WO2022184942A2

WO2022184942A2 - Biomarkers

Info

Publication number: WO2022184942A2
Application number: PCT/EP2022/055760
Authority: WO
Inventors: Mike Fisher; Hans-Dieter Zucht; Petra Budde; Manuel BRAUTIGAN; Jana GAJEWSKI
Original assignee: Oncimmune Germany Gmbh
Priority date: 2021-03-05
Filing date: 2022-03-07
Publication date: 2022-09-09
Also published as: WO2022184942A3

Abstract

The present invention relates to autoantibody biomarkers associated with viral infections, and in particular severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The autoantibody biomarkers can be used in a variety of methods including: methods of selecting a COVID-19 patient for treatment; methods of predicting the risk of a COVID-19 patient developing certain diseases or disorders; and methods of predicting a COVID-19 patient's responsiveness to treatment. The invention also relates to kits suitable for use in such methods. The autoantibodies can be used as therapeutic targets for SARS-CoV-2 infection.

Description

BIOMARKERS

FIELD OF THE INVENTION

The present invention relates to autoantibody biomarkers associated with viral infections, and in particular severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The autoantibody biomarkers can be used in a variety of methods including: to stratify coronavirus disease 2019 (COVID-19) patients who are at risk of developing mild, moderate or severe infection; methods of selecting COVID-19 patients for treatment; methods of predicting responsiveness to treatment; methods of predicting survival responsive to treatment, method of predicting those patients at risk of immune-related adverse events (irAEs) associated with SARS-CoV-2 infection and methods for long term surveillance of flares of autoimmunity reaction in post infection disease management and long COVID syndrome.. The autoantibodies can be used as therapeutic targets for SARS-CoV-2 infection.

BACKGROUND TO THE INVENTION

Starting in December 2019, the world experienced a pandemic due to SARS-CoV-2 that has as of February 2021 claimed in excess of 2.5 million lives worldwide. According to the Centre for Disease Control and Prevention (CDC), about 81% of people with COVID-19 develop only mild or moderate symptoms such as fever or cough. However, about 14% of all patients go on to develop breathing difficulties and low blood oxygen levels. Approximately 5% become critically ill and may need treatment in intensive care units (ICU) for acute respiratory distress syndrome (ARDS), septic shock, refractory metabolic acidosis, coagulation disorders and multiple organ failure. In addition to the above, there have been reported cases of insulin-dependent diabetes occurring following SARS-CoV-2 infection.

There is therefore a need for biomarkers that are capable of stratifying COVID-19 patients to identify the optimal treatment regime to avoid patients becoming critically ill from COVID-19 related complications such as acute respiratory distress syndrome (ARDS), septic shock, refractory metabolic acidosis, coagulation disorders and multiple organ failure. During investigations into potential biomarkers for COVID-19 patients, we have surprisingly found that there are a number of autoimmune related antibodies that are increased in COVID-19 patients compared to normal controls that will have utility in such a stratification and can serve the demand in post infection analysis of triggered autoimmunity, autoimmune flaring, severity of long COVID and subsequent organ and tissue damage by triggered autoimmune reactions that especially target the proteins identified. Antibodies, and in particular autoantibodies, can serve as biological markers of disease or disease susceptibility. Autoantibodies are naturally occurring antibodies directed to an antigen which an individual’s immune system recognises as foreign even though that antigen actually originated in the individual. They may be present in the circulation as circulating free autoantibodies or in the form of circulating immune complexes consisting of autoantibodies bound to their target protein. Differences between a wild type protein expressed by “normal” cells and an altered form of the protein produced by a diseased cell or during a disease process may, in some instances, lead to the altered protein being recognised by an individual’s immune system as “non-self” and thus eliciting an immune response in that individual. This may be a humoral (i.e. B cell-mediated) immune response leading to the production of autoantibodies immunologically specific for the altered protein.

SUMMARY OF INVENTION

The present application reports the identification of autoantibody biomarkers in samples from COVID-19 patients that are increased compared to samples from healthy control subjects.

Accordingly, the present invention provides a method of selecting a COVID-19 patient for treatment, the method comprising:

(a) determining in a sample obtained from the patient the level(s) of autoantibodies specifically binding to one or more of the antigens selected from MPO, APOH, CADM3, IL-6, EXOSC10,

INS, MIF, MX1 , NCL, SLC30A8, SRP19, ELANE, SMD3, VEGFA, TOP1 , SSB, SOX13, TPO, SNRPD1 , DBT, CTLA4,CHD3, HARS, IFNA4, RNF41 , S100A9, and TROVE2; and

(b) comparing the level(s) of autoantibodies determined in (a) with a predetermined cut-off value for autoantibodies specifically binding to the same one or more antigens, wherein if the level of autoantibodies determined in the patient sample is higher than the predetermined cut-off value, the patient is selected for treatment.

In certain embodiments, the method comprises (a) determining the level(s) of autoantibodies specifically binding to one or more, or all, of the antigens selected from MPO, APOH, CADM3, IL- 6, EXOSC10, INS, MIF, MX1 , NCL, SLC30A8, SRP19, ELANE, SMD3, VEGFA, TOP1 , SSB, SOX13, TPO, SNRPD1 , DBT, CTLA4 and CHD3.

In certain embodiments, the method comprises (a) determining the level(s) of autoantibodies specifically binding to one or more, or all, of the antigens selected from INS, MIF, MX1 , SPR19, VEGFA, CTL4A, CHD3, HARS, IFNA4, RNF41 , S100A9, and TROVE2. In certain embodiments, the method comprises (a) determining the level(s) of autoantibodies specifically binding to one or more, or all, of the antigens selected from INS, MIF, MX1 , SPR19, VEGFA, CTL4A, and CHD3.

In certain embodiment, the predetermined cut-off value for autoantibodies is the average level of autoantibodies specifically binding to the antigen determined for a control cohort.

In certain embodiments, the patient sample is a serum sample.

In certain embodiments, the COVID-19 patient has or is at risk of developing one or more of the following disorders: acute respiratory distress syndrome (ARDS), septic shock, refractory metabolic acidosis, multiple organ failure, diabetes, vasculitis, myositis, systemic sclerosis, multiple sclerosis, chronic pulmonary diseases, systemic lupus erythematosus, Guillain Barre syndrome, rheumatoid arthritis, Sjogren’s syndrome, mixed connective tissue disease, idiopathic inflammatory myopathies, and coagulation disorders including but not limited to venous thromboembolism, anti-phospholipid syndrome and catastrophic anti-phospholipid syndrome.

