Identification of antimicrobial-resistant bacteria using deep RNA sequencing data

Abstract

Blood and lung infection is a worldwide health problem, and about five millions of cases of death each year. Early treatments can help rescue life but require better tests to identify these infections. The invention recognizes pathogens, in particular those having antibiotic resistance, more quickly. The invention allows appropriate antibiotic treatment to be performed at an earlier point in time. Data from deep RNA sequencing of human blood is used to create a faster diagnostic test against infection and related antimicrobial resistance.

Description

Field of the Invention

[0001] The present invention relates primarily to the chemical analysis of biological materials, using nucleic acid products used in nucleic acid analysis, such as primers or probes for diseases caused by genetic alterations of genetic material.

[0002] Statement Regarding Federally Sponsored Research or Development

[0003] This invention was made with government support under grants P20GM121344, R35 GM118097, R01 GM127472, and R35 GM142638 awarded by the National Institutes of Health. The government has certain rights in the invention.

[0004] Cross - Reference to Related Applications

[0005] Under 35 U.S.C.§119(e), this patent application claims the benefit of priority of the following provisional patent applications: U.S. Serial No. 63 / 378,365 and U.S. Serial No. 63 / 378,366, both filed on October 4, 2022. Background Art

[0006] Antimicrobial - resistant bacteria cause nearly 5 million deaths annually (Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis (2022)). Early identification of pathogenic agents and their antibiotic resistance patterns is central to infection management by focusing on antimicrobial administration. Typical clinical practice in infected patients would benefit from starting broad - spectrum antibiotic treatment as early as possible.

[0007] Although broad - spectrum antibiotics have benefits in reducing infection mortality, they also have negative consequences. Broad - spectrum antibiotics are costly and labor - intensive. They increase the risk of Clostridioides difficile colitis and select for new antibiotic - resistant pathogens.

[0008] Culture of the infected site identifies the pathogen and its associated antibiotic resistance, but it takes several days to generate actionable information. Antibiotics administered before sample collection reduce culture yields.

[0009] There is a need in the biomedical field for diagnostic tests for the diagnosis and treatment of sepsis. Summary of the Invention

[0011] The present invention provides molecular diagnostic tests to overcome the limitations of conventional microbiological methods for diagnosing and treating sepsis.

[0012] In a first embodiment and as a proof of principle, the inventors developed polymerase chain reaction (PCR) diagnostic tests for four common bacteria: Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Haemophilus influenzae.

[0013] In a second embodiment, the inventors established tests for clinically relevant resistance genes associated with these four pathogens. These bacteria are the pathogens of interest in the Request for Applications (RFA-AI-22-010) regarding bacteremia (Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa) and pneumonia (Staphylococcus aureus, Pseudomonas aeruginosa, Haemophilus influenzae). These pathogens are also the most common causes of bacteremia and hospital-acquired pneumonia in Rhode Island Hospital. These four pathogens have antimicrobial resistance attributable to specific genes, requiring a change in antibiotic stewardship. These four pathogens are the top organisms causing death due to resistance (Global burden of bacterial antimicrobial resistance in 2019 (2022)).

[0014] In a third embodiment, the present invention provides tests for clinically relevant resistance genes associated with other pathogens causing sepsis.

[0015] In a fourth embodiment, the present invention provides a diagnostic PCR test based on bacterial RNA. A PCR test for respiratory pathogens was developed. See Covert, Bashore, Edds, & Lewis (2021). A PCR test was developed to identify the DNA of bacteria such as Staphylococcus aureus in the target site. Palavecino (2020). Previous pathogen identification was done by sequencing cell-free DNA from blood. Camargo et al. (2019).

[0016] In the diagnostic PCR test, the most abundant RNA targets are selected from the blood of patients with these infections, making the method more sensitive than single-copy DNA targets. Antibiotic resistance is closely related to gene expression. By stabilizing RNA, the risk of RNA degradation is significantly reduced. Since the targets are from RNA sequencing data, these RNAs are abundant and measurable in infected patients.

[0017] In one aspect, unmapped RNA reads from infected patients that match a pathogen can form a better diagnostic test. The PCR targets for diagnosis are from a dataset created by deep sequencing (>100 million reads) of the blood of patients with bacteremia or pneumonia. Pathogen identification is performed using standard culture techniques. RNA sequences from pathogens are typically discarded in transcriptional analysis because they do not match the human genome. These "unmapped reads" are identified in the patient's blood and aligned with a custom "genome" derived from the pathogen of interest to identify pathogenic organisms. RNA that matches resistance genes is also identified.

[0018] In a fifth embodiment (A1a), the present invention provides a reverse transcriptase polymerase chain reaction (RT-qPCR) test directly from blood without culturing for bacteria that cause bacteremia (in particular Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa), which is based on RNA identified in patients with bacteremia caused by these organisms.

[0019] In a sixth embodiment (A1b), the present invention provides a method for validating these RT-qPCR tests in samples from patients with and without bacteremia.

[0020] In a seventh embodiment (A2a), the present invention provides a reverse transcriptase polymerase chain reaction (RT-qPCR) test directly from blood without culturing for bacteria that cause pneumonia (in particular Staphylococcus aureus, Pseudomonas aeruginosa, Haemophilus influenzae), which is based on RNA identified in patients with pneumonia caused by these organisms.

[0021] In an eighth embodiment (A2b), the present invention provides a method for validating these RT-qPCR tests in samples from patients with and without pneumonia.

[0022] In an eighth embodiment (A3a), using RNA from infected patients, the present invention provides RT-PCR for the most common resistance genes that affect the treatment of Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Haemophilus influenzae.

[0023] In a ninth embodiment (A3b), the present invention provides a method for validating these PCR tests for resistance genes in samples from patients with and without infection.

[0024] In a tenth embodiment, the present invention provides an RT-PCR test directly from blood without culturing and phenotypic microbial resistance identification for bacteremia caused by Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa.

[0025] In the eleventh embodiment, the present invention provides an RT-PCR test that is performed directly from blood without culturing and phenotypic identification of microbial resistance, which is used for pneumonia caused by Staphylococcus aureus, Pseudomonas aeruginosa, and Haemophilus influenzae.

[0026] In the twelfth embodiment, all tests provided by the present invention can obtain results within less than 4 hours from the sample collection time.

[0027] In the thirteenth embodiment, the present invention provides the ability to standardize and scale these tests for use in a clinical microbiology setting. See Table 3 below.

[0028] In another aspect, the present invention provides a direct form of a blood PCR panel (e.g., using the top 12 pathogens) that identifies pathogens and resistance profiles faster (less than 4 hours) than current bacteremia and hospital-acquired pneumonia techniques. The present invention transforms deep RNA sequencing data into a product: rapid PCR, to identify Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Haemophilus influenzae, as well as potential resistance genes, without culturing or specimens other than blood.

[0029] Recently, several factors have provided improvements to RNA sequencing, making RNA sequencing available for the diagnosis and treatment of sepsis using a set of standard test conditions. These improvements are supported by the literature and preliminary data, indicating that RNA sequencing can directly identify bacterial pathogens from the blood of patients with these infections.

[0030] Excluding processing time, an Illumina machine (NovaSeq X+) should only take 13 hours to obtain 1.6 billion reads.

[0031] Sequences that are commonly identified and organism-specific (for Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Haemophilus influenzae) are used as templates for designing oligonucleotide primers in RT-qPCR tests. Future efforts to expand diagnostic tests to other pathogens may require RNA sequencing of patients with pneumonia caused by other pathogens.

[0032] For these assays, direct RNA sequencing should be performed. This RNA-to-DNA conversion takes time. Direct RNA sequencing allows for faster processing times, completed within a 4-hour time frame. Focusing on RNA rather than DNA improves the phenotypic correlation with antimicrobial resistance.

[0033] RNA sequencing can be a valuable tool for the personalized care of sepsis patients. Based on these advancements, this tool will be used by clinicians in the intensive care unit for the care of sepsis patients. The above improvements have extended the technology from research laboratories to clinical microbiology laboratories.

[0034] The improvements listed enable the direct blood-based reverse transcriptase polymerase chain reaction (RT-qPCR) test for bacteria without culturing, and methods to validate these RT-qPCR tests in samples from patients. RT-qPCR tests can be used for the diagnosis and treatment of sepsis. The improvements listed above also enable ways to combat the scourge of drug-resistant bacteria.

[0035] The improvements listed can be used to design better platforms and reagents for the direct blood-based reverse transcriptase polymerase chain reaction (RT-qPCR) test for bacteria without culturing. QIAGEN (Germantown, MD, USA) is a manufacturer of platforms and reagents for RNA isolation and sequencing. Abbott (Abbott Park, IL, USA), Cepheid (Sunnyvale, CA, USA), Thermo Fisher Scientific (Waltham, MA, USA) and ELITech Group (Puteaux, FR) are also manufacturers of platforms and reagents for RNA isolation and sequencing.

[0036] RNA sequencing can be a medically useful tool for the personalized care of sepsis patients. Based on these advancements, this tool will be used by clinicians in the intensive care unit for the care of sepsis patients. Description of the Drawings

[0037] For illustration, some embodiments of the present invention are shown in the drawings described below. The same numbers in the drawings always represent the same elements. The present invention is not limited to the exact arrangements, dimensions, and instruments shown.

[0038] Figure 1 RT-qPCR validation of sequencing results is shown. The SARS-CoV-2 N gene in cDNA from patient RNA was tested using real-time PCR. Figure 1 A is a map of the N gene showing the positions of the peaks of the sequencing reads (red box) and the primers pointing to the peaks or elsewhere in the gene ("non-peaks"). Figure 1 B is a bar graph showing the relative expression levels of the peak and non-peak sequences in 15 COVID-19 patients. The peak sequences are shown in red, and the non-peak sequences are shown in black.

[0039] Figure 2AIt is a schematic diagram showing the gene structure of SARS-CoV2 - nucleocapsid (N2). Figure 2B It is a bar chart shown. Figure 2C It is a bar chart shown.

[0040] Figure 3 It is a schematic diagram showing the significant differences in the minimum free energy of RNA - Seq reads among different genes. The lines between the N gene and ORF1ab, between the N gene and ORF6, and between ORF6 and the S gene represent the significant differences between genes.

[0041] Figure 4 It is a schematic diagram showing the significant differences in the ensemble free energy of RNA - Seq reads among different genes. The lines between the N gene and ORF1ab, between the N gene and ORF6, between ORF6 and the S gene, and between ORF6 and ORF3a represent the significant differences between genes.

[0042] Figures 5A - 5C It is a set of bar charts showing examples of the effect of motifs on energy. Figure 5A It shows the results of the minimum free energy (left) and ensemble free energy (right) of samples with motif MEME - ChIP 9 compared to samples without motif MEME - ChIP 9. The MEME - ChIP 9 motif can increase the minimum free energy (see the left figure). This motif can destabilize the RNA sequence reads and can increase the ensemble free energy (see the right figure). Figure 5B It shows the results of the minimum free energy (left) and ensemble free energy (right) of samples with motif MEME - 28 compared to samples without motif MEME - 28. The MEME - 28 motif can decrease the minimum free energy (see the left figure). This motif can stabilize the RNA sequence reads (see the right figure) and decrease the ensemble free energy. Figure 5C It shows the results of the minimum free energy (left) and ensemble free energy (right) of samples with motif MEME - 15 compared to samples without motif MEME - 15. The MEME - 15 motif does not seem to affect the minimum free energy (see the left figure). This motif may not affect the stability of RNA sequence reads (see the right figure) and does not seem to affect the ensemble free energy.

