an increased susceptibility to a respiratory infection, the method comprising: contacting cord blood mononuclear cells (CBMCs) from an individual with a TLR4 agonist; - measuring the expression levels of interferon module biomarkers, Differential expression of interferon module biomarkers in CBMCs contacted with a TLR4 agonist compared to CBMCs not contacted with a TLR4 agonist indicates that the individual has increased susceptibility to respiratory infections.

[0015] In another aspect, the present invention provides a method for determining whether an individual has an increased susceptibility to a respiratory infection, the method comprising: contacting B cells and T cells from umbilical cord blood from the individual with a TLR4 agonist; - measuring the expression levels of interferon module biomarkers in B cells and T cells; - comparing the expression levels of biomarkers from the individual to a reference dataset, the reference dataset comprising information on the expression levels of the same biomarkers in B cells and T cells contacted with a TLR4 agonist from one or more individuals with or without increased susceptibility to respiratory infections, The expression level of the interferon module biomarkers in the individual relative to the reference dataset indicates that the individual has an increased susceptibility to respiratory infections.

[0016] In another aspect, the present invention provides a method for determining whether an individual has an increased susceptibility to a respiratory infection, the method comprising: contacting cord blood mononuclear cells (CBMCs) from an individual with a TLR4 agonist; - Measuring the expression levels of interferon module biomarkers in CBMCs; - comparing the expression level of a biomarker from the individual with a reference dataset, the reference dataset comprising information on the expression level of the same biomarker in CMBC contacted with a TLR4 agonist from one or more individuals with or without increased susceptibility to respiratory infections; The expression level of the interferon module biomarkers in the individual relative to the reference dataset indicates that the individual has an increased susceptibility to respiratory infections.

[0017] In any embodiment, the interferon module biomarkers are those listed in Table 3.

[0018] In another aspect, the present invention provides a method for determining whether an individual has an increased susceptibility to a respiratory infection, the method comprising: contacting B cells and T cells from umbilical cord blood from the individual with a TLR4 agonist; - measuring the expression levels of IFN module biomarkers regulated by the IRF1 regulon, Differential expression of IFN module biomarkers regulated by the IRF1 regulon in B cells and T cells contacted with a TLR4 agonist compared to B cells and T cells not contacted with a TLR4 agonist indicates that an individual has increased susceptibility to respiratory infections.

[0019] In another aspect, the present invention provides a method for determining whether an individual has an increased susceptibility to a respiratory infection, the method comprising: contacting cord blood mononuclear cells (CBMCs) from an individual with a TLR4 agonist; - measuring the expression levels of IFN module biomarkers regulated by the IRF1 regulon, Differential expression of IFN module biomarkers regulated by the IRF1 regulon in CBMCs contacted with a TLR4 agonist compared to CBMCs not contacted with a TLR4 agonist indicates that an individual has increased susceptibility to respiratory infections.

[0020] In another aspect, the present invention provides a method for determining whether an individual has an increased susceptibility to a respiratory infection, the method comprising: contacting B cells and T cells from umbilical cord blood from the individual with a TLR4 agonist; - measuring the expression levels of IFN module biomarkers regulated by the IRF1 regulon in B cells and T cells; - comparing the expression levels of biomarkers from the individual to a reference dataset, the reference dataset comprising information on the expression levels of the same biomarkers in B cells and T cells contacted with a TLR4 agonist from one or more individuals with or without increased susceptibility to respiratory infections, The expression levels of IFN module biomarkers regulated by the IRF1 regulon in an individual relative to a reference dataset indicates that the individual has increased susceptibility to respiratory infections.

[0021] In another aspect, the present invention provides a method for determining whether an individual has an increased susceptibility to a respiratory infection, the method comprising: contacting cord blood mononuclear cells (CBMCs) from an individual with a TLR4 agonist; - Measuring the expression levels of IFN module biomarkers regulated by the IRF1 regulon in CBMCs; - comparing the expression level of a biomarker from the individual with a reference dataset, the reference dataset comprising information on the expression level of the same biomarker in CMBC contacted with a TLR4 agonist from one or more individuals with or without increased susceptibility to respiratory infections; The expression levels of IFN module biomarkers regulated by the IRF1 regulon in an individual relative to a reference dataset indicates that the individual has increased susceptibility to respiratory infections.

[0022] In any aspect, the expression level of a biomarker can be an absolute level or a differential level of expression.

[0023] In any embodiment, the IFN module biomarkers regulated by the IRF1 regulon are those listed in Table 4.

[0024] In any aspect, the method further comprises applying a machine learning algorithm, preferably a random forest analysis, to the differential expression or absolute levels of expression of the biomarkers, thereby indicating that the individual is at increased risk of susceptibility to a respiratory infection.

[0025] In any aspect, the individual is less than 1 year old or 2 years old or less. In any embodiment, the individual is 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days, 2 weeks, 1 month, 2 months, 3 months, 6 months, 1 year old, or 2 years old. In any embodiment, the individual is at least 1 day old, at least 2 days old, at least 3 days old, at least 4 days old, at least 5 days old, at least 6 days old, at least 7 days old, at least 2 weeks old, at least 1 month old, at least 2 months old, at least 3 months old, at least 6 months old, at least 1 year old, but not more than 2 years old. In any embodiment, the individual is about 1 day to about 7 days old, about 1 day to about 2 weeks old, about 1 week to about 6 months old, about 1 month to about 6 months old, about 1 month to about 3 months old, about 6 months to about 1 year old, about 1 month to 2 years old, about 6 months to 2 years old, or about 1 year to 2 years old.

[0026] In any aspect, the method determines increased susceptibility to respiratory infection when the individual is about 2 years old, about 3 years old, about 4 years old, or about 5 years old, or when the individual is 2, 3, 4, or 5 years old. In any embodiment, the method determines increased susceptibility to respiratory infection when the individual is about 2 to about 5 years old, about 2 to about 4 years old, about 2 to about 3 years old, about 3 to about 5 years old, or about 4 to about 5 years old.

[0027] In any embodiment, the method determines an increased susceptibility to respiratory infection when the individual is at least 5 years of age.

[0028] In any aspect, the respiratory infection is a lower respiratory tract infection. In any aspect, the respiratory infection is a bacterial or viral respiratory infection. In any aspect, the respiratory infection is a severe lower respiratory tract infection. In any embodiment, the viral respiratory infection includes rhinovirus and respiratory syncytial virus (RSV), and the bacterial respiratory infection includes Haemophilus Influenzae, Staphyloccocus aureus, and Moraxella spp.

[0029] In any aspect, the differential expression is greater than 1.5-fold increase or decrease. In any embodiment, biomarkers with differential decrease include IFI6, IFI27, RHEBL1, CCDC194, BATF2, CARD16, IFIT1, ISG20, IFITM3, VAMP5, TNFSF13B, SAMD9, RNF213-AS1, IFIT2, XRN1, CD38, LRRN2, and CCDC194. In any embodiment, biomarkers with differential increase include biomarkers described herein, including those listed in Tables 3 and 4, other than IFI6, IFI27, RHEBL1, CCDC194, BATF2, CARD16, IFIT1, ISG20, IFITM3, VAMP5, TNFSF13B, SAMD9, RNF213-AS1, IFIT2, XRN1, CD38, LRRN2, and CCDC194.

[0030] In another aspect, the invention provides a method for measuring the expression levels of biomarkers KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11 and PMAIP1 in CBMCs stimulated with a TLR4 agonist.

[0031] In another aspect, the invention provides a method for measuring the expression levels of interferon module biomarkers in B cells and T cells or CBMCs stimulated with a TLR4 agonist.

[0032] In another aspect, the invention provides a method for measuring the expression levels of IFN module biomarkers regulated by the IRF1 regulon in B and T cells or CBMCs stimulated with a TLR4 agonist.

[0033] In another aspect, the present invention provides a method for producing a composition comprising: contacting B cells and T cells from umbilical cord blood from the individual with a TLR4 agonist; - measuring expression levels of biomarkers KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11 and PMAIP1 in B cells and T cells.

[0034] In another aspect, the present invention provides a method for producing a composition comprising: contacting B cells and T cells from umbilical cord blood from the individual with a TLR4 agonist; - measuring expression levels of interferon module biomarkers in B cells and T cells.

[0035] In another aspect, the present invention provides a method for producing a composition comprising: contacting B cells and T cells from umbilical cord blood from the individual with a TLR4 agonist; - measuring expression levels of IFN module biomarkers regulated by the IRF1 regulon in B cells and T cells.

[0036] In another aspect, the present invention provides a method for producing a composition comprising: contacting cord blood mononuclear cells (CBMCs) from an individual with a TLR4 agonist; - measuring expression levels of biomarkers KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11 and PMAIP1 in CBMCs.

[0037] In another aspect, the present invention provides a method for producing a composition comprising: contacting cord blood mononuclear cells (CBMCs) from an individual with a TLR4 agonist; - measuring expression levels of interferon module biomarkers in CBMCs.

[0038] In another aspect, the present invention provides a method for producing a composition comprising: contacting cord blood mononuclear cells (CBMCs) from an individual with a TLR4 agonist; - measuring expression levels of IFN module biomarkers regulated by the IRF1 regulon in CBMCs.

[0039] In another aspect, the invention provides a method comprising measuring the expression levels of biomarkers KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11 and PMAIP1 in CBMCs contacted with a TLR4 agonist.

[0040] In another aspect, the invention provides a method comprising measuring the expression level of an interferon module biomarker in B cells and T cells, or CBMCs, that have been contacted with a TLR4 agonist.

[0041] In another aspect, the invention provides a method comprising measuring the expression levels of IFN module biomarkers regulated by the IRF1 regulon in B and T cells, or CBMCs, that have been contacted with a TLR4 agonist.

[0042] In any embodiment, the B cells and T cells, or CBMCs are or have been contacted with the TLR4 agonist for at least 4, 6, 12, 18 or 24 hours.

[0043] In any embodiment, the CBMCs are or have been contacted with a TLR4 agonist and then only the expression levels of the relevant biomarkers in B cells and T cells are measured or determined.

[0044] In any aspect or embodiment, the biomarker is a protein, a nucleic acid, such as an RNA, or an amplification product. When the biomarker is a nucleic acid or an amplification product, the method includes determining the level or amount of expression of a gene or an RNA. Preferably, the biomarker is one or more nucleic acids that include a nucleotide sequence from a gene or an RNA transcript of a gene.

[0045] In any aspect or embodiment described herein, when reference is made to RNA or an amplification product thereof, the invention also includes, in some cases, determining or measuring the presence, level or amount of the corresponding protein (translated from the RNA).

[0046] In any aspect of the invention, the level or amount of one or more biomarkers may be the level or amount of RNA. Preferably, the RNA is either pre-mRNA or mature mRNA, and changes to the level or amount of RNA may be determined using any of the methods described herein, including RNA sequencing.

[0047] In any embodiment, the method further comprises administering to the individual a treatment that reduces susceptibility to respiratory infections. Preferably, the treatment is palivizumab, prednisolone, omalizumab, or a polybacterial agent, or any combination thereof.

[0048] In any aspect, the B and T cells, or CBMCs, may be purified from cord blood. Alternatively, in any aspect, the B and T cells, or CBMCs, may be present in a sample of cord blood from an individual. In any embodiment, the B and T cells, or CBMCs, are present in cord blood, such that any reference herein to contacting B and T cells, or CBMCs, with a TLR4 agonist includes contacting cord blood containing B and T cells, or CBMCs (e.g., untreated, whole cord blood, or unpurified cord blood) with a TLR4 agonist. In one embodiment, the cord blood may be depleted of red blood cells. In one embodiment, when the B and T cells, or CBMCs, are contacted with a TLR4 agonist, red blood cells are not present or are not present at significant levels. In another embodiment, red blood cells are present at normal levels, i.e., they are not depleted from the cord blood.

[0049] In any aspect, the CBMCs comprise or consist of lymphocytes (T cells and B cells). Preferably, the CBMCs further comprise CD14+ monocytes and conventional dendritic cells (cDCs) and optionally plasmacytoid DCs (pDCs). In any embodiment, the CBMCs comprise or consist of CD4+ T cells, CD8+ T cells, and B cells.

[0050] In any aspect, the TLR4 agonist is any one of those described herein.Preferably, the TLR4 agonist is derived from bacteria.More preferably, the TLR4 agonist is LPS.In any embodiment, the LPS may be purified.Alternatively, the LPS may be contained in a bacterial preparation.

[0051] In another aspect, the invention provides a kit, panel or microarray comprising at least two diagnostic reagents as described herein, each reagent identifying a different biomarker as described herein. In one embodiment, the kit comprises diagnostic reagents that bind to or individually complex with 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or more biomarkers. In one embodiment, the kit comprises a set of biomarkers: -KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11 and PMAIP1; - an interferon module biomarker listed in Table 3; or - diagnostic reagents that bind to or individually complex with each of the IFN module biomarkers regulated by the IRF1 regulon listed in Table 4.

[0052] In another aspect, a kit for or when used according to the methods of the invention is provided that comprises at least two diagnostic reagents as described herein, each reagent identifying a different biomarker as described herein. In one embodiment, the kit comprises diagnostic reagents that bind to or individually complex with 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or more biomarkers. In one embodiment, the kit comprises the biomarkers: -KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11 and PMAIP1; - an interferon module biomarker listed in Table 3; or - diagnostic reagents that bind to or individually complex with each of the IFN module biomarkers regulated by the IRF1 regulon listed in Table 4.

[0053] As used herein, unless the context otherwise requires, the term "comprise", and variations of terms such as "comprising", "comprises" and "comprised" are not intended to exclude additional additives, components, integers or steps.

[0054] Further aspects of the invention and further embodiments of the aspects described in the previous paragraphs will become apparent from the following description, given by way of example with reference to the accompanying drawings, in which: [Brief description of the drawings]

