Chromosome interactions

Abstract

The process of analyzing chromosomal interactions related to coronavirus infections.

Description

[Technical field]

[0001] The present invention relates to infectious disease processes. [Background technology]

[0002] Coronaviruses are a group of related RNA viruses that cause disease in mammals and birds. In humans and birds, coronaviruses cause respiratory infections that range from mild to fatal. Mild illness in humans includes cases of the common cold, but more lethal strains exist, including COVID-19. Summary of the Invention

[0003] The inventors have identified a chromosomal conformational signature associated with the prognosis of coronavirus infections, and COVID-19 in particular. This allows for stratifying patients and prognostically identifying high-risk individuals who, if exposed to coronaviruses, particularly COVID-19, will progress to severe deterioration and require ICU support. The decision to admit a patient to the ICU is primarily based on the patient's individual situation and is made when there is clear clinical evidence of complications that do not respond to clinical standard of care. Coronavirus complications, in all its wide range of manifestations, are associated with hyper-inflammation and immune hyper-responses targeting individual organs. The stable 3D genomic whole-body profile analyzed by the inventors conveys powerful prognostic information for long-lasting coronavirus infections, distinguishing between asymptomatic, mild and severe (ICU) outcomes, before the outcomes manifest themselves.

[0004] Thus, the present invention provides a method of detecting the prognosis of a coronavirus infection in an individual, comprising determining the presence or absence of one or more chromosomal interactions represented by the probes set forth in Tables 1 or 3, thereby determining a prognosis in the individual. The present invention also provides a method of detecting the prognosis of a coronavirus infection in an individual, comprising determining the presence or absence of one or more chromosomal interactions represented by the probes set forth in Table 7, thereby determining a prognosis in the individual. The present invention further provides a method of detecting the prognosis of a coronavirus infection in an individual, comprising determining the presence or absence of one or more chromosomal interactions represented by the probes set forth in any of Tables 8, 9, 10 and 11, thereby determining a prognosis in the individual.

[0005] The present invention provides a method for detecting the prognosis of a coronavirus infection in an individual, comprising determining the presence or absence of one or more chromosomal interactions represented by the probes set forth in any of Tables 14, 15, 6 and 17, thereby determining the prognosis in the individual.

[0006] The present invention also provides a method for detecting the presence of or susceptibility to sepsis in an individual, the method comprising determining the presence or absence of one or more chromosomal interactions represented by the probes set forth in Tables 12 or 13.

[0007] Preferably, the method is performed to select individuals to receive a therapy or treatment. The method may be performed on individuals who have been preselected, for example, based on physical characteristics, the presence of risk factors or symptoms. [Brief description of the drawings]

[0008] [Figure 1] A preferred method for carrying out the marker detection step of the present invention is shown. [Diagram 2] Indicates the lineage of white blood cells. [Diagram 3] The study design is shown. [Figure 4] PCA for 38 patients (from 3 cohorts) with asymptomatic (squares), mild (triangles) and severe ICU disease (circles) is shown. [Diagram 5] PCA for mild (circles) and ICU (squares) only is shown. [Figure 6] The analysis pipeline is shown. [Figure 7] Shown are the 20 Hallmark GeneSets for ICU vs. mild. The middle row represents GeneSets shared between ICU and mild. The four sets on the bottom right represent immune processes associated with ICU patients. These 20 gene sets were identified based on the top EpiSwitch markers and their localization at gene locations described by the GeneSets. [Figure 8] BioCarta pathways for ICU vs. mild are shown. The shared gene set is shown in the middle, and the seven gene set in the left pons represents immune processes associated with ICU. This analysis identifies which overlapping genes are part of which pathways based on the genomic location of the top EpiSwitch markers. [Figure 9] The top 20 reactome pathways for ICU vs. mild are shown. The seven gene sets to the right of ICU are related to immune processes. Immune and angiotensin processes are seen in mild. In this analysis, EpiSwitch locations were compared to the same genomic locations described in the Reactome database. [Figure 10]The top 100 significant markers for mild associated with immune processes are shown. This is not a two-dimensional PCA but a similar one-dimensional standard linear discriminant analysis (all complexities transform into one linear score for the three clinical outcomes / phenotypes). It is generated based on the top 100 significant markers that are only present in mild cases, not in asymptomatic or severe ICU, and that overlap their location within immune loci in the genome (hence the name immune EpiSwitch markers). The analysis includes 80 patients, including 42 patients from Lima, Peru, in addition to the top three cohorts from the UK (1) and US (2), all taken during the period when Lima had the highest number of COVID-19 deaths. The top of the figure is on the left side of the page. The top set of circles are severe (bottom left of the page). The middle set of circles are mild (top of the page). The bottom set of circles are asymptomatic (middle right of the page). [Figure 11] The genome-wide profile of the markers in FIG. 10 is shown. [Figure 12] The top 100 significant markers for ICU associated with immune processes are shown, demonstrating that ICU is a distinct phenotype. This analysis is done in the same way as in Figure 10, except that the top markers used are all statistically significant and present in the ICU (severe) group of patients and not present in the mild or asymptomatic. This demonstrates that based on the top markers that are specific to ICU outcomes and present prior to complications at blood draw and initial COVID-19 test, we can prognostically identify and differentiate the profile present in severe outcomes as distinct 3D genomics phenotypes associated with distinct clinical outcomes. The top of the figure is on the left side of the page. The top set of circles are severe (top left of page). The middle set of circles are mild (second set of circles from the bottom of the page). The bottom set of circles are asymptomatic (bottom right of page). [Figure 13] The genome-wide profile of the markers in FIG. 12 is shown. [Figure 14]How the top 50 ICU markers associated with immune processes were selected for LDA analysis and classification statistics: Markers were selected using a training sample set of 30 and then used to classify a test set of 12. [Figure 15] Pathway enrichment using the top 50 marker enriched gene locations is shown. [Figure 16] Compound enrichment using gene locations enriched with the top 50 markers is shown. [Figure 17] Shown is an LDA plot of the 30 training sets using the top 50 markers. The left side of the page is the top of the figure. The top triangle is severe (top left of the page). The bottom circle is mild (bottom right of the page). [Figure 18] 1 shows an LDA plot of 30 training sets with 12 test sets. [Figure 19] We show the efficacy of patient calling by LDA and prognostic stratification by LDA calling for 42 patients in the Lima cohort. [Figure 20] Standard STRING network analysis-Functional protein association network is shown, where immune genes with ICU / severe coronavirus-associated EpiSwitch significant markers are overlaid on the known interaction network. This is in very good agreement with the known network, showing a highly connected part of the regulatory network through EpiSwitch dysregulated genes. No additional nodes had to be formed outside the EpiSwitch list to complete the network. Table 5 shows the names of the important genes involved and provides further data. [Figure 21] Here is how Table 6 should be interpreted. [Figure 22]The analysis of markers found in this study identified pathways related to both coronavirus infection and sepsis. In particular, PSMA5 is involved, and HG38_1_109341939_109348573_109359719_109366704_RR (OBD183_q481.q483) is a common marker. PSMA5, CD3D, and CD3G are coincident genes in pathways related to antigen processing cross-presentation, while KLRG1, CD3D, and CD3G are coincident genes in pathways related to immune regulatory interactions between lymphoid and non-lymphoid cells. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Terminology used herein The method of the present invention is sometimes referred to herein as the "process" of the present invention.

[0010] The chromosomal interactions that are classified may also be referred to herein as "markers," "CCSs," "chromosome conformation signatures," "epigenetic interactions," or "EpiSwitch markers."

[0011] The word "type" can be interpreted depending on the context, but generally refers to the detection of the presence or absence of a particular chromosomal interaction.

[0012] Epigenetic interactions relevant to the present invention The chromosomal interactions classified in the present invention are usually interactions between distal regions of chromosomes, which are dynamic and change, form or break depending on the state of the chromosomal regions, which state reflects different aspects of coronavirus infection, and therefore the present invention can be used to detect the prognosis of infection, in particular the susceptibility to severe disease (which may be characterized, for example, by any of the adverse effects of the immune response mentioned herein).

[0013] Chromosomal interactions may reflect, for example, whether a chromosome is transcribed or repressed. Chromosomal interactions specific to coronavirus infection as defined herein have been found to be stable, thus providing a reliable means to measure differences between two subgroups (e.g., reflecting different outcomes of infection).

[0014] Chromosomal interactions specific to coronavirus infections usually occur earlier in the disease process compared to other epigenetic markers, such as, for example, changes to methylation or histone protein binding. Thus, the process of the present invention can detect disease at an early stage. This allows for earlier intervention (e.g., treatment) that is more effective as a result. Chromosomal interactions can also be used to assess changes in disease state, as they reflect the current state of the individual. Furthermore, there is little variation in relevant chromosomal interactions between individuals within the same subgroup. The detection of chromosomal interactions is highly informative, with up to 50 different possible interactions per gene, so that the process of the present invention can collate, for example, 500,000 different possible interactions.

[0015] Chromosomal interactions may overlap and include regions of chromosomes that have been shown to code for related or undescribed genes, but may equally occur in intergenic regions. It is further noted that the inventors have found that chromosomal interactions in all regions are equally important in determining the state of a chromosomal locus.

[0016] The chromosomal interactions detected by the present invention can be influenced by the changes to the underlying DNA sequence by environmental factors, DNA methylation, non-coding antisense RNA transcripts, non-mutagenic carcinogens, histone modification, chromatin remodeling and specific local DNA interactions.However, it should be noted that the chromosomal interactions defined herein are themselves regulatory modalities, and do not correspond one-to-one with any genetic marker (DNA sequence change) or any other epigenetic marker.

[0017] The chromosomal interactions detected in the methods of the invention are to any gene, chromosomal region defined in the Tables, or to any pathway set forth herein (e.g., to any gene in such a pathway).

[0018] Chromosomal interactions can be influenced by changes in the underlying nucleic acid sequence that do not directly affect gene products or gene expression modes themselves. Such changes can be, for example, intragenic and / or extragenic SNPs, gene fusions and / or intergenic DNA deletions, microRNAs, and non-coding RNAs. For example, it is known that approximately 20% of SNPs are present in non-coding regions, so the process described is also useful in non-coding situations. In one embodiment, the regions of chromosomes that come together to form interactions are less than 5kb, 3kb, 1kb, 500 base pairs, or 200 base pairs apart on the same chromosome.

[0019] The chromosomal interaction to be detected may be within a gene, such as any of the genes mentioned herein. However, the chromosomal interaction may also be upstream or downstream of the gene, for example, up to 50,000, up to 30,000, up to 20,000, up to 10,000, or up to 5000 bases upstream or downstream from the gene or coding sequence.

[0020] The process of the present invention The process of the present invention includes a typing system for detecting chromosomal interactions associated with the prognosis of coronavirus infection. Any suitable typing method can be used, such as a method in which the proximity of chromosomes in an interaction is measured. The typing method can be any of the EpiSwitch typing methods referred to herein. TM The system can be used to perform, for example, the following steps (e.g., in a sample from a subject); (i) bridging regions of chromosomes that come together in chromosomal interactions; (ii) optionally isolating the cross-linked DNA from the chromosomal locus; (iii) exposing the crosslinked DNA to cleavage; and (iv) ligating the nucleic acids present in the bridge entity to derive a ligated nucleic acid having sequences from both regions that formed the chromosomal interaction. The method can be carried out by a method comprising:

[0021] The detection of this ligated nucleic acid allows the determination of the presence or absence of specific chromosomal interaction.The ligated nucleic acid thus acts as a marker for the presence of chromosomal interaction.Preferably, the ligated nucleic acid is detected by PCR or probe-based method, such as qPCR method.

[0022] In the method, the chromosomes can be crosslinked by any suitable means, for example by a crosslinking agent, which is typically a chemical compound. In a preferred embodiment, the interactions are crosslinked using formaldehyde, but can also be crosslinked by any aldehyde, or D-biotinoyl-e-aminocaproic acid-N-hydroxysuccinimide ester or digoxigenin-3-O-methylcarbonyl-e-aminocaproic acid-N-hydroxysuccinimide ester. Paraformaldehyde can crosslink DNA strands that are 4 angstroms apart. Preferably, the chromosomal interactions are on the same chromosome. Usually, the chromosomal interactions are 2-10 angstroms apart.

[0023] Crosslinking is preferably in vitro. Cleavage is preferably by restriction digestion with an enzyme such as TaqI. The ligation may form a DNA loop.

[0024] When PCR (polymerase chain reaction) is used to detect or identify the ligated nucleic acid, the size of the PCR product generated can be indicative of the specific chromosomal interaction present, and can therefore be used to identify the state of the locus. In a preferred embodiment, the primers shown in any table herein are used, for example the primer pairs shown in Table 1 or 3 (corresponding to the chromosomal interaction to be detected). In another preferred embodiment, the primers shown in Table 8, 9, 10, 11, 12, 13, 14, 15, 16 or 17 (corresponding to the chromosomal interaction to be detected) are used. Homologs of such primers or primer pairs can also be used, and they can have at least 70% identity to the original sequence.