[0043] Figure 6 It is a set of calculated sequence results from MEME - ChIP analysis. Only 10 results are required, among which 3 significantly affect the minimum free energy and ensemble free energy of the read sequences.

[0044] Figure 7 is a set of exemplary secondary structures of reads based on the minimum free energy and discovered motifs. Figure 7A It shows MEME - ChIP 8.Figure 7B Shows MEME-ChIP 9. Figure 7C Shows MEME-ChIP 10. Figure 7D Displays MEME-9. Figure 7E : MEME-10. Figure 7F : MEME-21. Figure 7G : MEME-22. Figure 7H : MEME-27. Figure 7I : MEME-28.

[0045] Figure 8 Is a chart of a density map for identifying genomic regions with the most reads.

[0046] Figure 9 Is a pair of reads from two groups of patients with positive blood cultures compared to patients found not to have Escherichia coli infection. DETAILED DESCRIPTION OF THE INVENTION

[0048] Industrial applicability

[0049] Faster pathogen identification for severe infections . Sepsis causes one-fifth of the deaths in the world. Rudd et al. (2020). The diagnosis of sepsis infection is a major challenge in sepsis care. Duncan, Youngstein, Kirrane, & Lonsdale (2021). The present invention provides better diagnosis of bacterial infections and related antimicrobial resistance to improve outcomes.

[0050] The Surviving Sepsis Campaign has standardized sepsis treatment, including blood cultures before broad-spectrum antibiotics and starting antibiotic treatment within 1 hour. See Evans et al. (2021). In a multivariate analysis of factors affecting the mortality of patients with septic shock, the time to start antibiotic treatment is the most influential variable. Kumar et al. showed this effect, reporting a survival rate of 79.9% for patients with septic shock who received antibiotics within the first hour, and a 7.6% reduction in survival rate for each hour of delay. Kumar et al. (2006). Vazquez-Guillamet et al. determined that the number of people needed to be treated with antibiotics to save one life is 5. Vazquez-Guillamet et al., (2014). Faster pathogen identification improves sepsis prognosis by guiding antibiotic selection. Current methods take too much time, such as days. And sepsis causes death within hours.

[0051] The present invention provides diagnostic tests for three pathogens that cause bacteremia and three pathogens that cause pneumonia to shorten the time to pathogen-specific treatment of diseases such as sepsis.

[0052] Antimicrobial resistance is a serious health problem 。 Antimicrobial - resistance Bacteria cause nearly 5 million deaths worldwide each year (Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis (2022)). In the United States, Antimicrobial - resistance bacteria cause over 100,000 deaths and more than $21 billion in losses. With each use of an antibiotic, resistance soon follows. See Clatworthy, Pierson, & Hung (2007). The United Nations held a high-level meeting on Antimicrobial resistance on September 21, 2016, which included statements from global leaders such as Ban Ki-moon, Secretary-General of the United Nations: "Drug resistance is imposing a huge toll on health systems, is causing growing and unnecessary loss of life, and poses a threat to reversing much of the progress we have made." Locally, the trend of increasing resistance is also rising in many pathogens. Kassakian & Mermel (2014).

[0053] Importance of phenotypic antimicrobial susceptibility 。Bacteria have multiple Antimicrobial resistance mechanisms and are easily transmitted, resulting in pathogens with a broad resistance spectrum. Harbottle, Thakur, Zhao, & White (2006). Despite advances in genomic testing, resistance genes found in DNA are not always associated with phenotypic resistance. Bortolaia et al. (2020). Some computational methods have been proposed to handle this data. Bortolaia et al. (2020). Reasons for the lack of correlation between genomic data and resistance phenotypes include the lack of transcription and DNA independent of living cells. Using RNA sequencing data allows for better correlation between genomic data and resistance expression. Using RNA data only identifies actively transcribed genes, thus measuring gene expression levels.

[0054] Facilitating antimicrobial stewardship 。Antibiotic stewardship has been recommended as a countermeasure against ResistanceMethods. Although broad-spectrum antibiotics are appropriate, they carry the risk of increased adjusted mortality. Webb et al. (2019). Other studies have specifically pointed out that broad-spectrum antibiotics increase the risk of in-hospital death when resistance is not identified. Rhee et al. (2020). 67.8% of patients received broad-spectrum coverage. Rhee et al. (2020). De-escalation is important because inappropriate antibiotic exposure leads to the emergence of new resistance every day. Teshome et al. (2019). In a hospital setting, empirical treatment de-escalates only 16% of the time. De Bus et al. (2020). When de-escalation is used, costs are reduced. Seok, Jeon, & Park (2020). Narrowing antibiotics reduces the workload in the ICU. Mei-Sheng, Riley & Olans (2021). Diagnostic tests that rapidly identify pathogens and their antimicrobial resistance should help Antimicrobial management.

[0055] In the twelfth embodiment above, PCR provided with information from a large dataset is performed within four hours, generating results directly from the blood without relying on culture. Al-Hasan, Winders, Bookstaver, & Justo recommended directly evaluating the management plan rather than looking for adverse events. Al-Hasan, Winders, Bookstaver, & Justo (2019). Using faster bacterial identification, serial testing can evaluate treatment efficacy. Serial testing would be an additional metric in the management plan as antibiotics can be discontinued more quickly.

[0056] Identifying bacterial RNA from unmapped reads using deep RNA sequencing。In the initial assessment of RNA sequencing data, reads are aligned to the genome of the species from which the sample was derived (usually the human genome). Unmapped reads can account for up to 20% of the data. These data are typically discarded. In samples from diseased individuals, there are more unmapped reads (~35%). Monaghan et al. (2021). The Read Origin Protocol (ROP) (Mangul et al. (2018)) and Kraken (Wood, Lu, & Langmead (2018)) have been developed to determine the origin of unmapped reads. ROP analysis of multiple datasets mapped 99.9% of all reads. Analyze the data that is typically discarded in a seven-step method. Due to its relevance to the patient population in this work, one step is of particular interest: bacterial reads. Using ROP or the more recent Kraken2, bacterial RNA was identified in blood samples from sepsis patients and was mapped to the bacteria found in blood cultures. RNA sequencing data can inform primer design to produce better diagnostic tests.

[0057] Diagnostic protocol 。The present invention utilizes a large dataset of unmapped reads from patients diagnosed with infection by the gold standard of bacterial culture. This deep RNA sequencing data suggests PCR primers for the identification of pathogens such as Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Haemophilus influenzae, as well as clinically relevant resistance genes. These tests do not rely on culture and allow testing directly from blood, where RNA is stabilized in PAXgene tubes. PAXgene Blood RNA Tubes (QIAGEN, MD, USA; Catalog number / ID: 762165) are used for in vitro diagnostic tests (IVD). Sensitivity and specificity meet FDA requirements as it relates to molecular diagnostic tests. Because RNA is used, phenotypic identification is better than attempts by DNA sequencing. See BMJ Global Health, 5(11) (2020).

[0058] Conceptual innovation 。Reads that cannot be matched to the target genome (human in these assays) are typically discarded. In the present invention, unmapped reads are the focus of the study to identify new PCR targets in patients with bacterial infections.

[0059] Deep RNA sequencing of over 100 million reads allows the identification of bacterial RNA in the blood of infected patients.

[0060] Focusing on RNA rather than DNA improves the phenotypic correlation with antimicrobial resistance.

[0061] Reducing globin and ribosomal RNA enhances the identification of expressed bacterial genes.

[0062] Clinical management is guided by these RNA-based PCR tests, which are designed to identify target genes that directly impact treatment decisions.

[0063] RNA-based PCR tests are developed with the aim of rapid dissemination to clinical microbiology laboratories.

[0064] Technological innovation Unmapped reads from deep RNA sequencing are an untapped new information resource. Typically, 30% of the reads are unmapped; thus, for deep RNA sequencing of 100 million reads, 30 million reads are available for further analysis.

[0065] The present invention uses analytical algorithms, including mapping reads to genomes created for each pathogen based on standard features of a large number of strains.

[0066] Customized algorithms and improved computing power enhance computational analysis and shorten the time for primer identification.

[0067] The workflow is optimized and automated to protect RNA, including PAXgene tubes for blood collection.

[0068] Deep RNA sequencing identifies pathogen RNA and provides information for PCR primers RNA sequencing of COVID-19 patients from the ICU is used to create preliminary data. Data from deep RNA sequencing assays indicate that limited regions of the viral genome are detected in the bloodstream of COVID-19 patients. This information is used to design primers to validate the sequencing results by different methods. Using two sets of primers targeting the N gene, quantitative, real-time, reverse transcriptase PCR is performed on cDNA generated from patient RNA. One primer pair corresponds to the peak of the sequencing reads. The other primer pair is selected at a different locus of the gene. See Figure 1 A. Using the standard SYBRGreen method, amplicons of the peak N sequence are identified in all test samples. Templates corresponding to the non-peak sequence are detected in only 9 out of 15 patients. When present, the abundance of the non-peak sequence is 4 to 16 times lower than that of the peak sequence. See Figure 1 A.

[0069] This work is important because it is difficult to detect SARS-CoV-2 in the blood. Yan, Chang, & Wang (2020).

[0070] Deep RNA sequencing can distinguish RNA from target pathogens。Deep RNA sequencing data were taken from two patients with bacteremia due to Escherichia coli infection. The unaligned reads were aligned to the Escherichia coli genome. Each patient had reads matching 14 genes in Table 1. Bacterial ribosomal RNAs were identified because the depletion kit was designed for human ribosomal RNAs. Although previous work has looked at ribosomal RNAs for pathogen identification, this method is different because the present inventors looked at RNA rather than DNA, so the present inventors could look for actively expressed genes. As in the probe design for SARS-CoV-2 described above, the present inventors identified the exact regions of the target genes covered by the RNA reads identified by the sequencing data. They could also target multiple genes by PCR based on the genes with the most reads in the diseased patients. Interestingly, the patient with more reads died while the other patient survived. In the present invention, this increase in read count based on clinical deterioration can be analogous to the molecular equivalent of the time required to obtain a positive culture, which is sometimes used clinically. et al. (2022).

[0071] Patient 2 died of ESBL Escherichia coli bacteremia. In this patient, the genes CTX-M (12 counts) and blaCTX-M (12 counts) were identified. These genes led to the ESBL pathogen, confirming the culture diagnosis.

[0072] These data demonstrate the ability to isolate RNA from blood, sequence the RNA, and use computational methods to identify bacterial sequences and create PCR primers to identify infection and resistance.

[0073]

[0074]

[0075]

[0076] On the one hand, the unaligned RNA reads from infected patients matching the pathogen can inform better diagnostic tests. The present invention uses deep sequencing (>100 million reads) to identify the most highly expressed RNAs in the blood of patients with bacteremia or pneumonia. Culture and antibiotic susceptibility testing are performed as the gold standard. In transcriptional analysis, RNA sequences from pathogens are usually discarded because they do not match the human genome. The present inventors identified these "unaligned reads" in the patient's blood and aligned them to a custom "genome" derived from the target pathogen to identify the causative organism. RNAs matching resistance genes were also identified. The sequences that are commonly identified and organism-specific are templates for designing oligonucleotide primers for RT-qPCR tests.