In certain embodiment, the COVID-19 patient exhibits or is at risk of developing one or more symptoms selected from the following: pneumonia, breathing difficulties, dyspnea, pleuritic chest pain, low blood oxygen levels, hemoptysis, syncope, high blood pressure, pulmonary embolism, deep vein thrombosis, muscle weakness, joint pain, inflammation, chills, conjunctivitis, dry cough, productive cough, diarrhoea or other gastrointestinal symptoms, fatigue, fever, headache, loss of appetite, muscle aches, nasal congestion, nausea, vomiting, runny nose, shortness of breath, skin changes, dermatomyositis, loss of smell and/or taste, sneezing, sore throat, and stroke symptoms.

The present invention also provides a method of predicting the risk of a COVID-19 patient developing diabetes, the method comprising:

(a) determining in a sample obtained from the patient the level(s) of autoantibodies specifically binding to one or more antigens selected from SOX13 and SLC30A8; and

(b) comparing the level(s) of autoantibodies determined in (a) with a predetermined cut-off value for autoantibodies specifically binding to the same one or more antigens, wherein if the level of autoantibodies determined in the patient sample is higher than the predetermined cut-off value, the patient is identified as having an increased risk of developing diabetes.

The present invention also provides a method of predicting the risk of a COVID-19 patient developing systemic lupus erythematosus, the method comprising: (a) determining in a sample obtained from the patient the level(s) of autoantibodies specifically binding to one or more antigens selected from IL-6, SNRPD1 and MX1 ; and

(b) comparing the level(s) of autoantibodies determined in (a) with a predetermined cut-off value for autoantibodies specifically binding to the same one or more antigens, wherein if the level of autoantibodies determined in the patient sample is higher than the predetermined cut-off value, the patient is identified as having an increased risk of developing systemic lupus erythematosus.

The present invention also provides a method of predicting the risk of a COVID-19 patient developing a coagulation disorders including but not limited to venous thromboembolism, anti- phospholipid syndrome and catastrophic anti-phospholipid syndrome, the method comprising:

(a) determining in a sample obtained from the patient the level(s) of autoantibodies specifically binding to one or more of antigens selected from APOH and VEGFA; and

(b) comparing the level(s) of autoantibodies determined in (a) with a predetermined cut-off value for autoantibodies specifically binding to the same one or more antigens, wherein if the level of autoantibodies determined in the patient sample is higher than the predetermined cut-off value, the patient is identified as having an increased risk of developing a coagulation disorder.

The present invention also provides a method of predicting the risk of a COVID-19 patient developing vasculitis, the method comprising:

(a) determining in a sample obtained from the patient the level(s) of autoantibodies specifically binding to one or more of antigens selected from MPO and ELANE; and

(b) comparing the level(s) of autoantibodies determined in (a) with a predetermined cut-off value for autoantibodies specifically binding to the same one or more antigens, wherein if the level of autoantibodies determined in the patient sample is higher than the predetermined cut-off value, the patient is identified as having an increased risk of developing vasculitis.

The present invention also provides a method of predicting the risk of a COVID-19 patient developing myositis or systemic sclerosis, the method comprising:

(a) determining in a sample obtained from the patient the level of autoantibodies specifically binding to EXOSC10; and

(b) comparing the level of autoantibodies determined in (a) with a predetermined cut-off value for autoantibodies specifically binding to the same antigen, wherein if the level of autoantibodies determined in the patient sample is higher than the predetermined cut-off value, the patient is identified as having an increased risk of developing myositis or systemic sclerosis. The present invention also provides a method of selecting a COVID-19 patient for treatment, wherein the patient is male, the method comprising:

(a) determining in a sample obtained from the patient the level(s) of autoantibodies specifically binding to one or more antigens selected from MX1 , VEGFA, IL6, INS, CADM3, SSB, SNRPD1 , HARS, CTLA4, IFNA4, TROVE2, S100A9, SLC30A8, EXOSC10, SOX13, ELANE, SRP19, MIF, APOH, TOP1 , DBT, CXCL8, HIST1 H41 , CTSG, CSF2, SNRNP70, IL17A, UBTF, AK4, ICA1 , NCL, CHD4, RNF41 , IL4, EIF4H, ANXA2, SNRPA, PRTN3, TGFB1 , RPLP2, IGF1 R, NMP1 , C3, TG, SET, DLAT, VIM, TRIM21 , RAE1 , AQP4, LYZ, IFNA6, ACE2, MDA5, ECE1 , MOV10, GRP, CHGA, SNRPB; and

(b) comparing the level(s) of autoantibodies determined in (a) with a predetermined cut-off value for autoantibodies specifically binding to the same one or more antigens, wherein if the level(s) of autoantibodies determined in the patient sample is higher than the predetermined cut-off value, the patient is selected for treatment.

The present invention also provides a method of selecting a COVID-19 patient for treatment, wherein the patient is female, the method comprising:

(a) determining in a sample obtained from the patient the level(s) of autoantibodies specifically binding to one or more antigens selected from INS, ELANE, HARS, SOX13, CHD3, SMD3,

TOP1 , RNF41 , S100A9, TPO, MX1 , EXOSC10, DBT, MOV10, SP100, PTPRN, CHGA, S100A8, SNRPC, ECE1 , CENPB, PRTN3, SRP54, CHD4, TRIM33, TGFB1 , UBTF, AQP4, ROS1 ,

GAD65, IL10, SET, HIST1 H4A, IFNA2, IGF1 R, C3, RPLP2, TG; and

The present invention also provides a kit for the detection of autoantibodies in a test sample obtained from a COVID-19 patient, the kit comprising:

(a) one or more antigens selected from MPO, APOH, CADM3, IL-6, EXOSC10, INS, MIF, MPO, MX1 , NCL, SLC30A8, SRP19, ELANE, SMD3, VEGFA, TOP1 , SSB, SOX13, TPO, SNRPD1 , DBT, CTLA4, CHD3, HARS, IFNA4, RNF41 , S100A9, and TROVE2; and

(b) a reagent capable of detecting complexes of the antigen bound to autoantibodies present in the test sample obtained from the COVID-19 patient.