[0055] [Figure 1-1] Dimensionality reduction of multi-omics datasets. (A) Schematic representation of the experimental and analytical design. (B) Immunophenotyping of baseline CBMC samples. Y-axis indicates percentage of cell types of all cell types identified from CBMCs. Scatter plots show median with 95% CI. (C) Multilevel dimensionality reduction for gene expression (PCA) and cytokine (PCA) datasets. Axes indicate percentage of total variability (%) accounted for by the first (x) and second (y) principal components or canonical variables. (D-E) Vertical bar plots showing top contributing features of the first (top row) and second (bottom row) principal components or cross-validated canonical variables of the corresponding (above) dimensionality reduction plots. The x-axis indicates absolute contribution (%) / loading, with red indicating positive / high and blue indicating negative / low. [Figure 1-2] Same as above. [Figure 1-3] Same as above. [Figure 2-1]Interferon and pro-inflammatory gene expression identified by differential expression and network analysis characterize innate CBMC responses. (A-C; left panels) Volcano plots showing significantly upregulated (right; red) and downregulated (left; blue) genes for LPS, imiquimod, and Poly(I:C) responses, respectively, compared to matched unstimulated samples. Arrows indicate the number of upregulated and downregulated genes. (A-C; right panels) Over-represented pathways from significantly upregulated genes in CBMC LPS, imiquimod, and Poly(I:C) responses, respectively, compared to matched unstimulated controls. (D-F) Modules identified from network analysis (WGCNA) of LPS, imiquimod, and Poly(I:C) responses, respectively. Groups are in the order of the figure key, as displayed from top to bottom. Modules are plotted by adjusted t-statistic (limma / voom) and show median, first and third quartiles, ±1.5×IQR, and outliers. Modules with median above the red line (upper line) (adjusted t-statistic=2) are considered significantly upregulated, and modules below the blue line (lower line) (-2) are considered significantly downregulated. Modules are labeled from left to right in the same order as in the legend. (G) Bar plot of the number of genes in the interferon and pro-inflammatory module for each response. (H) Heat map showing Spearman's Rho values ​​of ranked expression and connectivity between CBMC response module genes. Expression of member genes from the interferon and pro-inflammatory module of each response was correlated with expression of the same genes from the other response. The p-values ​​associated with all correlations were less than 0.01. [Figure 2-2] Same as above. [Figure 2-3] Same as above. [Figure 3-1]IFN module gene connectivity and drivers of CBMC responses, from birth to 5 years of age. (A) Density plots of LPS, Imiquimod, and Poly(I:C) CBMC response IFN module connectivity, respectively. Dashed lines indicate mean (light grey) and median (dark grey). Lillefors p-values ​​>0.05 indicate normal distributed connectivity. (B-D; left panels) Network wiring diagrams of the top 20 most connected genes in LPS, Imiquimod, and Poly(I:C) CBMC IFN modules, respectively. Node size represents the number of connections (degrees) across the network, and edges indicate the strength of connections (red edges, i.e., dark lines, indicate correlations >0.8). (B-D; right panels) Top 10 master regulators identified by VIPER analysis for LPS, Imiquimod, and Poly(I:C) CBMC IFN modules. Bar plots show normalized enrichment scores (NES) of transcription factors that are significantly activated (NES>2, red line) or inactive / inhibited (NES<-2, blue line). Grey shading indicates adjusted P-values ​​less than 0.05. (E) Network wiring diagram of the top 20 most connected cord blood LPS-induced IFN module genes from matched CBMC (i) and 5-year-old PBMC (ii) samples. Network properties are the same as above (Figure 3B-D). (F) Network connectivity density plot of cord blood interferon module gene connectivity of matched CBMC and 5-year-old PBMC response to LPS stimulation. (G) Top significant drivers of cord blood interferon module genes identified for matched CBMC (i) and 5-year-old PBMC (ii, n=9 significant drivers) samples. Bar plot properties are the same as above (Figure 3C). (H and I) Network connectivity density plots of cord blood interferon module gene connectivity of matched CBMCs and 5-year-old PBMC responses to imiquimod and Poly(I:C) stimulation. [Figure 3-2] Same as above. [Figure 3-3] Same as above. [Figure 4-1]LPS-inducible IFN genes predict sLRI susceptibility at birth. (A) Random forest classifiers were trained on LPS-, imiquimod-, and Poly(I:C)-inducible IFN module genes from 25 (50%) randomly selected subjects and validated on the remaining 25 (50%) subjects. Each RF model was optimized with respect to the number of genes used at each split and the number of trees grown. The plot shows the area under the receiver operator characteristic (ROC) curve defined by the false (x-axis, 1 specificity) and true (y-axis, sensitivity) positive rates. (B) RF model predictions were repeated by resampling the training / validation set (50 / 50 random assignment) 2,000 times. The plot shows the area under the ROC curve for each resample with median (solid line) and 95% CI (dashed line). (C) Network connectivity density plots of LPS-(i), imiquimod-(ii) and Poly(I:C)-(iii) induced IFN module gene networks stratified by individuals who recorded (orange; i.e., lighter lines) and did not record (grey) sLRI in the first year of life. (D-E(i)) Network wiring diagrams of the top 20 most connected genes of the LPS-induced IFN module gene network from CBMC samples from individuals who were resistant (D(i)) and sensitive (E(i)) to sLRI in infancy. Node and edge characteristics are the same as in Figure 3B. (D-E(ii)) Top 10 master regulators identified by VIPER analysis for the CBMC LPS-induced IFN response module of individuals who were resistant (D(ii)) and sensitive (E(ii)) to sLRI in infancy. Bar plot characteristics are the same as in Figure 3C. (F) Box and whisker plot of cord blood LPS-inducible IFN module eigengenes grouped by individuals with (orange; i.e., lighter line) and without (gray) recorded sLRI in the first year of life. Boxes indicate median, first and third quartiles, ±1.5×IQR, and outliers; P values ​​are determined by Mann-Whitney U test. (G) Plot of IFN module eigengenes from left to right.CBMC responses to LPS (green; groups 1, 2, 7, and 8), imiquimod (blue; groups 3, 4, 9, and 10), and Poly(I:C) (red; groups 5, 6, 11, and 12) were grouped by individuals who were resistant (-) and sensitive (+) to LRI and sLRI during infancy. P values ​​and significant results determined by Mann-Whitney U test reflect Figure 4G. Plots show median (symbols) and 95% CI (bars). [Figure 4-2] Same as above. [Figure 4-3] Same as above. [Figure 4-4] Same as above. [Figure 5-1]Validation of in vitro CBMC cultured IFN module genes, multi-omics integration of LPS-induced biological features, and IRF1 gene expression correlation in external gene expression datasets. (A-C) Random forest classifiers were trained on unstimulated and LPS or Imiquimod / Poly(I:C) CBMC gene expression data (n=50) and used to predict: (A) children (<17 years old) hospitalized with bacterial (n=52) and viral infections (n=92) respectively from healthy controls (n=52) from blood-derived gene expression profiles; (B) infants (<18 months old; n=15) and children (18 months-5 years old; n=16) representing acute viral respiratory infections compared to convalescents from PBMC samples; and (C) asthmatic children (6-17 years old) with cold-like symptoms with (n=193) and without (n=105) detectable airway viral infections from nasal-derived gene expression profiles. Plots show the area under the ROC curve. (D) Multi-layer risk profile of sLRI susceptibility in infancy determined from multi-omics data integration. Inter-layer co-expression was maximized, showing positive (red) and negative (blue) correlations stronger than ±0.8, respectively. The perimeter line represents the relative expression of features from individuals who were resistant (gray) or susceptible (orange: i.e. lighter line) to sLRI in the first year of life. Input data were adjusted with respect to matched unstimulated samples (excluding baseline immunophenotypic data). Transcription factor IDs (green) were appended with "R_" to distinguish them from gene names (blue). (E-G) Plots of associations of LPS-induced IRF1 gene expression with IFN and pro-inflammatory genes (G), virus-associated receptors (H), and chemokines / cytokines (I). Data were adjusted with respect to matched unstimulated samples, and plots show Spearman's Rho values ​​(symbols) and 95% CIs (bars, 1000 bootstraps). Red and blue data points / labels represent positive and negative correlations (non-overlapping 95% confidence intervals above or below 0, respectively) with a BH adjusted FDR < 0.05.It will be understood that the invention disclosed and defined herein extends to all alternative combinations of two or more of the individual features mentioned or evident from this specification or the drawings, all of which different combinations constitute various alternative aspects of the invention. [Figure 5-2] Same as above. [Figure 6] (A) 10 selected significantly over-represented pathways for genes contained within the IFN module of CBMC response to LPS (InnateDB). (B) Original VIPER plot (before trimming by motif binding sites) of the LPS CBMC response interferon module. Plots show (LR) p-values, positive (right; red) and negative (left; blue) interactions, transcription factor gene names, activated (top panel; red) or inactivated / inhibited (bottom panel; blue) status (NES), and relative expression of TF genes. [Figure 7] Density plot and putative driver analysis (as above) of cord blood CBMC responses when input genes are restricted to only those genes common to the LPS-induced IFN module. [Figure 8] Drivers for CBMC Imiquimod (A) and Poly(I:C) (B) inducible IFN modules in matched CBMC (left) and 5-year PBMC (right) samples. [Figure 9] Top 30 most significant genes for CBMC LPS-(A), Imiquimod-(B), and Poly(I:C)-induced (C) IFN module random forest classifiers, respectively. The x-axis shows the accuracy loss for each model by removing each variable. [Figure 10-1](A,B) RF model predictions were repeated by resampling the training / validation sets (50 / 50 random assignment, 2000 resamplings) with the original (optimized) RF parameters of each model remaining consistent. AUC-ROC (A) as density of each resample. Solid lines indicate the median AUC-ROC and dashed lines indicate the upper and lower 95% confidence intervals. These values ​​were as follows; LPS: median = 0.619, CIlo = 0.615, CIhi = 0.623; Imiquimod: median = 0.338, CIlo = 0.335, CIhi = 0.341; Poly(I:C): median = 0.393, CIlo = 0.389, CIhi = 0.397. Grey dashed line indicates AUC-ROC of 0.5 (random chance). (B) Resampled AUC-ROC values ​​are displayed as box-and-whisker plots. Matched resample permutations are connected with grey lines. Matched resample AUC-ROC values ​​are significantly different between all groups (Wilcoxon SRT p-values ​​<0.00001). [Figure 10-2] Same as above. [Figure 11] (A) CBMC LPS-induced IFN module differential connectivity as determined by assessment using a distinct connectivity measure (Spearman's Rho) between individuals resistant and susceptible to sLRI during infancy. (B, C) Network connectivity density plots of imiquimod-induced (B) and Poly(I:C)-induced (C) IFN module gene networks, restricting input genes to only those common to the LPS-induced IFN module, stratified by individuals who did (orange; i.e., lighter line) and did not (grey) record sLRI in the first year of life. [Figure 12] (A) Box and whisker plot of cord blood LPS-inducible IFN module eigengenes grouped by asthmatic / non-asthmatic individuals at 5 years of age. (B) Box and whisker plot of cord blood LPS-inducible IFN module eigengenes grouped by individuals who had wheezing or not at 5 years of age. [Figure 13-1](A) Principal component analysis of blood-derived gene expression profiles of children hospitalized with febrile bacterial and viral infections (GSE72809). The gene expression dataset was restricted to available CBMC LPS-inducible genes, imiquimod-inducible genes, and Poly(I:C)-inducible IFN module genes, respectively. (B) Top 30 most significant genes for CBMC LPS-, imiquimod-, and Poly(I:C)-inducible IFN module random forest classifiers, respectively. [Figure 13-2] Same as above. [Figure 14-1] (A-C) Analysis of IFN and pro-inflammatory mediator and virus-associated receptor genes in relation to sLRI susceptibility in the first year of life. Data were adjusted for matched unstimulated samples and plots show Mann-Whitney U test estimates and 95% CI for CBMC data of individuals susceptible compared to resistant to sLRI in infancy. Red data points / labels (darker labels) indicate increased expression with p-values ​​<0.05. (D) Spearman correlation between IFIH1 and IRF1 / STAT1 in LPS-stimulated CBMCs (n=50) and associated p-values. Blue dotted lines represent loess fits of the data. [Figure 14-2] Same as above. [Figure 15-1] Differential gene expression from single-cell RNA sequencing analysis comparing lymphoid cells taken from cord blood with or without LPS treatment. Genes colored red (darker dots to the right of the rightmost dotted line) are considered upregulated and genes colored blue (darker dots to the left of the leftmost dotted line) are considered downregulated. [Figure 15-2] Same as above. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0056] It will be understood that the invention disclosed and defined herein extends to all alternative combinations of two or more of the individual features mentioned or evident from this specification or the drawings, all of which different combinations constitute various alternative aspects of the invention.

[0057] Further aspects of the invention and further embodiments of the aspects described in the previous paragraphs will become apparent from the following description, given by way of example with reference to the accompanying drawings, in which:

[0058] Reference will now be made in detail to specific embodiments of the invention. While the invention will be described in conjunction with the embodiments, it will be understood that the intention is not to limit the invention to those embodiments. Rather, the invention is intended to cover all alternatives, modifications, and equivalents that may be included within the scope of the present invention as defined by the claims.

[0059] The present invention is based on the development of a method that can predict at birth which children will experience early respiratory infections, including viral infections associated with the sequelae of asthma. Our findings suggest that susceptibility to severe respiratory viral infections (e.g., sLRI) in the first year of life is primarily determined by antibacterial-antiviral innate immune pathways, providing a rationale for identifying at-risk infants for early intervention. In this regard, the data presented herein suggest that the response to pathogenic bacteria is a more important determinant of sLRI susceptibility than the response to viral stimuli.

[0060] The findings described herein that an enhanced bacteria-mediated TLR4-induced IFN response / gene network connectivity pattern at birth conferred risk of viral sLRI in infancy are surprising given that IFN responses are nearly universally protective during acute viral infections.

[0061] Each year, thousands of children are admitted to emergency departments with severe respiratory viral infections. Severe viral lower respiratory tract infections (sLRIs) are a leading cause of hospitalization in infants and children and constitute a major risk factor for the subsequent development of asthma.

[0062] Furthermore, some of these children will develop asthma, a chronic inflammatory disease of the airways that affects 300 million people worldwide. In particular, the trajectory to the presence of asthma in utero and during the first few years of life represents a critical period during which the immune system is functionally immature and undergoes increased plasticity during which it is susceptible to infection. The plasticity of the immune system during infancy provides an ideal "window of opportunity" for the administration of immunomodulatory drugs to reprogram the immune system and minimize disease risk. The present invention allows for very early identification of high-risk infants who can be treated with appropriate interventions to modulate innate immunity and reduce or prevent severe respiratory viral infections and the subsequent development of asthma.

[0063] Therefore, early identification of high-risk infants would allow these children to be treated with immunomodulatory drugs, thereby minimizing the risk of severe respiratory viral infections and the subsequent development of asthma. Preventing emergency room visits due to severe respiratory viral infections would save the health care system billions of dollars and improve the quality of life for millions of children and their families.

[0064] Overview Throughout this specification, unless specifically stated otherwise or the context requires otherwise, references to a single step, composition of matter, group of steps, or group of compositions of matter should be construed to include one and more than one (i.e., one or more) of that step, composition of matter, group of steps, or group of compositions of matter. Thus, as used herein, the singular forms "a," "an," and "the" include plural aspects, and vice versa, unless the context clearly indicates otherwise. For example, a reference to "a" includes two or more as well as the singular, a reference to "an" includes two or more as well as the singular, a reference to "the" includes two or more as well as the singular, etc.

[0065] Those skilled in the art will appreciate that the present invention is susceptible to variations and modifications other than those specifically described. It is to be understood that the present invention includes all such variations and modifications. The present invention also includes all of the steps, features, compositions, and compounds referred to or shown in this specification, individually or collectively, and any and all combinations, or any two or more of such steps or features.

[0066] One skilled in the art will recognize many methods and materials similar or equivalent to those described herein, which could be used in the practice of the present invention, and the present invention is in no way limited to the methods and materials described.

[0067] All patents and publications mentioned herein are incorporated by reference in their entirety.

[0068] The present invention is not to be limited in scope by the specific examples described herein, which are for the purpose of illustration only. Functionally equivalent products, compositions, and methods are clearly within the scope of the invention.

[0069] Any example or embodiment of the invention herein applies mutatis mutandis to any other example or embodiment of the invention, unless expressly stated otherwise.

[0070] Unless specifically defined otherwise, all technical and scientific terms used herein should be understood to have the same meaning as commonly understood by one of ordinary skill in the art (e.g., in cell culture, molecular genetics, immunology, immunohistochemistry, protein chemistry, and biochemistry).

[0071] Unless otherwise indicated, the recombinant protein, cell culture, and immunological techniques utilized in this disclosure are standard procedures, well known to those skilled in the art. Such techniques are described in J. Perbal, A Practical Guide to Molecular Cloning, John Wiley and Sons (1984), J. Sambrook et al. Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory Press (1989), TA Brown (editor), Essential Molecular Biology: A Practical Approach, Volumes 1 and 2, IRL Press (1991), DMG Lover and BDHames (editors), DNA Cloning: A Practical Approach, Volumes 1-4, IRL Press (1995 and 1996), and FM Ausubel et al. (editors), Current Protocols in Molecular Biology, Greene Pub. Associates and Wiley-Interscience (1988, including all updates to date), Ed Harlow and David Lane (editors), Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory, (1988), and JE Coligan et al. al. (editors) Current Protocols in Immunology, John Wiley & Sons (including all updates to date), and other sources.

[0072] The term "and / or" is to be understood as meaning either "X and Y" or "X or Y", for example, "X and / or Y" and is to be interpreted as explicitly endorsing both meanings or either meaning.

[0073] As used herein, the term "derived from" should be construed to indicate that a specified integer may be obtained from a particular source, although not necessarily directly from that source.

[0074] As used herein, the TLR4 agonist may be selected from the group consisting of lipopolysaccharide (LPS), monophosphoryl lipid A (MPLA), heat shock proteins, S100A8, S100A9, RSV F protein, fibrinogen, heparin sulfate or a fragment thereof, hyaluronic acid or a fragment thereof, nickel, opoids, alpha 1 acid glycoprotein (AAG), aminoacylglucoaminide 4-phosphate (AGP), RC-529, murine beta-defensin 2, and complete Freund's adjuvant (CFA).

[0075] The term "susceptibility" as described herein refers to the tendency of an individual to develop a particular condition (e.g., a particular trait, phenotype, or disease) or to be less able to resist a particular condition than the average individual. The term encompasses both increased susceptibility and decreased susceptibility. Thus, certain biomarkers, including those described herein, can be characteristic of increased susceptibility (i.e., increased risk) of respiratory infection (e.g., sLRI), for example, as characterized by a relative risk (RR) or odds ratio (OR) greater than 1 for the particular biomarker. Alternatively, the biomarker is characteristic of decreased susceptibility (i.e., decreased risk) of respiratory infection (e.g., sLRI), as characterized by a relative risk less than 1.

[0076] Measures of susceptibility or risk include measures such as relative risk (RR), odds ratio (OR), and absolute risk (AR), as described in more detail herein.

[0077] In certain embodiments, increased susceptibility refers to a risk having a RR or OR value of at least 1.10, at least 1.11, at least 1.12, at least 1.13, at least 1.14, at least 1.15, at least 1.16, at least 1.17, at least 1.18, at least 1.19, at least 1.20, at least 1.21, at least 1.22, at least 1.23, at least 1.24, at least 1.25, at least 1.30, at least 1.35, at least 1.40, at least 1.45, at least 1.50, at least 1.55, at least 1.60, at least 1.65, at least 1.70, at least 1.75, and / or at least 1.80. Other non-integer numbers greater than 1 can also characterize risk, and such numbers are also within the scope of the invention.

[0078] The increased susceptibility may also involve a comparison with a reference dataset. The reference dataset may be from one or more individuals who have been determined to be (a) at increased, elevated, high or higher risk or susceptibility to respiratory infections (e.g., sLRI) (also referred to as an increased or high risk reference dataset), or (b) at no increased risk or susceptibility to respiratory infections (also referred to as a normal risk reference dataset or a no increased risk reference dataset). Thus, if the expression level of the biomarker in a sample from an individual whose risk is to be determined is the same as or significantly different from the increased or high risk reference dataset, a determination may be made that the individual has an increased or high risk of respiratory infection. Alternatively, if the expression level of the biomarker in a sample from an individual whose risk is to be determined is significantly different from the increased or high risk reference dataset, a determination may be made that the individual does not have an increased or high risk of respiratory infection. Alternatively, if the expression level of the biomarker in a sample from an individual whose risk is to be determined is the same as or significantly different from the normal or no increased risk reference dataset, a determination may be made that the individual does not have an increased or high risk of respiratory infection. Alternatively, a determination can be made that an individual has an increased or high risk of respiratory infection if the expression level of the biomarker in a sample from an individual for whom risk is being determined is significantly different from a reference dataset of normal or no increased risk.

[0079] In any method or use of the present invention, the determination may be an increased, elevated, high or higher risk or susceptibility of respiratory infection (e.g., sLRI), or the determination may be a non-increased, non-elevated, non-higher or normal risk or susceptibility of respiratory infection. As used herein, a reference to a determination of an increased, elevated, high or higher risk or susceptibility of respiratory infection may be interpreted as a reference to a determination that the individual requires intervention in the form of pre-emptive therapy. Thus, in any method or use of the present invention in which a determination is made that the individual requires intervention in the form of pre-emptive therapy, the method or use further comprises administering an intervention in the form of pre-emptive therapy (e.g., any pre-emptive therapy described herein).

[0080] The term "protein" is intended to include a single polypeptide chain, i.e., a series of consecutive amino acids linked by peptide bonds, or a series of polypeptide chains that are covalently or non-covalently bonded to one another (i.e., a polypeptide complex). For example, a series of polypeptide chains can be covalently bonded using suitable chemical bonds or disulfide bonds. Examples of non-covalent bonds include hydrogen bonds, ionic bonds, van der Waals forces, and hydrophobic interactions.

[0081] The term "polypeptide" or "polypeptide chain" is understood from the previous paragraph to mean a series of consecutive amino acids linked by peptide bonds.

[0082] The term "microarray" refers to an ordered arrangement of binding / composite array elements or ligands, such as antibodies, on a substrate.

[0083] The term "polynucleotide", when used in the singular or plural, generally refers to any polyribonucleotide or polydeoxyribonucleotide, which may be unmodified RNA or DNA, or modified RNA or DNA. Thus, for example, polynucleotides as defined herein include, but are not limited to, single-stranded and double-stranded DNA, DNA containing single-stranded and double-stranded regions, single-stranded and double-stranded RNA, and RNA containing single-stranded and double-stranded regions, hybrid molecules containing DNA and RNA that may be single-stranded, more typically double-stranded, or may contain single-stranded and double-stranded regions. Additionally, as used herein, the term "polynucleotide" refers to triple-stranded regions containing RNA or DNA, or both RNA and DNA. The term "polynucleotide" specifically includes cDNA. This term includes DNA (including cDNA) and RNA containing one or more modified bases. In general, the term "polynucleotide" encompasses all chemically, enzymatically, and / or metabolically modified forms of unmodified polynucleotides, as well as the characteristic DNA and RNA chemical forms of viruses and cells, including simple and complex cells.

[0084] The term "oligonucleotide" refers to a relatively short polynucleotide of less than 20 bases, including, but not limited to, single-stranded deoxyribonucleotides, single- or double-stranded ribonucleotides, RNA:DNA hybrids, and double-stranded DNA. Oligonucleotides, such as single-stranded DNA probe oligonucleotides, are often synthesized by chemical methods, for example, using commercially available automated oligonucleotide synthesizers. However, oligonucleotides can be produced by a variety of other methods, including in vitro recombinant DNA-mediated techniques, and by expression of DNA in cells and organisms.

[0085] As used herein, the term "subject" should be taken to mean any animal, including humans, e.g., mammals. Exemplary subjects include, but are not limited to, humans and non-human primates. For example, the subject is a human.

[0086] sample In any aspect, the B and T cells, or CBMCs, may be purified from cord blood. Alternatively, in any aspect, the B and T cells, or CBMCs, may be present in a sample of cord blood from an individual. In any embodiment, the B and T cells, or CBMCs, are present in cord blood, such that any reference herein to contacting CBMCs with a TLR4 agonist includes contacting cord blood containing B and T cells, or CBMCs (e.g., untreated, whole cord blood, or unpurified cord blood) with a TLR4 agonist. In one embodiment, the cord blood may be depleted of red blood cells. In one embodiment, when the B and T cells, or CBMCs, are contacted with a TLR4 agonist, red blood cells are not present or are not present at significant levels. In another embodiment, red blood cells are present at normal levels, i.e., they are not depleted from the cord blood.