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[0080] Definition

[0081] For convenience, the meanings of some terms and phrases used in the specification, examples, and claims are listed below. Unless otherwise stated or indicated from the context, these terms and phrases shall have the following meanings. These definitions assist in describing specific embodiments but are not intended to limit the claimed invention. Unless otherwise defined, all technical and scientific terms have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. If any apparent discrepancy arises between the meaning provided in this specification and the use of the term in the biomedical field, the meaning of the term provided in this specification shall prevail.

[0082] Acute respiratory distress syndrome (ARDS) has the meaning recognized in the biomedical field. ARDS is a type of respiratory failure characterized by widespread inflammation that occurs rapidly in the lungs. Symptoms include shortness of breath, rapid breathing, and bluish skin. Causes can include sepsis, pancreatitis, trauma, pneumonia, and inhalation.

[0083] Alternative splicing (AS) has the meaning defined in the biomedical field. RNA splicing is a molecular function that occurs directly after RNA transcription but before protein translation in all cells, in which introns are removed and exons are joined. Alternative splicing, or alternative RNA splicing, or differential splicing, is a regulatory process during gene expression that results in a single gene encoding multiple proteins. The exons of a gene can either be included in the final processed messenger RNA (mRNA) produced by that gene or excluded from that mRNA. Proteins translated from alternatively spliced mRNAs can contain differences in their amino acid sequences and typically differences in their biological functions.

[0084] COVID-19 has a recognized meaning in the biomedical field. The global pandemic of SARS-CoV-2 has had a significant impact on global public health. The viral genome is relevant and relatively small, approximately 30 kb. It encodes two large overlapping open reading frames for 16 non-structural proteins and four open reading frames for structural proteins. The small size of the genome and the small number of gene regions provide a good size for our analysis. Wu et al., Virology Journal, 20(1), 6(2023). The outbreak of SARS-CoV-2 that became the COVID-19 global pandemic has had a huge impact on global health and the economy. Cascella et al., Features, Evaluation, and Treatment of Coronavirus (COVID-19). SARS-CoV-2 remains a continuing threat to human health. El-Sadr, Vasan, & El-Mohandes, N. Engl. J. Med., 388(5), 385-387(2023).

[0085] Ensemble free energy has a recognized meaning in the field of physics. Ensemble free energy is estimated based on the partition function algorithm that is also included in the RNAfold program. Lorenz et al., ViennaRNA Package 2.0. Algorithms Mol. Biol., 6, 26(2011); McCaskill, Biopolymers, 29(6-7), 1105-19(1990); Zuker & Stiegler, Nucleic Acids Res, 9(1), 133-48(1981).

[0086] Mann Whitney U tests have a meaning defined in the field of statistics. The Mann-Whitney U test (also known as the Mann-Whitney-Wilcoxon (MWW), Wilcoxon rank sum test, or Wilcoxon-Mann-Whitney test) is a non-parametric test of the null hypothesis that the probability of a value randomly selected from one population being less than or greater than a value randomly selected from a second population is equal. This test can investigate whether two independent samples are selected from populations with the same distribution.

[0087] Minimum free energy has a recognized meaning in the field of physics. Minimum free energy can be estimated using the minimum free energy algorithm that produces the optimal structure. Zuker & Stiegler, Nucleic Acids Res, 9(1), 133-48(1981).

[0088] Motif analysis has a well - recognized meaning in the biomedical field. A DNA sequence motif is a subsequence of a DNA sequence, which is a short and similar nucleotide repeat pattern and has many biological functions. A DNA motif refers to a short and similar nucleotide repeat pattern with biological significance. See Hashim, Mabrouk, & Al - Atabany, Review of different sequence motif finding algorithms. Avicenna J. Med. Biotechnol., 11(2), 130–148 (April - June 2019). The motif analysis tool called XSTREME can be used to input sequences of any length. XSTREME uses two well - established motif discovery programs, MEME and STREME, to identify motifs and uses the SEA algorithm for motif enrichment analysis. Motifs are discovered and analyzed using MEME - ChIP and compared with RNA databases. The sequences are input into the online version 5.5.1 MEME suite.

[0089] The mountainClimber algorithm is a cumulative - sum - based method for identifying alternative transcription start sites (ATSs) and alternative polyadenylation (APA) as change points. Different from many existing methods, mountainClimber runs on a single sample and identifies multiple ATS or APA sites at any position in the transcript. Cass & Xiao, Cell Systems, 9(4), 23, 393 - 400.e6 (October 2019).

[0090] Next - generation sequencing (NGS) has a well - recognized meaning in the biomedical field. NGS technology is generally highly scalable and can sequence an entire genome at once. Typically, this is done by fragmenting the genome into small segments, randomly sampling the segments, and sequencing them using various techniques.

[0091] The nucleocapsid gene (N gene) has a well - recognized meaning in the biomedical field, which is the protein that packages the positive - sense RNA genome of the coronavirus to form a ribonucleoprotein structure enclosed within the viral capsid. For example, the N gene can be the gene of the SARS - CoV2 - nucleocapsid (N2) gene. See Wu et al., Virology Journal, 20(1), 6 (2023).

[0092] Principal component analysis (PCA) has the meaning defined in the biomedical field. Principal component analysis is a statistical process that uses an orthogonal transformation to convert the observations of a set of potentially correlated variables (entities, each presenting various numerical values) into values of a set of linearly uncorrelated variables called principal components.

[0093] "Read" has the meaning defined in the biomedical field, that is, reading the sequencing results to determine the nucleotide base structure.

[0094] "Read origin protocol" (ROP) has the meaning in the computer field, that is, a computational protocol used to discover the origin of all reads, including those derived from repetitive sequences, rearranged B and T cell receptors, and microbial communities. The read origin protocol was developed to determine what the unmapped reads represent. Mangul et al., Genome Biology 19, 36 (2018). Recent developments of the read origin protocol (ROP) have shown that unmapped reads match bacterial, viral, fungal, and B / T rearranged genomes.

[0095] "RNA sequencing" (RNA-Seq) has the meaning recognized in the biomedical field, that is, a sequencing technology that uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample, representing an aggregated snapshot of the dynamic pool of RNA in cells, also known as the transcriptome. In RNA-Seq, reads can be mainly aligned to certain regions, and there are usually duplicate identical read sequences. Deschamps-Francoeur, Simoneau, & Scott, Handling multi-mapped reads in RNA-seq., Comput. Struct. Biotechnol. J., 18, 1569-1576 (2020). Several factors determine the extent of repetitive sequences, especially many processing steps in the RNA-Seq procedure. Fu et al., BMC Genomics, 19(1), 531 (2018).

[0096] RNAfold is a computer program from the ViennaRNA package, which is used to predict the minimum free energy of the secondary structure of RNA-Seq read sequences. RNAfold uses a loop-based energy model and a dynamic programming algorithm to estimate the MFE based on the RNA sequence. Lorenz et al., ViennaRNA Package 2.0. Algorithms Mol Biol, 6, 26 (2011).

[0097] "Sepsis" has the meaning defined in the biomedical field, that is, a life-threatening condition that occurs when the body's response to infection damages its tissues and organs. Bone et al., Chest, 101, 1644-1655 (1992); Singer et al., JAMA, 315, 801-810 (February 2016).

[0098] The STAR alignment tool is the Spliced Transcripts Alignment to a Reference (STAR), a fast RNA-seq read mapper that supports spliced-junction and fusion-read detection. Using a suffix-array index, STAR aligns reads by finding the maximal mappable prefix (MMP) hits between the reads (or read pairs) and the genome. Different parts of a read can be mapped to different genomic locations, corresponding to splicing or RNA fusions. The genomic index includes known spliced junctions from annotated gene models, allowing sensitive detection of spliced reads. STAR performs local alignment and automatically soft-clips the ends of reads with a high number of mismatches. Dobin, A. et al., STAR: Ultrafast universal RNA-seq aligner. Bioinformatics, 29(1), 15–21 (January 2013).

[0099] The treatment of sepsis has a meaning recognized in the medical field. Sepsis is treatable, and the timely implementation of targeted interventions has improved outcomes. Mayo Clinic informs the public that several medications are used to treat sepsis and septic shock. They include antibiotics. Broad-spectrum antibiotics that are effective against a variety of bacteria are typically used first. After learning the results of blood tests, doctors may switch to different antibiotics that are targeted against the specific bacteria causing the infection. Other medications include low-dose corticosteroids, insulin to help maintain stable blood sugar levels, medications that alter the immune system response, and pain medications or sedatives.

[0100] Whippet (OMICS_29617) is a program that can detect and measure alternative RNA splicing events of any complexity, and its computational requirements are compatible with a laptop. Whippet applies the idea of lightweight algorithms to event-level splicing measurements via RNAseq. The software can assist in analyzing alternative splicing events ranging from simple to complex that function in normal and disease physiologies. Use Whippet to identify alternative splicing events with high entropy. Sterne-Weiler et al., Molecular Cell, 72, 187-200.e186 (2018). Whippet can generate entropy values for alternative splicing and transcriptional events identified for each gene. These entropy values are created without using any groups in gene expression analysis. To visualize this data, principal component analysis (PCA) can be performed to reduce the dimensionality of the dataset and obtain an unsupervised overview of the trends in entropy values in the samples. The raw entropy values from all samples can be concatenated into a matrix, and missing values can be replaced with column means. Mortality can be overlaid on the PCA plot to evaluate the ability of these raw entropy values to predict this outcome in this sample set. This analysis was done in R (version 3.6.3).

[0101] Unless otherwise defined, the scientific and technical terms used in this application shall have the meanings commonly understood by those of ordinary skill in the biomedical field. The present invention is not limited to the methods, protocols, reagents, etc. described herein and can be altered.

[0102] This specification does not relate to methods of cloning humans, methods of altering the germline genetic identity of humans, the use of human embryos for industrial or commercial purposes, or procedures for altering the genetic identity of animals, such as procedures that cause suffering without substantial medical benefit to humans or animals, and animals produced by such procedures.

[0103] Materials and methods guidance

[0104] When making and using the present invention, those of ordinary skill in the biomedical field can use these materials and methods as a guide to predictable results:

[0105] Human subjects The inventors timely obtained a sufficient number of samples. The inventors are recruiting patients with sepsis in the intensive care unit and sending their blood for deep RNA sequencing. After approval by the Institutional Review Board, when blood Fluid cultures are required, patients are recruited from the emergency department and inpatients for this research project. Through alerts from the electronic health record (EPIC), research assistants are informed when blood Fluid cultures are needed. Blood is collected FluidPrior to culturing, patient consent was obtained. Blood samples were collected in collaboration with the blood collection service and bedside nurses. The blood was collected in two PAXgene tubes, 5 ml of blood, and stored in a -80°C freezer until RNA was isolated for sequencing. Hospital data from the most recent six months were reviewed. There were many available samples. Over a six-month period, 2,453 patients had blood Fluid cultured in the emergency department, and 602 patients had blood Fluid cultured in the intensive care unit. Blood was also collected from patients who underwent bronchoalveolar lavage (BAL) in the intensive care unit for the diagnosis of pneumonia. As described, samples were collected and stored prior to bronchoalveolar lavage. Over a six-month period, 46 patients had bronchoalveolar lavage samples obtained in the emergency department, and 51 patients had bronchoalveolar lavage samples obtained in the intensive care unit.

[0106] In Example 4, the Lifespan Institutional Review Board approved the study protocol in accordance with the Declaration of Helsinki. Written informed consent was provided by the participants or their legally authorized representatives prior to enrollment.