In certain embodiments, the kit comprises (a) one or more, or all, of the antigens selected from MPO, APOH, CADM3, IL-6, EXOSC10, INS, MIF, MX1 , NCL, SLC30A8, SRP19, ELANE, SMD3,

VEGFA, TOP1 , SSB, SOX13, TPO, SNRPD1 , DBT, CTLA4, and CHD3. In certain embodiments, the kit comprises (a) one or more, or all, of the antigens selected from INS, MIF, MX1 , SPR19, VEGFA, CTL4A, CHD3, HARS, IFNA4, RNF41 , S100A9, and TROVE2.

In certain embodiment, the kit comprises (a) one or more, or all, of the antigens selected from INS, MIF, MX1 , SPR19, VEGFA, CTL4A, and CHD3.

(a) one or more antigens selected from MX1 , VEGFA, IL6, INS, CADM3, SSB, SNRPD1 , HARS, CTLA4, IFNA4, TROVE2, S100A9, SLC30A8, EXOSC10, SOX13, ELANE, SRP19, MIF, APOH, TOP1 , DBT, CXCL8, HIST1 H41 , CTSG, CSF2, SNRNP70, IL17A, UBTF, AK4, ICA1 , NCL, CHD4, RNF41 , IL4, EIF4H, ANXA2, SNRPA, PRTN3, TGFB1 , RPLP2, IGF1 R, NMP1 , C3, TG, SET, DLAT, VIM, TRIM21 , RAE1 , AQP4, LYZ, IFNA6, ACE2, MDA5, ECE1 , MOV10, GRP, CHGA, SNRPB; and

(a) one or more antigens selected from INS, ELANE, HARS, SOX13, CHD3, SMD3, TOP1 , RNF41 , S100A9, TPO, MX1 , EXOSC10, DBT, MOV10, SP100, PTPRN, CHGA, S100A8, SNRPC, ECE1 , CENPB, PRTN3, SRP54, CHD4, TRIM33, TGFB1 , UBTF, AQP4, ROS1 , GAD65, IL10, SET, HIST1 H4A, IFNA2, IGF1 R, C3, RPLP2, TG; and

BRIEF DESCRIPTION OF DRAWINGS

Fig. 1 Heat map showing the 22 antigens with increased reactivity in COVID-19 patients compared to healthy controls. From the heat map it can be seen that high log2 MFI values are more abundant in the COVID-19 patient cohort compared to the healthy patients for all markers presented. The 22 antigens that were determined to have elevated levels of autoantibodies in COVID-19 patient samples compared to healthy controls are MPO, APOH, CADM3, IL-6, EXOSC10, INS, MIF, MX1 , NCL, SLC30A8, SRP19, ELANE, SMD3, VEGFA, TOP1 , SSB,

SOX13, TPO, SNRPD1 , DPTDBT, CTLA4, and CHD3. Fig. 2 ROC curve that illustrates how the identified markers can be combined to a full panel in a logistic regression analysis increasing predictive performance. Fig. 3 Histogram showing the performance of combinations of markers in panels of three. Each count on the histogram represents one panel AUC. Most panels perform reasonably well but some show extraordinarily high ROC AUC values illustrating the complementary value of marker panels. Fig. 4 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the MPO antigen.

Fig. 5 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the APOH antigen.

Fig. 6 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the CADM3 antigen.

Fig. 7 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the IL6 antigen.

Fig. 8 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the SLC30A8 antigen. Fig. 9 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the INS antigen.

Fig. 10 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the VEGFA antigen.

Fig. 11 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the EXOSC10 antigen.

Fig. 12 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the SRP19 antigen.

Fig. 13 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the ELANE antigen. Fig. 14 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the NCL antigen.

Fig. 15 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the SMD3 antigen.

Fig. 16 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the MIF antigen.

Fig. 17 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the MX1 antigen.

Fig. 18 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the SOX13 antigen.

Fig. 19 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the SNRPD1 antigen.

Fig. 20 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the CHD3 antigen.

Fig. 21 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the TPO antigen.

Fig. 22 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the TOP1 antigen.

Fig. 23 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the DBT antigen.

Fig. 24 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the SSB antigen.

Fig. 25 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the CTLA4 antigen. Fig. 26 Sex-specific clustering of symptoms and autoantibodies in men and women previously infected with SARS-CoV-2. Symptoms and autoantibodies were grouped based on similar directions and magnitudes of the beta coefficients from age-adjusted regression association analyses, with clusters selected based on a threshold of h=0.5 for autoantibodies and h=1 .5 for symptoms from Ward hierarchical clustering. Results are shown for men and women. A distinct set of autoantibodies to 59 antigens were highly correlated with reported symptoms in the male population (CXCL8, HIST1 H41 , CTSG, CSF2, SNRNP70, IL17A, UBTF, AK4, ICA1 , NCL, TOP1 , CHD4, RNF41 , IL4, EIF4H, ANXA2, MX1 , VEGFA, SNRPA, PRTN3, CADM3, IL6, INS, SSB, SNRPD1 , TGFB1 , RPLP2, HARS, CTLA4, IGF1 R, IFNA4, NMP1 , C3, TG, TROVE2, S100A9, SET, SLC30A8, DLAT, VIM, TRIM21 , EXOSC10, SOX13, ELANE, RAE1 , DBT, AQP4, LYZ, IFNA6, ACE2, SRP19, MDA5, MIF, ECE1 , MOV10, GRP, CHGA, APOH, and SNRPB). A distinct set of autoantibodies to 38 antigens were highly correlated with reported symptoms in the female population (INS, ELANE, MOV10, SP100, PTPRN, CHGA, S100A8, SNRPC, ECE1 , HARS, CENPB, SOX13, PRTN3, CHD3, SRP54, CHD4, DBT, TRIM33, TGFB1 , SMD3, UBTF, TOP1 , AQP4, ROS1 , RNF41 , GAD65, IL10, S100A9, TPO, SET, HIST1 H4A, MX1 , EXOSC10, IFNA2, IGF1 R, C3, RPLP2 and TG).

Fig. 27 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the SNRPB, IL17A and APOH antigen in patients with dry cough.

Fig. 28 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the SOX13 antigen in patients with runny nose.

Fig. 29 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the GRP and NCL antigen in patients with nausea.