[0087] In any embodiment, the individual is less than 1 year old or 2 years old or less. In any embodiment, the individual is 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days, 2 weeks, 1 month, 6 months, 1 year old, or 2 years old. In any embodiment, the individual is at least 1 day old, at least 2 days old, at least 3 days old, at least 4 days old, at least 5 days old, at least 6 days old, at least 7 days old, at least 2 weeks old, at least 1 month old, at least 2 months old, at least 3 months old, at least 6 months old, at least 1 year old, but not more than 2 years old. In any embodiment, the individual is about 1 day to about 7 days old, about 1 day to about 2 weeks old, about 1 week to about 6 months old, about 1 month to about 6 months old, about 1 month to about 3 months old, about 6 months to about 1 year old, about 1 month to 2 years old, about 6 months to 2 years old, or about 1 year to 2 years old.

[0088] In any embodiment, the cord blood is from an individual within 24 hours of birth. In any embodiment, the cord blood is from an individual within 48 hours of birth. After the cord blood is obtained, it may be frozen for up to two years before being used in the method of the present invention. For example, the cord blood may be obtained and frozen for a time equal to or less than the current age of the individual subjected to the method of the present invention described herein. Thus, the CBMCs may be obtained from fresh cord blood within 24 hours or within 48 hours, or from cord blood that has been frozen for up to two years since the birth of the individual.

[0089] In any embodiment, cord blood red blood cells are depleted by ammonium chloride lysis, density gradient techniques, hypotonic lysis, immunomagnetic cell separation or sedimentation, flow cytometric sorting, or equivalent methods understood by one of skill in the art.

[0090] In any embodiment, the CBMCs are cultured in a nutrient medium at or near physiological conditions. For example, the CBMCs are cultured at 37° C., 5% CO 2 In some embodiments, the CBMCs are cultured in nutrient medium containing non-heat inactivated serum.

[0091] In any embodiment, the B cells and T cells are cultured in a nutrient medium at or near physiological conditions. B and T cell culture media are known in the art.

[0092] Stimulation with TLR4 agonists In any aspect of the invention, B and T cells, or CBMCs, or cord blood may be stimulated with a TLR4 agonist.

[0093] B and T cells, or CBMCs, or cord blood may be stimulated with a TLR4 agonist for at least 6 hours, at least 12 hours, at least 18 hours, or at least 24 hours. In any embodiment, CBMCs are stimulated with at least 1×10 6 The cells may be suspended at 200 cells / mL and then stimulated with a TLR4 agonist.

[0094] In any embodiment, the TLR4 agonist is provided at an effective concentration to stimulate TLR4 activation. An example of an effective concentration is 0.025 ng / ml to 100 ng / ml, preferably 1 ng / ml, of LPS. For other TLR4 agonists, the effective concentration is any amount that results in the same activation of TLR4 as 1 ng / ml of LPS. A person skilled in the art will also recognize methods that can be used to determine the effective concentration of any TLR4 agonist to stimulate TLR4 activation, as described herein. For example, a person skilled in the art may perform an assay for the effective concentration of any TLR4 agonist. An exemplary assay includes stimulating B and T cells, CBMCs, or cord blood in vitro overnight with a TLR4 agonist and measuring NK-κB-mediated transcriptional activity compared to unstimulated B and T cells, CBMCs, or cord blood, respectively, where an increase in NK-κB-mediated transcriptional activity indicates an effective concentration of the TLR4 agonist.

[0095] Biomarker detection and measurement It is understood that the biomarker in a sample can be measured by any suitable method known in the art. Measurement of the expression level of a biomarker can be direct or indirect. For example, the level of RNA or protein present can be directly quantified. Alternatively, the amount of a biomarker can be determined indirectly by measuring the level of cDNA, amplified RNA or DNA present, or by measuring the amount or activity of RNA, protein, or other molecules that indicate the expression level of the biomarker.

[0096] In one embodiment, the expression level of a biomarker is determined by measuring the polynucleotide level of the biomarker. The level of a transcript of a particular biomarker gene can be determined from the amount of mRNA or polynucleotide derived therefrom present in a sample. Polynucleotides can be detected and quantified by a variety of methods, including, but not limited to, microarray analysis, polymerase chain reaction (PCR), reverse transcriptase polymerase chain reaction (RT-PCR), Northern blot, serial analysis of gene expression (SAGE), total RNA sequencing, mRNA sequencing, cap analysis gene expression (CAGE) sequencing, single cell RNA sequencing, or NanoString nCounter. See, e.g., Draghici Data Analysis Tools for DNA Microarrays, Chapman and Hall / CRC, 2003; Simon et al. Design and Analysis of DNA Microarray Investigations, Springer, 2004; Real-Time PCR: Current Technology and Applications, Logan, Edwards, and Saunders eds., Caister Academic Press, 2009; Bustin AZ of Quantitative PCR (IUL Biotechnology, No. 5), International University Line, 2004; Velculescu et al. (1995) Science 270:484-487; Matsumura et al. (2005) Cell. Microbiol. 7:11-18; Serial Analysis of Gene Expression (SAGE): Methods and Protocols (Methods in Molecular Biology), Humana Press, 2008, which are incorporated by reference in their entireties.

[0097] In one embodiment, a microarray is used to measure the levels of the biomarkers. The advantage of microarray analysis is that the expression of each of the biomarkers can be measured simultaneously, and the microarray can be specifically designed to provide an expression profile of a particular disease or condition, regulon or network.

[0098] Microarrays are prepared by selecting probes that contain polynucleotide sequences and then immobilizing such probes on a solid support or surface. For example, the probes may contain DNA sequences, RNA sequences, or DNA and RNA copolymer sequences. The polynucleotide sequences of the probes may also contain DNA and / or RNA analogs, or combinations thereof. For example, the polynucleotide sequences of the probes may be complete or partial fragments of genomic DNA. The polynucleotide sequences of the probes may also be synthetic nucleotide sequences, such as synthetic oligonucleotide sequences. The probe sequences may be synthesized in vivo, enzymatically in vitro (e.g., by PCR), or non-enzymatically in vitro.

[0099] The probes used in the methods of the invention are preferably immobilized on a solid support, which may be either porous or non-porous. For example, the probes may be polynucleotide sequences bound to nitrocellulose or nylon membranes, or covalently filtered at either the 3' or 5' end of the polynucleotide. Such hybridization probes are well known in the art (see, for example, Sambrook, et al., Molecular Cloning: A Laboratory Manual (3rd Edition, 2001). Alternatively, the solid support or surface may be a glass or plastic surface. In one embodiment, the hybridization level is measured against a microarray of probes consisting of a solid phase on the surface of which is immobilized a population of polynucleotides, such as a population of DNA or DNA mimics, or alternatively a population of RNA or RNA mimics. The solid phase may be a non-porous or, optionally, a porous material, such as a gel.

[0100] In one embodiment, a microarray comprises a support or surface having an ordered array of binding (e.g., hybridization) sites or "probes," each representing one of the biomarkers described herein. Preferably, the microarray is an addressable array, more preferably a positionally addressable array. More specifically, each probe of the array is preferably located at a known, predetermined location on the solid support such that the identity (i.e., sequence) of each probe can be determined from its location in the array (i.e., on the support or surface). Each probe is preferably covalently attached to the solid support at a single site.

[0101] Microarrays can be made in a number of ways, some of which are described below. Regardless of how they are produced, microarrays share certain properties. Arrays are reproducible, allowing multiple copies of a given array to be produced and easily compared to one another. Preferably, microarrays are made from materials that are stable under binding (e.g., nucleic acid hybridization) conditions. Microarrays are generally small, e.g., up to 1 cm 2 ~25cm 2 Although small arrays between 100 and 1500, larger arrays may also be used, for example in screening arrays. Preferably, a given binding site or unique set of binding sites within the microarray specifically binds (e.g., hybridizes) to the product of a single gene within a cell (e.g., a particular mRNA, or a particular cDNA derived therefrom).

[0102] Generally, however, other related or similar sequences will cross-hybridize to a given binding site.

[0103] As mentioned above, the "probe" to which a particular polynucleotide molecule specifically hybridizes comprises a complementary polynucleotide sequence. The probes of the microarray typically comprise a nucleotide sequence of 1,000 nucleotides or less. In some embodiments, the probes of the array comprise a nucleotide sequence of 10-1,000 nucleotides. In one embodiment, the nucleotide sequence of the probe ranges from 10-200 nucleotides in length and is the genome sequence of one organism species, such that there are multiple different probes whose sequences are complementary and thus capable of hybridizing to the genome of such organism species, tiled sequentially across all or a portion of the genome. In other embodiments, the probes are in the range of 10-30 nucleotides in length, 10-40 nucleotides in length, 20-50 nucleotides in length, 40-80 nucleotides in length, 50-150 nucleotides in length, 80-120 nucleotides in length, or 60 nucleotides in length.

[0104] The probes may comprise DNA or DNA "mimics" (e.g., derivatives and analogs) that correspond to a portion of an organism's genome. In another embodiment, the microarray probes are complementary RNA or RNA mimics. DNA mimics are polymers composed of subunits capable of specific Watson-Crick-like hybridization with DNA or specific hybridization with RNA. Nucleic acids can be modified at the base moiety, sugar moiety, or phosphate backbone (e.g., phosphorothioate).

[0105] DNA can be obtained, for example, by polymerase chain reaction (PCR) amplification of genomic DNA or cloned sequences. PCR primers are preferably selected based on known sequences of the genome, resulting in amplification of specific fragments of genomic DNA. Computer programs well known in the art are useful for designing primers with the required specificity and optimal amplification properties, such as Oligo version 5.0 (National Biosciences). Typically, each probe on the microarray is between 10 and 50,000 bases long, usually 300 to 1,000 bases long. PCR methods are well known in the art and are described, for example, in Innis et al., eds., PCR Protocols: A Guide To Methods And Applications, Academic Press Inc., San Diego, Calif. (1990), which is incorporated herein by reference in its entirety. It will be apparent to those skilled in the art that controlled robotic systems are useful for isolating and amplifying nucleic acids.

[0106] An alternative preferred means for generating polynucleotide probes is by synthesis of synthetic polynucleotides or oligonucleotides, for example, using N-phosphonate or phosphoramidite chemistry (Froehler et al., Nucleic Acid Res. 14:5399-5407 (1986); McBride et al., Tetrahedron Lett. 24:246-248 (1983)). Synthetic sequences are typically about 10 to about 500 bases in length, more typically about 20 to about 100 bases in length, and most preferably about 40 to about 70 bases in length. In some embodiments, synthetic nucleic acids include unnatural bases such as inosine, but are in no way limited thereto. As mentioned above, nucleic acid analogs may be used as binding sites for hybridization. An example of a suitable nucleic acid analogue is peptide nucleic acid (see, e.g., Egholm et al., Nature 363:566-568 (1993); U.S. Patent No. 5,539,083).

[0107] Probes are preferably selected using an algorithm that takes into account binding energy, base composition, sequence complexity, cross-hybridization binding energy, and secondary structure. See Friend et al., International Patent Publication No. WO 01 / 05935, published Jan. 25, 2001; Hughes et al., Nat. Biotech. 19:342-7 (2001).

[0108] One of skill in the art will also understand that the array should include positive control probes, e.g., probes that are known to be complementary and hybridizable to sequences in the target polynucleotide molecules, and negative control probes, e.g., probes that are not complementary and known to be hybridizable to sequences in the target polynucleotide molecules. In one embodiment, the positive controls are synthesized along the perimeter of the array. In another embodiment, the positive controls are synthesized in diagonal stripes across the array. In yet another embodiment, the reverse complement of each probe is synthesized next to the location of the probe and serves as a negative control. In yet another embodiment, sequences from other species are used as negative or "spike-in" controls.

[0109] The probe is bound to a solid support or surface, which may be made of, for example, glass, plastic (e.g., polypropylene, nylon), polyacrylamide, nitrocellulose, gel, or other porous or non-porous materials. One method for attaching nucleic acid to a surface is by printing on a glass plate, as generally described by Schena et al., Science 270:467-470 (1995). This method is particularly useful for preparing microarrays of cDNA (see also DeRisi et al., Nature Genetics 14:457-460 (1996); Shalon et al., Genome Res. 6:639-645 (1996); and Schena et al., Proc. Natl. Acad. Sci. USA 93:10539-11286 (1995), which are incorporated herein by reference in their entirety).

[0110] A second method for making microarrays produces high-density oligonucleotide arrays. Techniques are known for generating arrays containing thousands of oligonucleotides complementary to defined sequences at defined locations on a surface using photolithographic techniques for in situ synthesis (see, e.g., Fodor et al., 1991, Science 251:767-773; Pease et al., 1994, Proc. Natl. Acad. Sci. USA 91:5022-5026; Lockhart et al., 1996, Nature Biotechnology 14:1675; U.S. Pat. Nos. 5,578,832, 5,556,752, and 5,510,270, which are incorporated herein by reference in their entireties) or other methods for rapid synthesis and deposition of defined oligonucleotides (see, Blanchard et al., Biosensors & Bioelectronics 11:687-690, which are incorporated herein by reference in their entireties). Using these methods, oligonucleotides (e.g., 60-mers) of known sequence are synthesized directly on a surface such as a derivatized glass slide. Typically, the arrays produced are redundant, with several oligonucleotide molecules per RNA.

[0111] Other methods for making microarrays, for example by masking (Maskos and Southern, 1992, Nuc. Acids. Res. 20:1679-1684, incorporated herein by reference in its entirety), can also be used. In principle, any type of array can be used, for example, dot blots on nylon hybridization membranes (Sambrook, et al., Molecular Cloning: A Laboratory Manual, 3rd Edition, 2001). However, as will be appreciated by those skilled in the art, very small arrays are often preferred because of the smaller hybridization volumes.

[0112] Microarrays can also be manufactured using an inkjet printing device for oligonucleotide synthesis, for example, using the methods and systems described by Blanchard in U.S. Patent No. 6,028,189, Blanchard et al., 1996, Biosensors and Bioelectronics 11:687-690, Blanchard, 1998, Synthetic DNA Arrays in Genetic Engineering, Vol. 20, JK Setlow, Ed., Plenum Press, New York, pp. 111-123, which are incorporated herein by reference in their entirety. Specifically, the oligonucleotide probes in such microarrays are synthesized in the array by sequentially depositing individual nucleotide bases in "microdroplets" of a high surface tension solvent, such as propylene carbonate, on a glass slide, for example. The microdroplets have small volumes (e.g., 100 pL or less, more preferably 50 pL or less) and are separated from one another (e.g., by hydrophobic domains) on the microarray to form circular surface tension wells that define the locations of the array elements (i.e., different probes). Microarrays produced by this inkjet method are typically high density, preferably with a density of at least about 2,500 different probes per cm. The polynucleotide probes are covalently attached to the support at either the 3' or 5' end of the polynucleotide. Biomarker polynucleotides that can be measured by microarray analysis can be expressed RNA or nucleic acids derived therefrom (e.g., cDNA or amplified RNA derived from a cDNA incorporating an RNA polymerase promoter) and include naturally occurring nucleic acid molecules, as well as synthetic nucleic acid molecules. In one embodiment, the target polynucleotide molecule is total cellular RNA, poly(A) +RNA includes, but is not limited to, messenger RNA (mRNA) or a fraction thereof, cytoplasmic mRNA, or RNA transcribed from cDNA (i.e., cRNA; see, e.g., Linsley & Schelter, U.S. Patent Application Serial No. 09 / 411,074, filed October 4, 1999, or U.S. Patent Nos. 5,545,522, 5,891,636, or 5,716,785). Total RNA and poly(A) + Methods for preparing RNA are well known in the art and are generally described, for example, in Sambrook, et al., Molecular Cloning: A Laboratory Manual (3rd Edition, 2001). RNA can be extracted from cells of interest using guanidinium thiocyanate lysis followed by CsCl centrifugation (Chirgwin et al., 1979, Biochemistry 18:5294-5299), silica gel-based columns (e.g., RNeasy (Qiagen, Valencia, Calif.) or StrataPrep (Stratagene, La Jolla, Calif.)), or using phenol and chloroform, as described in Ausubel et al., eds., 1989, Current Protocols In Molecular Biology, Vol. Ill, Green Publishing Associates, Inc., John Wiley & Sons, Inc., New York, pp. 13.12.1-13.12.5). Poly(A) + RNA can be selected, for example, by oligo-dT cellulose selection, or alternatively, by oligo-dT primed reverse transcription of total cellular RNA. RNA can be selected by methods known in the art, for example, by ZnCl 2 The RNA can be fragmented by incubation with

[0113] In one embodiment, total RNA, mRNA, or nucleic acids derived therefrom are isolated from stimulated samples. Biomarker polynucleotides that are under-expressed in certain cells can be enriched using normalization techniques (Bonaldo et al., 1996, Genome Res. 6:791-806).

[0114] As described above, biomarker polynucleotides can be detectably labeled with one or more nucleotides. Any method known in the art may be used to label target polynucleotides. Preferably, the label incorporates the label uniformly along the length of the RNA, and more preferably, the labeling is performed with high efficiency. For example, polynucleotides can be labeled by oligo-dT primed reverse transcription. Random primers (e.g., 9-mers) can be used for reverse transcription to incorporate labeled nucleotides uniformly throughout the entire length of the polynucleotide. Alternatively, random primers can be used in conjunction with PCR methods or T7 promoter-based in vitro transcription methods to amplify polynucleotides.

[0115] The detectable label may be a luminescent label. For example, fluorescent labels, bioluminescent labels, chemiluminescent labels, and colorimetric labels may be used in the practice of the present invention. Fluorescent labels that may be used include, but are not limited to, fluorescein, fluorophores, rhodamine, or polymethine dye derivatives.

[0116] Additionally, commercially available fluorescent labels may be used, including, but not limited to, FluorePrime (Amersham Pharmacia, Piscataway, NJ), Fluoredite (Miilipore, Bedford, Mass.), FAM (ABI, Foster City, Calif.), and fluorescent phosphoramidites such as Cy3 or Cy5 (Amersham Pharmacia, Piscataway, NJ). Alternatively, the detectable label may be a radiolabeled nucleotide.

[0117] In one embodiment, biomarker polynucleotide molecules from a sample are labeled differently than corresponding polynucleotide molecules of a reference sample. The reference can include polynucleotide molecules from a normal biological sample (i.e., a control sample, e.g., stimulated CMBC from an individual not susceptible to sLRI) or from a reference biological sample (e.g., stimulated CMBC from an individual susceptible to sLRI).

[0118] Nucleic acid hybridization and washing conditions are selected so that the target polynucleotide molecule specifically binds to or hybridizes with the complementary polynucleotide sequence of the array, preferably the specific array site where its complementary DNA is located. Arrays with double-stranded probe DNA located thereon are preferably subjected to denaturing conditions to make the DNA single-stranded before contacting with the target polynucleotide molecule. Arrays containing single-stranded probe DNA (e.g., synthetic oligodeoxyribonucleic acid) may need to be denatured before contacting with the target polynucleotide molecule, for example, to remove hairpins or dimers formed due to self-complementary sequences.