[0107] Biological variables . Recruitment was not limited by gender. Variables were collected and compared across groups, such as age (patients were included across the lifespan), weight, and medical comorbidities. If these variables or gender were significantly different (t-test or rank sum), the analysis would adjust for these factors by regression.

[0108] Variables were collected and compared across groups, such as age (patients were included across the lifespan), weight, and medical comorbidities. If these variables or gender were significantly different (t-test or rank sum), these factors were adjusted for in the analysis by regression.

[0109] Blood sample collection。Blood samples were collected on day 0 of admission to the intensive care unit. Clinical data, including COVID-specific therapies, were prospectively collected from the electronic medical records, and the participants were followed up until discharge or death. Ordinal scales, such as those described by Beigel et al., New England Journal of Medicine (2020); and the Sepsis-related Organ Failure Assessment (SOFA) score and the diagnosis of ARDS, were collected. See Singer et al., The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA, 315:801–810 (2016); Ferguson et al., The Berlin definition of ARDS. Intensive Care Medicine, 38:1573–1582 (2012).

[0110] For the assays described in Example 5, during 2020, blood from COVID-19 patients in the ICU was collected in Paxgene tubes. RNA sequencing was performed as described by Fredericks et al., Science Reports, 12(1), 15755 (2022).

[0111] RNA extraction and sequencing 。Whole blood can be collected in PAXgene tubes (QIAGEN, Germantown, MD, USA) and sent to GeneWiz (South Plainfield, NJ, USA) for RNA extraction, ribosomal RNA removal, and sequencing. Sequencing can be performed on an Illumina HiSeq machine to provide 150-base pair paired-end reads. Libraries with three samples per lane were prepared. Each lane provided 350 million reads, ensuring that each sample had >100 million reads.

[0112] RNA isolation and sequencingBlood samples from patients were collected using PAXgene tubes (PreAnalytiX, Switzerland). All samples required at least 1400 ng of RNA for deep sequencing. Using the PAXgene system, typically >3000 ng was obtained. After the RNA samples were processed, they were sent out for RNA sequencing. Due to the high concentration of globin and ribosomal RNA in the blood samples, these samples were subsequently further processed at the sequencing company to reduce globin RNA and human ribosomal RNA. This optimized the yield of clinically relevant reads. Each sample was sent out for deep RNA sequencing with the goal of 100 million reads per sample.

[0113] RNA sequencing was performed on a non-CLIA machine because the data was not used for clinical practice. The vendor had a Clinical Laboratory Improvement Amendment (CLIA)-certified machine, which allowed for easier translation in future studies. Not all of the blood samples collected were sent out for deep RNA sequencing. One of the two PAXgene tubes was retained for PCR testing.

[0114] Sample size calculation Patients with bacteremia were compared to those without bacteremia to identify targets for creating PCR. Based on the positive culture rates (Table 6), the inventors would collect 2200 blood Fluid cultures to obtain 50 Staphylococcus aureus positive cultures. These rates applied to all samples. The inventors collected from the emergency department and intensive care unit, and thus the positive rates were higher. 3500 samples were obtained to get a representative representation of each type of organism. The institution performed an average of 3000 blood cultures every six months in the emergency department and intensive care unit. This test yielded at least 60 patients with Staphylococcus aureus, 30 with Escherichia coli, and 10 with Pseudomonas aeruginosa. For these three pathogens, all samples with the corresponding positive blood Fluid cultures were sent out for deep RNA sequencing. The inventors also sent out samples with the corresponding positive blood Fluid cultures for deep RNA sequencing, including approximately 135 samples judged to be contaminants, and an additional 115 samples from patients with negative blood Fluid cultures. This process resulted in 350 samples being sent out for the RNA sequencing of Example 1.

[0115] The second PAXgene tube drawn from these patients was used to validate the PCR test. Patients with pneumonia were compared with those without pneumonia to identify the targets for creating the PCR. Based on the positive culture rate (see Table 6), the inventors would ideally collect all patients undergoing bronchoalveolar lavage from the emergency department or intensive care unit. During a six-month period, this process would include approximately 100 patients, and 11 patients with Staphylococcus aureus, 10 patients with Pseudomonas aeruginosa, and 4 patients with Haemophilus influenzae would be obtained. The inventors collected samples for 18 months to obtain approximately 300 blood tubes for sequencing for the pneumonia portion of the present invention. Since two pathogens are being studied, these patients underwent complementary bronchoalveolar lavage and blood Fluid cultures simultaneously. The same samples collected were used to identify resistance genes.

[0116] Clinical information assessment . The RNA sequencing data was interpreted with clinical data collected from the electronic medical record, including endpoints such as mortality, intensive care unit length of stay, hospital length of stay, SOFA score (Shankar-Hari et al. (2016)), number of days on ventilator, renal failure, ARDS (Ferguson et al. (2012)). The culture data was based on the test results of the microbiology laboratory and was the gold standard. The clinical response to antibiotics was also tracked to see if the treatment based on the microbiology data was correct. Changes in treatment were evaluated to ensure that culture data was used in the treatment and antimicrobial stewardship practices were followed.

[0117] Polymerase chain reaction (PCR) designOptimized PCR parameters ensure the accuracy and reproducibility of qPCR reactions. See Bustin & Huggett (2017); Bustin, Mueller, & Nolan (2020). Preliminary data show that bacterial readings can be measured from patients with bacteremia and pneumonia, and these readings can be aligned with the genomes of the organisms. RNA sequencing data accumulated from patients with bacteremia or pneumonia caused by a specific pathogen are used to identify target sequences. These sequences are compared to the pan-genome of the same organism to confirm that the targets are generalizable to the pathogen. Wang et al. (2022). The present inventors designed several primer / probe combinations for the sequences using Beacon Designer (Premier Biosoft), and their specificity was confirmed by BLAST searches. Primers with low specificity, dimer formation, or production of amplicons with complex secondary structures were excluded. Bustin & Huggett (2017). Primer-BLAST (NCBI) was used as an independent, complementary design strategy; primers identified by both methods were prioritized. The temperature of the PCR reaction and the primer concentration used in the master mix were optimized in the laboratory. The goal was to create a set of standard test conditions.

[0118] Testing PCR The PCR test was validated in two ways. First, the cDNA library used for RNA sequencing was tested. Subsequently, RNA from the blood of infected and uninfected patients was used as a template for cDNA synthesis and then PCR was performed. PCR was applied to samples from RNA sequencing and an independent patient cohort to validate the assay. Several primer combinations were evaluated for each target sequence. Bustin & Huggett (2017). The SYBR Green method was used to prioritize different primer combinations. Then, for the prioritized primer combinations, hydrolysis ("Taqman") probes designed for qPCR that had been designed together with the primers were synthesized.

[0119] Rigor and reproducibility Preliminary data showed RNA isolated from patients and high-quality RNA sequencing results. The present inventors also focused on standard isolation methods and that they could be easily applied subsequently so that the results could be translated into clinical practice. To enhance robustness during the development process, it is standard practice to perform each step of PCR (setup, cycling, analysis) in a separate isolation chamber to reduce reactions contaminated with amplicons from past runs.

[0120] Computing resources。Computational biology work is performed on on-premises servers. These servers are protected as they contain clinical data. All HIPAA standards apply. The servers run on 6x VxRail E560F nodes (PowerEdge R640 1U rack-mounted servers) and have dual Intel Xeon Platinum 8260 (24C) 2.4 GHz, along with 1,152 GB of RAM, 2x 1.6 TB SAS SSD cache, 8x 7.68 TB SAS SSD capacity, 4x 10 Gb data ports, and 1x 1 Gb iDRAC management port. This server includes vSphere Enterprise Plus, with each node configured for 3-year 24x7 mission-critical support to provide the computational infrastructure. The server consists of a 288c (691.2 GHz) CPU and 6.75 TB of RAM. Storage estimates reflect 368.64 TB RAW / 222 TB usable memory on a RAID6 configuration, and 20% vSAN overhead. This server manages all large datasets from RNA sequencing. Due to the sequencing depth for RNA splicing analysis (100 million reads vs. 40 million), both sequencing and analysis generate more data. In a preliminary project, the inventors generated 1 TB of sequencing data and an additional 1 TB from genome alignment. Since RNA sequencing data is always identifiable, data from humans is treated as protected health information (PHI), even without typical identifiers (such as name, date of birth, etc.) associated with the data.

[0121] The following pipeline includes typical analyses: Differential expression is done using Whippet, RNA analysis (Sterne-Weiler et al. (2018)). Thereafter, for microbial RNA, the unmapped reads are analyzed. The inventors selected reference genomes for all identified species of Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Haemophilus influenzae. This was done using the genomes described in Table 4 and adding plasmids. Bacterial rearrangements are common between strains. The tool realigns rearrangements with the consensus genome to align the unmapped reads to it. Noureen, Tada, Kawashima, & Arita (2019). The tool allows visualization and construction of the consensus genome. Conserved sequences and strain-specific sequences are retained. Tada, Tanizawa, & Arita (2017). Targets are preferentially selected from conserved regions. Strain-specific targets are used if clinically relevant. The STAR alignment tool is also used to search for specific resistance genes in the unmapped reads.

[0122] Cloud - based computingDue to the depth of sequencing for RNA splicing analysis (100 million reads vs. 40 million), both sequencing and analysis generated more data (a small study generated 1 TB of sequencing data and another 1 TB was generated from genome alignment). Given the prediction of such large amounts of data, the ability to scale storage space and computing power in the cloud up and down is an ideal option. The server stores and analyzes data from mouse and human samples. Since RNA sequencing data is always identifiable, data from humans is treated as protected health information (PHI), even without typical identifiers (such as name, date of birth, etc.) associated with the data. The cloud server can only be accessed through the hospital virtual desktop, and the data is only stored on Azure servers or hospital computers. The data is encrypted both at rest and during transfer to and from the hospital. Any linkage to typical identifiers is kept separate from the sequencing data. The cloud-based server allows for large-scale data analysis, where the computing and storage requirements change per use. The Azure server is Linux-based and uses R and Python programming. The following pipeline includes typical analyses: differential expression is done using Whippet, RNA analysis. This also includes entropy measurement, and the target genes undergo gene ontology term analysis. Genes with alternative transcription start and stop sites identified by Whippet are related to the findings from mountainClimber analysis.

[0123] Computational analysis and statistics First, the quality of the RNA sequencing data is checked using FASTQC. RNA sequencing data is collected from the GTEx Consortium and differential gene processing analysis is done using the Whippet software. Alternative transcription events are those identified by Whippet as "tandem transcription start sites", "tandem alternative polyadenylation sites", "alternative first exons", and "alternative last exons". "Alternative RNA splicing events" are those labeled as "core exons", "alternative acceptor splice sites", "alternative donor splice sites", and "retained introns". Alternative mRNA processing events are determined by a log2 fold change greater than 1.5 + / - 0.2. Statistical significance is calculated by the chi-square p-value of a contingency table from 1000 simulations based on the probability of each result.