[0025] When the probe is used to detect or identify the ligated nucleic acid, this is genetically due to Watson-Crick-based base pairing between the probe and the ligated nucleic acid. The probe sequence as shown in any table herein can be used, for example, the probe sequence shown in Table 1 or 3 (corresponding to the chromosomal interaction to be detected) can be used. The probe sequence shown in Tables 8, 9, 10, 11, 12, 13 and 14 can also be used (corresponding to the chromosomal interaction to be detected). Homologs of such probe sequences can also be used, and they can have at least 70% identity to the original sequence.

[0026] Classification by the process of the present invention can be carried out at multiple time points, for example to monitor the progression of disease.It can be carried out at one or more defined time points, for example at least 1, 2, 5, 8 or 10 different time points.The period between at least 1, 2, 5 or 8 time points can be at least 5, 10, 20, 50, 80 or 100 days.Usually, 3 time points are separated by at least 50 days.

[0027] Subgroups and individualized treatment As used herein, "subgroup" refers preferably to a population subgroup, more preferably to a subgroup within a population of a particular organism, such as a particular eukaryote, animal, bird or mammal. Most preferably, "subgroup" refers to a subgroup within a human population. Thus, the process of the present invention is preferably carried out to detect the presence of a corona infection in a eukaryote, such as an animal, mammal or bird, preferably in a human. The process of the present invention may be carried out for diagnostic or prognostic purposes.

[0028] The present invention involves detecting and treating specific subgroups within a population that typically have different prognoses for coronavirus infection. The inventors have discovered that chromosomal interactions differ between subsets (e.g., at least two subsets) within a relevant population. Identifying these differences allows a physician to classify a patient as part of one subset of the population. Thus, the present invention provides a physician with a process for personalizing medications for patients based on their epigenetic chromosomal interactions. Such testing can be used to subsequently select how to treat the patient, for example, the type of drug, and / or its dose, and / or its frequency of administration.

[0029] Individuals being tested The individuals to be tested in the process of the invention may have been selected in several ways, for example, based on risk factors or physical characteristics: Individuals may have been selected on the basis of being asymptomatic for a given disease, or being at an early stage of the disease, or having a mild form of the disease.

[0030] The individual may be susceptible to any of the conditions described herein and / or in need of any of the treatments described herein. The individual may be receiving any of the treatments described herein. In particular, the individual may have or be suspected of having a coronavirus infection. The individual may have or be suspected of having a COVID-19 infection. Thus, the present invention includes a process of classifying patients to determine the prognosis of a corona infection, which is equivalent to determining the subgroup to which they belong.

[0031] An individual may have one or more of the following characteristics: - Receiving an organ transplant - Receiving chemotherapy or antibody treatment for cancer, such as immunotherapy -Having undergone an intensive course of radiation therapy (definitive radiation therapy) for lung cancer - Receiving targeted cancer treatments that can affect the immune system (protein kinase inhibitors or PARP inhibitors) - Currently or has had a blood or bone marrow cancer (e.g., leukemia, lymphoma, or myeloma) - Have or have had severe lung disease (e.g. cystic fibrosis, severe asthma or severe COPD) -High risk of infection (e.g. SCID or sickle cell anemia) - Taking medications that make the individual more susceptible to infections (such as high doses of steroids or immunosuppressants) - Pregnant - Have a spleen problem or have had your spleen removed (splenectomy) -Down syndrome - Are undergoing dialysis or have severe (stage 5) long-term kidney disease - Classified as clinically extremely vulnerable based on clinical judgement and assessment of needs -Age 70 or older - Have a non-severe pulmonary condition (asthma, COPD, emphysema or bronchitis) - Heart disease (heart failure, etc.) -Diabetic - Liver disease (e.g. hepatitis) -Conditions affecting the brain or nerves (such as Parkinson's disease, motor neurone disease, multiple sclerosis or cerebral palsy) - Being very obese (e.g., BMI over 40).

[0032] Prognosis to be determined The process of the present invention preferably determines the prognosis for the severity of the disease caused by coronavirus, and preferably by COVID-19.Therefore, the process can determine whether an individual has a prognosis of severe disease or mild disease.The process can determine whether an individual has a prognosis of severe disease that will require ICU treatment, leading to cytokine storm (cytokine release syndrome), hyperinflammation or sepsis.

[0033] Tables provided herein Tables 1, 2, 3 and 4 show specific markers that can be used to detect coronavirus infection, i.e., their presence or absence can be used in such detection (i.e., they are "disseminating" markers). Tables 1 and 2 show markers present in individuals with a severe disease prognosis. Tables 3 and 4 show markers present in individuals with a mild disease prognosis. Tables 2 and 4 are subsets of Tables 1 and 3, respectively. The process of the invention can be carried out with markers from any of Tables 1-4, or with markers from more than one table, for example, with markers from both Tables 1 and 3.

[0034] Table 7 shows a further set that can be used to detect prognosis for coronavirus infection. The process of the present invention can be carried out using only the markers in Table 7, or these markers can be combined with other markers disclosed herein.

[0035] Tables 8, 9, 10, 11, 12, 13 and 14 show further sets of markers that can be used to detect prognosis for coronavirus infection. The process of the present invention can be performed using only any of the markers in Tables 8, 9, 10, 11, 12, 13 and 14, or these markers can be combined with other markers disclosed herein.

[0036] Table 8 includes markers associated with a severe phenotype. Markers with a "1" in the LS column are associated with a severe phenotype in this table. These are preferred markers to be used in the present invention.

[0037] Table 9 shows preferred markers that are all associated with the severe phenotype.

[0038] Table 10 includes markers associated with a mild phenotype. Markers with "-1" in the LS column are associated with a mild phenotype in this table. These are preferred markers to be used in the present invention.

[0039] Table 11 shows preferred markers that are all associated with the mild phenotype.

[0040] Tables 12 and 13 show markers that can be used to determine sepsis status. The results in Table 12 relate to "S_SS" and "qPCR_ICU". These are 27 markers that are ICU prognostic markers, but are also found to be significant and specific for severe sepsis in the analysis of patients with sepsis versus severe sepsis. The results in Table 13 relate to "H_SS" and "qPCR_ICU". These are 5 markers that are ICU prognostic markers, but are also significant and specific for severe sepsis in the analysis of patients with severe sepsis when compared to healthy subjects.

[0041] Tables 14 and 15 show the preferred severe (ICU) disease markers, with a "1" in the CCS column representing that phenotype.

[0042] Tables 16 and 17 show preferred mild disease markers, with "-1" in the CCS column representing that phenotype.

[0043] The markers are defined using a probe sequence (which detects the ligation product as defined herein). The first two sets of Start-End positions indicate the probe positions, and the second two sets of Start-End positions indicate the relevant 4 kb region. The markers may be defined with reference to the Start-End positions provided to uniquely identify the marker in the same way that a probe sequence does.

[0044] The following information is provided in the probe data table: RP - the sum of rank product statistics evaluated for each chromosomal interaction Frequency of FC-interactions (positive or negative) Pfp - the estimated percentage of false positive predictions (pfp), taking into account both positive and negative chromosomal interactions P-value - Estimated p-value for each positive and negative CCS Adjusted P-value (FDR) - False discovery rate adjusted p-value Loop Detected - A loop has been found

[0045] Simple permutation-based estimation is used to determine the likelihood that a given RP value or greater will be observed in a random experiment. It has the following steps: 1. Create a permutation p of a list of rank k of length n. 2. Calculate the rank product of the nth CCS in the p permutation. 3. Count the number of times the rank product of the CCS in a permutation is less than or equal to the observed rank product. Set this value to c. 4. Calculate the average expected value of the rank product by Erp(g) = c / p. 5. Calculate the percentage of false positives as follows: pfp(g)=Erp(g) / Rank(g), where Rank(g) is the rank of CCSg in the list of all n CCSs sorted in ascending order of RP.

[0046] The rank product statistic ranks chromosomal interactions according to their intensity within each microarray and calculates the product of these ranks across multiple microarrays. This technique can identify chromosomal interactions that are consistently detected among the most distinct chromosomal interactions across multiple replicate microarrays. If the p-value is 0, this indicates that there is little variation in the rank product of CCSs between samples and is a good example of signal to noise and effect size of CCSs. If the p-value is 0 and pfp is 0, this means that the permutation rank product does not differ from the actual observed rank product. These methods are described in Breitling R and Herzyk P (2005) Rank-based methods as a non-parametric alternative of the t-test for the analysis of biological microarray data. J Bioinf Comp Biol 3, 1171-1189.

[0047] The FC indicates the prevalence of the marker in each comparison, with 2 indicating 2 times the average test, 1.5 indicating 1.5 times the average test, etc., and thus the FC indicates the weight of the marker for the phenotype / group. The FC value can be used to give an indication of the number of markers required for a highly efficient test. Typically, 5-10 markers can give a highly efficient test, but a smaller number of markers may also give a functional test for the detection of coronavirus prognosis.

[0048] The probe is designed to be 30 bp away from the Taq1 site. In the case of PCR, PCR primers are typically designed to detect the ligated product, but at a variable position from the Taq1 site. Probe Location: Start1 - 30 bases upstream of the TaqI site on fragment 1 End1 - TaqI restriction site on fragment 1 TaqI restriction site on Start2-fragment 2 End2 - 30 bases downstream of the TaqI site on fragment 2 4kb sequence location: Start1 - 4000 bases upstream of the TaqI site on fragment 1 End1 - TaqI restriction site on fragment 1 TaqI restriction site on Start2-fragment 2 End2 - 4000 bases downstream of TaqI on fragment 2

[0049] Table 5 relates to the network analysis shown in FIG. 20 and lists the String functional sub-networks represented in the network, along with the names of the key genes within the sub-network that are associated with EpiSwitch markers.

[0050] Table 6 provides a list of therapeutic compounds that have an associated set of affected genes (see #gene column) and have associations with individual genes from that set (see next column, #matched genes) that are associated with EpiSwitch significant markers specific for severe disease outcomes (all of which are immune-related genes). In the table: - The Total Number of Genes column indicates the genes affected by the therapeutic agent; - The Matched Genes column indicates the number of affected genes matching the EpiSwitch marker. The high scores obtained confirm the non-random selection of matched genes, i.e. the influence of the 3D genome architecture in severely diseased patients.

[0051] Table 7 shows a set of preferred markers, as well as preferred probes and primers for use in the present invention. The specific prognosis associated with the presence of each marker is shown in the first column. "Mild" indicates clinical symptoms of COVID-19 (as opposed to asymptomatic), patients may be hospitalized, but respond to standard treatment and remain in a stable condition. "ICU" indicates severe symptoms in which hospitalized patients do not respond to standard treatment in the ward and require intensive care unit (ICU) support. Examples of severe (ICU) are hyperinflammation and the need for mechanical ventilation.

[0052] Table 8 shows a set of markers for use in the present invention. Any marker from this table can be used. The preferred markers with a "1" in the LS column are associated with severe phenotypes in this table, and therefore all these markers represent a preferred subset of markers from which markers can be selected for use in the present invention.

[0053] Table 9 shows markers that are all associated with the severe phenotype. The table also shows preferred primer and probe sequences. These primers and probes can be used in a qPCR format for detection of the associated markers.

[0054] Table 10 shows a set of markers for use in the present invention. Any marker from this table can be used. The preferred markers with "-1" in the LS column are associated with a mild phenotype in this table, and therefore all these markers represent a preferred subset of markers from which markers can be selected for use in the present invention.

[0055] Table 11 shows markers that are all associated with the mild phenotype. This table also shows preferred primer and probe sequences. These primers and probes can be used in a qPCR format for detection of the associated markers.

[0056] Tables 14 and 16 show preferred probe and primer sequences that can optionally be used in a qPCR format. Tables 15 and 17 show preferred primers that can optionally be used in a nested PCR format.

[0057] Preferred Marker Sets The present invention relates to detecting the prognosis of coronavirus infection by classifying chromosomal interaction markers, such as any of the specific markers disclosed herein, for example in Tables 1 or 3, or preferably combinations of markers, or markers in the specific defined regions disclosed herein. Markers present in the genes and regions mentioned in the tables may be classified. Specific markers are defined herein by location or by probe and / or primer sequences. Thus, preferred markers are those represented by the probe and / or primer pairs disclosed in the tables herein.

[0058] The present invention relates to detecting the prognosis of coronavirus infection by classifying chromosomal interaction markers such as any of the specific markers disclosed in Tables 8, 9, 10, 11, 14, 15, 16 or 17, or preferred combinations of markers from any of these tables, or markers in defined specific regions disclosed in any of these tables.

[0059] The present invention further relates to detecting septic conditions by typing chromosomal interaction markers, such as any of the specific markers disclosed in Tables 12 or 13, or preferred combinations of markers from any of these tables.

[0060] Marker combinations can be done in a variety of ways, e.g.: - by ranking according to any parameter defined herein, or - Any "part" of Table 1 or 3 -By referring to the "number" of the marker listed in the left column of the table It can be defined.