[0119] Optimal hybridization conditions depend on the length (e.g., oligomers vs. polynucleotides greater than 200 bases) and type (e.g., RNA or DNA) of the probe and target nucleic acid. Those skilled in the art will understand that as oligonucleotides become shorter, their length may need to be adjusted to achieve a relatively uniform melting temperature for satisfactory hybridization results. General parameters for specific (i.e., stringent) hybridization conditions for nucleic acids are described in Sambrook, et al. Molecular Cloning: A Laboratory Manual (3rd Edition, 2001) and Ausubel et al., Current Protocols In Molecular Biology, vol. 2, Current Protocols Publishing, New York (1994). Typical hybridization conditions for Schena et al.'s cDNA microarrays are 4 hours of hybridization at 65° C. in 5×SSC plus 0.2% SDS, followed by a wash in low stringency wash buffer (1×SSC plus 0.2% SDS) at 25° C., followed by a wash in high stringency wash buffer (0.1×SSC plus 0.2% SDS) for 10 minutes at 25° C. (Schena et al., Proc. Natl. Acad. Sci. USA 93:10614 (1993)). Useful hybridization conditions are also provided, for example, in Tijessen, 1993, Hybridization With Nucleic Acid Probes, Elsevier Science Publishers BV, and Kricka, 1992, Nonisotopic Dna Probe Techniques, Academic Press, San Diego, Calif. Particularly preferred hybridization conditions include hybridization in 1 M NaCl, 50 mM MES buffer (pH 6.5), 0.5% sodium sarcosine, and 30% formamide at or near the average melting temperature of the probes (e.g., within 51°C, more preferably within 21°C).

[0120] When using fluorescently labeled gene products, the fluorescence emission at each site of the microarray can be preferably detected by scanning confocal laser microscopy. In one embodiment, a separate scan is performed for each of the two fluorophores used, using the appropriate excitation line. Alternatively, a laser can be used that simultaneously illuminates the specimen with wavelengths specific to the two fluorophores, allowing the emission from the two fluorophores to be analyzed simultaneously (see Shalon et al., 1996, "A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization," Genome Research 6:639-645, which are incorporated by reference in their entirety for all purposes). The array can be scanned with a laser fluorescence scanner equipped with a computer-controlled XY stage and a microscope objective. Sequential excitation of the two fluorophores is achieved with a multi-line mixed gas laser, and the emitted light is split by wavelength and detected with two photomultiplier tubes. Fluorescent laser scanning devices are described in Schena et al., Genome Res. 6:639-645 (1996), and other references cited herein. Alternatively, a fiber optic bundle described by Ferguson et al., Nature Biotech. 14:1681-1684 (1996) can be used to simultaneously monitor mRNA abundance levels at multiple sites.

[0121] Polynucleotides may also be analyzed by other methods, including, but not limited to, Northern blotting, nuclease protection assays, RNA fingerprinting, polymerase chain reaction, ligase chain reaction, Qbeta replicase, isothermal amplification, strand displacement amplification, transcription-based amplification systems, nuclease protection (S1 nuclease or RNAse protection assay), SAGE, and the methods disclosed in WO 88 / 10315 and WO 89 / 06700, and International Application Nos. PCT / US87 / 00880 and PCT / US89 / 01025, which are incorporated by reference in their entireties.

[0122] Standard Northern blot assays can be used to confirm RNA transcript size and identify the relative amounts of alternatively spliced ​​RNA transcripts and mRNA in a sample according to conventional Northern hybridization techniques known to those skilled in the art. In Northern blots, RNA samples are first separated by size by electrophoresis in an agarose gel under denaturing conditions. The RNA is then transferred to a membrane, crosslinked, and hybridized with a labeled probe. Nonisotopic or high specific activity radiolabeled probes can be used, including random-primed, nick-translated, or PCR-generated DNA probes, in vitro transcribed RNA probes, and oligonucleotides. In addition, sequences with only partial homology (e.g., cDNAs from different species or genomic DNA fragments that may contain exons) may be used as probes. Labeled probes containing full-length single-stranded DNA or fragments of that DNA sequence, e.g., radiolabeled cDNAs, may be at least 20, at least 30, at least 50, or at least 100 contiguous nucleotides in length. Probes can be labeled by any of a number of different methods known to those skilled in the art. The most commonly used labels in these studies are radioactive elements, enzymes, chemicals that fluoresce when exposed to ultraviolet light, etc. Several fluorescent materials are known and can be utilized as labels. These include, but are not limited to, fluorescein, rhodamine, auramine, Texas Red, AMCA Blue, and Lucifer Yellow. A particular detection material is an anti-rabbit antibody prepared in goats and conjugated with fluorescein via an isothiocyanate. Proteins can also be labeled with radioactive elements or enzymes. Radioactive labels can be detected by any of the currently available counting procedures. Isotopes that can be used include: 3 H, 14 C. 32 P, 35 S, 36 Cl, 35 Cr, 57 Co, 58 Co, 59 Fe, 90 Y, 125 I,131 I, and 186 Re. Enzyme labels are also useful and can be detected by any of the currently available colorimetric, spectrophotometric, fluorospectrophotometric, amperometric or gasometric methods. The enzyme is conjugated to the selected particle by reaction with a bridging molecule such as carbodiimide, diisocyanate, glutaraldehyde, etc. Any enzyme known to those skilled in the art can be utilized. Examples of such enzymes include, but are not limited to, peroxidase, beta-D-galactosidase, urease, glucose oxidase + peroxidase, and alkaline phosphatase. U.S. Patent Nos. 3,654,090, 3,850,752, and 4,016,043 are referenced by way of example for disclosure of alternative labeling materials and methods.

[0123] Nuclease protection assays (including both ribonuclease protection assays and S1 nuclease assays) can be used to detect and quantify specific mRNAs. In a nuclease protection assay, an antisense probe (e.g., radiolabeled or nonisotopically labeled) is hybridized in solution to an RNA sample. After hybridization, the single-stranded nonhybridized probe and RNA are degraded by nucleases. An acrylamide gel is used to separate the remaining protected fragments. Typically, solution hybridization is more efficient than membrane-based hybridization and can accommodate up to 100 μg of sample RNA, compared to a maximum of 20-30 μg for blot hybridization. The most common type of nuclease protection assay, the ribonuclease protection assay, requires the use of an RNA probe. Oligonucleotides and other single-stranded DNA probes can only be used in assays that contain S1 nuclease. Single-stranded antisense probes typically must be perfectly homologous to the target RNA to prevent cleavage of the probe:target hybrid by nucleases.

[0124] Sequential analysis of gene expression (SAGE) can also be used to determine the RNA abundance in a cell sample. See, for example, Velculescu et al., 1995, Science 270:484-7; Carulli, et al., 1998, Journal of Cellular Biochemistry Supplements 30 / 31:286-96, which are incorporated by reference in their entirety. SAGE analysis does not require special devices for detection and is one of the preferred analysis methods for simultaneously detecting the expression of multiple transcripts. First, polyA +RNA is extracted from cells. Then, the RNA is converted to cDNA using a biotinylated oligo(dT) primer, and a four-base recognition restriction enzyme (anchor enzyme: AE) results in AE-treated fragments containing a biotin group at the 3' end. The AE-treated fragments are then incubated with streptavidin for binding. The bound cDNA is divided into two fractions, and then each fraction is ligated to a different double-stranded oligonucleotide adaptor (linker) A or B. These linkers are composed of (1) a protruding single-stranded portion having a sequence complementary to the sequence of the protruding portion formed by the action of the anchor enzyme, (2) a 5' nucleotide recognition sequence of a type IIS restriction enzyme that functions as a tagging enzyme (TE) (cut at a predetermined position within 20 bp from the recognition site), and (3) an additional sequence of sufficient length to construct a PCR-specific primer. The linker-bound cDNA is cleaved using the tagging enzyme, leaving only the linker-bound cDNA sequence portion that exists in the form of a short sequence tag. The pool of short sequence tags from the two different types of linkers are then ligated together, followed by PCR amplification using primers specific for linkers A and B. As a result, an amplification product is obtained as a mixture containing an infinite number of sequences of two adjacent sequence tags (ditags) attached to linkers A and B. The amplification product is treated with an anchor enzyme, and the free ditag moieties are ligated into a chain in a standard ligation reaction. The amplification product is then cloned. Determination of the nucleotide sequence of the clones can be used to obtain a readout of a continuous length of ditags. The presence of mRNA corresponding to each tag can then be identified from the nucleotide sequence of the clones and the information on the sequence tags.

[0125] Quantitative reverse transcriptase PCR (qRT-PCR) can also be used to determine the expression profile of biomarkers (see, e.g., U.S. Patent Application Publication No. 2005 / 0048542A1; incorporated herein by reference in its entirety). The first step in gene expression profiling by RT-PCR is the reverse transcription of the RNA template into cDNA, followed by its exponential amplification in a PCR reaction. The two most commonly used reverse transcriptases are aviromyoblastosis virus reverse transcriptase (AMV-RT) and Moloney murine leukemia virus reverse transcriptase (MLV-RT). The reverse transcription step is typically primed using specific primers, random hexamers, or oligo-dT primers, depending on the situation and the goal of expression profiling. For example, extracted RNA can be reverse transcribed using a GeneAmp RNA PCR kit (Perkin Elmer, Calif., USA) according to the manufacturer's instructions. The derived cDNA can then be used as a template in a subsequent PCR reaction.

[0126] The PCR step can use a variety of thermostable DNA-dependent DNA polymerases, but typically employs Taq DNA polymerase, which has 5'-3' nuclease activity but lacks 3'-5' proofreading endonuclease activity. Thus, TAQMAN PCR typically utilizes the 5'-nuclease activity of Taq or Tth polymerase to hydrolyze hybridization probes bound to its target amplicon, although any enzyme with equivalent 5' nuclease activity can be used. Two oligonucleotide primers are used to generate an amplicon typical of a PCR reaction. A third oligonucleotide, or probe, is designed to detect the nucleotide sequence located between the two PCR primers. The probe is non-extendable by the Taq DNA polymerase enzyme and is labeled with a reporter fluorescent dye and a quencher fluorescent dye. Laser-induced emission from the reporter dye is quenched by the quenching dye when the two dyes are positioned in close proximity as they are on the probe. During the amplification reaction, the Taq DNA polymerase enzyme cleaves the probe in a template-dependent manner. The resulting probe fragments dissociate in solution, and the signal from the released reporter dye is free of the quenching effect of the second fluorophore. Each time a new molecule is synthesized, one molecule of reporter dye is liberated, and detection of the unquenched reporter dye provides the basis for quantitative interpretation of the data.

[0127] TAQMAN RT-PCR can be performed using commercially available instruments such as, for example, the ABI PRISM 7700 Sequence Detection System. (Perkin-Elmer-Applied Biosystems, Foster City, Calif., USA) or the Lightcycler (Roche Molecular Biochemicals, Mannheim, Germany). In a preferred embodiment, the 5'-nuclease procedure is performed on a real-time quantitative PCR device such as the ABI PRISM 7700 Sequence Detection System. The system is composed of a thermocycler, a laser, a charge-coupled device (CCD), a camera, and a computer. The system includes software to run the instrument and analyze the data. The 5'-nuclease assay data is initially expressed as Ct, or threshold cycle. Fluorescence values ​​are recorded during each cycle and represent the amount of product amplified up to that point in the amplification reaction. The point at which the fluorescent signal is first recorded as being statistically significant is the threshold cycle (Ct).

[0128] To minimize the effects of error and sample-to-sample variation, RT-PCR is usually performed using an internal standard. An ideal internal standard is expressed at a constant level among different tissues and is not affected by experimental treatments. The RNAs most frequently used to normalize patterns of gene expression are the mRNAs of the housekeeping genes glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) and beta-actin.

[0129] A more recent variation of the RT-PCR technique is real-time quantitative PCR, which measures the accumulation of PCR products via a dual-labeled fluorogenic probe (i.e., TAQMAN probe). Real-time PCR is compatible with both quantitative competitive PCR, in which an internal competitor of each target sequence is used for normalization, and quantitative comparative PCR, which uses a normalization gene contained within the sample or a housekeeping gene for RT-PCR. For further details, see, for example, Held et al., Genome Research 6:986-994 (1996).

[0130] Next generation sequencing methods such as RNA sequencing (RNA-seq) can also be used to assess RNA abundance in sample cells. Exemplary protocols for performing RNA-seq include the RNA ACCESS® protocol or the TRUSEQ® RIBO-ZERO® protocol (ILLUMINA®). Those skilled in the art will also recognize many methods for performing RNA-seq, including but not limited to total RNA sequencing, mRNA sequencing, 3'mRNA sequencing, 5'mRNA sequencing, and CAGE-Seq.

[0131] Biomarker data may be analyzed by a variety of methods to identify biomarkers and determine the statistical significance of differences in levels of biomarkers observed between test and reference expression profiles to assess whether a patient is susceptible to a sLRI. In certain embodiments, patient data is analyzed by one or more methods, including, but not limited to, multivariate linear discriminant analysis (LDA), receiver operating characteristic (ROC) analysis, principal component analysis (PCA), ensemble data mining methods, Bayesian generalized linear models, Gaussian processes, naive Bayes, elastic nets, k-nearest neighbors, lasso, penalized logistic regression, partial least squares, predictive analysis of microarrays (PAM), Poisson linear discriminant analysis, negative binomial linear discriminant analysis, neural networks, support vector machines, significance analysis of microarrays (SAM), cell-specific significance analysis of microarrays (csSAM), spanning tree progression analysis of density-normalized events (SPADE), and multidimensional protein identification technology (MUDPIT) analysis.(See, e.g., Hilbe (2009) Logistic Regression Models, Chapman & Hall / CRC Press; McLachlan (2004) Discriminant Analysis and Statistical Pattern Recognition. Wiley Interscience; Zweig et al. (1993) Clin. Chem. 39:561-577; Pepe (2003) The statistical evaluation of medical tests for classification and prediction, New York, NY: Oxford; Sing et al. (2005) Bioinformatics 21:3940-3941; Tusher et al. (2001) Proc. Natl. Acad. Sci. USA 98:5116-5121; Oza (2006) Ensemble data mining, NASA Ames Research Center, Moffett Field, CA, USA; English et al. al. (2009) J. Biomed. Inform. 42(2):287-295, Zhang (2007) Bioinformatics 8:230, Shen-Orr et al. (2010) Journal of Immunology 184:144-130, Qiu et al. al.(2011) Nat.Biotechnol.29(10):886-891, Ru et al.(2006) J.Chromatogr.A.1111(2):166-174, Jolliffe Principal Component Analysis(Springer Series in Statistics,2. nd edition, Springer, NY, 2002; Koren et al. (2004) IEEE Trans Vis Comput Graph 10:459-470).

[0132] A preferred method of analyzing the biomarker data, i.e., gene expression data, is by a random forest classifier.

[0133] Random Forest Classifier A random forest classifier is an ensemble classifier that is composed of many decision trees and outputs a class that is the mode of the classes output by the individual trees. Random forests utilize bootstrapping instead of cross-validation. At each iteration, random samples (with replacement) are drawn and the largest possible tree is grown. Each tree receives a vote in the final class prediction. The number of trees (e.g., bootstrap iterations) is specified to fit the random forest. The random forest algorithm measures the importance of a biomarker by the average decrease in training accuracy. The random forest method uses a large number of different decision trees. A biomarker is considered to have discriminatory significance if it serves as a decision split of a decision tree from a significant random forest analysis.

[0134] A random forest (or forests) is an ensemble classifier that consists of many decision trees and outputs a class that is the mode of the classes output by the individual trees. (Breiman,Leo(2001).“Random Forests”.Machine Learning 45(1):5-32). Random forests are one of the most accurate learning algorithms available, i.e., they generate highly accurate classifiers for a data set. (Caruana,Rich;Karampatziakis,Nikos;Yessenalina,Ainur(2008)“An empirical evaluation of supervised learning in high dimensions.”Proceedings of the 25th International Conference on Machine Learning(ICML)). This method combines “bagging” with random selection of features to build an ensemble of decision trees with controlled variation. The selection of a random subset of features is an example of a random subspace method, which is a method to implement probabilistic discrimination. Bootstrap distribution is used as a method to estimate the variance of statistics based on the original data. For each tree grown in a bootstrap sample, say 150 or 500, the error rate of observations left outside the bootstrap sample is monitored. This is called the "out-of-bag" error rate.

[0135] Each tree is constructed using the following algorithm: (1) let N be the number of training examples and M be the number of variables in the classifier, (2) let m be the number of input variables used to determine the decision at the node of the tree (m should be much smaller than M), (3) select a training set for this tree by selecting n times with replacement from all N available training examples (i.e., taking a bootstrap sample), and estimate the error of the tree by using the remaining examples to predict their class, (4) for each node of the tree, randomly select m variables to base the decision at that node on, and compute the best split based on these m variables in the training set, and (5) each tree is fully grown and is not pruned (as can be done when building a regular tree classifier).

[0136] For prediction, new samples are pushed down the tree. The terminal nodes are assigned the labels of the training samples. This procedure is repeated for all trees in the ensemble, and the mode votes of all trees are reported as the Random Forest prediction.

[0137] In one embodiment, random forest analysis, including classification and regression based on forests of trees using random inputs, is performed using "randomForest: Breiman and Cutler's random forests for classification and regression" (Depends: R (>=2.5.0), stats) (Version: 4.6-6) (2012-01-06) (Fortran original by Leo Breiman and Adele Cutler, R port by Andy Liaw and Matthew Wiener). See A. Liaw and M. Wiener (2002). Classification and Regression by randomForest. R News 2(3), 18-22.

[0138] Random forests are further described in Liaw and Wiener, R News Vol. 2 / 3, December 2002, pgs. 18-22; Dfaz-Uriarte and Alvarez, BMC Bioinformatics. 2006 Jan. 6;7:3); Statnikov et al., BMC Bioinformatics. 2008 Jul. 22;9:319; Shi et al., Mod Pathol. 2005 April;18(4):547-57; Breiman, 1999, "Random Forests-Random Features," Technical Report 567, Statistics Department, UC Berkeley, September 1999 (each of which is incorporated by reference in its entirety).

[0139] respiratory infections The present invention provides a method for determining susceptibility to a respiratory infection. Preferably, the respiratory infection is a lower respiratory tract infection. In any embodiment, the infection can be a bacterial or viral infection. The bacterial infection can be any of those described herein. The viral infection can be any of those described herein.

[0140] As used herein, the term respiratory infection refers to a viral or bacterial infection anywhere in the respiratory tract. Examples of respiratory infections include, but are not limited to, cold, sinusitis, throat infection, tonsillitis, laryngitis, bronchitis, pneumonia, or bronchiolitis. Preferably, in any embodiment of the present invention, the respiratory infection is a cold.

[0141] Individuals may be identified as having a respiratory tract infection by virus testing, and may show symptoms of itchy eyes, watery eyes, runny nose, stuffy nose, sneezing, sore throat, cough, headache, fever, malaise, fatigue and weakness.In one embodiment, subjects with respiratory infections may not have any other respiratory conditions.Detection of the presence or amount of virus can be performed by PCR / sequencing of RNA isolated from clinical samples (nasal wash, sputum, BAL) or serology.

[0142] Influenza (commonly referred to as "the flu") is an infectious disease caused by an RNA virus of the family Orthomyxoviridae (influenza virus) that affects birds and mammals. The most common symptoms of the disease are chills, fever, sore throat, muscle aches, severe headache, cough, weakness / fatigue, and general discomfort.

[0143] Influenza viruses constitute three of the five genera in the family Orthomyxoviridae. Influenza A and B viruses cocirculate during seasonal epidemics and can cause severe influenza infections. Influenza C virus infections are less common but can be severe and cause endemic disease.