[0124] Computational biology and statistical analysisAll computational analyses can be completed without knowledge of clinical data. The data can be quality control evaluated using FastQC. See Andrews, A quality control tool for high throughput sequence data. FastQC (2014). The RNA sequencing data can be aligned to the human genome using the STAR aligner. Dobin et al., Bioinformatics (Oxford, England), 29, 15–21 (2013). Reads that match the human genome can be separated and called “mapped” reads. Reads that do not match the human genome, which are typically discarded in standard RNA sequencing analysis, are identified as “unmapped” reads. The unmapped reads are then aligned to a relevant comparator and counted for each sample using Magic-Blast. See Boratyn et al., BMC Bioinformatics, 20, 405 (2019). The unmapped reads are further analyzed using Kraken2. See Wood, Lu, & Langmead, Genome Biology, 20, 257 (2019). The analysis uses the PlusPFP index to identify additional bacterial, fungal, archaeal, and viral pathogens. See the Kraken2 / Bracken Refseq index maintained by Benlangmead, which uses a modified version of the Kutay B. Sezginel minimal GitHub page theme.

[0125] Reads that match the human genome (i.e., mapped reads) can also be analyzed for gene expression, alternative RNA splicing, and alternative transcription start / end sites by Whippet. See Sterne-Weiler et al., Molecular Cell, 72, 187-200.e186 (2018). When comparing between two groups (dead vs. alive), differential gene expression can be set at a threshold of P<0.05 and + / -1.5 log2 fold change. Alternative splicing is defined as core exons, alternative acceptor splice sites, alternative donor splice sites, retained introns, alternative first exons, and alternative last exons. Alternative transcription start / termination events can be defined as tandem transcription start sites and tandem alternative polyadenylation sites. Alternative RNA splicing and alternative transcription start / termination events can be compared between groups. See Sterne-Weiler et al., Molecular Cell, 72, 187-200.e186 (2018). As described by Fredericks et al., Intensive Care Medicine (2020), significance is set at greater than 2 log2 fold change. Genes identified from the mapped read analysis can be evaluated by GO enrichment analysis (PANTHER Overrepresentation Release 20200728). See Mi et al., Nature Protocols, 8, 1551–1566 (2013).

[0126] Kraken2 These tools are compatible with both Kraken1 and Kraken2. Both tools can help users analyze and visualize Kraken results. Bracken allows users to estimate the relative abundances in a specific sample from Kraken2 classification results. Bracken uses a Bayesian model to estimate abundances at any standard taxonomic level, including species / genus level abundances. Pavian has also been developed as a comprehensive visualization program that can compare Kraken2 classifications between multiple samples. KrakenTools is a set of scripts to help analyze Kraken results. For more information, those of ordinary skill in the biomedical arts can refer to Wood, Lu, & Langmead, Improved metagenomic analysis with Kraken2, Genome Biology (November 28, 2019).

[0127] In Example 5, the present inventors presented an analysis of the stability of targets for diagnosing COVID-19. RNAfold from the Vienna package was used to predict the minimum free energy of the secondary structure of RNA-Seq read sequences. RNAfold was also used to calculate the minimum free energy value and the overall free energy value of the structure to compare the stability between different read sequences. The energy parameters used for the calculation were set at 37 °C. In addition to RNAfold, different statistical tools were used to evaluate the stability or instability of the secondary structure.

[0128] Sequences with abnormal lengths were ignored in the analysis. Only sequences with a length of less than 175 nucleotides were analyzed. The energy parameters used for the calculation were set at 37 °C. R Studio was used to perform the statistical analysis. First, Welch ANOVA was performed to compare the minimum free energy (MFE) values and the overall free energy (EFE) values of the reads located within known gene regions.

[0129] The assignment to a certain gene region depends on which gene region the middle of the read sequence is located in. The Games-Howell Post Hoc test was performed for pairwise comparison of the free energy values between genes. A similar statistical analysis was performed for the nucleocapsid (N) gene.

[0130] The Welch T-test was performed to compare the minimum free energy and the overall free energy of sequences with and without motifs. The ideal length of the sequences input into MEME-ChIP was 500 letters, while the longest read sequence length used in our analysis was 151 nucleotides. MEME-ChIP was only used to find the top ten motifs. Negative binomial regression was used to analyze the effect of destabilizing motifs on the number of replicate reads.

[0131] First, Welch ANOVA was performed to compare the minimum free energy values and the overall free values of the reads at the start, middle, or end of the N gene. The N gene was divided into three equally long regions, and the assignment of the N gene region was determined by the middle of the read sequence. The Games-Howell Post Hoc test was performed for pairwise comparison of the free energy values between N gene regions. Since the early gene region of the N gene had zero variance, the Welch t-test was performed to compare individuals within the gene region. The chi-square goodness-of-fit test was performed to evaluate the distribution of reads between genes. This determined whether certain genes had more or fewer reads relative to other genes.

[0132] The Gene Ontology Resource Knowledgebase was used for the evaluation Gene Ontology theory (GO)。Ashburner et al., Nature Genetics, 25, 25 - 29 (2000); The Gene Ontology Resource. Nucleic Acids Research, 47, D330 - d338 (2019). The input comes from the genes being analyzed and the output is shown. The output of the Gene Ontology is not related to the actual increase or decrease in gene expression, but is related to the expectation based on a set of input genes.

[0133] Pipeline 。The following pipeline includes typical analyses: differential expression is done using Whippet, and RNA analysis (Sterne - Weiler et al. (2018)). Thereafter, for microbial RNA, the unmapped reads are analyzed. The inventors selected the reference genomes of all identified species of Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Haemophilus influenzae. Bacterial rearrangements are common between strains. The tool is adjusted for rearrangements as the inventors made a pan - genome to which the unmapped reads are aligned. Noureen, Tada, Kawashima, & Arita (2019). The tool allows visualization and construction of the pan - genome. Conserved sequences and strain - specific sequences are retained. Tada, Tanizawa, & Arita (2017). Targets are preferentially selected from conserved regions. Strain - specific targets are used if clinically relevant. The STAR alignment tool is also used to search for specific resistance genes in the unmapped reads.

[0134] The following examples are provided to illustrate the invention and should not be taken as limiting the scope of the invention in any way.

[0135] Example 1: Designing a reverse transcription quantitative polymerase chain reaction (RT - qPCR) test (A1a) for bacteria causing bacteremia (specifically Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa) directly from blood without culturing, based on RNA identified in patients with bacteremia caused by these organisms (especially Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa) Basic principle

[0136] Fluid 。Blood Assay 1: Evaluating RNA sequencing data of patients with bloodstream infections caused by Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa Culture is the current gold standard for pathogen diagnosis but takes several days. Blood cultures have a known contamination rate that can have an adverse impact on treatment and disease progression, as shown in the COVID pandemic. Yu et al. (2020).

[0137] RNA sequencing is an emerging technology that can enhance diagnostic capabilities. Unmapped reads, which are reads that cannot be matched to the human genome, are typically discarded in RNA sequencing data from humans. When the depth of RNA sequencing is sufficient, these unmapped reads can provide useful clinical information. Unmapped reads found in the blood of patients with bacteremia are used to inform the development of diagnostic PCR.

[0138] Gene expression in bacteria differentiates between infection and simple colonization. D’Mello et al. (2020). Targeting RNA is more specific than DNA by eliminating signals from free DNA of dead bacteria or pathogen DNA released from immune cells against infection. Opota, Jaton, & Greub (2015).

[0139]

[0140] Assay 2: Creating RT - qPCR primers to identify Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa causing bacteremia Unmapped reads or reads that cannot be matched to the target organism are typically discarded. These reads are used to identify bacterial RNA in blood. This was initially done using Kraken2. Wood, Lu, & Langmead (2019). For finer granularity, the present inventors assembled custom genomes and aligned the unmapped reads to them using the STAR RNA-sequencing alignment tool. Dobin et al. (2013). These genomes are based on the common genomes in Table 4 but also include sequences from other chromosomes and plasmids attributed to these bacteria, resulting in a pan-genome. Eizenga et al. (2020). Samples from patients with Staphylococcus aureus were used to identify significant reads matching this bacterium and were repeated for other target pathogens. This gave the total read count for each bacterium and the genomic portion with the highest read abundance. From these abundant reads, PCR primers were based on large reads in common regions among many patients to detect the pathogen.

[0141] Expected results Using the targets of interest from deep RNA-sequencing data, PCR primers cover these portions of the identified bacterial genomes. Multiple primers for multiple targets can identify one pathogen, but this is done by multiplexing using the NeuMoDx instrument from an industry partner. The targets of these primers are RNA in blood, and reverse transcriptase reactions are used to generate cDNA for PCR.

[0142] Potential alternatives。Preliminary data show that the blood of bacteremia patients has bacterial RNA associated with pathogenic organisms (Table 1). During an infection, there should be a set of highly expressed genes from each bacterium, which can serve as the basis for identification. The present inventors expect genes such as coagulase genes to be detected in patients with Staphylococcus aureus bacteremia. Cheng et al. (2010). The present inventors prioritize PCR targets that are unique to the bacteria being tested and different from other bacteria. Further findings include observing that bacterial gene expression can also determine colonization and infection based on expression patterns and abundances. D’Mello et al. (2020). The number of reads, i.e., transcript abundance, can be correlated with the patient's condition or patient outcome. Abundant clinical data are patient-related for the derived samples. For correlation, the read frequency or abundance on RT-qPCR is evaluated.

[0143] Example 1A: Validating these RT - PCR tests in samples from patients with and without bacteremia (A1b) 。Bacteria identified by sequencing may be unrelated to microbial culture. This may be due to blood cultures being negative in 50% of bloodstream infections, either because of low bacterial numbers in the blood or the effect of antibiotics before sample acquisition. Opota, Jaton, & Greub (2015). Blood cultures may identify the wrong pathogen, while another pathogen may be causing the infection, i.e., a contaminant. The method involves using Kraken2 to align the unaligned reads to identify the background level of sequences from unrelated bacteria that may be commensals or contaminants. A single gene may not uniquely identify an organism, thus reducing the specificity of the test. In this case, the present inventors tested gene combinations as described above. Alternatively, unique alleles / SNPs are used to define a specific pathogen. Established techniques are used to measure SNPs in the form of RT-qPCR.

[0144] (A1b) Basic principle 。

[0145] Fluid 。RT-qPCR allows the direct identification of pathogens from blood in less than 4 hours without culturing. RT-qPCR also allows for faster, pathogen-specific antibiotic selection. It is recommended to perform blood Fluid culture collection before administering antibiotics to improve the diagnostic sensitivity of blood Assay 1: Testing PCR primers on samples used for RNA sequencing cultures. See Evans et al., (2021). Administering this diagnostic test proposed herein, it is expected that antibiotics do not affect the RNA present at the time of blood draw.

[0146] Assay 2: Validating PCR primers on samples collected to simulate clinical useAs an initial test of the PCR primers, access the cDNA library created for RNA sequencing. Since the RNA sequencing data determined the presence of these RNA fragments, this is the first step in evaluating the utility of these novel PCR assays for bacteria. cDNA from all samples of positive cultures from each bacterium was used for this assay. For all pathogens, each cDNA sample was tested with the PCR primers. As a negative control for specificity, the inventors also used cDNA from the blood of patients without infection and normal controls.

[0147] Expected results To obtain sensitivity and specificity that meet FDA requirements, samples from patients with and without confirmed bloodstream infections caused by the target pathogen were identified from a stock of clinical specimens, including PAXgene tubes. PAXgene tubes were collected and stored at the time of blood culture collection. Empirically, PAXgene tubes stabilize high-quality RNA. To enhance the robustness of the test, these tubes were unknown to the team performing the PCR assay. RNA was extracted and globin and rRNA were reduced using a commercially available kit. A cDNA library was prepared with reverse transcriptase and then PCR was completed with primers. PAXgene tubes were used, but RT-qPCR was performed immediately to ensure results were returned within less than 4 hours.