[0061] In a preferred embodiment, at least 10 markers are ranked from the top 40 markers for any parameter mentioned in the table, such as FC.

[0062] In one embodiment, one or more markers are classified, which are: (i) in any one of the regions listed in Tables 1 or 3; and / or (ii) corresponds to any one of the chromosomal interactions represented by any of the probes shown in Tables 1 or 3; and / or (iii) is present in a 4,000-base region containing or adjacent to (i) or (ii).

[0063] In a preferred embodiment, - at least 5 chromosomal interactions classified from Table 1, and / or - At least five chromosomal interactions are classified from Table 3.

[0064] In another preferred embodiment, the at least five chromosomal interactions are - the top 40 interactions in Table 1 defined using any parameter, and / or -The top 40 interactions in Table 3 defined using arbitrary parameters A category is selected from.

[0065] The marker combination can be defined by any "portion" of Tables 8, 9, 10, 11, 12, 13, 14, 15, 16 or 17.

[0066] In a preferred embodiment, at least 10 markers are ranked from the top 40 markers for any parameter mentioned in the table, such as FC.

[0067] In one embodiment, one or more markers are classified, which are: (i) in any one of the regions listed in Tables 8, 9, 10 or 11; and / or (ii) corresponds to any one of the chromosomal interactions represented by any of the probes shown in Tables 8, 9, 10 or 11; and / or (iii) is present in a 4,000-base region containing or adjacent to (i) or (ii).

[0068] In a preferred embodiment, - at least 5 chromosomal interactions classified from Table 9, and / or At least five chromosomal interactions are classified from Table 11.

[0069] In another preferred embodiment, the at least five chromosomal interactions are - the top 40 interactions in Table 8 defined using any parameter, and / or -Top 40 interactions in Table 10 defined using arbitrary parameters A category is selected from.

[0070] Preferred number of markers to be sorted Categorizing a very small number of markers disclosed herein is a valid test due to their nature of regulation by chromosomal interactions, including their network-oriented properties. Different numbers and combinations of markers will result in different performance characteristics. As will be further understood, markers can be selected from Tables 1 or 3 as a whole, or from the portions of the tables defined by numbers and letters (e.g., "a2"). Similarly, markers can be selected from any of Tables 8, 9, 10, 11, 14, 15, 16, or 17 in whole or in part. Markers can also be selected from Tables 12 and 13 in whole or in part.

[0071] In one embodiment, the process comprises classifying at least 3, 5, 8, 10, 15, 20, 50, 100, 150, 200, 250 or 300 of the chromosomal interactions represented by the probes of Table 1. In one embodiment, at least 10 of the chromosomal interactions represented by the probes of Table 1 are classified.

[0072] In one embodiment, the process comprises classifying at least 3, 5, 8, 10, 15, 20, 25 or 30 of the chromosomal interactions represented by the probes of Table 2. In one embodiment, at least 10 of the chromosomal interactions represented by the probes of Table 2 are classified.

[0073] In one embodiment, the process comprises classifying at least 3, 5, 8, 10, 15, 20, 50, 80, 100, 150 or 200 of the chromosomal interactions represented by the probes of Table 3.

[0074] In one embodiment, the process comprises classifying at least 3, 5, 8 or 10 of the chromosomal interactions represented by the probes of Table 4. In one embodiment, at least 10 of the chromosomal interactions represented by the probes of Table 4 are classified.

[0075] In one embodiment, at least 3, 5, 8, 10, 15 or 20 chromosomal interactions are classified from the top 40 markers of Table 1 defined using any parameter, and / or at least 3, 5, 8, 10, 15 or 20 chromosomal interactions are classified from the top 40 markers of Table 3 defined using any parameter.

[0076] In one embodiment, 2, 3, 4, 5 or 6 markers are grouped from Table 7. All six markers of Table 7 may be grouped. In another embodiment, all six markers of Table 7 are grouped together with at least 3, 5, 8, 10, 15 or 20 chromosomal interactions of Table 1. In another embodiment, all six markers of Table 7 are grouped together with at least 3, 5, 8, 10, 15 or 20 markers of Table 3.

[0077] In one embodiment, the process comprises classifying at least 3, 5, 8, 10, 15, 20, 50, 100, 150, 200, 250 or 300 of the chromosomal interactions represented by the probes of Table 8. Preferably, at least 10 of the chromosomal interactions represented by the probes of Table 8 are classified.

[0078] In one embodiment, the process comprises classifying at least 3, 5, 8, 10, 15, 20, 50, 100 or all of the chromosomal interactions shown in Table 8 with a "1" in the LS column. Preferably, at least 10 chromosomal interactions shown in Table 8 with a "1" in the LS column are classified.

[0079] In one embodiment, the process comprises classifying at least 3, 5, 8, 10, 15, 20, 30 or all of the chromosomal interactions shown in Table 9. Preferably, at least 10 of the chromosomal interactions represented by the probes in Table 9 are classified.

[0080] In one embodiment, the process comprises classifying at least 3, 5, 8, 10, 15, 20, 50, 100, 150, 200 or all of the chromosomal interactions represented by the probes of Table 10. Preferably, at least 10 chromosomal interactions represented by the probes of Table 10 are classified.

[0081] In one embodiment, the process comprises classifying at least 3, 5, 8, 10, 15, 20, 50, 80 or all of the chromosomal interactions shown in Table 10 with the LS column being "-1". Preferably, at least 10 chromosomal interactions shown in Table 10 with the LS column being "-1" are classified.

[0082] In one embodiment, the process comprises classifying at least 3, 5, 8, 10 or all of the chromosomal interactions shown in Table 11. Preferably, at least 10 chromosomal interactions represented by the probes of Table 11 are classified.

[0083] In one embodiment, the process comprises classifying at least 3, 5, 8, 10, 20, 25 or all of the chromosomal interactions shown in Table 12. Preferably, at least 10 chromosomal interactions represented by the probes in Table 12 are classified.

[0084] In one embodiment, the process comprises classifying at least 2, 3 or 5 of the chromosomal interactions shown in Table 13. Preferably, at least 10 chromosomal interactions represented by the probes of Table 13 are classified.

[0085] In one embodiment, the process comprises classifying at least 3, 5, 8, 10, 15, 20, 50, 70 or all of the chromosomal interactions represented by the probes of Table 14. Preferably, at least 10 chromosomal interactions represented by the probes of Table 14 are classified.

[0086] In one embodiment, the process comprises classifying at least 3, 5, 8, 10, 15, 20, 50, 100, 120 or all of the chromosomal interactions represented by the probes of Table 15. Preferably, at least 10 chromosomal interactions represented by the probes of Table 15 are classified.

[0087] In one embodiment, the process comprises classifying at least 3, 5, 8, 10, 15, 20, 40 or all of the chromosomal interactions represented by the probes of Table 16. Preferably, at least 10 chromosomal interactions represented by the probes of Table 16 are classified.

[0088] In one embodiment, the process comprises classifying at least 3, 5, 8, 10, 15, 20, 50, 80 or all of the chromosomal interactions represented by the probes of Table 17. Preferably, at least 10 chromosomal interactions represented by the probes of Table 17 are classified. Preferably, at least 10 chromosomal interactions represented by the probes of Table 17 are classified.

[0089] Types of chromosomal interactions In one embodiment, the locus (including the gene and / or location where the chromosomal interaction is detected) may contain a CTCF binding site. This is any sequence that can bind to the transcriptional repressor CTCF. The sequence may consist of or include the sequence CCCTC, which may be present in one, two, or three copies at the locus. The CTCF binding site sequence may include the sequence CCGCGNGGNGGCAG (IUPAC notation). The CTCF binding site may be within at least 100, 500, 1000, or 4000 bases of the chromosomal interaction or any of the chromosomal regions shown in Table 1.

[0090] When detection is performed using a probe, typically sequences from both regions of the probe (i.e., from both sites of chromosomal interaction) can be detected. In preferred embodiments, a probe that contains or consists of the same or complementary sequence as any of the probes shown in the table is used in the process. In some embodiments, a probe that contains a sequence that is homologous to any of the probe sequences shown in the table is used.

[0091] Approaches taken to identify markers and panels of markers The invention described herein relates to chromosome interaction profile and 3D architecture as its own regulatory modality closely linked to phenotype. Biomarker discovery is based on annotation by pattern recognition and screening in representative cohorts of clinical samples that represent phenotypic differences. We annotated and screened key parts of genome across coding and non-coding parts and across all non-coding 5' and 3' large swings of known genes to identify statistically disseminating consistent conditional spreading chromosome conformations that anchor to non-coding sites inside (intronic) or outside open reading frames.

[0092] In selecting the best marker, we are driven by statistical data and p-values ​​for marker leads.The chromosome conformation in the selected and verified signature spreads stratification entities on its own, regardless of the expression profile of the gene used as reference.In addition, research can also be carried out on related regulatory modalities, such as SNPs at anchor sites, changes in gene transcription profiles, changes in the level of H3K27ac, etc.

[0093] We address the issue of clinical phenotypic differences and their stratification from the basis of fundamental biology and epigenetic control over phenotypes, for example from a regulatory network framework. Therefore, to aid in stratification, network changes can be captured, preferably through a signature of several biomarkers, for example by a subsequent machine learning algorithm for marker reduction, including assessing the optimal number of markers to stratify the test cohort with the least noise. This may result in ending up with 3-20 markers.

[0094] Markers for a panel may be selected by cross-validation statistical performance (eg, rather than by the functional relatedness of adjacent genes used in the reference names).

[0095] The panel of markers (named for their neighboring genes) is the product of a clustered selection from a screen across a significant part of the genome, in an unbiased way that analyzes the statistical diffusion power over 14,000-60,000 annotated EpiSwitch sites across a significant part of the genome. This should not be understood as a tailored capture of chromosomal conformations of genes whose functional value is known for the stratification problem. The total number of sites for chromosomal interactions is 1.2 million, and the number of possible combinations is 1.2 million to the power of 1.2 million. Nevertheless, the approach we followed allows to identify relevant chromosomal interactions.

[0096] The specific markers provided by the present application pass the selection and are statistically (significantly) associated with the condition or subgroup, as the data in the associated table attests. Each marker can be seen as representing a biological epigenetic event as part of the network deregulation that is manifested in the associated condition. In fact, this means that these markers are spread across the entire group of patients compared to the controls. By way of example, on average, an individual marker may typically be present in 80% of the patients tested and 10% of the controls tested.

[0097] A simple addition of all markers cannot directly represent the network interrelationships between several deregulations. Here, a standard multivariate biomarker analysis GLMNET (R package) is introduced. The GLMNET package helps to identify interdependencies between several markers that reflect their joint role in achieving the deregulations that lead to the disease phenotype. Modeling and subsequent examination of markers with the highest GLMNET scores not only identifies the minimum number of markers that correctly identify the patient cohort, but also the minimum number that shows the fewest false positive results in the patient control group due to the background statistical noise of the low prevalence in the control group. Typically, a group (combination) of selected markers (e.g., 3-10) provides the best balance between both sensitivity and specificity of detection, which emerges in the context of a multivariate analysis from the individual properties of all statistically significant markers selected for the condition.

[0098] The table herein gives the reference names of the array probes (60mer) for array analysis, the junctions between the long-range interaction sites, the chromosome numbers, and the Start and End of the two juxtaposed chromosomal fragments that overlap. The name of each marker listed in the table gives the chromosomal location numbers of the two regions recognized by the associated probe, providing an alternative way to define chromosomal interactions in a unique way.

[0099] Samples and sample treatment The process of the present invention is usually carried out on a sample. The sample can be obtained at a defined time point, for example at any time point defined herein. The sample will usually contain DNA from an individual. The sample will usually contain cells. In one embodiment, the sample is obtained by minimally invasive means, and can be, for example, a blood sample. DNA can be extracted and cut into pieces with standard restriction enzymes. This is done to determine which chromosome conformations are maintained and which EpiSwitch TMBy synchronizing chromosomal interactions between tissues and blood, including horizontal transfer, blood samples can be used to detect chromosomal interactions in tissues, such as disease-associated tissues.

[0100] Preferred Embodiments for Sample Preparation and Detection of Chromosomal Interactions Methods for preparing samples and detecting chromosome conformation are described herein. Optimized (non-conventional) versions of these processes can be used, for example, as described in this section.

[0101] Typically, samples are at least 2 × 10 5 The samples contain up to 5 × 10 5 In one embodiment, the sample may contain 2×10 cells. 5 From 5.5 × 10 5 Contains cells of.

[0102] The cross-linking of epigenetic chromosomal interactions present at chromosomal loci is described herein. This can be performed before cell lysis occurs. Cell lysis can be performed for 3-7 minutes, such as 4-6 minutes or about 5 minutes. In some embodiments, cell lysis is performed for at least 5 minutes and less than 10 minutes.

[0103] Digestion of DNA with restriction enzymes is described herein. Typically, restriction of DNA is carried out at about 55 to about 70° C., such as about 65° C., for a period of about 10 to 30 minutes, such as about 20 minutes.