[0144] Influenza A viruses can be subdivided into different serotypes or subtypes based on the antibody response to these viruses. Influenza A viruses are divided into subtypes based on two proteins on the surface of the virus: hemagglutinin (H) and neuraminidase (N). There are 18 different hemagglutinin subtypes and 11 different neuraminidase subtypes (H1-H18 and N1-N11, respectively). The subtypes identified in humans are H1N1, H1N2, H2N2, H3N2, H5N1, H7N2, H7N3, H7N7, H9N2, and H10N7.

[0145] Influenza has a major impact on public health and causes devastating health problems, including morbidity and even mortality, as well as serious economic consequences. Thus, there is a need for therapeutic agents that can prevent infection or reduce the severity of infection in individuals.

[0146] In any embodiment, the influenza infection for which prevention is required is an infection with a virus selected from the group consisting of influenza A, B, or C. Influenza A viruses can be subdivided into different serotypes or subtypes based on the antibody response to these viruses. Influenza A viruses are divided into subtypes based on two proteins on the surface of the virus: hemagglutinin (H) and neuraminidase (N). There are 18 different hemagglutinin subtypes and 11 different neuraminidase subtypes (H1-H18 and N1-N11, respectively). The subtypes identified in humans are H1N1, H1N2, H2N2, H3N2, H5N1, H7N2, H7N3, H7N7, H9N2, and H10N7.

[0147] In any embodiment of the invention, the condition may be caused by rhinovirus or respiratory syncytial virus (RSV). Further, in any embodiment of the invention, the virus-mediated exacerbation is rhinovirus or RSV mediated. The rhinovirus or RSV may be of any serotype described herein. Typically, the rhinovirus is a member of the RV-A, RV-B, or RV-C rhinovirus species.

[0148] In another aspect of the invention, the condition may be caused by viruses of the families / genera influenza, parainfluenza, coronavirus, adenovirus, and metapneumonvirus.

[0149] Treatment, Administration, Dosage and Formulations The present invention allows for the identification of individuals susceptible to respiratory infections, such as severe lower respiratory tract infections, early in life, which provides an opportunity for intervention in the form of pre-emptive therapy.

[0150] Exemplary pre-emptive therapies include palivizumab, prednisolone, omalizumab, or polybacterial preparations, and any combination thereof.

[0151] The term "respiratory" refers to the process by which oxygen is taken in and carbon dioxide is expelled through the bodily systems that include the nose, throat, larynx, trachea, bronchi, and lungs.

[0152] As used herein, the upper respiratory tract may include the following regions: nose and nasal passages, paranasal sinuses, pharynx, and the portion of the larynx above the vocal cords. Typically, the lower respiratory tract includes the portion of the larynx below the vocal cords, the trachea, the bronchi, and the bronchioles. The lungs may be included in the lower respiratory tract and include the respiratory bronchioles, alveolar ducts, alveolar sacs, and alveoli.

[0153] The term "respiratory disease" or "respiratory condition" refers to any one of several diseases that involve inflammation and affect components of the respiratory system, including the upper (including the nasal passages, pharynx, and larynx) and lower respiratory tract (including the trachea, bronchi, and lungs).

[0154] Symptoms of respiratory disease may include coughing, excessive sputum production, a feeling of shortness of breath, or chest tightness with audible wheezing. Exercise capacity may be very limited. In asthma, the FEV 1.0 The percentage of forced expiratory volume in 1 second (FEV) may be reduced, as may the peak expiratory flow rate during forced expiration. In COPD, FEV as a percentage of FVC 1.0 is typically reduced to less than 0.7. The impact of each of these conditions can also be measured by days missed from work / school, sleep disturbances, need for bronchodilators, and need for glucocorticoids, including oral glucocorticoids.

[0155] The presence, amelioration, treatment, or prevention of respiratory disease may be determined by any clinically or biochemically relevant method in a subject or by a biopsy therefrom. For example, parameters measured may be the presence or degree of lung function, signs and symptoms of obstruction, exercise tolerance, nighttime awakenings, days lost from school or work, bronchodilator use, inhaled corticosteroid (ICS) dose, oral (glucocorticoid) GC use, need for other medications, need for treatment, hospitalization.

[0156] The term "treatment" or "treating" of a subject includes the application or administration of a therapeutic compound as described herein for the purpose of delaying, retarding, stabilizing, curing, curing, alleviating, relieving, altering, repairing, non-aggravating, ameliorating, improving, or affecting a disease or condition, symptoms of a disease or condition, or the risk (or susceptibility) of a disease or condition. The term "treating" refers to any indicia of success in treating or ameliorating an injury, pathological condition, or condition, including any objective or subjective parameter, such as relief, remission, reduced rate of exacerbation, reduced severity of disease, stabilization, alleviation of symptoms, or making the injury, pathological condition more tolerable to the subject, slowing the rate of degeneration or decline, not debilitating the end point of degeneration, or improving the physical or mental well-being of the subject.

[0157] A positive response to therapy may be the prevention or attenuation of worsening of respiratory symptoms, such as asthma symptoms (exacerbations), following respiratory viral infection. This can be assessed by comparing the mean change in disease score from baseline to the end of the study period based on the Juniper Asthma Control Questionnaire (ACQ-6), and also by assessing the lower respiratory tract symptom score (LRSS-symptoms of chest pain, wheezing, shortness of breath, and cough) daily after infection / onset of cold symptoms. Changes from baseline lung function (peak expiratory flow PEF) can also be assessed, and a positive response to therapy may be a significant attenuation in the reduction of PEF. For example, a placebo-treated group will show a significant reduction of 15% in morning PEF at the peak of exacerbation, while a treated group will show a significant reduction in PEF of less than 15% change from baseline.

[0158] The treatment for use in the methods of the present invention is administered in an effective amount. The phrase "therapeutically effective amount" or "effective amount" refers to a treatment described herein that (i) treats a particular disease, condition, or disorder, (ii) alleviates, improves, or eliminates one or more symptoms of a particular disease, condition, or disorder, or (iii) delays the onset of one or more symptoms of a particular disease, condition, or disorder described herein. Unwanted effects, e.g., side effects, may appear along with the desired therapeutic effect. Thus, a practitioner balances the potential benefits against the potential risks when determining what is an appropriate "effective amount."

[0159] The exact amount required will vary from subject to subject, depending on the species, age, and general condition of the subject, mode of administration, etc. Thus, it may be impossible to specify an exact "effective amount." However, an appropriate "effective amount" in any individual case can be determined by one of ordinary skill in the art using only routine experimentation.

[0160] The treatments described herein may be formulated for intranasal administration, including as a dry powder, spray, mist, or aerosol, which may be particularly preferred for the treatment of respiratory infections.

[0161] Suitable formulations wherein the carrier is a liquid include aqueous or oily solutions of the active ingredient, for administration as, for example, a nasal spray or nasal drops. Alternatively, the treatment may be presented as a dry powder and administered only to the upper respiratory tract as defined herein.

[0162] The selection of a suitable carrier will depend on the particular type of administration envisaged. For administration via the upper respiratory tract, e.g., nasal mucosal surface, the active compounds of the treatments described herein may be formulated as buffered or unbuffered solutions, e.g., water or isotonic saline, or suspensions, for intranasal administration as drops or sprays. Preferably, such solutions or suspensions are isotonic with respect to nasal secretions and have about the same pH, e.g., in the range of about pH 4.0 to about pH 7.4, or pH 6.0 to pH 7.0. The buffer should be physiologically compatible and include, by way of example only, phosphate buffer. For example, a representative nasal decongestant is described as being buffered to a pH of about 6.2 (Remington's, supra, p. 1445). Of course, one skilled in the art can easily determine the appropriate saline content and pH of a non-toxic aqueous carrier for nasal and / or upper respiratory tract administration.

[0163] Other ingredients such as preservatives, colorants, lubricating or viscous mineral or vegetable oils, fragrances, natural or synthetic plant extracts such as aromatic oils, and humectants and viscosity enhancers, e.g., glycerol, known in the art, may also be included to provide additional viscosity, water retention, and a pleasant texture and odor to the formulation. For nasal administration of the treatments described herein, a variety of devices are available in the art for generating drops, droplets, and sprays. For example, the treatments described herein can be administered to the nasal passages by a simple dropper (or pipette) that includes a manually operated pump attached to one end, e.g., a glass, plastic, or metal dispensing tube whose contents are dripped by air pressure provided by a flexible rubber valve. EXAMPLES

[0164] Example 1 - Materials and Methods Study population Subjects were a subset of 50 individuals from the Childhood Asthma Study, a 10-year prospective birth cohort enrolled prenatally due to high risk for developing asthma, as previously described (Kusel et al., J Allergy Clin Immunol, 2007, 119:1105-1110; Holt et al., J Allergy Clin Immunol, 2019, 143:1176-1182 e1175; Kusel et al., Pediatr Infect Dis J, 2006, 25:680-686; Kusel et al., Eur Respir J, 2012, 39:876-882; Holt et al., J Allergy Clin Immunol, 2010, 125:653-659; Kusel et al., J Allergy Clin Immunol, 2005, 116:1067-1072). Acute respiratory infection was considered as sLRI if wheezing and / or fever were present in addition to chest rattle. Chest rattle was defined as a wet, noisy breathing sound heard from the child's chest, whereas wheezing was defined as an audible, expiratory, high-pitched whistling sound. Fever was defined by recording a temperature >38°C (digital thermometer) on two occasions taken >1 hour apart 48 hours after the onset of respiratory infection syndrome. History of respiratory viral infection was determined from detailed evaluation and nasopharyngeal aspirates (RT-PCR) collected during a home visit within 48 hours of symptom onset (Kusel et al., J Allergy Clin Immunol, 2007, 119:1105-1110; Kusel et al., Pediatr Infect Dis J, 2006, 25:680-686). Wheezing at age 5 years (Crwz5) was defined as any wheezing event (parent assessment) recorded in the 12 months prior to the 5-year follow-up. Asthma at age 5 years was defined as having a ever physician diagnosis of asthma, a prescription for asthma medication, and wheezing at age 5 years. A non-asthmatic determination at age 5 years was not having any of these criteria. Umbilical cord blood was collected at birth and peripheral blood was collected at 0.5, 1, 2, 3, 4, 5, and 10 years of age (as close to the date of birth as possible).

[0165] Immunophenotyping Cryopreserved CBMCs were thawed and washed with RPMI 1640 (Gibco) containing 10% non-heat inactivated FBS (Serana Australia). 10 μl of the cell mixture was stained with trypan blue and counted in a hemocytometer. For each sample, approximately 1 × 10 6 Aliquot 0.25 x 10 cells for the unstained control. 6The cells were pelleted by centrifugation at 1500 rpm (approximately 500 g) for 5 minutes at 4° C., and excess medium was removed by vacuum aspiration. Each sample was diluted with 50 μl of a master mix of monoclonal antibodies (CD19-FITC RRID:AB_395812, CD3-AF700 RRID:AB_396952, CD4-V500 RRID:AB_1937323, CD14-APC-Cy7 RRID:AB_1645464, HLA-DR-PerCP-Cy5.5 Cat. No. 347364, CD25-BV421 RRID:AB_11154578, CD127-BV605 RRID:AB_2738138, CD123-CF594 RRID:AB_11153664, CD11c-PE-Cy7 RRID:AB_10611859 [BD Bioscience] and FcεRIα-APC RRID:AB_10671394 [eBioscience]) were stored in cold FACS buffer (PBS + 1% BSA) in the dark at 4 °C for 30 min. Cells were washed, fixed, permeabilized with (Cytofix / Cytoperm buffer (BD Biosciences)) for 1 h and incubated with FoxP3-PE (intracellular, BD Biosciences) for 30 min. The same antibody batch was used for all samples at the dilution recommended by the manufacturer. Individual cells were acquired after quality control assessment (Rainbow calibration and CS&T beads (BD Biosciences)) before each cytometry run using the LSR-Fortessa platform with FACSDiva software (BD Biosciences), and unstained controls were included for each sample. First, samples were compensated and gated with FlowJo 10.3 software. Compensated FCS files were imported into the R (3.6.2) statistical environment and preprocessed with flowWorkspace and flowCore packages. Logicle transformation (flowCore) and batch correction (sva) were applied to all samples. Nonparametric paired (Wilcoxon sign rank test) or unpaired (Mann-Whitney U test) tests were used to determine differences between groups.

[0166] In vitro cell culture Samples were assigned to randomized blocks and cultured sequentially by the same personnel using consistent reagent / stimulus stocks. Cord blood erythrocytes were immunomagnetically depleted (EasySep kit, StemCell) and each sample was incubated in RPMI + 5% AB serum (non-heat inactivated, Sigma-Aldrich) with LPS (Enzo Biochem, 1 ng / ml), imiquimod (Invivogen, 5 μl / ml) and Poly(I:C) (Invivogen, (50 μl / ml)) and matched unstimulated controls for 18 h (37°C, 5% CO 2 ) for cytokine quantification. Aliquots of culture supernatants were stored at -20°C. Cell pellets were stored in Trizol (Invitrogen) at -20°C for RNA extraction.

[0167] Data Generation RNA-Seq: RNA was extracted in batches using the RNeasy MinElute kit (Qiagen) and extraction batch information was recorded. RNA concentration was measured (Bioanalyzer; Agilent, Santa Clara, USA) and found to be of good quality (RIN score; mean = 8.514, 95% CI = 8.46-8.567). A low-yield protocol was used with sequencing libraries prepared with the NEBNext UltraII kit (New England BioLabs, Massachusetts, USA) and sequenced on a NovaSeq 6000 (Illumina, San Diego, USA) platform at the Australian Genome Research Facility (AGRF, Melbourne, Australia) for sequencing (100 bp paired-end).

[0168] Cytokines: Concentrations of 48 cytokines (Bio-plex Pro, BioRad) were quantified simultaneously with a Luminex 200 system (Luminex). Analyte quantification (pg / ml) was determined by alignment to a standard curve. The cytokine panel includes CTACK, basic FGF, eotaxin, G-CSF, GM-CSF, GRO-α, HGF, IFN-α2, IFN-γ, IL-1β, IL-1ra, IL-1α, IL-2, IL-2Rα, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12(p40), I Contained L-12(p70), IL-13, IL-15, IL-16, IL-17A, IL-18, IP-10, LIF, MCP-3, MCP-1, M-CSF, MIF, MIG, MIP-1α, MIP-1β, β-NGF, PDGF-BB, SCF, SCGF-β, SDF-1α, RANTES, TNF, TRAIL, and VEGF. Nine cytokines were outside the detection limit in >20% of samples and were removed.

[0169] Data Preprocessing RNAseq: Binary base call (BCL) sequence files were converted to fastq files using the bcl2fastq pipeline (Illumina). Sequence data were processed with the MEdical Sequence Analysis Pipeline (MESAP) and aligned to the hg38 genome using HISAT2 (Pertea et al., Nat Protoc, 2016, 11:1650-1667), and counts were quantified using the summarizeOverlaps function from the GenomicAlignments R package. Pre- and post-alignment QC was assessed using FastQC and SAMStat, respectively.

[0170] Cytokines: Cytokines were excluded if more than 30% of the samples (excluding unstimulated samples) recorded out-of-range (OOR) values. This removed CTACK, IL-3, IL-7, IL-8, IL-13, IL-18, PDGF-BB, SCGFβ, SDF-1α from further analysis. The remaining OOR values ​​were imputed below / above the minimum / maximum values ​​based on a truncated normal distribution using the truncnorm function from the truncnorm R package, so that partial information is used to define values ​​below / above the detection limit of imputation. The most appropriate method of transformation and normalization was tested (data not shown), and ArcSinh transformation and Loess normalization were applied. Batch effects were removed using linear modeling (removeBatchEffect function from the limma R package).

[0171] Dimensionality reduction: The experimental design ensured that matched data were generated from the four conditions (unstimulated, LPS-, imiquimod-, and Poly(I:C)-stimulated) for each individual in the same batch. This allowed for a multilevel design for dimensionality reduction analysis (principal components analysis), whereby within-subject variance was decomposed from between-subject variance, greatly improving the power and interpretability of subsequent multivariate analyses. For this purpose, the withinVariation function was adapted from the mixOmics package in R (Rohart et al., PLoS Comput Biol, 2017, 13:e1005752).

[0172] Transcripts: After preprocessing, CBMC gene expression data for matched unstimulated, LPS-, imiquimod-, and Poly(I:C)-stimulated samples for 50 individuals were filtered to reduce noise to only significantly variable genes using the varianceBasedfilter function in R. For this analysis, genes were filtered at a ratio of 2.88 × 10, as determined by 0.05 / number of genes. -6A p-value lower than a strict threshold of 0.01 was considered significant (n=17356). This resulted in 5,885 genes for dimensionality reduction. Within variation of interest was calculated using the withinVariation function, genes were scaled to unit variation, and the PCA function from the FactoMineR package was used for principal component analysis (Le et al., J of Statistical Software, 2008, 25:1-18) and principal component scores and variability contributions were used to plot.

[0173] Cytokines: After pretreatment, within-subject variability was calculated (as above) from CBMC cytokine concentration data (n=39) for matched unstimulated, LPS-, imiquimod-, and Poly(I:C)-stimulated samples for 50 individuals. Principal component analysis was applied as above.

[0174] Transcriptome analysis EdgeR: A total of 50,019 raw transcripts were available for analysis after preprocessing. Raw transcripts were removed if they had no counts in any sample, lacked annotation, or had counts less than 0.5 per million in ≦25 samples. This strategy generated 17,363 transcripts for analysis. Data were normalized with the trimmed mean of M values ​​(TMM normalization (Robinson et al., Genome Biol, 2010, 11:R25)). Since the experimental design involved randomization of blocks (with respect to age and stimulation) into batches, there was no batch effect associated with cell culture batch number for these pairwise comparisons. For unpaired comparisons, no discernible batch effect was observed, but culture batch was still included as a covariate in the unpaired analyses (i.e., sLRI susceptibility in infancy). Unwanted variation was identified and removed using the RUVg function from the RUVSeq R package (Risso et al., Nat Biotechnol, 2014, 32:896-902), which models a set of empirical control genes (not significantly different between any comparisons of interest) to determine estimated technical effects (lanes, sequencing, etc.). A pairwise design was used for the analysis between matched stimulated and unstimulated samples, including trends identified by RUVg as covariates. The EdgeR (Robinson et al., Bioinformatics, 2010, 26:139-140) pipeline was run with default parameters, including estimateDisp, glmQLFit, glmLRT, and topTags. It fits a negative binomial generalized log-linear model to the counts for each gene and performs a genetic likelihood ratio test. For analyses between CBMC and matched samples at age 5 years (n=27), a paired design was employed that modeled differences between matched unstimulated and matched stimulated samples, as well as RUVg trends. Differences between primary outcomes were determined using an unpaired design that modeled unstimulated / stimulated and RUVg trends.Genes were considered significantly different if their p-value was less than 0.01 with the FDR-controlled Benjamini-Hochberg method and the Log2 fold change was greater than 1 (upregulated) or -1 (downregulated). For downstream network analysis, a matrix of corrected gene counts was generated. Size factors were estimated using the median ratio method with the estimateSizeFactors function, and a variance stabilizing transformation (VST) was applied to the count data using the varianceStabilizingTransformation function from the DESeq2 package (Love et al., Genome Biol, 2014, 15:550). RUVg trends associated with technical variation and culture batch were removed as covariates with the linear model with the removeBatchEffect function from the limma package.