[0148] Potential alternatives A set of PCR primers was developed and optimized on a machine that can be easily converted into a clinical microbiology laboratory. The test directly identifies Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa from blood by extracting RNA, reducing globin and rRNA, and creating cDNA for PCR. These tests have sensitivity and specificity that meet FDA requirements. These PCRs directly from blood were initially performed on Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa. By collecting samples from patients with other infections in PAXgene tubes, the PCR panel can be expanded as new targets from more pathogens are identified. PCR is rapid and can monitor treatment efficacy, which has not been done to date because cultures take several days to give feedback. If successful treatment is detected, the antibiotic course can be shortened and antimicrobial stewardship can be enhanced.

[0149] Example 2: Designing a reverse transcription quantitative polymerase chain reaction (RT - qPCR) test (A2a) for bacteria causing pneumonia (specifically Staphylococcus aureus, Pseudomonas aeruginosa, Haemophilus influenzae) directly from blood without culturing, based on RNA identified in patients with pneumonia caused by these organisms Blood interferes with PCR when identifying DNA rather than RNA. Sidstedt et al. (2018). Deep RNA sequencing can discover genes that identify infection, but the PCR conditions cannot be optimized to replicate this finding. This problem can be solved when the cost and time of RNA sequencing are reduced. At a depth of 100 million or more reads, RNA sequencing should take less than 4 hours.

[0150] (especially Staphylococcus aureus, Pseudomonas aeruginosa, Haemophilus influenzae) Basic principle 。

[0151] Fluid 。The diagnosis of hospital - acquired pneumonia is complex. Modi & Kovacs (2020). Bronchoalveolar lavage (BAL) is the gold standard, as is blood Fluid culturing for bacteremia. Since bronchoalveolar lavage requires an invasive intervention (bronchoscopy) that can worsen the clinical presentation, screening tools are used to decide when to perform it, and thus the yield is higher than that of blood Assay 1: Evaluating RNA sequencing data of patients with pneumonia caused by Staphylococcus aureus, Pseudomonas aeruginosa, and Haemophilus influenzae culturing. Direct blood tests that provide the same diagnosis avoid the need for invasive bronchoscopic interventions. The assays described below are similar to those in Example 1, where an independent cohort of patients was diagnosed with pneumonia and underwent BAL.

[0152] Assay 2: Creating RT - PCR primers to identify Staphylococcus aureus, Pseudomonas aeruginosa, and Haemophilus influenzae causing pneumonia 。As described above, for bloodstream infections, unlocalized reads are aligned to the target genome (Table 4) to identify genes with increased expression in patients diagnosed with infection by BAL. At the time of BAL, blood is collected in PAXgene tubes. The identified Staphylococcus aureus and Pseudomonas aeruginosa genes are compared to the genes identified for bacteremia. From these reads, PCR primers are developed.

[0153] Expected results 。Using the reads generated by Assay 1, PCR primers are developed to identify the pathogens causing hospital - acquired pneumonia and applied to sequenced samples and an independent cohort.

[0154] Potential alternatives 。Preliminary data show that patients in the ICU have bacterial RNA in their blood. During the infection process, there is a set of highly expressed genes from the bacteria that can be used as a basis for identification. As a result, these genes are different from those expressed during bacteremia because some believe that bacterial gene expression changes according to the site of infection / colonization. The present inventors have RNA sequencing data from patients with bacteremia and pneumonia due to similar pathogens and can see if different genes are expressed at a higher rate. Primers can be developed for each pathogen according to the site of infection to guide diagnosis. Another result is that the same target sequences are found in bacteremia and pneumonia. This will simplify product development on the NeuMoDx and require integrating the test into other clinical diagnoses, such as X - rays.

[0155] Example 2A: Validating these RT - qPCR tests in samples from patients with and without pneumonia (A2b)。This technical method is similar to Example 1, and the present inventors have shown that this is possible. See Table 1. Since the infection is located in the lungs, bacterial RNA may not be identified in the blood of these patients, but other studies have questioned this possibility. D’Mello et al. (2020). Bacterial DNA was detected in the blood of patients with pneumonia. Langelier et al. (2020). The lungs have a large surface area for gas exchange, which would facilitate the transfer of RNA from the infection to the bloodstream in stable RNA or microvesicles. Blenkiron et al. (2016). Depending on the severity, bacteremia complicates pneumonia in 6 - 17% of cases. Zhang, Yang, & Makam (2019). A subgroup of patients with pneumonia is expected to have shared target sequences with the patients studied in Example 1. If sequencing analysis cannot distinguish whether a subgroup of patients with pneumonia is accompanied by bacteremia, clinical data are used to guide treatment.

[0156] (A2b) Basic principle 。

[0157] Assay 1: Testing PCR primers on samples used for RNA sequencing 。The PCR targets identified by sequencing can be used clinically. Hospital-acquired pneumonia usually causes rapid deterioration of patients. RT-qPCR allows the direct identification of pathogens from blood without culturing in less than 4 hours and faster selection of antibiotics against the pathogens. The goal is to eliminate invasive bronchoscopy, which delays antibiotic administration and increases the risk to the patient.

[0158] ​ 。The RNA sequencing cDNA library was again the initial test for the PCR primers. cDNA from all samples of positive bronchoalveolar lavage cultures from each bacterium was used for this assay. For all pathogens, each cDNA sample was tested with the PCR primers. The present inventors also used cDNA from patients without hospital-acquired pneumonia.

[0159] Assay 2: Validation of PCR primers on samples collected to mimic clinical use 。This test was completed to obtain sensitivity and specificity that meet FDA requirements. PAXgene tubes were identified, which were from patients with and without confirmed hospital-acquired pneumonia caused by the target pathogen. When collecting bronchoalveolar lavage fluid, PAXgene tubes were collected. The infection status of the patients was unknown to the researchers performing the PCR assay. Stable RNA was extracted from the PAXgene tubes, and globin and human rRNA were removed using a commercial kit from New England Biolabs. cDNA was prepared with reverse transcriptase and then PCR was completed with primers. PAXgene tubes were used. RT-PCR was completed immediately to ensure results were obtained in less than 4 hours.

[0160] Expected results。Specific RT-qPCR assays confirmed the sequencing and diagnosis of hospital-acquired pneumonia caused by Staphylococcus aureus, Pseudomonas aeruginosa, and Haemophilus influenzae directly from blood samples within less than 4 hours. Target abundances varied among patients (see, e.g., Table 1), which were correlated with the severity of pneumonia. The effort was directed towards finding primers for diagnosing pneumonia, which, although caused by the same pathogens, were different from those used for bacteremia.

[0161] Example 3: Using RNA from infected patients, design RT-qPCR for the most common resistance genes, the expression of which will affect the treatment of Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Haemophilus influenzae genes (A3a) 。

[0162] Rationale 。Delayed antibiotic use worsened the prognosis of all patients, including those carrying drug-resistant bacteria. Bonine et al. (2019). Over-treatment of organisms not carrying resistance determinants also worsened the outcome. Rhee et al. (2020). The aim of this example was to use data from RNA sequencing to inform clinically relevant PCR-based antimicrobial resistance diagnosis. Reads from the above sequencing study were aligned to the "genome" of the target resistance genes, and then new PCR primers were tested on clinical specimens. These were "phenotypic" measurements of antibiotic resistance, as gene expression and resistance phenotype are closely related. Suzuki, Horinouchi, & Furusawa (2014).

[0163] Assay 1: Evaluate RNA sequencing data from patients with infections caused by Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa and Haemophilus influenzae for resistance genes 。"Genomes" were prepared using clinically relevant resistance genes. For Staphylococcus aureus, the inventors included mecA (methicillin resistance) (Chambers & Deleo (2009); Guo et al. (2020)), qacA, norA, smr (efflux transporters for quinolones and tetracyclines) Guo et al. (2020), beta-lactamase (hydrolyzing cefazolin) (Guo et al. (2020)), and VRSA (vanA, vanB, vanC, vanX, vanY, vanA). Escherichia coli targets included multiple beta-lactamases: alkaline beta-lactamase cleaving ampicillin, ESBL genes (TEM-1, TEM-2, and SHV-1), CTX-M (see Table 1), ampC, carbapenemases: KPC (class A), metallo-carbapenemases (class B: IMP, VIM, NDM-1), OXA (class D) (Bajaj, Singh, & Virdi (2016)), GyrA and ParC (fluoroquinolone resistance) (Tchesnokova et al. (2019)), acrB (Karczmarczyk et al. 2011), ompF, efflux pump PabetaN, and qnr (Salah et al. (2019)). For Pseudomonas aeruginosa, ampC, oprM, mexY (efflux transporter for quinolones and aminoglycosides) (Islam et al. (2009)), bla, gyrA, gyrB, parC (for quinolones) (Yang et al. (2015)), and aac(6')-Ib, aphA1 and aadB (aminoglycosides) (Teixeira et al. (2016)). For Haemophilus influenzae, TEM-1 and ROB-1 (Gutmann, Williamson, Collatz, & Acar (1988); Tristram, Jacobs, & Appelbaum (2007)). (52,53). Using these genes, PCR primers were identified based on RNA data from patients with these infections. These are also the primers for RT-PCR as the target is RNA. Using RNA as the target produced better results than DNA. Such a tool suitable for use with RNA data can enhance phenotypic correlation using this dataset. Bortolaia et al. (2020).

[0164] Assay 2: Create RT-PCR primers to identify resistance genes . Using target targets from deep RNA sequencing data, the PCR primers cover resistance genes. Multiple primers can identify the resistance genes of a pathogen. This is done by multiplexing, which may be related to the machines of industry partners. The target of these primers is the RNA in the blood, and reverse transcriptase is used to produce cDNA for primer interaction. Targeting RNA has better phenotypic correlation compared to the targeted DNA from the pathogen as RNA indicates that the gene is actively expressed.

[0165]

[0166] Potential alternatives。In a detailed study of transcription and protein abundance in Escherichia coli, there was a lack of correlation between RNA and protein levels. Taniguchi et al. (2010). Although the overall abundance was not correlated, enzyme transcription and translation were closely related. Taniguchi et al. (2010). Some resistance phenotypes, such as fluoroquinolone resistance caused by gyrA, gyrB, and parC, are mediated by SNPs. In such cases, PCR primers are suitable for SNP detection, such as TaqMan assays. Easterday, Van Ert, Zanecki, & Keim (2005). For some resistance mechanisms, such as beta-lactamases, there are too many individual genes to test. In such cases, the present inventors used k-mer analysis to identify primers capable of detecting all types of beta-lactamases. Marini et al. (2022). Finally, there are concerns about whether RNA-based resistance detection is comprehensive enough for clinical practice. Regulatory RNAs may play a role in resistance but cannot be detected by sequencing methods. Dersch, Khan, Mühlen, & (2017). In such cases, the present inventors evaluated more patient specimens and changed the sequencing protocol to detect unconventional RNAs.

[0167] Example 3A: Validate these PCR tests for resistance genes in samples from patients with and without infections (A3b) Assay 1: Test PCR primers on samples used for RNA sequencing 。

[0168] Figure 1 。The cDNA library for RNA sequencing is the initial test for PCR primers. See Assay 2: Test PCR primers on samples collected to mimic clinical use 。The cDNA of all samples from positive cultures with resistance was positive. Each cDNA sample was tested with PCR primers for all resistance genes to evaluate primer specificity.