Fig. 30 Box-and-whisker plot showing the results of the levels of autoantibodies binding to the SNRPB and GRP antigen in patients with chest pain.

DETAILED DESCRIPTION

A. Definitions

Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terms “a”, “an”, and “the” do not denote a limitation of quantity, but rather denote the presence of “at least one” of the referenced item. In this application and the claims, the use of the singular includes the plural unless specifically stated otherwise. In addition, use of "or" means "and/or" unless stated otherwise. Moreover, the use of the term "including", as well as other forms, such as "includes" and "included", is not limiting.

As used herein, "autoantibody" means an antibody produced by the immune system of a subject that is directed to and specifically binds to an "autoantigen”, “self-antigen" or an "antigenic epitope" thereof. The terms “specifically bind" and/or "specifically recognize" as used herein, refer to the higher affinity of a binding molecule for a target molecule compared to the binding molecule's affinity for non-target molecules. A binding molecule that specifically binds a target molecule does not substantially recognize or bind non-target molecules, e.g., an antibody "specifically binds" and/or "specifically recognizes" another molecule, meaning that this interaction is dependent on the presence of the binding specificity of the molecule structure, e.g., an antigenic epitope.

As used herein, the term “autoantibody biomarker” refers to an autoantibody, the levels of which are associated with a particular phenotype, response or outcome. As described herein, the levels of autoantibody biomarkers can be detected in samples obtained from subjects/patients and the levels can be compared with pre-determined cut-off values. This assessment of autoantibody biomarkers can be used to detect/diagnose diseases and disorders as well as inform decisions relating to treatment of patients.

As used herein, the terms "diagnose" or "diagnosis" or "diagnosing" refer to determining the nature or the identity of a condition or disease or disorder, e.g., acute respiratory distress syndrome (ARDS). A diagnosis may be accompanied by a determination as to the severity of the disorder.

As used herein, the term "sample" refers to a sample obtained from a mammalian subject or a patient for evaluation in vitro. The sample can be any sample that is expected to contain antibodies and/or immune cells. The sample can be taken from blood, e.g., serum, peripheral blood, peripheral blood mononuclear cells (PBMC), whole blood or whole blood pre-treated with an anticoagulant such as heparin, ethylenediamine tetraacetic acid, plasma or serum. A sample may be pre-treated prior to use, such as by preparing plasma from blood, diluting viscous liquids, or the like. Methods of treating a sample may also involve separation, filtration, distillation, concentration, inactivation of interfering components, and/or the addition of reagents. As used herein, the terms "treat," "treatment," "treating," or "amelioration" refer to therapeutic treatments, wherein the object is to reverse, alleviate, ameliorate, inhibit, slow down or stop the progression or severity of disease, an associated condition and/or a symptom thereof. The term "treating" includes reducing or alleviating at least one adverse effect or symptom of the disease. Treatment is generally "effective" if one or more symptoms or clinical markers are reduced. Alternatively, or in addition, treatment is "effective" if the progression of a disease is reduced or halted.

B. Methods using autoantibody biomarkers in COVID-19 patients

Methods of selecting COVID-19 patients for treatment

The present invention provides methods of selecting COVID-19 patients for treatment. The methods comprise a step of analysing a sample obtained from the COVID-19 patient to determine the levels of autoantibodies specifically binding to one or more target antigens. The sample is typically removed from the body such that the analysis of the sample is carried out in vitro.

The patient may be a patient previously diagnosed with COVID-19 or suspected of having COVID-19. The patient may have received prior treatment or may be newly-diagnosed having received no prior treatment.

The sample obtained for in vitro analysis in accordance with the methods described herein may be any sample expected to contain autoantibodies and/or immune cells. The sample may be taken from blood, e.g., serum, peripheral blood, peripheral blood mononuclear cells (PBMC), whole blood or whole blood pre-treated with an anticoagulant such as heparin, ethylenediamine tetraacetic acid, plasma or serum. The sample is preferably serum. The sample may be pre- treated prior to testing, such as by preparing plasma from blood, diluting viscous liquids, or the like. Methods of treating the sample prior to testing may also involve separation, filtration, distillation, concentration, inactivation of interfering components, and/or the addition of reagents. The sample may also be stored prior to testing. In certain embodiments, the sample may be any one of plasma, serum, whole blood, urine, sweat, lymph, faeces, cerebrospinal fluid, ascites fluid, pleural effusion, seminal fluid, sputum, nipple aspirate, post-operative seroma, saliva, amniotic fluid, tears or wound drainage fluid. In accordance with the methods of the invention, the sample obtained from the patient is assessed for autoantibodies, also referred to herein as “autoantibody biomarkers”. The autoantibody biomarkers analysed in accordance with this first aspect of the invention can be used to select COVID-19 patients for treatment on the basis that the autoantibodies have been linked to one or more immune-related disorders.

In a first study, the inventors identified 22 antigens that were determined to have elevated levels of autoantibodies in COVID-19 patient samples compared to healthy controls. Accordingly, in certain embodiments, the patient sample is tested for autoantibody biomarkers that bind to one or more of the antigens selected from: MPO, APOH, CADM3, IL-6, EXOSC10, INS, MIF, MX1 , NCL, SLC30A8, SRP19, ELANE, SMD3, VEGFA, TOP1 , SSB, SOX13, TPO, SNRPD1 , DBT, CTLA4, and CHD3. In a second study, the inventors identified 12 antigens (for which there is significant overlap with the first study) that were determined to have elevated levels of autoantibodies in COVID-19 patient samples compared to healthy controls. Accordingly, in certain embodiments, the patient sample is tested for autoantibody biomarkers that bind to one or more of the antigens selected from: CHD3, CTLA4, HARS, IFNA4, INS, MIF, MX1 , RNF41 , S100A9, SRP19, TROVE2, and VEGFA. In view of these studies, the inventors hypothesise that a COVID-19 patient can be selected for treatment by determining in a sample obtained from the patient the level(s) of autoantibodies specifically binding to one or more of these 27 antigens. Accordingly, in certain embodiments, the patient sample is tested for autoantibody biomarkers that bind to one or more of the antigens selected from: MPO, APOH, CADM3, IL-6, EXOSC10, INS, MIF, MX1 , NCL, SLC30A8, SRP19, ELANE, SMD3, VEGFA, TOP1 , SSB, SOX13, TPO, SNRPD1 , DBT, CTLA4, CHD3, HARS, IFNA4, RNF41 , S100A9, and TROVE2. In certain embodiments, the patient sample is tested for autoantibody biomarkers that bind to one or more of the antigens selected from: INS, MIF, MX1 , SPR19, VEGFA, CTL4A, and CHD3.