[0175] Limma-voom: Transcript filters, normalization, and model design were performed the same as described for the EdgeR analysis. Data were transformed to log2 counts per million and mean-variance relationships were estimated to generate weights using the voom function. The limma (Ritchie et al., Nucleic Acids Res, 2015, 43:e47) pipeline was run with default parameters including lmFit, contrasts.fit, eBayes, and topTable functions. To determine gene significance, the same criteria as in the EdgeR analysis were applied. From this analysis, adjusted t-statistics calculated for each gene were plotted by module for each network as a way to display which modules were differentially regulated between stimulated and matched unstimulated samples. Adjusted t-statistics are expressed as M-values ​​(logarithmic) against their standard errors. 2The ratio of the fold change (fold change) of the module eigenvalues ​​(module specific genes) to the fold change (fold change) of the module eigenvalues ​​(module specific genes) was calculated as "adjusted" (empirical Bayes) across all genes. Applying the module specific genes instead of the adjusted t-statistics yielded the same overall results for module up / downregulation (data not shown). Modules with a median adjusted t-statistic above 2 are considered significantly upregulated and those below 2 are considered significantly downregulated.

[0176] Weighted Gene Co-expression Network Analysis: The corrected count data (described above) were used as input containing 17,363 genes for the analysis. For this analysis, three perturbation networks were created, each of which included the unstimulated samples and the corresponding stimulated samples (i.e., LPS, Imiquimod, and Poly(I:C) networks) (WGCNA (Zhang et al., Stat Appl Genet Mol Biol, 2005, 4:Article 17; Langfelder et al., BMC Bioinformatics, 2008, 9:559)). The varianceBasedfilter function was used to filter significantly variable genes (p-value < 0.01) for each condition, as well as the connected genes between the unstimulated samples and the respective stimulated samples. This strategy resulted in 6561, 6757, and 6764 genes available for the LPS, Imiquimod, and Poly(I:C) networks, respectively. Soft power was calculated with the pickSoftThreshold function with the networkType parameter set to "signed". This resulted in soft powers of 7, 8, and 7 for the LPS, imiquimod, and Poly(I:C) networks, respectively. Adjacency and topological overlap matrices (TOM) were created with the adjacency and TOMsimilarity functions, respectively, specifying the "signed" network. The TOM dissimilarity matrix was calculated by the 1-TOM similarity matrix (1-TOM). Modules were identified by hierarchical clustering using the hclust function (method = "average") and cutreeDynamic function (method = "hybrid", deepSplit = 2, minClusterSize = 50). TOM plots were created using the TOMplot function. Module eigengenes were calculated with the module Eigengenes function. Modules were determined by their eigengenes, hierarchical clustering, and dendrogram correlation cut at 0.1 using the mergeCloseModules function, and were merged if they were similar.Network statistics and intramodular connectivity were calculated using the intramodularConnectivity function. Modules were annotated with a consensus approach by assessing significantly enriched pathways from: Gene Ontology Term Enrichment (GOenrichmentAnalysis), ReactomePA (Yu et al., Mol Biosyst, 2016, 12:477-479) and clusterProfiler (Yu et al., Omics, 2012, 16:284-287) R packages, InnateDB (Breuer et al., Nucleic Acids Res, 2013, 41:D1228-1233), and identifying top module genes (Log. 2-FC / gene connectivity). Module preservation between networks was calculated using the modulePreservation function with 200 permutations, networkType set to "signed", and "gold" (random) module size set to the average module size for each comparison. Ranked expression was calculated as the (rank) average expression of each gene across all samples, and ranked connectivity was calculated using the (rank) softConnectivity function, type set to "signed", and power set to the soft power of the corresponding network. Soft connectivity is defined as the sum of the adjacencies (co-expression measures) of each gene to all other genes in the network. We determined connectivity density with density functions and used the Sheather-Jones smoothed bandwidth method. Connectivity density was assessed for normal distribution using the Lilliefors test of normality (lillie.test function). We also calculated a Spearman correlation matrix for each module to assess intra-module connectivity, defined as the sum of the correlation values ​​of each gene to all other genes, separately. A network wiring diagram of the top 20 most connected genes was constructed using the graph_from_adjacency_matrix function from the igraph R package. Node sizes represent the number of connections (degrees) across the network, and edges indicate the strength of connections (red edges indicate correlations >0.8).

[0177] Master Regulator Analysis Gene regulator networks were reverse engineered with ARACNe (Margolin et al., BMC Bioinformatics, 2006, 7Suppl1, S7) and transcription factor activities were inferred with VIPER (Alvarez et al., Nat Genet, 2016, 48:838-847). Significant (p<0.05) TFs were considered drivers of the response if they had known binding motifs in the regions of regulon target genes determined by RcisTarget (Aibar et al., Nat Methods, 2017, 14:1083-1086). Normalized expression scores (NES) output from VIPER were retained for downstream analysis.

[0178] Machine Learning Gene expression data were randomly assigned to training (50%) and validation (50%) sets and filtered to only the respective module genes for each analysis. The same random assignment was applied to all models. For the validation model, the CAS cohort data was filtered to the respective module genes and used as the training set, and the external gene expression data was used for validation (filtered to the same input genes).

[0179] The Random Forest package was used in R for model building, and the number of decision trees (ntrees) and candidate variables (mtry) were optimized according to the out-of-bag error rate. Random Forest (RF) analysis was performed using modules defined by WGCNA, which clusters genes according to their co-expression, revealing that module member genes exhibit high multicollinearity, a recognized effect on RF interpretation (Strobl et al., BMC Bioinformatics, 2008, 9:307; Tolosi et al., Bioinformatics, 2011, 27:1986-1994). However, collinearity mainly affects the interpretation of variable importance and not the overall model prediction accuracy. For this reason, the RF classifier used in this study was mainly used to study the utility of IFN module genes for predicting outcomes, and the variable importance measures (although reported in the figures) should be considered as an underestimate of their true value.

[0180] CAS cohort: To account for potential differences in baseline / unstimulated CBMC gene expression between individuals, we took delta values ​​from the matched stimulated gene expression profiles (e.g., LPS stimulated gene expression - matched unstimulated gene expression = adjusted LPS stimulated gene expression matrix) and used these as input. Genes were filtered to only those present in the IFN module of the corresponding response. Subjects (n=50) were randomly assigned to either the test or validation set (50 / 50 split) and the same random assignment was applied to the LPS, imiquimod and Poly(I:C) datasets. The randomForest function (randomForest R package) optimized each RF model with respect to the number of variables randomly sampled as candidates at each split ("mtry") and the number of decision trees to grow ("ntree") to classify individuals who had and had not experienced sLRI in infancy. For the mtry parameter, we defined a sequence of 1 increments from the lower of 10 or the square root of the number of input genes up to a maximum of 5 times the square root of the number of input genes. RF classifiers were constructed for all numbers in the sequence with ntree set to 1000, and the mtry value that recorded the smallest out-of-bag error rate (OOBer) was selected as optimal. In the case of ties for OOBer, the smallest number was selected. For the ntree parameter, sequences from 500 to 10,000 in increments of 100 were defined, and RF classifiers were constructed for all numbers in the sequence with mtry set optimally as defined above. The optimal ntree was selected as the one that produced the smallest OOBer (the smallest number in the case of ties). This approach yielded mtry values ​​of 11, 23, and 121, and ntree values ​​of 5400, 1000, and 1000 for the LPS, imiquimod, and Poly(I:C) RF models, respectively. Trained models internally bootstrapped (70 / 30 split) on the training set reported OOBers of 36%, 72%, and 52% for the LPS, imiquimod, and Poly(I:C)RF models, respectively.Following optimization, the final RF classifier was used to predict the primary outcome status of the corresponding validation set (without class labels) using a prediction function (STATS R package). Predictions were compared to true values ​​and calculated true and false positive rates as well as the area under the receiver operating characteristic (ROC) curve using prediction and performance functions from the ROCR R package.

[0181] Training / validation set resampling: To test the reproducibility of the RF classifiers, the adjusted LPS, Imiquimod, and Poly(I:C) datasets were randomly resampled 2000 times each with respect to their training / validation set assignment (50 / 50 split). The same 2000 random assignments were used for the LPS, Imiquimod, and Poly(I:C) classifiers. The RF classifiers were built on the training set and tested on the validation set (as above) for each resample (using the optimal parameters pre-determined above), and the area under the ROC curve was recorded each time to determine the prediction accuracy. To evaluate whether different proportional assignments of the training and validation sets yielded similar results, the RF classifiers of the adjusted LPS, Imiquimod, and Poly(I:C) datasets were randomly resampled 1000 times with training / validation assignments of 60% / 40% and 70% / 30% (with respect to the training / validation set assignments), respectively. The same 1000 random assignments were used for the LPS, Imiquimod, and Poly(I:C) classifiers, which were constructed with the parameters (visualized) above. The area under the ROC curve was recorded for each resampling to determine the prediction accuracy.

[0182] Validation on external cohorts: We trained RF classifiers on CBMC data and used them to classify samples derived from a set of publicly available datasets from the Gene Expression Omnibus. In general, unstimulated samples from the CAS cohort were used to represent "healthy" individuals (absence of infection), and stimulated samples were used to represent anti-bacterial (LPS) and anti-viral (Imiquimod / Poly(I:C)) innate immune responses in infants / children with confirmed infection. GSE72809: RF classifiers were trained on conditioned LPS and Imiquimod / Poly(I:C) stimulated CBMC gene expression datasets (n=50 each) and used to predict children hospitalized with bacterial (n=52) and viral (n=92) infections from healthy controls (n=52) from blood-derived gene expression profiles, respectively. Model optimization was performed and model prediction accuracy was tested on external gene expression profiles as previously described (above). GSE113211:RF classifiers were trained on adjusted Imiquimod and Poly(I:C) stimulated CBMC gene expression datasets (n=50 each) and used to predict hospitalization with acute viral bronchiolitis in infants (<18 months, n=15) and toddlers (18 months-5 years, n=16) from matched samples taken post-recovery (asymptomatic, 8.8±2.5 weeks) from PBMC samples. Model optimization was applied and the predictive accuracy of the model was tested on external gene expression profiles for all subjects, as well as for infants and children, as previously described (above). GSE115770: RF classifiers were trained on adjusted Imiquimod and Poly(I:C) stimulated CBMC gene expression datasets (n=50 each) and used to predict study visits from asthmatic children (ages 6-17 years) with viral-associated (n=193) "cold"-like illness to asthmatic children (n=105) with non-viral "cold"-like illness (samples taken 1-6 days after onset), some of whom later experienced an exacerbation (58 did, 25 did not).Model optimization was performed and model prediction accuracy was tested separately for nasal and blood-derived gene expression profiles for viral infections given cold symptoms and for viral infections given exacerbations, as previously described (above).

[0183] Multi-omics Data Integration A DIABLO (Singh et al., Bioinformatics, 2019, 35:3055-3062) model was constructed for supervised multi-omics data integration, which generalizes partial least squares analysis to maximize co-expression between matched datasets. Prior to analysis, all datasets (except immunophenotyping) were baseline adjusted and gene expression data were filtered to significantly variable genes (n=6353) to reduce noise. The number of components and feature selection parameters were adjusted with 5-fold cross-validation.

[0184] statistical analysis All statistical analyses were calculated in the R environment (version 3.6.2) and graphs were generated from R or Prism software (version 8, GraphPad Software, La Jolla California USA). Non-parametric statistical methods were applied to test group differences (Mann-Whitney U test (unpaired analysis) and Wilcoxon signed rank test (paired analysis); wilcox.test function [stats R package]) and correlations (Spearman's rank correlation coefficient; cor.test function [stats R package]). For comparison of study population characteristics, Fisher's exact test (fisher.test function [stats R package]) was used to calculate odds ratios, 95% confidence intervals, and associated P values ​​for categorical variables, and Mann-Whitney U test was used to determine P values ​​for continuous variables.

[0185] Example 2 - Clinical characteristics of the study population The study population consisted of a subset of 50 children within the Childhood Asthma Study (CAS) cohort. Twenty-three subjects (46%) experienced at least one wheeze and / or febrile sLRI during the first year (infancy), which was the primary outcome of interest (Table 1). The prevalence of wheeze (OR=2.48) and asthma (OR=2.86) at age 5 years was higher in susceptible individuals, but this was not statistically significant. No differences were found with regard to sex, gestational age, birth weight, skin prick test positivity rate, and the primary outcome URI in infancy. Overall, this subset was found to be representative of the CAS cohort (n=263) with regard to key clinical characteristics (Table 1). Rhinovirus was the most frequent viral agent identified from the first year of life in this subset (present in 56.9% of infectious nasopharyngeal samples), followed by RSV (13.125%) (data not shown).

[0186] [Table 1]

[0187] Example 3 - Baseline Flow Cytometry An 11-color flow cytometry panel was applied to baseline cord blood mononuclear cell (CBMC) samples to assess cellular composition. Lymphocytes (T and B cells) constituted the majority of cell types identified among CBMC (Figure 1B). CD14+ monocytes and conventional dendritic cells (cDCs) were identified among the myeloid compartment, along with smaller proportions of plasmacytoid DCs (pDCs) and basophils (Figure 1B). There were no differences in baseline cellular composition with respect to sLRI in the first year of life (data not shown).

[0188] Example 4 - Multi-omics profiling of innate immune responses in CBMCs CBMCs from all 50 subjects were cultured for 18 hours with LPS (TLR4), or imiquimod (TLR7), or Poly(I:C) (TLR3) to induce innate immune responses along with unstimulated controls (Figure 1A). This time point was chosen to capture downstream signaling cascades of the immediate and secondary response programs (Shalek et al., Nature, 2014, 510:363-369; Jovanovic et al., Science, 2015, 347:1259038; Lawlor et al., Front Immunol, 2021, 12:636720). Gene expression was profiled from cell pellets (RNAseq) and cytokines were profiled using supernatants (multiplex assays). Matched PBMC samples collected at age 5 years were available for a subset of subjects (n=27) and these were cultured in parallel under the same conditions. After data pre-processing and filtering, 17,363 transcripts and 39 cytokines were available for analysis (see Example 1 for methods). For exploratory data analysis, unsupervised principal component analysis (PCA) dimensionality reduction was applied. As expected, samples from each omics layer clustered by stimulus (Figure 1C). For transcripts and cytokines, the first two principal components captured interferon (IFN) and pro-inflammatory signatures (e.g., CXCL10 / IP-10, IL-1β, IL-6) (Figure 1D).

[0189] Example 5 - IFN and pro-inflammatory gene expression programs are upregulated in CBMC responses Initial analyses focused on transcriptomics data as these data provide genome-wide coverage. Using differential expression analysis, 641 differentially expressed genes (DEGs) were identified for the cord blood LPS response (Log2 fold change >1, adjusted P value <0.01) and over 1000 DEGs for the imiquimod and Poly(I:C) responses (Figure 2A-C, left panels). Pathway analysis (InnateDB; Breuer et al., Nucleic Acids Res, 2013, 41:D1228-1233) identified enrichment of cytokine and chemokine signaling pathways from genes upregulated in all responses, with the IFN signaling pathway being prominent in the imiquimod and Poly(I:C) CBMC responses (Figure 2A-C; right panels). In particular, viral stimulation triggered a common set of 429 upregulated genes, constituting a core antiviral response shared between TLR3 and TLR7 activation (data not shown). In addition, 462 and 243 genes were identified that were specifically upregulated in response to Poly(I:C) or imiquimod, respectively, indicating unique signaling pathways downstream of TLR3 or TLR7 activation (data not shown). CIBERSORTx was then used to infer the post-culture cellular composition from the RNA-Seq data (Newman et al., Nat Biotechnol, 2019, 37:773-782). Prominent cell types included monocytes, B cells, and CD4+ T cells (data not shown). The proportion of red blood cells was negligible as a result of immunomagnetic depletion (see Example 1 for methods). Changes in cellular composition were identified between stimulation and age, but not sequence order, sex, or encounter with sLRI during infancy. Further investigation of variations in innate immune gene expression was also performed in matched samples collected at birth versus 5 years of age (n=27 per age / stimulus) (data not shown). Interestingly, LPS responses at 5 years of age were characterized by upregulation of IFN-related genes, including IRF1, STAT1, and IFIT1-3, compared with those at birth (Table 2).In contrast, no differential expression of IFN-related genes was observed between birth and age 5 years following imiquimod or Poly(I:C) stimulation (data not shown). Finally, there were no genes that were significantly different between sLRI-resistant and -susceptible individuals in infancy for any condition from this analysis, suggesting that sLRI risk is not conferred solely by individual gene expression magnitude (data not shown).

[0190] [Table 2]

[0191] Example 6 - Identification of co-expression networks underlying innate immune function Genes do not function in isolation, but work together in networks. To elucidate the overall connectivity and functional organization of gene expression, we used weighted gene co-expression network analysis (WGCNA). This analysis identified 11, 11, and 8 co-expression modules for LPS, imiquimod, and Poly(I:C) responses, respectively (Figure 2D-F). All responses showed upregulation of IFN and pro-inflammatory modules, and because essential components of the cord blood innate response had already been identified, they were carried forward for downstream analysis (Figure 2D-F, Figure 6A). The LPS response had the smallest IFN module (180 genes; Table 3) compared to imiquimod (1114 genes) and Poly(I:C) (2201 genes), while the reverse was true for the proinflammatory module (LPS, 2297 genes; imiquimod, 924 genes; Poly(I:C), 646 genes) (Figure 2G). Notably, there was substantial overlap between the IFN and proinflammatory module genes of the different stimuli, especially between Poly(I:C) IFN and LPS proinflammatory modules (n=385 genes) (data not shown).

[0192] [Table 3-1]

[0193] [Table 3-2]

[0194] Next, gene network patterns between each response were compared. First, module preservation statistics were calculated, and the results showed that the LPS-induced IFN module was highly conserved within the IFN module of the imiquimod and Poly(I:C) responses, but not vice versa (data not shown). The IFN modules associated with the imiquimod and Poly(I:C) responses were preserved within each other, and the pro-inflammatory modules were preserved among all responses (data not shown). Second, ranked gene expression and ranked connectivity were calculated to compare the modules. Significant differences were observed between the expression magnitude (rho=0.88 and 0.82) and intramodule connectivity (rho=0.57 and 0.59) between the cord blood LPS-induced IFN module genes and the same genes after imiquimod and Poly(I:C) stimulation, respectively (Figure 2H). To examine intramodular connectivity, we plotted connectivity density across all genes within each module and identified the most connected genes (Figure 3A, Figure 3B-D, left panels). Connectivity in the LPS-induced IFN module was characterized by a normal distribution, whereas viral stimulation produced a left-skewed distribution (Figure 3A). Key IFN signaling genes (e.g., IRF1, STAT1) were present among the most connected genes within the LPS-induced IFN module, but the intensity of the most connected genes was reduced compared to the virally stimulated IFN module (Figure 3B-D; left panels). The LPS-induced proinflammatory module showed greater connectivity compared to the imiquimod or Poly(I:C)-induced proinflammatory modules (data not shown). Genes encoding innate immune / proinflammatory cytokines (e.g., IL1A / B, CXCL2 / 3 / 8) were among the most connected genes within the proinflammatory modules of all responses at birth (data not shown). In summary, viral and bacterial stimuli activated overlapping sets of proinflammatory and IFN-responsive genes, but the underlying network structures were strikingly different.