[0169] Expected results 。These tests were completed to obtain sensitivity and specificity that meet FDA requirements. PAXgene tubes from patients with and without confirmed resistant infections were used as negative controls. Assays included positive control genes such as actin to confirm the PCR reaction in each specimen.

[0170] Fluid 。The set of PCR primers identified in this example detected resistance in these validation studies. The most straightforward test is for the presence of RNA encoding resistance mechanisms, such as mecA in MRSA. Although molecular tests are used in clinical microbiology laboratories to diagnose MRSA, the test requires a positive blood Potential alternatives culture bottle. The goal is to confirm an RNA-based blood test that changes the treatment described in Table 5. It returns results in less than four hours without culturing the patient's blood.

[0171] Example 4: Improve PCR primers for SARS-CoV-2 viremia through RNA sequencing information The main concerns are the level of the target sequence found in the blood, i.e., sensitivity, and the ability to identify primers that amplify the expected sequence, i.e., specificity. Strategies to increase sensitivity include using more cDNA in the PCR reaction and performing nested PCR. The inventors have not encountered evidence of inhibition of the PCR reaction due to the additional processing involved in using RNA as a PCR template. The inventors also continue to use a positive reference gene such as actin to detect PCR inhibitors. There may be a large number of potential sequences that can confer a phenotype, such as the large family of beta-lactamases. K-mer analysis is used to identify sequences representative of the family and primers are designed for that analysis. When mutations in pre-existing genes confer a resistant phenotype (such as SNPs in gyrA and ParC responsible for fluoroquinolone resistance), modified PCR reactions such as TaqMAMA are used. Another possibility is that important resistance mechanisms such as carbapenemase production are rarely encountered in the patient population. To create a more comprehensive test in these cases, the inventors can evaluate appropriate resistant strains in vitro, such as from the CDC&FDA Antibiotic Resistance Isolate Bank available to researchers. Another theoretical concern is that the test finds the target sequence in patients without infection or normal controls. RT-qPCR has distinct advantages compared to endpoint PCR, so the inventors can establish a threshold cutoff for a positive test using relative abundance measurements of the target by ΔCt calculation: Ct of the assay - Ct of the actin gene.

[0172] Figure 2A 。

[0173] The diagnosis of COVID-19 uses nucleic acid and antigen tests, where reverse transcription polymerase chain reaction (RT-PCR) is considered the gold standard. Peeling, Heymann, Teo, & Garcia, Lancet, 399(10326), 757-768 (2022). Although nasopharyngeal swabs are commonly used for diagnostic testing, recent studies have shown that SARS-CoV-2 viremia or RNAemia is associated with disease severity and patient mortality. Fajnzylber et al., Nature Commun., 11(1), 5493 (2020); Heinrich, F et al., Open Forum Infect Dis., 8(11), ofab509 (2021); Jacobs et al., Clin Infect Dis., 74(9), 1525-1533 (2022); and Rodriguez-Serrano et al., Science Reports, 11(1), 13134 (2021). In these studies, many patients did not show detectable viremia, which partly reflects the severity of the disease. However, improving the sensitivity of viral load measurement can provide more information about prognosis.

[0174] SARS-CoV-2 detection typically uses existing assays based on primers designed by the CDC, which were selected for specificity using in silico analysis. Lu et al., Emerg Infect Dis., 26(8), 1654-65 (2020). When creating these primers, little information about their design was available other than the SARS-CoV-2 sequence. Viral RNA sequences are unevenly present in the bloodstream of severely ill COVID-19 patients. Lu et al., Emerg Infect Dis., 26(8), 1654-65 (2020). Deep RNA sequencing showed an overrepresentation of two peaks in the alignment with the SARS-CoV-2 genome, suggesting that RT-PCR primers targeting these sites could better detect viremia. In this example, the inventors designed primers for measuring the peaks of the nucleocapsid (N) gene to be comparable to the widely used CDC-N1 primers ( Figure 2B ), with similar GC percentages and amplicon lengths. BLAST analysis showed results similar to those of the CDC primers and no significant cross-reactivity with other sequences, but their positions on the N gene were different.

[0175] Primers were first compared using cDNA generated from RNA sourced from an original sequencing cohort. See Fredericks, Sci. Rep., 12(1), 15755 (2022). Quantitative (q) RT-PCR reactions were performed. The CDC-N1 primer was used as a reference to compare Ct values. When detecting the N-gene, the N-peak primer was approximately 2 to 100 times more sensitive than the CDC-N1 primer ( Figure 2C ).

[0176] Due to the inherent variability observed among patients and to validate the findings, viremia was tested in a second patient cohort. Using a similar method, the inventors found that the N-peak primer was approximately 10 times more sensitive than the CDC-N1 primer ( Example 5: Stability and motif analysis of RNA-Seq reads from COVID-19 patients ).

[0177] Enhancing the sensitivity of RNAemia can improve the ROC curve and understand the lower range of viremia measurements. More information can improve prognostic assessment, especially for those patients who may develop into more severe diseases. The results of this example show the value of using RNA sequencing data to inform qRT-PCR primer design, as certain sequences or genes may unexpectedly go out of the normal range during in vivo infection. Enhanced sensitivity can lead to a diagnosis using molecular tests performed directly from blood.

[0178] RNA was isolated as described in Fredericks, Sci. Rep., 12(1), 15755 (2022). According to the manufacturer's instructions, in a final volume of 20 μL, 100 ng of total blood RNA depleted of globin and ribosomal RNA was used for cDNA synthesis with the Superscript IV First Strand Synthesis System (Invitrogen, USA) and the cDNA was stored at -20 °C for later use. Real-time qPCR was performed using the iTaq Universal SYBR Green Supermix (Bio-Rad, USA) according to the manufacturer's instructions. The final volume of each qPCR reaction was 10 μL, including 1 μL of cDNA, and the final concentration of the primers in each reaction was 400 nM. Before thermal cycling, all qPCR reactions were centrifuged at 455 RCF for 1 minute. qPCR was performed using a CFXConnect or CFX96 instrument (Bio-Rad, USA) controlled by the CFX Maestro software (Bio-Rad, USA), and the thermal cycling protocol was as follows: 95 °C for 30 seconds and 40 cycles of the following steps: (1) 95 °C for 5 seconds; (2) 60 °C for 30 seconds.

[0179]

[0180] Measure the cycle threshold (Ct) with β-actin as the reference gene and CDC-N1 primers as the calibrator.

[0181]

[0182] Results

[0183] RNA sequencing has increasingly been incorporated into clinical diagnosis and management. See Ketkar, Burrage, & Lee, JAMA, 329(1), 85 - 86 (2023); Mortazavi et al., Nature Methods, 5(7), 621 - 8 (2008); and Peymani, Farzeen, & Prokisch, Pediatr. Investig., 6(1), 29 - 35 (2022). The technique has various clinical uses, such as analyzing the transcriptome of cancer and determining the type of infection. Huang, Wang, & Yao, Microb. Cell, 8(9), 208 - 222 (2021). RNA sequencing has also been used to elucidate the pathogenesis and potential treatments of certain diseases. See Huang, Wang, & Yao, Microb. Cell, 8(9), 208 - 222 (2021). This laboratory technique can detect different transcript isoforms from alternative splicing, chimeric gene fusions, and other genetic changes. Mortazavi et al., Nature Methods, 5(7), 621 - 8 (2008). By alignment with the pathogen's biogenome, comparisons can be made between the gene expressions of pathogens. Fredericks et al., Science Reports, 12(1), 15755 (2022).

[0184] Regulatory RNAs regulate the metabolic and virulence functions of certain pathogens, showing increasing pressure to clinically expand the capabilities of RNA sequencing to create a complete transcriptome. See Oliva, Sahr, & Buchrieser, FEMS Microbiol. Rev., 39(3), 331 - 49 (2015); and Papenfort & Vogel, Front. Cell Infect. Microbiol., 4, 91 (2014). RNA undergoes multiple cellular processes that can affect gene expression.

[0185] Up to 92 - 94% of human multi - exon genes undergo alternative splicing. Houseley & Tollervey, Cell, 136(4), 763 - 76(2009). Mutations in RNA - modifying enzymes are associated with more than 100 human diseases. It is estimated that the median half - life of mRNA in the human body is 10 hours, and mRNAs in different functional groups decay at different rates. Yang et al., Genome Res, 2003.13(8), 1863 - 72.

[0186] It has also been found that altered mRNA stability changes gene expression and mRNA lifespan. RNA viruses evade degradation by maintaining mRNA stability. See Houseley & Tollervey, Cell, 136(4), 763 - 76(2009); and Moon, Barnhart, & Wilusz, Curr. Opin. Microbiol., 15(4), 500 - 5(2012). The stability of RNA has been measured by the minimum free energy (MFE) of the structure and the ensemble free energy (EFE) of the structure. See Ding, Chan, & Lawrence, RNA, 11(8), 1157 - 66(2005); Doshi et al., BMC Bioinformatics, 5, 105(2004); Wuchty et al., Biopolymers, 49(2), 145 - 65(1999); Lorenz et al., ViennaRNA Package 2.0. Algorithms Mol. Biol., 6, 26(2011); Trotta, PLoS One, 9(11), e113380(2014); and Vasudevan & Steitz, Cell,.128(6), 1105 - 18(2007).

[0187] This example provides an analysis of the stability of RNA - Seq reads from COVID - 19 infected patients. The inventors established RNA motifs that increase or decrease the stability of RNA - Seq read fragments. The inventors also evaluated whether the destabilizing RNA motifs affect repeated RNA - Seq reads.

[0188] Figure 3Out of 676 reads from RNA-Seq, there were 137 unique sequences. Thus, 539 reads were identical to another read. Among all unique read sequences, the average minimum free energy (MFE) was -30.46 kcal / mol, and the average ensemble free energy (EFE) was -32.94 kcal / mol. Among the repetitive sequences, one sequence was repeated 328 times. The minimum free energy of this sequence was -33.00 kcal / mol, and the ensemble free energy was -35.20 kcal / mol. Despite being highly repetitive, it had only the 48th lowest MFE and the 60th lowest EFE.

[0189] To analyze the minimum free energy and ensemble free energy values of read sequences across the entire gene region, read sequences were found in 6 genes: the nucleocapsid (N) gene, the ORF1ab gene, the ORF3a gene, the ORF6 gene, the ORF8 gene, and the spike (S) gene. The N gene and the S gene encode integral structural proteins, and the ORF3a, ORF6, and ORF8 genes encode accessory genes. The ORF1ab gene encodes other non-structural proteins.

[0190] Welch's ANOVA analysis showed that for one gene, at least one average minimum free energy was significantly different from the average minimum free energy of another gene (p = 0.0004907). Post hoc analysis evaluated 15 pairs among the 6 genes and showed 3 significant relationships. The average minimum free energy of the N gene was significantly different from the average minimum free energy of the ORF1ab gene (p = 2.81e-7). The average MFE of the N gene was also significantly different from that of the ORF6 gene (p = 0.23). The average minimum free energy of the ORF6 gene was significantly different from the average minimum free energy of the S gene (p = 0.037). See the schematic diagram in the figure. Figure 4 and Figure 6 。

[0191] For the overall free energy, Welch's ANOVA analysis showed that for one gene, at least one average overall free energy was significantly different from the average overall free energy of another gene (p = 0.002398). Post hoc analysis found four significant paired comparisons. The average overall free energy of the N gene was significantly different from the average overall free energy of the ORF1ab gene (p = 0.005). The average overall free energy of the N gene was also significantly different from the ORF6 gene (p = 0.03). The average overall free energy of the ORF3a gene was significantly different from the average overall free energy of the ORF6 gene (p = 0.027). The average minimum free energy of the ORF6 gene was significantly different from the average overall free energy of the S gene (p = 0.036). To analyze the minimum free energy and overall free energy values within the N gene, Welch's ANOVA analysis was completed. Subsequently, Welch's t-tests were completed, comparing each of the individual genomes to each other.