Autoantibody biomarkers that bind to one or more of the antigens listed in these groups may be considered positive predictive biomarkers for patient selection in this aspect of the invention.

The levels of these autoantibodies have been reported as increased in COVID-19 patients. For embodiments wherein one or more of the positive predictive biomarkers listed above is analysed, a higher level of autoantibodies in the patient sample as compared with a pre-determined cut-off value identifies the patient as a patient suitable for treatment.

The sensitivity of the methods may be increased by testing for multiple autoantibodies i.e. autoantibodies that bind to multiple different antigens. In this regard, the patient sample may be tested for autoantibodies binding to panels of two or more antigens. In certain embodiments, the patient sample is tested for autoantibodies binding to a panel of two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more antigens from the above list. In certain embodiments, the patient sample is tested for autoantibodies binding to a panel of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13 or 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, or 27 antigens from the above list. In particularly preferred embodiments, the patient sample is tested for autoantibodies binding to a panel of three or more of any of the antigens listed herein. Panel embodiments as described herein are contemplated for use in all aspects of the invention.

Certain antigens on their own may not induce a significant autoantibody response in COVID-19 patients; however, specific clusters of autoantibody responses were identified. These clusters include autoantibodies specific for one or more of the 27 antigens identified as elevated in samples from COVID-19 patients compared to samples from healthy control subject, but may also include autoantibodies specific for one or more additional antigens. For instance, a “virus infection” cluster was characterized by reactivity of a preponderance of autoantibodies in an individual against immune defence proteins (MX1 , TOP1 , SP100, SNRPD1 , SNRPC, SPP1 ). Accordingly, in certain embodiments, the patient sample is tested for autoantibody biomarkers that bind to one or more of the antigens selected from: MX1 , TOP1 , SP100, SNRPD1 , SNRPC, and SPP1 . A further cluster was identified relating to targets associated with autoimmune diseases, such as systemic lupus erythematosus, and was composed of autoantibodies reactive against TROVE2, DLST, IFNA4, HARS, C3, CTLA4 and SMD3. Accordingly, in certain embodiments, the patient sample is tested for autoantibody biomarkers that bind to one or more of the antigens selected from: TROVE2, DLST, IFNA4, HARS, C3, CTLA4 and SMD3. A further cluster was identified relating to novel autoantibody targets implicated in organ-specific chronic inflammation, such as the thyroid in Graves' disease (VEGFA, PTPRN, IGF1 R and TG). Accordingly, in certain embodiments, the patient sample is tested for autoantibody biomarkers that bind to one or more of the antigens selected from: VEGFA, PTPRN, IGF1 R and TG. A further cluster was identified relating to targets implicated in type I diabetes (INS, S100A9, ICA1 ). Accordingly, in certain embodiments, the patient sample is tested for autoantibody biomarkers that bind to one or more of the antigens selected from: INS, S100A9, and ICA1 .

In certain embodiments, the methods in accordance with the first aspect of the invention may involve the analysis of autoantibody levels for autoantibody biomarkers binding to any combination of antigens described in the context of this first aspect of the invention.

The inventors have identified a specific autoantibody profile in COVID-19 patients that correlate with pre-existing clinical and symptom traits. Interesting, the inventors also found that disease- associated autoantibodies are not only elevated in COVID-19 patients with pre-existing conditions, but are also elevated in COVID-19 patients that do not present with the condition. The presence of disease-associated autoantibodies may therefore be useful as a prognostic marker that precedes clinical manifestation. For example, the inventors found that the diabetes- associated autoantibodies INS, SLC30A8 and SOX13 are elevated in COVID-19 patients with pre-existing diabetes and also in COVID-19 patients with no known history of the disease. These results suggest the potential induction of diabetes as a consequence of SARS CoV-2 infection.

Accordingly, the inventors hypothesise that serological tests can be used to predict the development of disease and/or severity of symptoms and allow for early monitoring and medical intervention.

In certain embodiments, the COVID-19 patient has or is at risk of developing one or more of the following disorders: acute respiratory distress syndrome (ARDS), septic shock, refractory metabolic acidosis, multiple organ failure, diabetes, vasculitis, myositis, systemic sclerosis, multiple sclerosis, chronic pulmonary diseases, systemic lupus erythematosus, Guillain Barre syndrome, rheumatoid arthritis, Sjogren’s syndrome, mixed connective tissue disease, idiopathic inflammatory myopathies, and coagulation disorders including but not limited to venous thromboembolism, anti-phospholipid syndrome and catastrophic anti-phospholipid syndrome. In certain preferred embodiments, the COVID-19 patient has or is at risk of developing acute respiratory distress syndrome (ARDS).

In certain embodiments, the patient is asymptomatic. In certain embodiments, the patient is symptomatic. In certain embodiments, the patient exhibits one or more symptoms selected from the following: pneumonia, breathing difficulties, dyspnea, pleuritic chest pain, low blood oxygen levels, hemoptysis, syncope, high blood pressure, pulmonary embolism, deep vein thrombosis, muscle weakness, joint pain, inflammation, chills, conjunctivitis, dry cough, productive cough, diarrhoea or other gastrointestinal symptoms, fatigue, fever, headache, loss of appetite, muscle aches, nasal congestion, nausea, vomiting, runny nose, shortness of breath, skin changes, dermatomyositis, loss of smell and/or taste, sneezing, sore throat, and stroke symptoms. In certain embodiments, the treatment alleviates one or more of these symptoms. In certain embodiments, the treatment prevents or reduces the risk of developing one or more of these symptoms. In certain embodiments, the patient has mild symptoms. In certain embodiments, the patient exhibits up to seven distinct symptoms. In certain embodiments, the patient has severe symptoms. In certain embodiments, the patient exhibits more than seven distinct symptoms. For some people, COVID-19 can cause symptoms that last weeks or months after the infection has gone. This is sometimes called post-COVID-19 syndrome or "long COVID". In certain embodiments, the patient has long COVID.