[0104] Preferably, frequent cutter restriction enzymes are used, resulting in ligated DNA fragments having an average fragment size of up to 4000 base pairs. Optionally, the restriction enzymes result in ligated DNA fragments having an average fragment size of about 200-300 base pairs, such as about 256 base pairs. In one embodiment, typical fragment sizes are 200-4000 base pairs, such as 400-2,000 or 500-1,000 base pairs.

[0105] In one embodiment of the EpiSwitch Process, no DNA precipitation step is performed between the DNA restriction digestion step and the DNA ligation step.

[0106] DNA ligation is described herein. Typically, DNA ligation is carried out for 5 to 30 minutes, such as about 10 minutes.

[0107] Proteins in the sample may be enzymatically digested, for example using a proteinase, optionally proteinase K. Proteins may be enzymatically digested for about 30 minutes to 1 hour, for example, about 45 minutes. In one embodiment, there is no crosslink reversal or phenol DNA extraction step after protein digestion, for example proteinase K digestion.

[0108] In one embodiment, PCR detection can detect a single copy of the ligated nucleic acid, preferably with a binary readout for the presence / absence of the ligated nucleic acid.

[0109] FIG. 1 illustrates a preferred process for detecting chromosomal interactions.

[0110] Processes and Uses of the Invention The process of the present invention can be described in various ways. It can be described as a process of making a ligated nucleic acid, which comprises the steps of: (i) in vitro cross-linking chromosomal regions that are joined in a chromosomal interaction; (ii) exposing the cross-linked DNA to cleavage or restriction digestion; and (iii) ligating the cross-linked and cleaved DNA ends to form a ligated nucleic acid, the detection of which can be used to determine the chromosomal status at a locus, preferably - the locus may be any of the loci or regions mentioned in Tables 1, 3, 8 or 10, and / or - the chromosomal interaction may be any of the chromosomal interactions mentioned herein or corresponding to any of the probes disclosed in Tables 1, 3, 8 or 10, and / or - The ligated product may have or contain: (i) a sequence that is identical or homologous to any of the probe sequences disclosed in Tables 1, 3, 8 or 10; or (ii) a sequence that is complementary to (ii).

[0111] The process of the present invention can be described as a process for detecting chromosomal states representing different subgroups within a population, the process comprising determining whether chromosomal interactions are present or absent within defined epigenetically active regions of the genome, preferably comprising: - Subgroups will be defined by prognosis of coronavirus infection, and / or - the chromosomal status may be of any locus or region mentioned in Tables 1 or 3, and / or -The chromosomal interactions may be any of those mentioned in Tables 1 or 3 or any of the probes disclosed in those tables.

[0112] One or more of the markers in Table 7, for example, three, four, five or all of the markers in Table 7, can be used in these aspects of the invention.

[0113] Additionally, one or more of the markers of Tables 8, 9, 10, 11, 12, 13, 14, 15, 16 or 17 may be used in these aspects of the invention, including any specific number or combination or markers from any of these tables disclosed herein.

[0114] homologue Homologs of polynucleotide / nucleic acid (e.g., DNA) sequences are referred to herein.Such homologs typically have at least 70% homology, preferably at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98% or at least 99% homology, for example, over a region of at least 10, 15, 20, 30, 100 or more consecutive nucleotides, or over a portion of the nucleic acid derived from a region of a chromosome involved in chromosomal interaction.Homology can be calculated based on nucleotide identity (sometimes referred to as "hard homology").

[0115] Thus, in certain embodiments, homologs of polynucleotide / nucleic acid (e.g., DNA) sequences are referred to herein by referring to percentage sequence identity.Typically, such homologs have at least 70% sequence identity, preferably at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98%, or at least 99% sequence identity, for example, over a region of at least 10, 15, 20, 30, 100 or more consecutive nucleotides, or over a portion of the nucleic acid derived from a region of a chromosome involved in chromosomal interaction.A homolog may have at least 70% sequence identity, preferably at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98%, or at least 99% sequence identity over the entire probe, primer, or primer pair.

[0116] For example, the UWGCG package provides the BESTFIT program, which can be used to calculate homology and / or % sequence identity (e.g., used with its default settings) (Devereux et al (1984) Nucleic Acids Research 12, p387-395). The PILEUP and BLAST algorithms can be used to calculate homology and / or % sequence identity and / or align sequences (such as to identify equivalent or corresponding sequences (typically with their default settings)), as described, for example, in Altschul SF (1993) J Mol Evol 36: 290-300; Altschul, S, F et al (1990) J Mol Biol 215: 403-10.

[0117] Software for performing BLAST analysis is publicly available through the National Center for Biotechnology Information. The algorithm involves first identifying high-scoring sequence pairs (HSPs) by identifying short words of length W in the matched sequences that match or meet some positive threshold score T when aligned with words of the same length in the database sequences. T is called the neighborhood word score threshold (Altschul et al, supra). These initial neighborhood word hits act as seeds to initiate searches to find HSPs that contain them. The word hits are extended in both directions along each sequence for as long as the cumulative alignment score can be increased. The extension of the word hits in each direction is stopped when: the cumulative alignment score falls by a quantity X from its maximum achieved value; the accumulation of one or more negatively scoring residue alignments causes the cumulative score to fall below zero; or the end of either sequence is reached. The BLAST algorithm parameters W5 T and X determine the sensitivity and speed of the alignment. The BLAST program uses as defaults a wordlength (W) of 11, the BLOSUM62 scoring matrix (see Henikoff and Henikoff (1992) Proc. Natl. Acad. Sci. USA 89:10915-10919) of alignments (B) of 50, expectation (E) of 10, M=5, N=4, and a comparison of both strands.

[0118] The BLAST algorithm performs a statistical analysis of the similarity between two sequences (see, e.g., Karlin and Altschul (1993) Proc. Natl. Acad. Sci. USA 90: 5873-5787). One measure of similarity provided by the BLAST algorithm is the minimum sum probability (P(N)), which provides an indication of the probability that a match between two polynucleotide sequences would occur by chance. For example, a sequence is considered to be similar to another sequence if the minimum sum probability in a comparison of the first sequence to the second sequence is less than about 1, preferably less than about 0.1, more preferably less than about 0.01, and most preferably less than about 0.001.

[0119] Homologous sequences typically differ by 1, 2, 3, 4 or more bases, such as less than 10, 15 or 20 bases (which may be nucleotide substitutions, deletions, or additions). These changes may be measured across any of the above-mentioned regions in conjunction with calculating homology and / or % sequence identity.

[0120] The homology of a "primer pair" can be calculated, for example, by considering the two sequences as a single sequence (as if the two sequences were joined together) for purposes of subsequent comparison to another primer pair (which is also considered as a single sequence).

[0121] EpiSwitch TM technology EpiSwitch TM EpiSwitch Microarray Technology for the Detection of Phenotype-Specific Epigenetic Chromosome Conformation Signatures TM Use of marker data. EpiSwitch utilizing nucleic acids ligated in the manner described herein. TMSuch embodiments have several advantages. For example, nucleic acid sequences from the first set of nucleic acids of the present invention either hybridize or do not hybridize with the second set of nucleic acids, so that their stochastic noise is at a low level. This provides a binary result that allows a relatively simple way of measuring complex mechanisms at the epigenetic level. EpiSwitch TM The technique also has a rapid processing time and low cost. In one embodiment, the processing time is 3-6 hours.

[0122] array All of the nucleic acids disclosed herein can be bound to the array, and in one embodiment, there are at least 15,000, 45,000, 100,000 or 250,000 different nucleic acids bound to the array, which preferably represent at least 300, 900, 2000 or 5000 loci.In one embodiment, one or more or all of the various populations of nucleic acids are bound to more than one separate region of the array, effectively repeating on the array, allowing for error detection.The array can be based on Agilent SurePrint G3 Custom CGH microarray platform.Detection of the binding of the first nucleic acid to the array can be carried out by a dual color system.

[0123] Detection Threshold The markers disclosed herein have been found to be "disseminating markers" capable of determining the status or subgroups of coronavirus infection. In practical terms, these markers mean that they are disseminating throughout a group of patients when compared to controls (e.g., as indicated by FC values). By way of example, on average, an individual marker may typically be present in 80% of the patients tested and 10% of the controls. Thus, in one embodiment of the method, an individual is considered part of a related coronavirus prognostic subgroup if at least 80% of the markers tested for that subgroup are present in that individual and / or if at least 80% of the markers tested related to the control are absent in the individual.

[0124] Therapeutic Drugs and Treatments This section: - a therapeutic agent to be administered to an individual selected by the process of the invention, and - Therapeutic agents selected based on the results of the process of the invention It concerns both.

[0125] The invention provides a therapeutic agent for use in preventing or treating a coronavirus infection or related sub-condition in a particular individual, such as an individual identified by the process of the invention. This may include administering a therapeutically effective amount of the agent to an individual in need thereof. The invention provides the use of the agent in the manufacture of a medicament for preventing or treating a condition in a particular individual. The disease or condition may be any type of coronavirus infection, a coronavirus infection sub-condition, such as severe disease, or a stage of coronavirus infection.

[0126] The formulation of the drug depends on the nature of the drug. The drug is provided in the form of a pharmaceutical composition containing the drug and a pharma- ceutically acceptable carrier or diluent. Suitable carriers and diluents include isotonic saline, such as phosphate buffered saline. Typical oral dosage forms include tablets, capsules, liquid solutions, and liquid suspensions. The drug can be formulated for parenteral, intravenous, intramuscular, subcutaneous, transdermal, or oral administration.

[0127] The dosage of the drug can be determined according to various parameters, in particular the substance used; the age, weight and condition of the individual to be treated; the route of administration; and the required regimen. A physician can determine the route of administration and dosage required for any particular drug. However, a suitable dosage can be, for example, 0.1-100 mg / kg body weight, such as 1-40 mg / kg body weight, taken once to three times a day.

[0128] The therapeutic agent may be any such agent disclosed herein or may target any "target" disclosed herein, including any protein or gene disclosed in any table herein. It is understood that any agent disclosed in combination should also be understood to be disclosed for administration alone.

[0129] The therapeutic agents and treatments that can be used in the present invention are as follows.

[0130] Antiviral drugs Remdesivir – reduces the virus in the lungs and lung damage. Lopinavir, ritonavir or lopinavir / ritonavir combination - reduces the virus in the lungs and lung damage. Umifenovir - inhibits viral entry into target cells and stimulates the immune response.

[0131] Immunomodulatory drugs · Dexamethasone - To suppress the body's immune system. Convalescent sera - contain high concentrations of polyclonal, pathogen-specific antibodies. These antibodies can confer passive immunity to the recipient.

[0132] The individual may be treated with any therapeutic agent selected from Table 6, preferably adapacept, afelimomab, angiotensin II, dexamethasone, imatinib, immunoglobulin, infliximab, nintedanib, rituximab, or ulixertinib.

[0133] Individuals identified as having a severe disease prognosis are - May be treated for hyperinflammation, - Specialized treatment may be given under close observation, May be treated with medications to reduce viral load, - May receive immunomodulatory drugs, -Are you willing to receive the vaccine? - May receive preventive treatment, - may be in quarantine isolation, -May receive medications to prevent or treat sepsis.

[0134] Alternative Treatment Modalities The present invention provides a therapy for an individual to prevent or treat any outcome referred to herein, including the outcome of severe coronavirus disease, such as any type of individual referred to herein, including risk factors, disease, susceptibility, or any other characteristic referred to herein. The individual may be identified as susceptible to severe coronavirus disease. Preferably, the present invention provides a therapy for preventing or treating severe coronavirus disease, optionally in an individual identified as susceptible to such disease. The individual may receive any therapy referred to herein, such as any of the agents listed in Table 6.

[0135] The present invention provides a therapeutic agent selected from any of the agents set forth in Table 6 for use in a method of treating severe coronavirus disease, the method comprising: - Identifying whether an individual is susceptible to severe coronavirus disease by the process of the present invention (coronavirus infection in an individual), and -Administered to any individual identified as susceptible to said drug Includes.

[0136] The invention provides methods of treatment comprising identifying whether an individual is susceptible to severe coronavirus disease by the classification methods of the invention, and administering to any individual identified as susceptible any agent listed in Table 6.

[0137] Preferred agents from Table 6 include adapacept, afelimomab, angiotensin II, dexamethasone, imatinib, immunoglobulin, infliximab, nintedanib, rituximab or ulixertinib.

[0138] The present invention provides a method for treating an individual who has been identified as susceptible to severe disease as a result of coronavirus infection according to the methods of the present invention, comprising administering to said individual a therapeutically effective amount of a coronavirus vaccine ... - Prevent or treat coronavirus infection, and / or -Prevent or treat adverse immune responses Provide medication.

[0139] individual treatment The present invention provides various types of individualized treatment.In one embodiment, an individual is treated or not treated based on the result of the method of the present invention, i.e., based on the detection of the presence or absence of one or more specific chromosomal interactions.The method of the present invention in this sense typically allows the selection of treatment based on the individual's prognosis or response to treatment.