[0195] Example 7 - Identification of master regulators of innate immune function at birth and 5 years of age VIPER (Alvarez et al., Nat Genet, 2016, 48:838-847) analysis was used to identify master regulators predicted to drive the module connectivity patterns. This approach revealed that the LPS-induced IFN module was putatively driven by BATF3, STAT3 and IRF1 transcription factors (TFs) at birth, whereas the top drivers of the imiquimod and Poly(I:C)-induced IFN modules included multiple STAT (e.g., STAT2) and IRF (e.g., IRF7) TFs (Figure 3B-D; right panel, Figure 6B). IRF1 was found to regulate 52 genes of the 180 LPS-induced IFN module genes (Table 4). Proinflammatory modules for all three responses were enriched for CEBPB, AP-1 (e.g., JUN, FOSL1) and NF-kB (e.g., NFKB2, RELB) (data not shown). Importantly, repeated analyses with input genes restricted to only those preserved from the LPS-responsive IFN (169 / 180, 93.89%) (Figure 7) and pro-inflammatory (443 / 2297, 19.29%) modules (data not shown) did not change the data. Finally, we compared gene network patterns between CMBC and matched PBMC samples (n=27) collected at 5 years of age. Connectivity of the LPS-induced IFN module was significantly higher at 5 years of age compared to birth, suggesting that the wiring of this module is subject to developmental regulation (Figures 3E and 3F). In addition, IRF1 enrichment was identified only from umbilical cord blood (Figure 3G). In contrast, IFN responses induced by imiquimod and Poly(I:C) stimulation showed relatively similar connectivity patterns between birth and 5 years of age, and in support of this, putative drivers were also comparable between birth and 5 years of age (e.g., STAT2, IRF7) (Figures 3H&I, Figure 8). Imiquimod and poly(I:C) pro-inflammatory modules were characterized by decreased intramodular connectivity in blood drawn at 5 years of age compared to birth (data not shown).

[0196] [Table 4]

[0197] Example 8 - Innate immune responses at birth predict sLRI in the first year of life To determine whether the innate immune response at birth could predict sLRI in the first year of life, we randomly assigned the dataset to a training (50%, n=25) and validation set (50%, n=25) and trained a random forest classifier on the CBMC IFN module. Surprisingly, the classifier trained on the LPS-induced IFN module genes was able to predict sLRI in the first year of life with 72% accuracy on the validation dataset (area under the receiver operating characteristic curve = 0.724) (Figure 4A, Figure 9). In contrast, classifiers built from the imiquimod or Poly(I:C) data did not predict sLRI in the first year of life (Figure 4A). The predictive random forest model also showed that some of the 180 genes provided higher accuracy than others, as quantified by the mean decrease Gini or mean decrease accuracy (Table 5 and Figure 9A). For Gini index importance scoring, a statistical threshold (two median absolute deviations above the median) was used to determine which genes were most predictive from the model. Fourteen genes were above this threshold (Table 5).

[0198] [Table 5]

[0199] To test whether this finding was reproducible given the relatively small number of samples available as input, we repeated the analysis by randomly resampling the membership of subjects in the training / validation sets, and again found that only LPS-induced IFN module genes were able to predict sLRI in infancy, on average, better than chance (Figure 10). Furthermore, a strikingly different connectivity pattern was observed for the LPS-induced IFN module with respect to sLRI susceptibility in infancy that was not evident from the Imiquimod or Poly(I:C) IFN modules (Figure 4B & C). Specifically, susceptible individuals had a stronger gene network pattern for the LPS-induced IFN module, although the putative drivers of response were comparable (IRF1, STAT3, BATF) (Figure 4B, DE(i)). Furthermore, restricting Imiquimod and Poly(I:C) responses to only those genes of the LPS-induced IFN module did not reveal any notable differences in connectivity patterns or drivers in relation to sLRI susceptibility in infancy (Figure 11). Whereas the connectivity density plot of LPS-induced IFN modules in CBMCs of susceptible individuals (Figure 4B) resembled the overall connectivity density of PBMC connectivity at age 5 years (Figure 3F), intramodule connectivity was not related (data not shown), suggesting that the similarity emerges from different processes. Module eigengenes were also calculated to summarize overall module expression and correlate with clinical traits. Data showed that cord blood LPS-induced IFN module eigengenes stratified individuals susceptible to sLRI in the first year of life (p=0.016), as well as individuals with asthma at age 5 years (p=0.015) and wheezing at age 5 years (p=0.02) (Figure 4F, Figure 12). This result was only significant for LPS responses and was specific to the IFN module (Figure 4G, Figure 12).

[0200] Example 9 - Validation of interferon response at birth in an external cohort To investigate whether the above findings relating to IFN module gene expression profiles induced by CBMCs in culture reflect naturally occurring IFN responses to pediatric infections in vivo, RF classifiers were trained on CBMC data and used to classify samples derived from a series of publicly available datasets from the Gene Expression Omnibus. The first dataset included whole blood gene expression profiles from children (<17 years) with febrile illness requiring hospitalization with confirmed bacterial (n=52) or viral (n=92) infections versus healthy controls (n=52) (GSE72809; Herberg et al., JAMA, 2016, 316:835-845). RF classifiers trained on LPS and Imiquimod / Poly(I:C) data were found to accurately predict children with bacterial (AUC=0.889) and viral (AUC=0.874 / 0.838) infections, respectively (Figure 5A-C, Figure 13). The second dataset consisted of PBMC samples from infants (<18 months, n=30) and toddlers (18 months-5 years, n=32) hospitalized with acute viral bronchiolitis (GSE113211; Jones et al., Am J Respir Crit Care Med, 2019, 199:1537-1549). Classifiers built on either unstimulated, imiquimod (AUC=0.8) or Poly(I:C) (AUC=0.877) treated CBMCs were able to accurately stratify samples collected during acute illness compared to matched post-recovery samples (asymptomatic, 8.8±2.5 weeks post-infection), regardless of age. The model performed well separately for infants (AUC=0.922, Poly(I:C); AUC=0.827, Imiquimod) and children (AUC=0.789, Poly(I:C); AUC=0.842, Imiquimod) (data not shown).The third dataset consisted of nasal-derived gene expression profiles from study visits of asthmatic children (ages 6-17) with virus-related or non-viral "cold"-like illness (1-6 days after onset), some of whom later experienced an exacerbation (n=83, 58 were virus positive) (GSE115770; Altman et al., Nat Immunol, 2019, 20:637-651). Symptomatic children with respiratory viral infections were accurately predicted from children who were symptomatic but virus negative by Imiquimod (AUC=0.8) and Poly(I:C) (AUC=0.832)-defined RF classifiers (Figure 5E). Furthermore, they classified virus-positive and virus-negative asthmatic children who subsequently experienced an exacerbation (within 10 days of symptom onset) with comparable accuracy (data not shown). In the same study, peripheral blood-derived gene expression profiles of viral vs. non-viral exacerbations were more accurately classified from imiquimod (AUC=0.671) than from the Poly(I:C)-defined classifier (AUC=0.627) (data not shown). However, it performed poorly when predicting virus-positive vs. virus-negative asthma independent of exacerbations from blood expression profiles in this cohort (data not shown).

[0201] Example 10 - Multi-omics integration Finally, multi-omics data integration (DIABLO; Singh et al., Bioinformatics, 2019, 35:3055-3062) was used to identify correlative molecular features across biological layers that confer sLRI risk. Input data consisted of CBMC baseline immune cell type ratios (n=8), variable mRNA transcripts (n=6353), VIPER-derived regulon activity scores (n=1224), and cytokine / chemokine proteins (n=39). The data reinforced that LPS-induced IFN signaling transcripts (IRF9, STAT1, GBP2 / 4) and IRF1 activity, in combination with T cell, monocyte and DC cell types, immune (HOXB4, NFIX) regulators, and pro-inflammatory cytokines / chemokines (IL-1β, MIP1α, MIF), are major determinants of risk for sLRI in the first year of life (data not shown). B and T cells harvested from cord blood were also shown to be a source of IFN signals, as indicated by the upregulation of IRF1 and STAT1 when stimulated with LPS (Figure 15). LPS-stimulated monocytes / dendritic cells and NK cells did not upregulate these genes (data not shown).

[0202] Since LPS-induced IRF1 activity was identified separately from network, master regulator, and integrative analyses, we further investigated IRF1 gene expression correlations. IRF1 gene expression at birth was positively correlated with selective STAT and IRF family transcription factors (e.g., STAT1, IRF9), pro-inflammatory mediators (e.g., IL-1β, IL-6, CCL3 / MIP-1α), and virus-associated receptors (e.g., ICAM1, IFIH1) (Figure 5D-F). In addition, CBMC STAT1 and IFIH1 gene expression was higher in response to LPS among individuals who were susceptible to sLRI in infancy, and IFIH1 expression correlated with IRF1 and STAT1 expression (Figure 14).

[0203] In summary, severe viral lower respiratory tract infections (sLRI) are a leading cause of hospitalization in infants and children and constitute a major risk factor for subsequent asthma development. Although it is increasingly recognized that bacterial and viral pathogens interact to drive the pathogenesis of sLRI, the underlying innate immune mechanisms are poorly understood. To address this knowledge gap, as described in the Examples herein, we used a multi-omics approach to systematically profile innate immune responses to bacterial (LPS / TLR4) and viral (Poly(I:C) / TLR3; Imiquimod / TLR7) stimuli at birth and to investigate response patterns associated with susceptibility to sLRI in the first year of life. Data showed that innate immune responses to a panel of stimuli contained overlapping proinflammatory and IFN-mediated gene expression programs, but that LPS, but not Poly(I:C) / Imiquimod response profiles, predicted sLRI. Moreover, susceptibility was determined by activation of a network of IFN genes, and the connectivity pattern of this network in cord blood LPS responses was significantly exaggerated among infants at risk for sLRI. Moreover, the connectivity pattern of these genes was also highly variable between cord and 5-year-old LPS responses, suggesting that the same mechanisms that determine sLRI risk are subject to developmental regulation. These findings were specific to LPS-induced IFN responses and were not observed for IFN responses induced by TLR3 / 7 stimulation nor from the pro-inflammatory genes of the responses tested, suggesting that the wiring of the LPS response is specifically altered in children at high risk for sLRI during infancy. It is noteworthy that expression of the LPS-induced IFN module was not associated with mild (non-wheezing / non-febrile) lower respiratory tract infections, highlighting that these findings are specifically related to the severity of the infection. Master regulator analysis identified IRF1 as a major driver of LPS-induced IFN responses at birth. By age 5 years, the putative activity of IRF1 was replaced by other members of the IRF transcription factor family, including IRF7. In contrast, IRF7 was the dominant driver of TLR3 / 7 IFN responses at birth and at age 5 years.Finally, using DIABLO, we identified a multi-omics signature associated with sLRI risk characterized by IRF1 regulation and IFN genes associated with pro-inflammatory cytokines and immune regulators. In summary, our findings suggest that susceptibility to sLRI in the first year of life is primarily determined by antibacterial versus antiviral innate immune pathways, providing a basis for identifying infants at risk for early intervention and identifying targets for drug development.

[0204] Recurrent episodes of rhinovirus-induced wheezing in infancy are often the first sign of early-onset asthma, as demonstrated from prospective cohort studies including CAS (Kusel et al., J Allergy Clin Immunol, 2007, 119:1105-1110) and COAST (Jackson et al., Am J Respir Crit Care Med, 2008, 178:667-672). However, viral infections are routinely detected in asthmatic children without severe symptoms, suggesting that recurrent sLRI throughout early life is not sufficient alone to drive asthma development, suggesting the involvement of additional disease cofactors. In this regard, the data presented herein suggest that the response to pathogenic bacteria is a more important determinant of sLRI susceptibility than the response to viral stimuli. Notably, a shift towards increased abundance of these bacterial communities in the airway microbiome over the first 5 years of life has been shown to frequently precede virus detection or the onset of respiratory symptoms. This suggests that the presence of pathogenic bacteria may "prime" the airways for the development of severe symptoms during subsequent viral infection.

[0205] This study found that enhanced LPS-induced IFN responses / gene network connectivity patterns at birth conferred risk for viral sLRI in infants, which is surprising given that IFN responses are nearly universally protective during acute viral infections. In this study, we used systems biology to identify IRF1 as a master regulator of the LPS-induced IFN network. IRF1 promotes constitutive expression of interferon-mediated antiviral programs at baseline, and inducible expression of these programs induced by respiratory viral infection. However, IRF1 is not essential for induction of interferon programs, providing a non-essential but complementary role in antiviral immunity. Examples described herein show that LPS-induced IRF1 gene expression at birth associates with distinct IFN signaling mediators (e.g., STAT1, but not JAK1 / TYK2), as well as pro-inflammatory gene / cytokine expression (e.g., CXCL9 / 10 / 11, IL1B) (Figure 5E-G). This is consistent with evidence that type I IFN-activated IRF1 promotes the induction of specific pro-inflammatory genes. Furthermore, the association of IRF1 with virus-sensing and attachment-related receptors ( Fig. 5F ) supports a role for IRF1 regulation of these receptors and may partially explain how increased IRF1 activation can prime the innate immune system for an exaggerated response to rhinovirus infection.

[0206] Innate immune responses are controlled by the coordinated activity of multiple cell types across multiple layers of molecular regulation. For this reason, an integrated biomarker profile of co-expression signatures was generated to capture the integrated response associated with sLRI risk in infancy. Strikingly, IFN signaling genes were prominent among the transcripts of the selected multi-omics risk profile after LPS stimulation of cord blood, consistent with key findings from the network-based approach (IRF1, STAT1). IFIH1, encoding the key virus-recognition receptor MDA5, was also identified. Furthermore, IFIH1 expression was significantly elevated in susceptible individuals and correlated with IRF1 and STAT1, suggesting that at-risk individuals may dysregulate key virus-sensing receptors upon TLR4 activation. CD8 by DC cross-presentation + Other important IRF1-dependent innate pathways are evident in the data, including MHC class I regulation (NLRC5, RFX5), which is important for T cell activation. Among the cytokines selected, IL-1β was particularly strongly associated with IFN-related transcripts, highlighting antiviral and proinflammatory response programs associated downstream of TLR4 activation. Finally, many features selected in the integrated profile of sLRI risk in infants are directly related to asthma. These include transcripts of asthma risk genes (IRF1, P2RY14, ABO), and features involved in remodeling (MMP7), airway inflammation (NLRC5), Th2 dysregulation (LGALS3BP), as well as cell types (T cells, monocytes / DCs, pDCs, and cytokines (e.g., IL-1β, IL-16, MIF). The supervised data integration approach used herein extends the risk profile across biological layers. This highlights the unique ability of integrated multi-omics methods to extract meaningful information from multiple biological levels.

[0207] This study focused on CBMC innate immune responses in a single birth cohort, and considering that the neonatal immune system undergoes dramatic developmental changes in the first weeks and months of life, it is valid to question the extent to which CBMC responses reflect immune responses to infections occurring at later ages in childhood. To address this question, a random forest classifier was trained on the provided in vitro data and applied to infection-associated host response data obtained from an external cohort. It was found that induced IFN responses following LPS or imiquimod / Poly(I:C) stimulation could be used to accurately stratify children presenting to the hospital with current bacterial and viral febrile infections, respectively, from whole blood samples. These data argue that the in vitro model described herein is relevant to respiratory infections in vivo. Furthermore, CBMC responses induced by imiquimod or Poly(I:C) also predicted respiratory viral infections in infants and children with viral bronchiolitis and asthma exacerbations in blood and airways, suggesting that the signatures are somewhat robust to variations in cellular composition between circulatory blood airway tissues. Also, the accuracy of the random forest model was higher when predicting infants (<18 months) compared to young children (18 months-5 years) (GSE113211; Jones et al., AM J Respir Crit Care, 2019, 199:1537-1549) or older children (6-17 years) (GSE115770; Altman et al., Nat Immunol, 2019, 20:637-651). Taken together, this supports that the IFN gene network identified from in vitro investigations of cord blood is a bona fide response mediator of infection in a real-world context.

[0208] In summary, the findings described herein demonstrate that LPS-induced IFN responses at birth predict risk of sLRI in the first year of life and identify cellular and molecular targets with potential utility in modifying the innate immune trajectory toward sLRI susceptibility and childhood asthma development.

Claims

1. 1. A method for determining whether an individual has an increased susceptibility to a respiratory infection, the method comprising: - contacting a sample comprising cord blood mononuclear cells (CBMCs) from an individual with a TLR4 agonist; - measuring the expression levels of the biomarkers KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11 and PMAIP1 in CBMCs; Including, Differential expression of the biomarkers KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11, and PMAIP1 in CBMCs contacted with a TLR4 agonist compared to CBMCs not contacted with a TLR4 agonist indicates that the individual has increased susceptibility to respiratory infections. The above method.

2. 1. A method for determining whether an individual has an increased susceptibility to a respiratory infection, the method comprising: - contacting a sample comprising cord blood mononuclear cells (CBMCs) from an individual with a TLR4 agonist; - measuring the expression levels of interferon module biomarkers in CBMCs; Including, Differential expression of interferon module biomarkers in CBMCs contacted with a TLR4 agonist compared to CBMCs not contacted with a TLR4 agonist indicates that the individual has increased susceptibility to respiratory infections. The above method.

3. 1. A method for determining whether an individual has an increased susceptibility to a respiratory infection, the method comprising: - contacting a sample comprising cord blood mononuclear cells (CBMCs) from an individual with a TLR4 agonist; - measuring the expression levels of IFN module biomarkers regulated by the IRF1 regulon in CBMCs; Including, Differential expression of IFN module biomarkers regulated by the IRF1 regulon in CBMCs contacted with a TLR4 agonist compared to CBMCs not contacted with a TLR4 agonist indicates that the individual has increased susceptibility to respiratory infections. The above method.

4. 1. A method for determining whether an individual has an increased susceptibility to a respiratory infection, the method comprising: - contacting a sample comprising cord blood mononuclear cells (CBMCs) from an individual with a TLR4 agonist; - measuring the expression levels of the biomarkers KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11 and PMAIP1 in CBMCs; - comparing the expression levels of biomarkers from the individual with a reference dataset, wherein the reference dataset contains information on the expression levels of the same biomarkers in CMBCs contacted with a TLR4 agonist from one or more individuals with or without increased susceptibility to respiratory infections; Including, the expression levels of the biomarkers KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11, and PMAIP1 in the individual compared to a reference dataset indicate that the individual has an increased susceptibility to respiratory infections; The above method.

5. 1. A method for determining whether an individual has an increased susceptibility to a respiratory infection, the method comprising: - contacting a sample comprising cord blood mononuclear cells (CBMCs) from an individual with a TLR4 agonist; - measuring the expression levels of interferon module biomarkers in CBMCs; - comparing the expression levels of biomarkers from the individual with a reference dataset, wherein the reference dataset contains information on the expression levels of the same biomarkers in CMBCs contacted with a TLR4 agonist from one or more individuals with or without increased susceptibility to respiratory infections; Including, the expression level of the interferon module biomarker in the individual compared to the reference dataset indicates that the individual has increased susceptibility to respiratory infections; The above method.

6. 1. A method for determining whether an individual has an increased susceptibility to a respiratory infection, the method comprising: - contacting a sample comprising cord blood mononuclear cells (CBMCs) from an individual with a TLR4 agonist; - measuring the expression levels of IFN module biomarkers regulated by the IRF1 regulon in CBMCs; - comparing the expression levels of biomarkers from the individual with a reference dataset, wherein the reference dataset contains information on the expression levels of the same biomarkers in CMBCs contacted with a TLR4 agonist from one or more individuals with or without increased susceptibility to respiratory infections; Including, the expression level of the IFN module biomarkers regulated by the IRF1 regulon in the individual compared to a reference dataset indicates that the individual has increased susceptibility to respiratory infections; The above method.