In certain embodiments, the treatment is selected from: an anti-severe acute respiratory syndrome monoclonal antibody, such as bamlanivimab, casirivimab, imdevimab and etesevimab, or any combination thereof; an antiviral agent such as Remdesivir; a corticosteroid such as dexamethasone, a kinase inhibitor, a cytokine inhibitor, an interleukin (IL)-6 inhibitor; and an anti- coagulation drug such as heparin.

In certain embodiments, the COVID-19 patient has an increased level of autoantibodies specifically binding to insulin (INS), SOX13 and/or SLC30A8. In certain embodiments, an increased level of autoantibodies specifically binding to INS, SOX13 and/or SLC30A8 may be indicative of diabetes or risk of developing diabetes. In certain embodiments, the treatment may target INS, SOX13 and/or SLC30A8. In certain embodiments, the treatment may treat diabetes, and/or the treatment may alleviate one or more symptoms associated with diabetes. For instance, the treatment may lower blood glucose levels. In certain embodiments, the treatment is any known treatment for diabetes.

In certain embodiments, the COVID-19 patient has an increased level of autoantibodies specifically binding to IL-6, SNRPD1 and/or MX1. In certain embodiments, an increased level of autoantibodies specifically binding to IL-6, SNRPD1 and/or MX1 may be indicative of systemic lupus erythematosus (SLE) or risk of developing SLE. In certain embodiments, the treatment may be an inhibitor of IL-6 and/or may decrease the levels of IL-6. In certain embodiments, the treatment may be an inhibitor of SNRPD1 and/or may decrease the levels of SNRPD1 . In certain embodiments, the treatment may be an inhibitor of MX1 and/or may decrease the levels of MX1 . In certain embodiments, the treatment may treat SLE, or may treat one or more of the symptoms of SLE. In certain embodiments, the treatment is any known treatment for SLE.

In certain embodiments, the COVID-19 patient has acute respiratory distress syndrome (ARDS) or is at risk of developing ARDS. In certain embodiments, the treatment may treat ARDS, or may treat one or more of the symptoms of ARDS. In certain embodiments, the treatment is any known treatment for ARDS. In certain embodiments, the treatment is an anti-severe acute respiratory syndrome monoclonal antibody, such as bamlanivimab; casirivimab, imdevimab and etesevimab, or any combination thereof.

In certain embodiments, the COVID-19 patient has an increased level of autoantibodies specifically binding to APOH and/or VEGFA. In certain embodiments, an increased level of autoantibodies specifically binding to APOH and/or VEGFA may be indicative of a coagulation disorder or risk of developing a coagulation disorder, such as anti-phospholipid syndrome. In certain embodiments, the treatment may be an inhibitor of APOH and/or may decrease the levels of APOH. In certain embodiments, the treatment may be an inhibitor of VEGFA and/or may decrease the levels of VEGFA. In certain embodiments, the COVID-19 patient has a coagulation disorder, or is at risk of developing a coagulation disorder, such as anti-phospholipid syndrome. In certain embodiments, the treatment may treat the coagulation disorder, or may treat one or more of the symptoms of the coagulation disorder. In certain embodiments, the treatment is any known treatment for coagulation disorders, such as heparin.

In certain embodiments, the COVID-19 patient has an increased level of autoantibodies specifically binding to MPO and/or ELANE. In certain embodiments, an increased level of autoantibodies specifically binding to MPO and/or ELANE may be indicative of vasculitis or risk of developing vasculitis. In certain embodiments, the treatment may be an inhibitor of MPO and/or may decrease the levels of MPO. In certain embodiments, the treatment may be an inhibitor of ELANE and/or may decrease the levels of ELANE. In certain embodiments, the treatment may treat vasculitis, or may treat one or more of the symptoms of vasculitis. In certain embodiments, the treatment is any known treatment for vasculitis.

In certain embodiments, the COVID-19 patient has an increased level of autoantibodies specifically binding to EXOSC10. In certain embodiments, an increased level of autoantibodies specifically binding to EXOSC10 may be indicative of myositis or systemic sclerosis or risk of developing myositis or systemic sclerosis. In certain embodiments, the treatment may be an inhibitor of EXOSC10 and/or may decrease the levels of EXOSC10. In certain embodiments, the treatment may treat myositis or systemic sclerosis, or may treat one or more of the symptoms of myositis or systemic sclerosis. In certain embodiments, the treatment is any known treatment for myositis or systemic sclerosis.

In certain embodiments, the COVID-19 patient has an increased level of autoantibodies specifically binding to CHD3 and/or MDA5. In certain embodiments, an increased level of autoantibodies specifically binding to CHD3 and/or MDA5 may be indicative of dermatomyositis or risk of developing dermatomyositis. In certain embodiments, the treatment may be an inhibitor of CHD3 and/or may decrease the levels of CHD3. In certain embodiments, the treatment may be an inhibitor of MDA5 and/or may decrease the levels of MDA5. In certain embodiments, the treatment may treat dermatomyositis, or may treat one or more of the symptoms of dermatomyositis. In certain embodiments, the treatment is any known treatment for dermatomyositis.

In certain embodiments, the COVID-19 patient has an increased level of autoantibodies specifically binding to SRP19. In certain embodiments, an increased level of autoantibodies specifically binding to SRP19 may be indicative of shortness of breath or risk of developing shortness of breath. In certain embodiments, the treatment may be an inhibitor of SRP19 and/or may decrease the levels of SRP19. In certain embodiments, the treatment may treat shortness of breath. In certain embodiments, the treatment is any known treatment for shortness of breath. In certain embodiments, the COVID-19 patient has an increased level of autoantibodies specifically binding to IFNA4. In certain embodiments, an increased level of autoantibodies specifically binding to IFNA4 may be indicative of diarrhoea or risk of developing diarrhoea. In certain embodiments, the treatment may be an inhibitor of IFNA4 and/or may decrease the levels of IFNA4. In certain embodiments, the treatment may treat diarrhoea. In certain embodiments, the treatment is any known treatment for diarrhoea.

In certain embodiments, the COVID-19 patient has an increased level of autoantibodies specifically binding to SNRPB, IL17A and/or APOH In certain embodiments, an increased level of autoantibodies specifically binding to SNRPB, IL17A and/or APOH may be indicative of flu-like symptoms, such as dry cough. In certain embodiments, the treatment may be an inhibitor of SNRPB, IL17A and/or APOH, and/or may decrease the levels of SNRPB, IL17A and/or APOH. In certain embodiments, the treatment may treat flu-like symptoms.