[0140] In one aspect, the present invention provides a method for producing a method for treating a cancer cell comprising: (i) detecting the presence or absence of one or more chromosomal interactions represented by the probes shown in Tables 1 or 3; (ii) administering to the individual a therapeutic agent that is selected based on the presence or absence of the chromosomal interaction in the individual. The present invention provides a method for treating an individual, comprising:

[0141] In one embodiment, a therapeutic agent is selected that targets a particular gene or gene product (e.g., an expressed protein), the gene being implicated in or having a chromosomal interaction.

[0142] In the embodiment of the personalized treatment of the present invention, any specific chromosomal interaction can be classified, as mentioned herein, for example, in any table. Any number or combination of interactions disclosed herein can be classified. One or more markers of table 7, for example, 3, 4, 5 or all markers of table 7 can be used in these embodiments of the present invention. Furthermore, one or more markers of table 8, 9, 10, 11, 12, 13, 14, 15, 16 or 17 can be used in these embodiments of the present invention, including any specific number or combination of markers from any of these tables, as disclosed herein.

[0143] Therapeutic drug screening The present invention provides methods of screening candidate agents (e.g., compounds) to determine whether they can be used for the treatment of any of the conditions mentioned herein, including susceptibility to severe coronavirus disease, preferably severe novel coronavirus disease.

[0144] In one aspect, the invention provides a method of determining whether a candidate agent is for the treatment of severe coronavirus disease, comprising determining whether the candidate agent is capable of altering one or more chromosomal interactions disclosed herein, such as those represented by the probes shown in Tables 1 or 3, including the number and combination of interactions disclosed herein. The method may determine whether the candidate agent is capable of creating or disrupting such chromosomal interactions.

[0145] In one aspect, the invention provides a method for determining whether a candidate agent can be used to prevent or treat severe coronavirus disease, comprising: (i) contacting a candidate agent with one or more chromosomes; (ii) determining whether the candidate agent is capable of creating or disrupting one or more chromosomal interactions represented by the probes set forth in Table 1 or 3 on that chromosome; thereby determining whether a candidate agent can be used to prevent or treat severe coronavirus disease.

[0146] One or more of the markers in Table 7 may be used in these embodiments of the invention, for example, three, four, five or all of the markers in Table 7. Additionally, one or more of the markers in Tables 8, 9, 10, 11, 12, 13, 14, 15, 16 or 17 may be used in these embodiments of the invention, including specific numbers or combinations of markers from any of these tables, as disclosed herein.

[0147] Properties of the Nucleic Acids of the Invention The present invention relates to certain nucleic acids, such as ligated nucleic acids, described herein as being used or produced in the process of the invention. These may be the same as the first and second nucleic acids referred to herein, or may have any of the properties of the first and second nucleic acids. The nucleic acids of the invention typically comprise two portions, each of which comprises a sequence from one of two regions of a chromosome that come together in a chromosomal interaction. Typically, each portion is at least 8, 10, 15, 20, 30 or 40 nucleotides in length, for example 10-40 nucleotides in length. Preferred nucleic acids comprise a sequence from any of the genes referred to in any of the tables. Typically, preferred nucleic acids comprise the specific probe sequences referred to in Tables 1 or 3, or fragments and / or homologues of such sequences.

[0148] Preferably, the nucleic acid is DNA. When a specific sequence is provided, it is understood that the present invention may use the complementary sequence required in a particular embodiment. Preferably, the nucleic acid is DNA. When a specific sequence is provided, it is understood that the present invention may use the complementary sequence required in a particular embodiment.

[0149] Primers shown in Tables 1 or 3 may also be used in the present invention as described herein. In one embodiment, primers are used that include any of the sequences shown in Tables 1 or 3; or fragments and / or homologues of any of the sequences shown in Tables 1 or 3.

[0150] One or more of the probes or primer pairs in Table 7 may be used in these aspects of the invention, for example three, four, five or all of the probes or primer pairs in Table 7. Additionally, one or more of the probes or primer pairs in any of Tables 8, 9, 10, 11, 12, 13, 14, 15, 16 or 17 may be used in these aspects of the invention.

[0151] Screening to identify relevant chromosomal interactions In one embodiment, one or more of the classified chromosomal interactions have been identified by a process of determining which chromosomal interactions are associated with a chromosomal status corresponding to a coronavirus infection subgroup of a population, comprising contacting a first set of nucleic acids from a subgroup having a different chromosomal status with a second set of index nucleic acids and allowing complementary sequences to hybridize, wherein the nucleic acids in the first and second sets of nucleic acids represent ligated products comprising sequences from both chromosomal regions that come together in the chromosomal interaction, and the pattern of hybridization between the first and second sets of nucleic acids makes it possible to determine which chromosomal interactions are specific to the subgroup.

[0152] The second set of nucleic acid sequences has the function of being a set of index sequences, and is essentially a set of nucleic acid sequences suitable for identifying specific sequences of a subgroup. They can represent "background" chromosomal interactions, which may or may not be selected in some way. They are generally a subset of all possible chromosomal interactions.

[0153] The second set of nucleic acids may be derived by any suitable process. They may be derived computationally or based on individual chromosomal interactions. They typically represent a larger population than the first set of nucleic acids. In one particular embodiment, the second set of nucleic acids represents all possible epigenetic chromosomal interactions in a specific set of genes. In another particular embodiment, the second set of nucleic acids represents a majority of all possible epigenetic chromosomal interactions present in the population described herein. In one particular embodiment, the second set of nucleic acids represents at least 50% or at least 80% of epigenetic chromosomal interactions in at least 20, 50, 100 or 500 genes, such as 20-100 or 50-500 genes.

[0154] The second set of nucleic acids typically represents at least 100 possible epigenetic chromosomal interactions that modify, regulate, or in some way mediate phenotypes in a population. The second set of nucleic acids may represent chromosomal interactions that affect disease states (typically associated with diagnosis or prognosis) in a species. The second set of nucleic acids typically includes sequences that represent epigenetic interactions that are both associated and not associated with prognostic subgroups.

[0155] In one particular embodiment, the second set of nucleic acids is at least partially derived from sequences of natural origin in the population, typically obtained by an in silico process. The nucleic acids may further comprise single or multiple mutations compared to the corresponding portion of the nucleic acid present in the nucleic acid of natural origin. Mutations include deletions, substitutions, and / or additions of one or more nucleotide base pairs. In one particular embodiment, the second set of nucleic acids may comprise sequences representing homologs and / or orthologs with at least 70% sequence identity to the corresponding portion of the nucleic acid present in the species of natural origin. In another particular embodiment, at least 80% sequence identity or at least 90% sequence identity to the corresponding portion of the nucleic acid present in the species of natural origin is provided.

[0156] A second set of properties of the nucleic acids In one particular embodiment, the second set of nucleic acids has at least 100 different nucleic acid sequences, preferably at least 1000, 2000 or 5000 different nucleic acid sequences, with up to 100,000, 1,000,000 or 10,000,000 different nucleic acid sequences. A typical number is 100-1,000,000, such as 1,000-100,000 different nucleic acid sequences. All or at least 90% or at least 50% of these correspond to different chromosomal interactions.

[0157] In one particular embodiment, the second set of nucleic acids represents chromosomal interactions at at least 20 different loci or genes, preferably at least 40 different loci or genes, more preferably at least 100, at least 500, at least 1000 or at least 5000 different loci or genes, such as 100 to 10,000 different loci or genes. The length of the second set of nucleic acids is suitable to specifically hybridize to the first set of nucleic acids according to Watson-Crick base pairing to allow identification of subgroup-specific chromosomal interactions. Typically, the second set of nucleic acids will include two portions that correspond in sequence to two chromosomal regions that come together in a chromosomal interaction. The second set of nucleic acids typically includes nucleic acid sequences that are at least 10 bases (nucleotides) long, preferably 20 bases long, preferably also 30 bases long. In another embodiment, the nucleic acid sequences can be up to 500 base pairs in length, preferably up to 100 base pairs, preferably also up to 50 base pairs. In a preferred embodiment, the second set of nucleic acids comprises a nucleic acid sequence between 17 and 25 base pairs. In one embodiment, at least 100%, 80% or 50% of the second set of nucleic acid sequences have a length as described above. Preferably, the different nucleic acids do not have any overlapping sequence, for example, at least 100%, 90%, 80% or 50% of the nucleic acids do not have the same sequence over at least 5 consecutive nucleotides.

[0158] The same set of second nucleic acids can be used with different sets of first nucleic acids representing subgroups for different features, considering the second set of nucleic acids acting as an "index", i.e., the second set of nucleic acids can represent a "universal" collection of nucleic acids that can be used to identify chromosomal interactions associated with different features.

[0159] First set of nucleic acids (screening for relevant chromosomal interactions) The first set of nucleic acids is typically from a subgroup associated with coronavirus infection. The first nucleic acid may have any of the characteristics and properties of the second set of nucleic acids mentioned herein. The first set of nucleic acids is typically associated with the treatments and processes described herein, particularly EpiSwitch. TM Typically, the first set of nucleic acids represents all or at least 80% or 50% of the chromosomal interactions present in the sample taken from the individual.

[0160] Typically, the first set of nucleic acids represents a smaller population of chromosomal interactions across the loci or genes represented by the second set of nucleic acids compared to the chromosomal interactions represented by the second set of nucleic acids, i.e., the second set of nucleic acids represents a background or index set of interactions at a defined set of loci or genes.

[0161] Nucleic acid library Any type of nucleic acid population referred to herein may be in the form of a library containing at least 200, at least 500, at least 1000, at least 5000, or at least 10000 different nucleic acids of that type, such as a "first" or "second" nucleic acid. Such libraries may be in the form of arrays bound to them. The libraries may contain some or all of the probe or primer pairs shown in any table disclosed herein. The libraries may contain all of the probe sequences from any of the tables disclosed herein.

[0162] Hybridization The present invention typically requires a means for allowing the fully or partially complementary nucleic acid sequences from the first set of nucleic acids and the second set of nucleic acids to hybridize, or between the first set of nucleic acids and the second set of nucleic acids, for example in the method of the present invention.In one embodiment, all of the first set of nucleic acids are contacted with all of the second set of nucleic acids in a single assay, i.e. in a single hybridization step.However, any suitable assay can be used.

[0163] Labeled Nucleic Acids and Hybridization Patterns The nucleic acid referred to herein can preferably be labeled using an independent label, such as a fluorophore (fluorescent molecule) or a radioactive label, which aids in the detection of successful hybridization.Certain labels can be detected under UV light.The pattern of hybridization, for example on the array described herein, represents the difference in epigenetic chromosomal interactions between two subgroups, thus providing a process of comparing epigenetic chromosomal interactions and determining which epigenetic chromosomal interactions are specific to subgroups in the population of the present invention.

[0164] The term "pattern of hybridization" broadly covers, for example, the presence or absence of hybridization between a first set of nucleic acids and a second set of nucleic acids, i.e., which specific nucleic acids from the first set hybridize to which specific nucleic acids from the second set, and thus is not limited to any particular assay or technique, or the need to have a surface or array capable of detecting the "pattern."

[0165] Forms of matter referred to herein Any of the substances, such as nucleic acids or therapeutic agents, referred to herein may be in purified or isolated form. They may be in a form different from that found in nature, e.g., they may be present in combination with other substances that do not occur in nature. Nucleic acids (including portions of sequences defined herein) may have sequences different from those found in nature, e.g., have at least one, two, three, four or more nucleotide changes in the sequence as described in the section on homology. Nucleic acids may have heterologous sequences at the 5' or 3' end. Nucleic acids may be chemically different from those found in nature, e.g., they may be modified in some way, but are still preferably capable of Watson-Crick base pairing. Where appropriate, nucleic acids are provided in double-stranded or single-stranded form. The invention provides all of the specific nucleic acid sequences referred to herein in single-stranded or double-stranded form, and thus includes the complementary strand to any sequence disclosed.

[0166] The present invention provides a kit for carrying out any process of the present invention, including the detection of chromosomal interactions related to prognosis.Such kits can include specific binding agents that can detect relevant chromosomal interactions, such as agents that can detect the ligated nucleic acid produced by the process of the present invention.Preferred agents present in the kit include probes that can hybridize to ligated nucleic acid or primer pairs, which can amplify ligated nucleic acid in PCR reaction, for example as described herein.

[0167] The present invention provides a device capable of detecting relevant chromosomal interactions. The device preferably comprises any specific binding agent, probe or primer pair capable of detecting chromosomal interactions, such as any such agent, probe or primer pair described herein.

[0168] Discovery Process In one embodiment, quantitative detection of ligated sequences associated with chromosomal interactions is carried out using a probe that is detectable upon activation during a PCR reaction, the ligated sequences comprising sequences from two chromosomal regions that come together in an epigenetic chromosomal interaction, the process comprising contacting the ligated sequences with a probe during a PCR reaction and detecting the degree of activation of the probe, the probe binding to the ligation site. The process can typically detect specific interactions in a MIQE-compliant manner using dual-labeled fluorescent hydrolysis probes.