[0192] Motif analysis of all read sequences using MEME-ChIP found 10 motifs, 6 of which had known or similar motifs in the database. See Discussion the sequences in. Three of these motifs had a high minimum free energy for the read sequences (p = 0.00502, p = 0.00000422, p = 0.00023) and a high overall free energy for the read sequences (p = 0.0034, p = 0.0000034, p = 0.00039). Motif analysis of all read sequences using XSTREME found 34 motifs. See Figure 7. For 6 of the identified motifs, the sequences with the motif had significantly different minimum free energy values compared to the sequences without the motif (p = 0.0275, p = 0.0204, p = 0.000455, p = 0.0082, p = 0.00175, p = 3.26e-79). These motifs were labeled MEME-9, MEME-10, MEME-21, MEME-22, MEME-27, and MEME-28. For all of these motifs except MEME-10, the sequences with the motif also had significantly different overall free energy values compared to the sequences without the motif (p = 0.0315, p = 0.000664, p = 0.00695, p = 0.00114, p = 0.000328). Negative binomial regression was performed to evaluate whether MEME-28 (the only stability motif found) was associated with sequences with a higher number of repeats. This analysis was not significant, with a P-value of 0.266.

[0193] Example 6: Deep RNA sequencing and alignment of unaligned reads from patients to custom genomes constructed and retrieved from the NCBI gene database。This initial chi-square goodness-of-fit test indicates that the proportions of reads from different genes are not equal. RNA-Seq does not collect reads uniformly from each gene. There may be factors that affect the specific sequences being detected. One known factor is RNA expression. Since the gene expression levels of different genes vary, at different time points in the cell, the number of certain RNA sequences may be greater than others.

[0194] Degradation and stability may be other factors that play a role in the detection of RNA-Seq assays. When designing PCR primers based on RNA sequencing data, the stability of the structure should be included not only in the design but also in the optimization of the workflow.

[0195] The results of this example show that the stabilities of reads from different genes are different. Since a gene such as ORF6 has a less stable sequence compared to a gene such as the S gene, ORF6 may be underrepresented in RNA-Seq analysis. If this is true, it will have a significant impact on our interpretation of RNA-Seq results. Genes that may be considered low-expressing and discarded (to focus on seemingly highly-expressing genes) may be new targets for re-analysis. The contribution of these genes to cell function may be underestimated.

[0196] In the N gene, the stabilities of reads from different regions are also different. Since all of these reads are from the N gene, the difference in the number of repeats is not caused by the expression of the N gene itself, but by the abundance of potentially alternatively spliced RNAs and the stability of RNA fragments. One sequence in the N gene is highly repetitive, with 328 repeats.

[0197] The motif analysis in this example shows a motif corresponding to RNA destabilization and a single motif corresponding to RNA stabilization. These motifs do not exist in most of the repetitive read sequences, but there may still be motifs in this read sequence that were not detected by our analysis. The most repetitive sequence may have motifs that confer increased stability or an increased chance of being detected by RNA-Seq, but it was not found.

[0198] The motif analysis in this example used the established motif analysis tool MEME-ChIP and the new motif analysis tool XSTREME. MEME-ChIP is optimized for sequences larger than our average sequence length. MEME-ChIP found three of the eight motifs affecting RNA stability that XSTREME could not find. These two motif analysis tools found different motifs. Both were applied to subsequent analyses.

[0199] This example provides the discovery of new motifs that can confer increased or decreased stability to RNA. Using the discovered motifs to alter stability while also constraining different gene expression levels and alternative splicing can further elucidate the relationship between stability and RNA-Seq read replicates, allowing for further analysis and broadening the research implications of already well-known RNA-Seq experiments.

[0200] Figure 8 Specificity for Escherichia coli .

[0201] Whole blood samples were collected from patients in the ICU, stored in PAXgene tubes to preserve specimen integrity, and submitted to a commercial sequencing service (Azenta / GeneWiz) for RNA sequencing.

[0202] In this example, the inventors used deep RNA sequencing and aligned unmapped reads from patients to a custom gene set constructed and retrieved from the NCBI gene database. See Table 5 in Example 1.

[0203] All analyses were performed blinded to clinical data and patient outcomes. Unmapped RNA sequencing reads were aligned to all four custom gene panels using the STAR alignment tool to classify, extract, and count unmapped reads.

[0204] Four custom genomes were created and aligned to the unmapped reads retrieved from the patients. Reads were counted as mapped to the pathogen genome when at least 100 base pairs of the read matched the pathogen genome. Then, the density plots (see Fluid ) to identify genomic regions with the most reads. When compared with clinical microbiology data (such as positive cultures) and clinical outcomes (such as mortality), targets from the pathogen genome were identified as targets to serve as the basis for PCR-based testing.

[0205] Figure 9 From blood Patent citations Readings from culture-positive patients were grouped together and compared with readings present in patients not found to have E. coli infection. Non-patent citations The reads from both groups are shown. When compared, there were significantly more reads in patients with positive E. coli infection. In addition, using these graphs, a PCR test on a clinical microbiology machine was designed with target genes.

[0206] Specifically for E. coli, the following genes and precise nucleic acid targets are used based on RNA sequencing data. Ribosomal RNA and mRNA targets will be used. The final targets will be BLAST searched against all known genomes to ensure there are no false signals.

[0207] · 50S subunit of rrlE ribosomal RNA

[0208] · Coordinates NC_000913.3:4210165 - 4210194

[0209] · ldtD peptidoglycan L,D - transpeptidase

[0210] · Coordinates NC_0009133:982647 - 982657

[0211] · pitB phosphate transporter

[0212] · Coordinates NC_0009133:3135153 - 3135165

[0213] These three sites were chosen for several reasons, including the increased numbers observed in patients infected with Escherichia coli, the targets of the mRNA, as it is known to persist for 5 - 8 minutes, and to ensure that the targets are uniquely specific only to that pathogen. It is also important to note that these targets are identified using methods such as PCR or nucleic acid probes, identified as the pathogenic agent, and treated with an appropriate antibiotic.

[0214] The work done to identify these targets can be repeated on other known pathogens and all resistance genes.

[0215] List of embodiments

[0216] Specific compositions and methods of the present invention have been described. The detailed description in this specification is illustrative, not restrictive or exhaustive. The detailed description is not intended to limit the disclosure to the precise forms disclosed. Other equivalent embodiments and modifications are possible besides those already described without departing from the inventive concept described in this specification, as recognized by those skilled in the biomedical arts. When the specification or claims recite method steps or functions in a sequence, alternative embodiments may perform these functions in a different sequence or substantially simultaneously. The subject matter of the present invention should not be limited except in the spirit of this disclosure.

[0217] When interpreting the present invention, all terms should be interpreted in the broadest possible manner consistent with the context. Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the biomedical arts to which this invention pertains. The present invention is not limited to the specific methods, protocols, reagents, etc. described in this specification and may vary in practice. The terms used in this specification are not intended to limit the scope of the present invention, which is defined only by the claims.

[0218] When a numerical range is provided, unless otherwise specified by the context, each intermediate value is one-tenth of the lower limit unit and lies between the upper and lower limits of the range and any other specified or intermediate value within that numerical range.

[0219] Some embodiments of the technology can be defined according to the following numbered paragraphs:

[0220] 1. A reverse transcriptase polymerase chain reaction (RT-qPCR) test for bacteria causing bacteremia, performed directly from blood without culturing, based on RNA identified in patients with bacteremia caused by these bacteria, which bacteria are optionally selected from Staphylococcus aureus, Pseudomonas aeruginosa, and Haemophilus influenzae.

[0221] 2. A method for validating these RT-qPCR tests in samples from patients with and without bacteremia.

[0222] 3. A reverse transcriptase polymerase chain reaction (RT-qPCR) test for bacteria causing pneumonia, performed directly from blood without culturing, based on RNA identified in patients with pneumonia caused by these bacteria, which bacteria are optionally selected from Staphylococcus aureus, Pseudomonas aeruginosa, and Haemophilus influenzae.

[0223] 4. A method for validating these RT-qPCR tests in samples from patients with and without pneumonia.

[0224] 5. An RT-PCR for the most common resistance genes, the expression of which will affect the treatment of bacteria, which bacteria are optionally selected from Staphylococcus aureus, Pseudomonas aeruginosa, and Haemophilus influenzae, and the RT-PCR uses RNA from infected patients.

[0225] 6. A method for validating these PCR tests for resistance genes in samples from patients with and without infection.

[0226] References

[0227] Those of ordinary skill in the biomedical field can rely on the following patents, patent applications, scientific books, and scientific publications to implement the methods:

[0228] Textbooks and technical references :

[0229] International Patent Publication WO2021163692A1 (Rhode Island Hospital), RNA sequencing to diagnose sepsis, published on August 19, 2021.

[0230] ​ :

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[0350] All patents and publications cited in this specification are hereby expressly incorporated by reference to disclose and describe the materials and methods that may be used in conjunction with the technology described in this specification. The publications discussed are provided solely for their disclosure prior to the filing date. They should not be construed as an admission that the inventors are not entitled to antedate such disclosure by virtue of an earlier invention or for any other reason. In the event of an apparent discrepancy between a prior patent or publication and the description provided in this specification, the specification (including any definitions) and the claims shall govern. All statements as to the date of these documents or as to the content of these documents are based on the information available to the applicant and do not constitute an admission as to the correctness of the date or content of these documents. The publication dates provided in this specification may differ from the actual publication dates. In the event of an apparent discrepancy between the publication date provided in this specification and the actual publication date provided by the publisher, the actual publication date shall govern.

Claims

1. A reverse transcription polymerase chain reaction (RT-qPCR) test for bacteria causing bacteremia that is performed directly from blood without culturing, said RT-qPCR test being based on RNA identified in patients suffering from bacteremia caused by these bacteria.

2. The RT-qPCR test according to claim 1, wherein the bacteria causing bacteremia are selected from Staphylococcus aureus, Escherichia coli, and Haemophilus influenzae.

3. A reverse transcription polymerase chain reaction (RT-qPCR) test for bacteria causing pneumonia that is performed directly from blood without culturing, said RT-qPCR test being based on RNA identified in patients suffering from pneumonia caused by these bacteria.

4. The RT-qPCR test according to claim 3, wherein the bacteria causing pneumonia are selected from Staphylococcus aureus, Pseudomonas aeruginosa, and Haemophilus influenzae.

5. A reverse transcription polymerase chain reaction (RT-qPCR) test for the most common resistance genes that is performed directly from blood without culturing, the expression of said most common resistance genes affecting the treatment of bacteria.

6. The PCR test according to claim 5, wherein the expression of the most common resistance genes affects the treatment of bacteria selected from Staphylococcus aureus, Pseudomonas aeruginosa, and Haemophilus influenzae, wherein RNA from infected patients is used.

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