In certain embodiments, the COVID-19 patient has an increased level of autoantibodies specifically binding to SOX13 In certain embodiments, an increased level of autoantibodies specifically binding to SOX13 may be indicative of cold-like symptoms, such as runny nose. In certain embodiments, the treatment may be an inhibitor of SOX13, and/or may decrease the levels of SOX13. In certain embodiments, the treatment may treat cold-like symptoms.

In certain embodiments, the COVID-19 patient has an increased level of autoantibodies specifically binding to GRP and/or NCL In certain embodiments, an increased level of autoantibodies specifically binding to GRP and/or NCL may be indicative of gastrointestinal symptoms, such as nausea. In certain embodiments, the treatment may be an inhibitor of GRP and/or NCL, and/or may decrease the levels of GRP and/or NCL. In certain embodiments, the treatment may treat gastrointestinal symptoms.

In certain embodiments, the COVID-19 patient has an increased level of autoantibodies specifically binding to SNRPB and/or GRP In certain embodiments, an increased level of autoantibodies specifically binding to SNRPB and/or GRP may be indicative of chest pain. In certain embodiments, the treatment may be an inhibitor of SNRPB and/or GRP, and/or may decrease the levels of SNRPB and/or GRP. In certain embodiments, the treatment may treat chest pain. Methods of predicting responsiveness to treatment

In a further aspect, the present invention provides methods of predicting a COVID-19 patient’s responsiveness to treatment. The methods of this further aspect comprises a step of analysing a sample obtained from a COVID-19 patient to determine the levels of autoantibodies specifically binding to one or more target antigens. The autoantibodies analysed in accordance with this further aspect of the invention serve as biomarkers of clinical response.

Accordingly, there is provided a method of predicting a COVID-19 patient’s responsiveness to treatment, the method comprising:

(a) determining in a sample obtained from the patient the level(s) of autoantibodies specifically binding to one or more antigens selected from MPO, APOH, CADM3, IL-6, EXOSC10, INS, MIF, MX1 , NCL, SLC30A8, SRP19, ELANE, SMD3, VEGFA, TOP1 , SSB, SOX13, TPO, SNRPD1 , DPT, CTLA4, CHD3, HARS, IFNA4, RNF41 , S100A9, and TROVE2; and

(b) comparing the level(s) of autoantibodies determined in (a) with a predetermined cut-off value for autoantibodies specifically binding to the same one or more antigens, wherein if the level of autoantibodies determined in the patient sample is higher than the predetermined cut-off value, improved responsiveness is predicted.

In certain embodiments, the method comprises (a) determining the level(s) of autoantibodies specifically binding to one or more, or all, of the antigens selected from MPO, APOH, CADM3, IL- 6, EXOSC10, INS, MIF, MX1 , NCL, SLC30A8, SRP19, ELANE, SMD3, VEGFA, TOP1 , SSB, SOX13, TPO, SNRPD1 , DBT, CTLA4,and CHD3.

In certain embodiments, the method comprises (a) determining the level(s) of autoantibodies specifically binding to one or more, or all, of the antigens selected from INS, MIF, MX1 , SPR19, VEGFA, CTL4A, CHD3, HARS, IFNA4, RNF41 , S100A9, and TROVE2.

In certain embodiments, the method comprises (a) determining the level(s) of autoantibodies specifically binding to one or more, or all, of the antigens selected from INS, MIF, MX1 , SPR19, VEGFA, CTL4A, and CHD3.

The steps of the method of this further aspect are similar to the steps described above for the methods in accordance with the first aspect of the invention. As such, all embodiments pertaining to the first aspect of the invention are equally applicable to this further aspect of the invention. In particular, these embodiments pertain to patients selected for testing, the nature of the patient sample, and the methods by which the autoantibody levels may be determined in the patient sample. Methods of predicting risk of developino certain disorders

In a further aspect, the present invention provides methods of predicting a COVID-19 patient’s risk of developing certain disorders. For instance, in certain preferred embodiments, the present invention provides a method of predicting a COVID-19 patient’s risk of developing a coagulation disorder.

Accordingly, there is provided a method of predicting the risk of a COVID-19 patient developing coagulation disorders including but not limited to venous thromboembolism, anti-phospholipid syndrome and catastrophic anti-phospholipid syndrome, the method comprising:

(a) determining in a sample obtained from the patient the level(s) of autoantibodies specifically binding to one or more antigens selected from MPO, APOH, CADM3, IL-6, EXOSC10, INS, MIF, MX1 , NCL, SLC30A8, SRP19, ELANE, SMD3, VEGFA, TOP1 , SSB, SOX13, TPO, SNRPD1 , DPT, CTLA4, and CHD3; and

In certain embodiments, there is provided a method of predicting the risk of a COVID-19 patient developing a coagulation disorders including but not limited to venous thromboembolism, anti- phospholipid syndrome and catastrophic anti-phospholipid syndrome, the method comprising:

The invention provides a method of predicting the risk of a COVID-19 patient developing diabetes, the method comprising:

The invention provides a method of predicting the risk of a COVID-19 patient developing systemic lupus erythematosus, the method comprising:

(a) determining in a sample obtained from the patient the level(s) of autoantibodies specifically binding to one or more antigens selected from IL-6, SNRPD1 and MX1 ; and

The invention provides a method of predicting the risk of a COVID-19 patient developing vasculitis, the method comprising:

The invention provides a method of predicting the risk of a COVID-19 patient developing myositis or systemic sclerosis, the method comprising:

(b) comparing the level of autoantibodies determined in (a) with a predetermined cut-off value for autoantibodies specifically binding to the same antigen, wherein if the level of autoantibodies determined in the patient sample is higher than the predetermined cut-off value, the patient is identified as having an increased risk of developing myositis or systemic sclerosis.

The steps of the methods of this further aspect are similar to the steps described above for the methods in accordance with the first aspect of the invention. As such, all embodiments pertaining to the first aspect of the invention are equally applicable to this further aspect of the invention. In particular, these embodiments pertain to patients selected for testing, the nature of the patient sample, and the methods by which the autoantibody levels may be determined in the patient sample.

The present inventors have found that the autoantibody response exhibits sex-specific patterns of frequency distribution as well as associations with symptomatology and variations in symptom burden. Accordingly, the methods of the invention, i.e. the antigens assayed, may be tailored depending on the sex of the patient.