[0169] The probe is generally labeled with a detectable label that has an inactive state and an active state, and is therefore only detected when activated. The degree of activation is related to the degree of template (ligated product) present in the PCR reaction. Detection can be performed during all or part of the PCR, for example, during at least 50% or 80% of the cycles of the PCR.

[0170] The probe can include a fluorophore covalently attached to one end of the oligonucleotide and a quencher attached to the other end of the nucleotide, so that the fluorescence of the fluorophore is quenched by the quencher. In one embodiment, the fluorophore is attached to the 5' end of the oligonucleotide and the quencher is covalently attached to the 3' end of the oligonucleotide. Fluorophores that can be used in the process of the present invention include FAM, TET, JOE, Yakima Yellow, HEX, Cyanine 3, ATTO 550, TAMRA, ROX, Texas Red, Cyanine 3.5, LC 610, LC 640, ATTO 647N, Cyanine 5, Cyanine 5.5 and ATTO 680. Quenchers that can be used with the appropriate fluorophore include TAM, BHQ1, DAB, Eclipse, BHQ2 and BBQ650, and optionally the fluorophore is selected from HEX, Texas Red and FAM. Preferred combinations of fluorophores and quenchers include FAM and BHQ1, and Texas Red and BHQ2.

[0171] Using probes in qPCR assays The hydrolysis probes of the present invention are typically optimized for temperature gradient with a concentration-matched negative control. Preferably, a single-step PCR reaction is optimized. More preferably, a standard curve is calculated. The advantage of using a specific probe that binds across the junction of the ligated sequence is that specificity for the ligated sequence can be achieved without using a nested PCR approach. The process described herein allows accurate and precise quantification of low copy number targets. Prior to optimization of the temperature gradient, the ligated sequence of the target can be purified, e.g., gel purified. The ligated sequence of the target can be sequenced. Preferably, the PCR reaction is performed using about 10 ng, or 5-15 ng, or 10-20 ng, or 10-50 ng, or 10-200 ng of template DNA. The forward and reverse primers are designed, e.g., by being complementary to the sequence, such that one primer binds to one of the sequences of the chromosomal regions represented by the ligated DNA sequence and the other primer binds to the other chromosomal region represented by the ligated DNA sequence.

[0172] Detection of the ligated nucleic acids may use any of the probes and / or primer pairs disclosed herein in any of the tables. In one embodiment, for homologs of such probes and primer pairs, the qPCR system uses the probes and primer pairs disclosed in Table 7. Preferably, the probes shown in the "Modified Sequence" column of Table 7 are used with the quencher and reporter types shown.

[0173] Selection of ligated DNA targets The present invention involves selecting primers and probes for use in a PCR process as defined herein, which includes selecting primers based on their ability to bind and amplify ligated sequences, and selecting a probe sequence based on the properties of the target sequence to which it binds, in particular the curvature of the target sequence.

[0174] Probes are typically designed / selected to bind to ligated sequences that are juxtaposed restriction fragments spanning restriction sites. In one embodiment of the invention, the predicted curvature of the ligated sequences that may be associated with a particular chromosomal interaction is calculated, for example, using certain algorithms referenced herein. Curvature can be expressed as degrees per helical turn, for example, 10.5° per helical turn. Ligated sequences are selected for targeting when the ligated sequences have a curvature propensity peak score of at least 5° per helical turn, typically at least 10°, 15° or 20° per helical turn, for example, between 5° and 20° per helical turn. Preferably, the curvature propensity peak score per helical turn is calculated for at least 20, 50, 100, 200 or 400 bases, such as between 20 and 400 bases upstream and / or downstream of the ligated site. Thus, in one embodiment, the target sequence in the ligated product has any of these levels of curvature. Target sequences can also be selected based on the lowest thermodynamic structural free energy.

[0175] Specific Aspects In one embodiment, only intrachromosomal interactions are classified / detected and not extrachromosomal interactions (between different chromosomes).

[0176] In certain embodiments, a particular chromosomal interaction, e.g., any specific interaction referred to herein (e.g., as defined by any probe or primer pair referred to herein), is not classified. In some embodiments, a chromosomal interaction is not classified in any of the genes associated with the chromosomal interaction referred to herein.

[0177] The data provided herein show that the marker is a "disseminating" marker that can distinguish between cases and non-cases of related disease conditions.Therefore, when implementing the present invention, the skilled artisan can determine which subgroup an individual is in by detecting interactions.In one embodiment, a detection threshold of at least 70% of the markers tested in a manner that correlates with related disease conditions can be used to determine whether an individual is in the related subgroup.

[0178] Screening Process The present invention provides a process for determining which chromosomal interactions are associated with chromosomal states corresponding to prognostic subgroups of a population, the method comprising contacting a first set of nucleic acids from subgroups with different chromosomal states with a second set of index nucleic acids and allowing the complementary sequences to hybridize, the nucleic acids in the first and second sets of nucleic acids representing ligated products comprising sequences from both chromosomal regions that come together in the chromosomal interaction, and the pattern of hybridization between the first and second sets of nucleic acids allows for determining which chromosomal interactions are specific to a prognostic subgroup. The subgroups can be any of the specific subgroups defined herein, for example with reference to a particular condition or treatment.

[0179] Publication and Prior Application Disclosures The contents of all publications mentioned in this specification are incorporated herein by reference and may be used to further define features relevant to the present invention. The contents of all priority applications are incorporated herein and may be used to further define features relevant to the present invention.

[0180] Techniques used to identify specific relevant chromosomal interactions EpiSwitch TMPlatform technology detects epigenetic regulatory signatures of regulatory changes between normal and abnormal states at gene loci. EpiSwitch TM The platform identifies and monitors fundamental epigenetic levels of gene regulation associated with the regulation of higher order structure of human chromosomes, also known as chromosome conformation signatures. Chromosome signatures are distinct key steps in the cascade of gene deregulation. These are higher order biomarkers with a unique set of advantages over biomarker platforms that utilize later epigenetic and gene expression biomarkers, such as DNA methylation and RNA profiling.

[0181] EpiSwitch TM Array assay Custom EpiSwitch TM The array screening platform has four densities of 15K, 45K, 100K, and 250K unique chromosome conformations, and each chimeric fragment is repeated four times on the array, bringing the effective densities to 60K, 180K, 400K, and 1 million, respectively.

[0182] Custom-designed EpiSwitch TM array 15K EpiSwitch TM The array is EpiSwitch TM The biomarker discovery technology can screen the entire genome, including approximately 300 loci collated. EpiSwitch TM The arrays were built on the Agilent SurePrint G3 Custom CGH microarray platform, a technology that offers four densities: 60K, 180K, 400K, and 1 million probes. TM As probes are displayed in quadruplicates, the density per array is reduced to 15K, 45K, 100K, and 250K, thus allowing statistical assessment of reproducibility. Potential EpiSwitches collated per locusTM The average number of markers is 50, so the numbers of loci that can be interrogated are 300, 900, 2000, and 5000.

[0183] EpiSwitch TM Custom Array Pipelines EpiSwitch TM The array is EpiSwitch TM After generation of the library, a dual color system with one set of samples labeled with Cy5 and another set of samples to be compared / analyzed (controls) labeled with Cy3. The arrays are scanned using an Agilent SureScan Scanner and the resulting features are extracted using Agilent Feature Extraction software. The data are then analyzed using the EpiSwitch TM Arrays were processed using array processing scripts. Arrays were generated using R: Limma * Arrays were processed using the standard dual color package at Bioconductor in labs at 107.45. Array normalization was performed using Limma * This was performed using the normalizedWithinArrays feature of the on-chip Agilent positive control and EpiSwitch TM Performed against a positive control. * For analysis using , data are filtered based on Agilent Flag calls, Agilent control probes are removed, and technical replicate probes are averaged. Probes are modeled based on the difference between the two scenarios being compared and corrected using the false discovery rate. Probes with a coefficient of variation (CV) <=30% that are <=-1.1 or =>1.1 and pass a p-value of p<=0.1 FDR are used for further screening. To reduce the probe set, further multiple factor analysis is performed using the FactorMineR package in R.

[0184] *NOTE: LIMMA is a linear model and empirical Bayesian process for assessing differential expression in microarray experiments. Limma is an R package for analyzing gene expression data resulting from microarrays or RNA-Seq.

[0185] A pool of probes is initially selected based on the adjusted p-value, FC and CV < 30% (arbitrary cutoff point) parameters for final picking. Further analysis and the final list are made based only on the first two parameters (adjusted p-value; FC).

[0186] Statistics Pipeline EpiSwitch TM Screening arrays are available using EpiSwitch TM High-value EpiSwitch for conversion to PCR platforms TM EpiSwitch in R to select markers TM The data are processed using an analytical package.

[0187] Process 1 Probes are selected based on their corrected p-value (false discovery rate, FDR), which is the product of a modified linear regression model. Probes with p-value <=0.1 are selected and then further reduced by epigenetic ratio (ER), probes ER must be <=-1.1 or =>1.1 to be selected for further analysis. The final filter is the coefficient of variation (CV), probes must be <=0.3.

[0188] Process 2 The top 40 markers from the statistical list are selected based on their ER for selection as markers for PCR conversion. The top 20 markers with the highest negative ER load and the top 20 markers with the highest positive ER load form the list.

[0189] Process 3 The resulting markers, statistically significant probes, from step 1 form the basis of an enrichment analysis using hypergeometric enrichment (HE). This analysis allows for marker reduction from the significant probe list and, together with the markers from step 2, the EpiSwitch TM A list of probes converted to PCR platforms is generated.

[0190] Statistical probes are processed by HE to determine which genetic locations have statistically significant probe enrichment, indicating which genetic locations are hubs of epigenetic differences.

[0191] The most significant enrichment loci based on corrected p-values ​​are selected for creation of a probe list. Genetic positions with p-values ​​less than 0.3 or 0.2 are selected. Statistical probes that map to these genetic positions using markers from step 2 are selected using the EpiSwitch TM It forms a high value marker for PCR conversion.

[0192] Array Design and Processing Array Design Loci are processed using SII software (currently v3.2) as follows. - Extract genomic sequence at these specific loci (50kb upstream and 20kb downstream gene sequence). -Defines the probability that sequences within this region are involved in CC. -Cut the sequence using a specific RE. -Determine which restriction fragments tend to interact in a particular orientation. -Ranking the likelihood that different CCs interact together. -Determine the array size and therefore the number of available probe positions (x). Elicit -x / 4 interactions. -For each interaction, define 30bp of sequence from part 1 to the restriction site and 30bp to the restriction site from part 2. Make sure those regions are not repeats, if they are, exclude them and write down the next interaction on the list. Join both 30bp to define the probe. Create a list of -x / 4 probes plus the control probe defined, replicated four times, to be created on the array. -Upload a list of probes to the Agilent Sure design website for custom CGH arrays. -Design Agilent custom CGH arrays using the probe set.

[0193] Array Processing -EpiSwitch for template creation TM Process samples using standard operating procedures (SOPs). -Clean up with ethanol precipitation by the array processing lab. -Process samples according to Agilent SureTag Complete DNA Labeling Kit -Agilent Oligonucleotide Array-based CGH for Genomic DNA Analysis Enzymatic labelling for Blood, Cells or Tissues. - Scanned using an Agilent C Scanner with Agilent feature extraction software.

[0194] EpiSwitch TMBiomarker signatures demonstrate high robustness, sensitivity and specificity in stratifying complex disease phenotypes. This technology takes advantage of the latest breakthrough in the science of epigenetics, the monitoring and evaluation of chromosome conformation signatures as a highly informative class of epigenetic biomarkers. Current research methods deployed in academic settings require 3-7 days of biochemical processing of cellular material to detect CCS. Those procedures have limited sensitivity and reproducibility and, furthermore, the EpiSwitch protocol has been inadequately developed at the design stage. TM They do not have the benefit of targeted insights provided by analytics packages.

[0195] EpiSwitch TM In silico marker identification of arrays Genome-wide CCS sites were analyzed using EpiSwitch in clinical samples from the study cohort for identification of all relevant stratified lead biomarkers TM Directly assessed by arrays. EpiSwitch TM An array platform is used for marker identification due to its high throughput capacity and its ability to rapidly screen a large number of loci. The arrays used were Agilent custom CGH arrays, which allow for cross-checking of markers identified by in silico software.

[0196] EpiSwitch TM PCR Then, EpiSwitch TM Potential markers identified by the array were used in the EpiSwitch TM The top PCR markers that are statistically significant and show the best reproducibility are selected as the final EpiSwitch markers. TM Selected for further reduction to a signature set and validated in an independent cohort of samples. TMPCR can be performed by trained technicians following established standardized operating procedure protocols. Manufacturing of all protocols and reagents is carried out under ISO 13485 and 9001 accreditation to ensure research quality and the ability to transfer protocols. EpiSwitch TM PCR and EpiSwitch TM The array biomarker platform is compatible with both whole blood and cell line analysis, and the test is sensitive enough to detect abnormalities at very low copy numbers using small amounts of blood.