7. イターフェロンモジュールバイオマーカー1、SIC40、SCR80、SCR11、SCR11、SCR19 22、SAT2、SAT1、SAT4、SAT5、SATS6 、HARIC3、HAR19、HARIC1、HARIC1、BYS3 C10、C1500、C1502、F00000000 H2M、SHYS10、SHYS3、SHYS10、SHYS 1 CY26 、 CYS7 、 CYSYSY、SYSYS、SYS1 FAMOUS2、HAR101、HY2、HYS22、CH44、S SYS1、SYS2、SYSYSYS、SYSYS、 LOVE FISH、 SHY3、 SHYSHA2 、 CLOVE 4 、 LOVE 9 、 LOVE 1 、SAM、SAMIS4、SAM22、SAM214、SAM35、 ROCK1、ROSH、ROSE9、ROSE4、ROSE1、STR 22、SYSCH1、SHOSE、SHOSE229、SHOSE1、5 SYS0、SYS1、SYS6、SYS17、SYS1 、SYS18、SYSY、SYSH10、SYS30、SYS3 ROSE、ROSE3、ROSE73、ROSE2、ROSE12 ROCK10、ROCK174、ROCK1、ROCK10、10 33、HARSH、DAR11、HAR155、HARSH11、4 ROY2328、SYSY28、SY1、SYSY22、SY12 J1、SAT1、SATS1、SATS4、SATS7、0 DA1、SYS3、DYS10、SYS15、HICH 1. SAD40、SSA110、SAR15、SSA10、SAS 44、SIC5、N00、SIC22、SIC5、SIC1 、KSYS、SYS1、SHKS0、SHKS9、SHYS、5 DA58、SYS10、SYS2、SYS7、SYSYS CHA8、DASHA33、DASHAS、DAS58、SHAS5、S 2001、DAYS3、DYS14、SHAY16、SHAYS6、 ROCKS30、DAY2002、CHAR3、DAYS、S H22、H27、H22、SHO31、SHO11 、SHORE1、SHORE33、SHORE140、SHORE213、4 CHA8、SYS20、SYS2、SYS3、CHARS1940、The method of claim 2 or 5, wherein the target gene is selected from the group consisting of CLEC2B, LAP3, LY6E, CCDC194, BATF2, EIF2AK2, TNFSF13B, LINC02528, DGLUCY, VAMP5, NUB1, SAMD9, STAT2, RNF213-AS1, ZDHHC4P1, PGAP1, IFIT2, OAS1, CD38, XRN1, GVINP1, NT5C3A, LRRN2, and FBXO39.

8. The IFN module biomarkers regulated by the IRF1 regulon are CLEC2B, CXCL9, ERVK-28, FAS, FBXO6, GBP1, GBP1P1, GBP2, GBP3, GBP4, GBP5, GBP6, GPR174, HAPLN3, IDO1, IFNG, IL12RB1, IL15, IL15RA, LINC02328, NFIX, NLRC5, and OR. 2I1P, PARP3, PLAAT4, PSMB10, PSMB9, PSME1, PSME2, ​​PTGES3P1, SAMD4A, SOCS1, STAT1, SYNPO2, TAP1, TAPBP, TCAF2, TCAF2C, TCAF2P1, TIFA, TRAFD1, UBD, UBE2L6, and USP30-AS1.

9. 7. The method of any one of claims 1 to 6, wherein the method further comprises applying a machine learning algorithm to the differential or absolute expression of biomarkers, thereby indicating that the individual has an increased susceptibility to respiratory infections.

10. The method of claim 9 , wherein the machine learning algorithm is a random forest analysis.

11. The method of any one of claims 1 to 3, wherein the expression level is differential expression, said differential expression being an increase or decrease of more than 1.5 fold.

12. 7. The method of any one of claims 4 to 6, wherein the increased sensitivity is a relative risk or odds ratio of at least 1.10, at least 1.11, at least 1.12, at least 1.13, at least 1.14, at least 1.15, at least 1.16, at least 1.17, at least 1.18, at least 1.19, at least 1.20, at least 1.21, at least 1.22, at least 1.23, at least 1.24, at least 1.25, at least 1.30, at least 1.35, at least 1.40, at least 1.45, at least 1.50, at least 1.55, at least 1.60, at least 1.65, at least 1.70, at least 1.75, or at least 1.80 compared to a reference dataset.

13. The method of any one of claims 1 to 6, wherein the individual is 1 year old or younger.

14. 7. The method of any one of claims 1 to 6, wherein the individual is 1 day old, 2 days old, 3 days old, 4 days old, 5 days old, 6 days old, 7 days old, 2 weeks old, 1 month old, 3 months old, 6 months old, or 1 year old.

15. 7. The method of any one of claims 1 to 6, wherein the method indicates an increased susceptibility to respiratory infections when the individual is about 2 years old, about 3 years old, about 4 years old, or about 5 years old, or when the individual is 2, 3, 4, or 5 years old.

16. 7. The method of any one of claims 1 to 6, wherein the method indicates an increased susceptibility to respiratory infections when the individual is at least 5 years old.

17. The method according to any one of claims 1 to 6, wherein the respiratory infection is a lower respiratory tract infection.

18. The method according to any one of claims 1 to 6, wherein the respiratory infection is a bacterial or viral respiratory infection.

19. 19. The method of claim 18, wherein the viral respiratory infection is selected from the group consisting of influenza, parainfluenza, coronavirus, adenovirus, metapneumonvirus, rhinovirus, and respiratory syncytial virus respiratory infections.

20. 20. The method of claim 19, wherein the viral respiratory infection is a rhinovirus or respiratory syncytial virus respiratory infection.

21. 19. The method of claim 18, wherein the bacterial respiratory infection is a Haemophilus influenzae, Staphylococcus aureus, or Moraxella respiratory infection.

22. The method according to any one of claims 1 to 6, wherein the respiratory infection is a severe lower respiratory tract infection.

23. The biomarkers with differential increases are IDO1, SOCS1, GBP4, CD80, GBP1, CXCL11, CXCL9, MT2A, TCAF2, STAT1, RTP4, GBP5, FBXO6, HAPLN3, CCL19, APOL1, PMAIP1, USP30-AS1, IL15RA, GBP2, PARP9, IRF1, TCAF2C, TNFSF10, APOL3, TCAF2P1, EPSTI1, UBE2L6, ETV7, TTC39A, CETP, XAF1, CMPK2, GBP1P1, HAS2, APOL2, IFI44, WAR S1, RSAD2, OR2I1P, LGALS3BP, IFNG, NLRC5, OAS3, APOL6, PSME2, ​​PTGES3P1, DLL4, TRIM69, ALPK1, PSMB9, NFE2L3, FAS, PLAAT4, HELZ2, BCL2L14, IFI35, TRIM21, UBD, IRF9, TNFSF4, RGS1, SSTR2, SMTNL1, TIFA, TMEM229B, PSME1, D DX60, CASZ1, HERC6, CASP17P, SAMD9L, USP18, NFIX, PSMB10, GBP6, DTX3L, PT PRK, IFIT3, KLHDC7B, TAPBP, PARP12, TAP2, RCN1, GPR174, TAP1, PARP10, GB P3, HLA-F, IFIH1, GPR155, PARP11, LINC02328, ERVK-28, MX1, TRIM22, IL12 RB1, TRAFD1, IRF1-AS1, OASL, ACOT7, GCH1, PARP3, OPTN, MOV10, ISG15, MDG A1, SAMD4A, SP110, IL15, CEACAM1, CASP4, PCGF5, NMI, SYNPO2, IFIT5, CXCL1 0, C4BPB, TRANK1, GSDMD, CD69, DHX58, RAB1AP1, OAS2, UBA7, BISPR, PSMB8, APOBEC3F, PLAT, DDX58, HERC5, E2F3P1, DLG3, PARP14, CALHM6, APOBEC3G, TO MM20P2, LAG3, HLA-A, TAFA2, MX2, BTN3A1, USP41, BTN3A3, TMEM140, RNF213 , CCL8, BST2, LINC01949, CLEC2B, LAP3, LY6E, EIF2AK2, LINC02528, DGLUCY,NUB1, STAT2, ZDHHC4P1, PGAP1, OAS1, GVINP1, NT5C3A, FBXO39, CLEC2B, CXCL9, ERVK-28, FAS, FBXO6, GBP1, GBP 1P1, GBP2, GBP3, GBP4, GBP5, GBP6, GPR174, HAPLN3, IDO1, IFNG, IL12RB1, IL15, IL15RA, LINC02328, NFIX, NLR The method of any one of claims 1 to 6, wherein the target gene is selected from the group consisting of C5, OR2I1P, PARP3, PLAAT4, PSMB10, PSMB9, PSME1, PSME2, ​​PTGES3P1, SAMD4A, SOCS1, STAT1, SYNPO2, TAP1, TAPBP, TCAF2, TCAF2C, TCAF2P1, TIFA, TRAFD1, UBD, UBE2L6, or USP30-AS1.

24. 7. The method of any one of claims 1 to 6, wherein the CBMCs are in contact with, or have been in contact with, the TLR4 agonist for at least 4, 6, 12, 18, or 24 hours.

25. The method of any one of claims 1 to 6, wherein the biomarker is a nucleic acid or an amplification product.

26. 7. A pharmaceutical composition for treating an individual determined to have an increased susceptibility to a respiratory infection according to any one of claims 1 to 6, comprising palivizumab, prednisolone, omalizumab or a polybacterial preparation, or any combination thereof.

27. The method of any one of claims 1 to 6, wherein the sample is umbilical cord blood.

28. The method of any one of claims 1 to 6, wherein the CBMCs are purified from umbilical cord blood.

29. The method of any one of claims 1 to 6, wherein the CBMCs comprise B cells and T cells.

30. 30. The method of claim 29, wherein the T cells are CD4+ and / or CD8+ T cells.

31. 30. The method of claim 29, wherein the T cells are CD4+ central memory T cells and / or CD8+ central memory T cells.

32. 30. The method of claim 29, wherein the expression levels of biomarkers in B cells and T cells are measured only in CBMCs.

33. 30. The method of claim 29, wherein the CBMCs further comprise CD14+ monocytes and conventional dendritic cells (cDCs).

34. 34. The method of claim 33, wherein the cDC is a plasmacytoid DC (pDC).

35. 7. The method of any one of claims 1 to 6, wherein the TLR4 agonist is selected from the group consisting of lipopolysaccharide (LPS), monophosphoryl lipid A (MPLA), heat shock proteins, S100A8, S100A9, RSV F protein, fibrinogen, heparin sulfate or a fragment thereof, hyaluronic acid or a fragment thereof, nickel, opoid, alpha-1-acid glycoprotein (AAG), aminoacylglucaminide 4-phosphate (AGP), RC-529, murine beta-defensin 2, and complete Freund's adjuvant (CFA).

36. 36. The method of claim 35, wherein the TLR4 agonist is LPS.

37. 36. The method of claim 35, wherein the TLR4 agonist is derived from a bacterium.

38. 38. The method of claim 37, wherein the TLR4 agonist is purified in or contained in a bacterial preparation.

39. 36. The method of claim 35, wherein the TLR4 agonist is LPS and is provided at a concentration effective to stimulate TLR4 activation.

40. 40. The method of claim 39, wherein the concentration of LPS is 0.025 ng / ml to 100 ng / ml.

41. 41. The method of claim 40, wherein the concentration of LPS is 1 ng / ml.

42. CBMCs were cultured at 1 × 10 cells prior to contact with TLR4 agonists. ６ The method according to any one of claims 1 to 6, wherein the cells are suspended in 1000 cells / mL.

43. Biomarkers: - KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11 and PMAIP1; ROY1、SYS1、SYS4、SYS80、SYS1、SYS 11、SYSLOVE、SYSY2、SYSY1、SYSYS4 2010. 2016. 2016. 2016. 2016. 2016 CAS1、SIC30001、C1500000200000000000000000000000000 10、DAM1、DAM20、DASS10、DAS13、S FAMOUS1、SHAS1、SHAS26、SHAS7、SHAS THIS LOVE2、CH44、SHA1、SHA22、SH210、CHAS CHRIST、KHYC、DYSH5、SYS3、SYSYS 2、SHASHK31、DASHK4、SHASHK9、SHASH1、2 CYD、DYS23、SYS、SYSYS4、SYS22 2214、CH35、SHY21、200、SHY9 SYS1、SYS2、SYSY1、SYS 229、SYS1、DYS60、SYS1、SYS6、S SYS17、SYD94、SYS18、SYS、SYS10、 CHRIST、CHS33、SCHS3、SHR33 SHAR12、SHAR2、HAR11、SH174、S 11、SHA10、SHASH3、SHASH、SHA10、SHA1 55、SHAS11、SHASY2328、SHASY28、S1 、ROSE22、RO1201、ROSE1、ROSE1 、DYS、DYS7、DYS1、DYS3、DYS 10、SYS15、SYS10、SYS10、1 5、SAMAS1、SAMASIS4、SAMAS5、DYS 2、CHS5、SHA10、SH4H10、SHASH10 0.009.006.0058.0001.

002. CHRIST、SHASH、SHASH8、SHASH30、SHASH、 CHR58、HARSH5、HARSH11、DARCH3、HARSH14、 ROCK16、ROCKS6、ROCKS30、ROSE202、 LOVE3、FAMOUS、LOVE27 LOVE27 11、SIC1、SIC41、SIC31 H140、S00213、SHA48、SYS20、SY22、1 LOVE3、LOVE1949、LOVE23、LOVE3、an interferon module biomarker selected from the group consisting of CCDC194, BATF2, EIF2AK2, TNFSF13B, LINC02528, DGLUCY, VAMP5, NUB1, SAMD9, STAT2, RNF213-AS1, ZDHHC4P1, PGAP1, IFIT2, OAS1, CD38, XRN1, GVINP1, NT5C3A, LRRN2, and FBXO39; or -CLEC2B, CXCL9, ERVK-28, FAS, FBXO6, GBP1, GBP1P1, GBP2, GBP3, GBP4, GBP5, GBP6, GPR174, HAPLN 3, IDO1, IFNG, IL12RB1, IL15, IL15RA, LINC02328, NFIX, NLRC5, OR2I1P, PARP3, PLAAT4, PSMB10, PS an IFN module biomarker regulated by the IRF1 regulon selected from the group consisting of MB9, PSME1, PSME2, ​​PTGES3P1, SAMD4A, SOCS1, STAT1, SYNPO2, TAP1, TAPBP, TCAF2, TCAF2C, TCAF2P1, TIFA, TRAFD1, UBD, UBE2L6, and USP30-AS1; and a diagnostic reagent that binds to or individually complexes with each of the following:

44. Biomarkers: - KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11 and PMAIP1; ROY1、SYS1、SYS4、SYS80、SYS1、SYS 11、SYSLOVE、SYSY2、SYSY1、SYSYS4 2010. 2016. 2016. 2016. 2016. 2016 CAS1、SIC30001、C1500000200000000000000000000000000 10、DAM1、DAM20、DASS10、DAS13、S FAMOUS1、SHAS1、SHAS26、SHAS7、SHAS THIS LOVE2、CH44、SHA1、SHA22、SH210、CHAS CHRIST、KHYC、DYSH5、SYS3、SYSYS 2、SHASHK31、DASHK4、SHASHK9、SHASH1、2 CYD、DYS23、SYS、SYSYS4、SYS22 2214、CH35、SHY21、200、SHY9 SYS1、SYS2、SYSY1、SYS 229、SYS1、DYS60、SYS1、SYS6、S SYS17、SYD94、SYS18、SYS、SYS10、 CHRIST、CHS33、SCHS3、SHR33 SHAR12、SHAR2、HAR11、SH174、S 11、SHA10、SHASH3、SHASH、SHA10、SHA1 55、SHAS11、SHASY2328、SHASY28、S1 、ROSE22、RO1201、ROSE1、ROSE1 、DYS、DYS7、DYS1、DYS3、DYS 10、SYS15、SYS10、SYS10、1 5、SAMAS1、SAMASIS4、SAMAS5、DYS 2、CHS5、SHA10、SH4H10、SHASH10 0.009.006.0058.0001.

002. CHRIST、SHASH、SHASH8、SHASH30、SHASH、 CHR58、HARSH5、HARSH11、DARCH3、HARSH14、 ROCK16、ROCKS6、ROCKS30、ROSE202、 LOVE3、FAMOUS、LOVE27 LOVE27 11、SIC1、SIC41、SIC31 H140、S00213、SHA48、SYS20、SY22、1 LOVE3、LOVE1949、LOVE23、LOVE3、an interferon module biomarker selected from the group consisting of CCDC194, BATF2, EIF2AK2, TNFSF13B, LINC02528, DGLUCY, VAMP5, NUB1, SAMD9, STAT2, RNF213-AS1, ZDHHC4P1, PGAP1, IFIT2, OAS1, CD38, XRN1, GVINP1, NT5C3A, LRRN2, and FBXO39; or -CLEC2B, CXCL9, ERVK-28, FAS, FBXO6, GBP1, GBP1P1, GBP2, GBP3, GBP4, GBP5, GBP6, GPR174, HAPLN 3, IDO1, IFNG, IL12RB1, IL15, IL15RA, LINC02328, NFIX, NLRC5, OR2I1P, PARP3, PLAAT4, PSMB10, PS an IFN module biomarker regulated by the IRF1 regulon selected from the group consisting of MB9, PSME1, PSME2, ​​PTGES3P1, SAMD4A, SOCS1, STAT1, SYNPO2, TAP1, TAPBP, TCAF2, TCAF2C, TCAF2P1, TIFA, TRAFD1, UBD, UBE2L6, and USP30-AS1; 7. A kit, panel or microarray for or when used in accordance with the method of any one of claims 1 to 6, comprising a diagnostic reagent which binds to or separately complexes with each of:

45. contacting B cells and T cells from umbilical cord blood from the individual with a TLR4 agonist; - measuring the expression levels of the biomarkers KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11 and PMAIP1 in B cells and T cells; The assay includes:

46. contacting B cells and T cells from umbilical cord blood from the individual with a TLR4 agonist; - measuring the expression levels of interferon module biomarkers in B cells and T cells; The assay includes:

47. contacting B cells and T cells from umbilical cord blood from the individual with a TLR4 agonist; - measuring the expression levels of IFN module biomarkers regulated by the IRF1 regulon in B cells and T cells; The assay includes:

48. contacting cord blood mononuclear cells (CBMCs) from the individual with a TLR4 agonist; - measuring the expression levels of the biomarkers KLHDC7B, IFNG, CASZ1, PSMB9, PARP3, ACOT7, NUB1, USP18, NLRC5, CCDC194, GCH1, PARP11, CXCL11 and PMAIP1 in CBMCs; The assay includes:

49. contacting cord blood mononuclear cells (CBMCs) from the individual with a TLR4 agonist; - measuring the expression levels of interferon module biomarkers in CBMCs; The assay includes:

50. contacting cord blood mononuclear cells (CBMCs) from the individual with a TLR4 agonist; - measuring the expression levels of IFN module biomarkers regulated by the IRF1 regulon in CBMCs; The assay includes:

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