[0197] Using the classifier The methods of the invention involve the analysis of chromosomal interactions identified in an individual and include the use of classifiers that can improve performance, e.g., sensitivity or specificity. Classifiers are typically "trained" on samples from a population, and such training can assist the classifier in detecting any of the subgroups referred to herein.

[0198] The invention is illustrated by the following examples. EXAMPLES

[0199] EpiSwitch showing changes in chromosome conformation upon infection with the Sars-Covid-2 virus TM Identification of chromosomal interaction markers using the platform Chromosomal interaction markers include being top immunogenetic markers and being identified based on “pure stats” and having other biological links to important clinical findings such as hypercalcemia (Ca homeostasis disruption, markers around calmodulin, calcineurin, etc.).

[0200] Prognostic stratification was based on blood collection immediately after COVID-19 testing in asymptomatic individuals or those who have just been admitted to hospital. Stratification was based on outcomes of mild COVID-19 disease (present in hospitalized patients who remain in the ward and respond to treatment) and severe COVID-19 disease, which is associated with patients transferred to the ICU (intensive care unit). Severe patients do not respond to treatments available in the hospital ward (additional oxygen, anti-inflammatory drugs, etc.) and deteriorate to ICU emergency support. The stratification provides a prognosis of immune health and hyper-inflammation in individuals when exposed to coronavirus infection.

[0201] Marker data were based on 80 patients from a mixed cohort from the UK, USA and Peru, representing asymptomatic, mild and ICU severe cases. 42 samples were from Lima, Peru, and were collected at a time when Lima was the world's hotspot for COVID-19 complications and deaths (Peru has the highest per capita death rate, 107 per 100,000 compared to 71 per 100,000 in the US and 76 per 100,000 in Brazil). There were 11 samples from the UK and 19 from the US.

[0202] Experimental studies Whole blood samples were collected from hospitalized patients with confirmed novel coronavirus disease (symptoms of SARS-CoV-2 infection) and divided into two definitions of severe and mild outcomes. The severe outcome definition included patients who were hospitalized and required more aggressive interventions in ICU care, or for whom this intervention would have been provided but for their coexisting illness. Patients who were hospitalized and received only oxygen therapy on the ward (not in the ICU) or only minimally invasive interventions were defined as mild.

[0203] EpiSwitch TMThe microarray platform was initially utilized to interrogate patient samples with known clinical outcomes to gain biological insight and identify a group of markers that could demarcate severe from mild patient samples. The aim was to identify the most statistically significant and biologically relevant markers. This study was performed on the whole genome array, interrogating over 900,000 selected anchor sites for each of the 80 patients shown in the LDA analysis in Figure 12, all converted to linear scores.

[0204] Top EpiSwitch TM Markers were subjected to primer design to identify interactions that could be successfully interrogated by nested PCR (nPCR) in the laboratory. Markers at the top of the list of successfully designed interactions were selected and used to convert the microarray markers to nPCR markers by screening the top 150 markers on the complete sample cohort.

[0205] This study has three main objectives: to investigate SARS-CoV-2 in whole blood samples, to identify top microarray interactions and translate them into nPCR, and to develop a whole blood-based EpiSwitch based on SARS-CoV-2 severity. TM The aim is to provide a proof of concept for nPCR products.

[0206] Whole blood and PBMCs often contain different cell populations, and it has been found that the majority of granulocytes (basophils, neutrophils, and eosinophils) contained in whole blood are lost during the density gradient process to purify the PBMC fraction from whole blood (see Figure 2). This needs to be kept in mind when interpreting the results. The study design is shown in Figure 3.

[0207] Forty-two patients were enrolled in the study. Venous blood samples were collected from each subject. Five milliliters or more of whole blood was administered to the EpiSwitch TM Blood was collected for biochemical processing.

[0208] Study Inclusion Criteria Subjects had to be enrolled within 72 hours of presenting to the clinic. Subjects will be recruited into two groups: - Severe cases: 24 patients in the ICU and / or mechanically ventilated - Mild COVID-19: 18 patients who were hospitalized but not in the ICU and not on mechanical ventilation (they may be receiving supplemental oxygen via a nasal cannula).

[0209] Whole blood samples were collected into SDTA K3 blood collection tubes. Clinical annotations accompanying the samples were: -Demographic composition -Travel history -Symptoms - Date of onset -Pre-existing medical conditions -SARS-CoV-2 RT-PCR results - Date of specimen collection -Other SOC respiratory diagnostic results (if available) - Date of admission -Date of transfer to ICU (if available) - Information on the use of mechanical ventilation and duration of use - Information on supplemental oxygen and duration of use It consists of:

[0210] After blood sampling, the EDTA tubes were inverted to mix the samples. EDTA is coated on the surface of the tubes to avoid clots. Samples were stored at -20°C or colder, ideally for 60 minutes before collection.

[0211] EpiSwitch library preparation The 42 samples obtained were EpiSwitch TM The prepared EpiSwitch libraries were quantified using Picogreen and Nanoquant (dsDNA and total nucleic acid measurements, respectively). TMEach library was quality controlled using a standard OBD nPCR positive control assay before being stored at -80°C until use in subsequent steps.

[0212] After the required amount of EpiSwitch library was generated for microarray processing, extraction was continued to generate the required amount of library for nPCR conversion.

[0213] Microarray processing EpiSwitch TM Whole Genome Medium Density Protein Coding focused arrays were utilized using single channel analysis. The arrays contained 973,355 EpiSwitch TM Interaction probes and 2500EpiSwitch TM A control probe was constructed with a design focused around protein-coding and long non-coding RNA loci in the genome (GRCh38).

[0214] 42 EpiSwitches TM Each of the libraries was processed and labeled for single-channel microarrays using only Cy3 dye. Each processed library was hybridized to a separate microarray. Quality control and quantification were performed using an in-line control cocktail of four external DNA fragments.

[0215] Microarray analysis EpiSwitch microarray data were analyzed to identify differentially detected interactions between two different disease phenotypic states. After identifying the top markers in statistical and biological analyses, nPCR primer combinations were designed for each putative marker. Only markers with designed primer combinations were converted to nPCR assays. The default parameters of metagenomic primer design were initially used to design optimal primer combinations for the markers, and then the parameter thresholds were increased or decreased as necessary.

[0216] EpiSwitch TM Discovery nested PCR conversion For the top 150 markers identified from the microarray and primer design, physical primers were ordered to enable screening of nPCR assays capable of distinguishing between severe and mild disease phenotypes. The entire 42 sample cohort was used for nPCR conversion. Each patient library was normalized to a 1ng / ul dsDNA concentration, and serial dilutions consisting of 1x, 1:2x, and 1:4x were made for assay screening.

[0217] End-point nPCR products were run on a LabChip GX Touch high-throughput capillary electrophoresis instrument using the 5KChip and reagent options. Appropriate controls, including a negative human genome control, were used for each assay to ensure that the products detected were actual chromosome conformation capture products and not non-specific binding of high copy number genomic DNA. NTCs were used to detect any cross-contamination of reagents.

[0218] EpiSwitch TM Discovery nested PCR analysis The EpiSwitch nested PCR platform data output was analyzed using multiple statistical techniques, including but not limited to Fisher's Exact test, GLMNET (logistic regression), and Bayesian logistic regression. TM The following statistical analyses were used to develop the classifier. - XGBoost. A gradient boosting decision tree algorithm. An ensemble of weak decision tree models is generated and combined to produce one strong classification model. -Logistic Principal Component Analysis optimized for use with two-way data - GLMNET, generalized linear models fitted via penalized maximum likelihood.

[0219] Implications for outcomes and treatment The markers identified in the above studies are shown in Tables 1-4, and the figures show further data and results. Separate sets of markers for nPCR and qPCR were developed in parallel as shown in the tables. Selection of qPCR markers from the arrays (when compared to nPCR) relied on additional filters to obtain abundance. In the tables, if the genomic location of the marker overlaps with an immune locus (genetic marker for immune regulation), it is noted as "immune" and if it coincides with a cardiovascular genetic marker, it is noted as "cardiac."

[0220] A prognostic test based on the identified markers is a measure of immune health and immune competence under exposure to COVID-19. High-risk groups (with a prognosis of severe disease) are more likely to develop hyperinflammation and show poor response to standard treatment, alerting doctors to the need for specialized treatment under close observation, such as reducing viral load and introducing immunomodulatory drugs. Furthermore, in terms of vaccine use and distribution, the test identifies high-risk groups that should be targeted for vaccination. At the same time, it may help identify high-risk groups in the general working population who need further preventive treatment, protection and isolation, and support low-risk groups in returning to work.

[0221] Gene pathway identification Chromosomal interaction markers correspond to regulatory networks. In our analysis, we investigated how the markers correspond to biological pathways. We obtained a statistical score indicating the relative overlap of loci representing a particular pathway with significant EpiSwitch 3D genomic markers. The higher the score, the more genes in that pathway colocalize with sites that are dysregulated in the genome architecture. The identified pathways show how the markers relate to immune health beyond the COVID-19 model of disease.

[0222] Top pathways affected by 3D deregulation at genetic location were: -Innate immune system -Toll-like receptor signaling pathway -ERK signaling -TGF-beta signaling pathway (Wikipathy) -Cytokine signaling in the immune system -TCR signaling (REACTOME) -Th17 cell differentiation -Class I MHC-mediated antigen processing and presentation -Apoptosis pathway -Rheumatoid arthritis -NFAT in immune response -ICos-ICosL pathway in helper T cells -Signal Transduction by GPCRs - Allograft rejection -IL12 signaling mediated by STAT4 -The immune response function of MEF2 in T lymphocytes -CXCR4-mediated signaling events -Apoptotic pathway in synovial fibroblasts -GPCR pathway Includes.

[0223] FIG. 20 shows the standard STRING network analysis that was performed, and Table 5 shows the names of significant genes in the network associated with EpiSwitch markers.

[0224] Identified treatments Therapeutic compounds were identified using the set of affected genes and gene sets associated with EpiSwitch significant markers specific to the severe disease group. Table 6 shows the list of compounds and Figure 21 explains how to interpret this table.

[0225] conclusion This study clearly shows that severe / ICU is a separate phenotype from mild or asymptomatic patients with regard to 3D genomic profiling with EpiSwitch markers (i.e. chromosomal interaction markers). Moreover, this is pre-prognostic. The identified markers are strongly localized around genes responsible for immunity. This effectively means that an individual is predisposed to immune health by 3D genomics of the immune gene portion of the genome. This predisposition means that when certain individuals are exposed to a SARS-CoV-2 infection, they will be exacerbated by the nature of their immune response and will not respond to standard treatments (and therefore will be transferred to the ICU). They will be hyper-inflamed and often target specific organs.

[0226] Further studies on the markers in Table 7 Marker selection from arrays The data was loaded and normalized using the R Limma package, first to remove the Agilent control probes, and then to exclude probes above the saturation point of the system, 65,525, which depends on the range of detection by the scanner. The next two steps involve normalizing the data, first there is background correction, and then inter-array normalization is performed using a quantile approach, which standardizes the probes on and between arrays, and both of these steps minimize non-biological variation.

[0227] Immediately after normalization, positives and spikes in the controls are removed before statistical analysis. Two statistical methods are employed for statistical analysis: linear modeling using the LIMMA package and rank product analysis using the BigRankProduct package. Linear analysis is a parametric approach, while rank product is a non-parametric approach. For a probe to be statistically significant, the adjusted p-value must be ≦0.05. The statistical lists from both approaches are compared, and the union of the two is further restricted by two steps. The next filter of the combined statistical list is abundance, and probes must be either -1.2≦ or ≧1.2 on the abundance scale. This list is then further filtered to translate to qPCR, taking into account the normalized intensities of the samples. All samples must be ≧1000 in intensity units, or ≧9.66 on the log2 scale. Once these filters are applied, the data is ranked by probe abundance value (descending for positive integers, ascending for negative integers) and the top 120–top 200 markers from positive and negative selection form the list of markers used for translation to the qPCR platform.

[0228] Final C19 model The final binary outcome COVID-19 model was constructed using the WHO classification, where severity is determined by whether the patient is ventilated or not (see below). JPEG2024504062000002.jpg24563

[0229] At-risk patients (very severe, YES to ventilation) are those who require ventilation, while patients who do not require ventilation form the other set, average-risk (NO to ventilation) in this classifier. Two boosting classifiers form the basis of the CST model, XGBBoost (extreme gradient boosting) and CatBoost (boosting on decision trees), and the probabilities from these models are combined, with high-risk calls being those patients who reach a joint probability score ≥ 0.75, and average-risk patients being those with a joint probability score < 0.75.

[0230] Final combined model state The first confusion table was the training and two test sets combined. Statistics on prediction of high risk (clinical / severe (ICU)) (n=116) Accuracy: 92% Sensitivity: 96% Specificity: 86% PPV:92% NPV: 93% See below: JPEG2024504062000003.jpg139103

[0231] The second is a combined test set of 28 ventilated patients (a mix of ICU (18) and non-ICU (10)) and deceased patients (11 ICU, 8 non-ICU, not ventilated). These two sets were not used for model building. This is shown below: TIFF2024504062000004.tif249133

[0232] Further conclusions In terms of test efficiency, the qPCR format has many advantages, and the highly effective design of paired primers and probes provides high detection efficiency.

[0233] The use of multiple markers together in a test provides additional technical advantages. The epigenetic readout of the EpiSwitch marker-based system captures the functional setup of biological network regulation at the cellular level that reflects the underlying specific pathological phenotype. Stratification models can be based on groups of biomarkers that reflect the interrelationships of regulatory networks and the synchronization between individual contributors. This can be combined with machine learning models, which can determine the "optimized modeling" such as the number of markers to use in the test.

[0234] sepsis research The markers were further analyzed to identify markers that correlate with the sepsis phenotype. The results are shown in Tables 12 and 13 and Figure 22. It should be noted that PSMAS5 is a gene that has already been identified as a possible therapeutic target in the field of sepsis and in coronavirus infection research. This study directly shows for the first time that the PSMAS5 locus is involved in the same dysregulation network at the chromosome conformation level in both COVID-19 and sepsis conditions, providing a marker for monitoring sepsis and prognosing the sepsis outcome in coronavirus patients.

[0235] [Table 1] TIFF2024504062000006.tif250164 TIFF2024504062000007.tif69164 TIFF2024504062000008.tif171163 TIFF2024504062000009.tif252163 TIFF2024504062000010.tif138163 TIFF2024504062000011.tif104163 TIFF2024504062000012.tif248163 TIFF2024504062000013.tif203163 TIFF2024504062000014.tif38164 TIFF2024504062000015.tif248164 TIFF2024504062000016.tif248164 TIFF2024504062000017.tif25164 TIFF2024504062000018.tif221164 TIFF2024504062000019.tif253164 TIFF2024504062000020.tif93164 TIFF2024504062000021.tif148157 TIFF2024504062000022.tif245157 TIFF2024504062000023.tif157157 TIFF2024504062000024.tif83164 TIFF2024504062000025.tif249164 TIFF2024504062000026.tif226164 TIFF2024504062000027.tif233163 TIFF2024504062000028.tif246163 TIFF2024504062000029.tif68163 TIFF2024504062000030.tif170163 TIFF2024504062000031.tif251163 TIFF2024504062000032.tif138163 TIFF2024504062000033.tif82164 TIFF2024504062000034.tif248164 TIFF2024504062000035.tif226164 TIFF2024504062000036.tif243163 TIFF2024504062000037.tif252163 TIFF2024504062000038.tif70163 TIFF2024504062000039.tif150164 TIFF2024504062000040.tif249164 TIFF2024504062000041.tif159164 TIFF2024504062000042.tif61163 TIFF2024504062000043.tif252163 TIFF2024504062000044.tif252163 TIFF2024504062000045.tif239163 TIFF2024504062000046.tif248163 TIFF2024504062000047.tif69163 TIFF2024504062000048.tif166163 TIFF2024504062000049.tif246163 TIFF2024504062000050.tif46163 TIFF2024504062000051.tif192155 TIFF2024504062000052.tif250155 TIFF2024504062000053.tif25155 TIFF2024504062000054.tif219163 TIFF2024504062000055.tif250163 TIFF2024504062000056.tif237145 TIFF2024504062000057.tif224145 TIFF2024504062000058.tif240149 TIFF2024504062000059.tif227149 TIFF2024504062000060.tif240149 TIFF2024504062000061.tif227149 TIFF2024504062000062.tif240164 TIFF2024504062000063.tif226164

[0236]

Table 2

[0237]

Table 3

[0238]

Table 4

[0239]

Table 5

[0240]

Table 6

[0241]

Table 7

[0242]

Table 8

[0243]

Table 9

[0244]

Table 10

[0245]

Table 11

[0246]

Table 12

[0247]

Table 13

[0248]

Table 14

[0249]

Table 15

[0250]

Table 16

[0251]

Table 17

Claims

1. A method for detecting the prognosis of a coronavirus infection in an individual, comprising determining the presence or absence of one or more chromosomal interactions represented by the probes shown in Table 1 or 3, thereby determining said prognosis in the individual.

2. (i) at least five chromosomal interactions are classified from Table 1; and / or (ii) at least five chromosomal interactions are classified from Table 3 The method of claim 1.

3. The method according to claim 1 or 2, wherein the coronavirus infection is a novel coronavirus infection.

4. (i) at least five chromosomal interactions are classified from Table 2, and / or (ii) at least five chromosomal interactions are classified from Table 4 The method according to any one of claims 1 to 3.

5. Chromosomal interactions in a sample from an individual, and / or by detecting the presence or absence of a DNA loop at the site of chromosomal interaction, and / or - by detecting the presence or absence of distal regions of chromosomes that converge into a chromosomal conformation, and / or by detecting the presence of ligated nucleic acids generated during sorting, the sequences of which contain two regions each corresponding to regions of the chromosomes that come together in a chromosomal interaction; and / or - by a process that detects the proximity of chromosomal regions that come together in chromosomal interactions The method according to any one of claims 1 to 4.

6. said detecting the presence or absence of a chromosomal interaction comprising: (i) in vitro bridging of existing epigenetic chromosomal interactions; (ii) optionally isolating the crosslinked DNA; (iii) subjecting the cross-linked DNA to cleavage; (iv) ligating the cross-linked and cleaved DNA ends to form a ligated DNA; and (v) identifying the presence or absence of the ligated DNA; thereby determining the presence or absence of a chromosomal interaction. The method according to any one of claims 1 to 5, which is by a process.

7. 7. The method of claim 5 or 6, wherein the ligated DNA is detected by PCR or by the use of a probe.

8. (i) the detection is by use of a probe, said probe having at least 70% identity to any of the probes shown in Table 1 or 3; or (ii) the detection is by use of PCR, and the PCR uses a primer pair having at least 70% identity to any of the primer pairs shown in Table 1 or 3; The method of claim 7.

9. (i) the method is performed prospectively or prognostically to detect high risk of subsequent severe hyperinflammatory complications in an individual upon exposure to a coronavirus, such as 2019-nCoV; and / or (ii) the method is performed to select an individual for receiving a therapy or treatment against a coronavirus infection; and / or (iii) the method is performed in an individual who has been preselected based on the presence of a physical characteristic, risk factor, or symptom, and / or (iv) the method is carried out to determine a prognosis regarding the severity of a coronavirus infection; and / or (v) the method is performed to determine a prognosis for developing sepsis as part of a coronavirus disease; and / or (vi) The method is performed to determine a prognosis for cytokine release syndrome as part of a coronavirus disease. The method according to any one of claims 1 to 8.

10. The individual, (i) suspected of having coronavirus or COVID-19; and / or (ii) Being hospitalized The method according to any one of claims 1 to 9.

11. At least five chromosomal interactions have been classified, which (i) any 4 kb region shown in Table 1 or 3, and / or (ii) a gene shown in Table 1 or 3 The method according to any one of claims 1 to 10, wherein

12. The method according to any one of claims 1 to 11, wherein at least 10, 20, 30 or 50 chromosomal interactions are classified, and preferably between 5 and 50 chromosomal interactions are classified.

13. A method for detecting prognosis of a coronavirus infection in an individual, comprising determining the presence or absence of one or more chromosomal interactions represented by the probes set forth in Table 7, thereby determining a prognosis in the individual.

14. The method of claim 13, wherein at least three, four, five or six chromosomal interactions are classified from Table 7 and / or the coronavirus infection is COVID-19.

15. Chromosomal interactions in a sample from an individual, and / or by detecting the presence or absence of a DNA loop at the site of chromosomal interaction, and / or - by detecting the presence or absence of distal regions of chromosomes that converge into a chromosomal conformation, and / or by detecting the presence of ligated nucleic acids generated during sorting, the sequences of which contain two regions each corresponding to regions of the chromosomes that come together in a chromosomal interaction; and / or - by a process that detects the proximity of chromosomal regions that come together in chromosomal interactions 15. The method according to claim 13 or 14, wherein

16. said detecting the presence or absence of a chromosomal interaction comprising: (i) in vitro bridging of existing epigenetic chromosomal interactions; (ii) optionally isolating the crosslinked DNA; (iii) subjecting the cross-linked DNA to cleavage; (iv) ligating the cross-linked and cleaved DNA ends to form a ligated DNA; and (v) identifying the presence or absence of the ligated DNA; thereby determining the presence or absence of a chromosomal interaction. The method according to any one of claims 13 to 15, which is by a process.

17. The method of any one of claims 13 to 16, wherein the ligated DNA is detected by PCR or by the use of a probe.

18. (i) detection is by use of a probe, said probe having at least 70% identity to any of the probes shown in Table 7; or (ii) detection is by use of PCR, and the PCR uses a primer pair having at least 70% identity to any of the primer pairs shown in Table 7.

20. The method of claim 17.

19. 19. The method of claim 17 or 18, wherein the ligated DNA is detected using a qPCR system, wherein the probe has at least 70% identity to any of the probes shown in Table 7 and the primer pair has at least 70% identity to any of the primer pairs shown in Table 7; preferably the probe sequence is or comprises the probe sequence shown in Table 7 and / or each primer has or comprises the sequence of a primer shown in Table 7.

20. 20. The method according to any one of claims 17 to 19, wherein the presence or absence of all six chromosomal interactions represented by the probes of Table 7 is detected, optionally using all the probes and / or all the primer pairs shown in Table 7.

21. (i) the method is performed prospectively or prognostically to detect high risk of subsequent severe hyperinflammatory complications in an individual upon exposure to a coronavirus, such as 2019-nCoV; and / or (ii) the method is performed to select an individual for receiving a therapy or treatment against a coronavirus infection; and / or (iii) the method is performed in an individual who has been preselected based on the presence of a physical characteristic, risk factor, or symptom, and / or (iv) the method is carried out to determine a prognosis regarding the severity of a coronavirus infection; and / or (v) the method is performed to determine a prognosis for developing sepsis as part of a coronavirus disease; and / or (vi) The method is performed to determine a prognosis for cytokine release syndrome as part of a coronavirus disease. The method according to any one of claims 13 to 20.

22. The individual, (i) suspected of having coronavirus or COVID-19; and / or (ii) Being hospitalized The method according to any one of claims 13 to 21.

23. Classification of chromosomal interactions comprises specific detection of the ligated products by quantitative PCR (qPCR) using primers capable of amplifying the ligated products and probes that bind to the ligation sites during a PCR reaction, said probes comprising sequences complementary to sequences from each chromosomal region that are assembled into the chromosomal interaction, preferably comprising: - an oligonucleotide that specifically binds to the ligation product, and / or - a fluorophore covalently attached to the 5' end of the oligonucleotide, and / or - a quencher moiety covalently attached to the 3' end of the oligonucleotide and Optionally, - said fluorophore is selected from HEX, Texas Red and FAM; and / or - said probe comprises a nucleic acid sequence of 10 to 40 nucleotide bases in length, preferably 20 to 30 nucleotide bases in length; 23. The method according to any one of claims 1 to 22.

24. 24. The method of any one of claims 1 to 23, comprising determining the presence or absence of one or more chromosomal interactions represented by the probes set out in any of Tables 8, 9, 10, 11, 12, 13, 14, 15, 16 or 17, thereby determining a prognosis in the individual.

25. - one or more chromosomal interactions associated with severe coronavirus infections, preferably those shown in Tables 8, 9, 14 or 15, and / or - one or more chromosomal interactions associated with mild coronavirus infections, preferably those shown in Tables 10, 11, 16 or 17. The method of any one of claims 1 to 24, comprising determining the presence or absence of:

26. The presence or absence of a chromosomal interaction is determined by the method of claim 17, and (i) the detection is by use of a probe, said probe having at least 70% identity to any of the probes shown in Tables 9 or 11; or (ii) the detection is by use of PCR, and the PCR uses a primer pair having at least 70% identity to any of the primer pairs shown in Tables 9 or 11; 26. The method according to any one of claims 1 to 25.

27. 27. The method of claim 26, wherein the ligated DNA is detected using a qPCR system, wherein the probe has at least 70% identity to any of the probes shown in Tables 9, 11, 14 or 16 and the primer pair has at least 70% identity to any of the primer pairs shown in Tables 9, 11, 14 or 16; preferably the probe sequence is or comprises the probe sequence shown in Tables 9, 11, 14 or 16, and / or each primer has the sequence of a primer shown in Tables 9, 11, 14 or 16 or comprises the sequence of a primer shown in Tables 9, 11, 14 or 16.

28. 28. The method of any one of claims 1 to 27, wherein the presence or absence of at least five of the chromosomal interactions represented by the probes of Table 9 or 11 is detected, optionally using all the probes and / or all the primer pairs shown in Table 9 or 11.

29. 29. The method of any one of claims 1 to 28, comprising detecting a sepsis condition in an individual by detecting the presence or absence of any of the chromosomal interactions set out in Tables 12 or 13.

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