In certain embodiments, the patient is male and the patient sample is tested for autoantibody biomarkers that bind to one or more of the antigens selected from: SNRPB, CHD4, and CHGA.

In certain embodiments, the COVID-19 patient has an increased level of autoantibodies specifically binding to SNRPB, CHD4, and/or CHGA. In certain embodiments, an increased level of autoantibodies specifically binding to SNRPB, CHD4, and/or CHGA may be indicative of productive cough and/or nasal congestion. In certain embodiments, the treatment may be an inhibitor of SNRPB, CHD4, and/or CHGA, and/or may decrease the levels of SNRPB, CHD4, and/or CHGA. In certain embodiments, the treatment may treat productive cough and/or nasal congestion.

In certain embodiments, the patient is female and the patient sample is tested for autoantibody biomarkers that bind to one or more of the antigens selected from: DBT and/or ROS1 .

In certain embodiments, the COVID-19 patient has an increased level of autoantibodies specifically binding to DBT and/or ROS1 . In certain embodiments, an increased level of autoantibodies specifically binding to DBT and/or ROS1 may be indicative of dry cough and/or loss of appetite. In certain embodiments, the treatment may be an inhibitor of DBT and/or ROS1 , and/or may decrease the levels of DBT and/or ROS1 . In certain embodiments, the treatment may treat dry cough and/or loss of appetite.

In certain embodiments, the patient is male and the patient sample is tested for autoantibody biomarkers that bind to one or more of the antigens selected from: CXCL8, HIST1 H41 , CTSG, CSF2, SNRNP70, IL17A, UBTF, AK4, ICA1 , NCL, TOP1 , CHD4, RNF41 , IL4, EIF4H, ANXA2, MX1 , VEGFA, SNRPA, PRTN3, CADM3, IL6, INS, SSB, SNRPD1 , TGFB1 , RPLP2, HARS, CTLA4, IGF1 R, IFNA4, NMP1 , C3, TG, TROVE2, S100A9, SET, SLC30A8, DLAT, VIM, TRIM21 , EXOSC10, SOX13, ELANE, RAE1 , DBT, AQP4, LYZ, IFNA6, ACE2, SRP19, MDA5, MIF, ECE1 , MOV10, GRP, CHGA, APOH, and SNRPB. In certain embodiments, the patient is male and the patient sample is tested for autoantibody biomarkers that bind to one or more of the antigens selected from: SNRPB, MOV10, CHGA, CHD4, HIST1 H4A, ACE2, IFNA6, LYZ, and RNF41 .

In certain embodiments, the patient is male and the patient sample is tested for autoantibody biomarkers that bind to C3 and TG.

In certain embodiments, the patient is male and the patient sample is tested for autoantibody biomarkers that bind to LYZ and IFNA6.

In certain embodiments, the patient is female and the patient sample is tested for autoantibody biomarkers that bind to one or more of the antigens selected from: INS, ELANE, MOV10, SP100, PTPRN, CHGA, S100A8, SNRPC, ECE1 , HARS, CENPB, SOX13, PRTN3, CHD3, SRP54, CHD4, DBT, TRIM33, TGFB1 , SMD3, UBTF, TOP1 , AQP4, ROS1 , RNF41 , GAD65, IL10,

S100A9, TPO, SET, HIST1 H4A, MX1 , EXOSC10, IFNA2, IGF1 R, C3, RPLP2 and TG.

In certain embodiments, the patient is female and the patient sample is tested for autoantibody biomarkers that bind to one or more of the antigens selected from: DBT and ROS1 .

In certain embodiments, the patient is female and the patient sample is tested for autoantibody biomarkers that bind to ECE1 and HARS.

In certain embodiments, the patient is female and the patient sample is tested for autoantibody biomarkers that bind to SMD3, UBTF and TOP1 .

In certain embodiments, the patient is female and the patient sample is tested for autoantibody biomarkers that bind to IL10 and S100A9.

In certain embodiments, the patient is female and the patient sample is tested for autoantibody biomarkers that bind to HIST1 H4A, MX1 and EXOSC10.

In certain embodiments, the patient is female and the patient sample is tested for autoantibody biomarkers that bind to RPLP2 and TG.

C. Assay methodology

The methods require the level of each autoantibody biomarker in the patient sample to be determined or measured. This measurement can be made using any suitable immunoassay technique for the detection of autoantibodies. The general features of immunoassays, for example ELISA, radio-immunoassays and the like, are well known to those skilled in the art (see Immunoassay, E. Diamandis and T. Christopoulus, Academic Press, Inc., San Diego, CA, 1996, the contents of which are incorporated herein by reference). Immunoassays for the detection of autoantibodies having a particular immunological specificity generally require the use of a reagent (antigen) that exhibits specific immunological reactivity with a relevant autoantibody. Depending on the format of the assay, this antigen may be immobilised on a solid support. A test sample is brought into contact with the antigen and if autoantibodies of the required immunological specificity are present in the sample they will immunologically react with the antigen to form antigen / autoantibody complexes which may then be detected or quantitatively measured. The immunoassay used to detect autoantibodies according to the invention may be based on standard techniques known in the art.

The detection of autoantibody may be carried out in any suitable format which enables contact between the sample suspected of containing the autoantibody (the “test sample”) and the antigen. Conveniently, contact between the patient sample and the antigen may take place in separate reaction chambers such as the wells of a microtitre plate, allowing different antigens or different amounts of antigen to be assayed in parallel, if required. For immunoassays in which varying amounts of the antigen are used, these can be coated onto the wells of the microtitre plate by preparing serial dilutions from a stock of antigen across the wells of the microtitre plate. The stock of antigen may be of known or unknown concentration. Aliquots of the test sample may then be added to the wells of the plate, with the volume and dilution of the test sample kept constant in each well. The absolute amounts of antigen added to the wells of the microtitre plate may vary depending on such factors as the nature of the target autoantibody, the nature of the test sample, dilution of the test sample etc. as will be appreciated by those skilled in the art. Generally, the amounts of antigen and the dilution of the test sample will be selected so as to produce a range of signal strengths which fall within the acceptable detection range of the read- out chosen for detection of antigen / autoantibody binding in the method.

In some embodiments, a patient sample, preferably serum, is contacted with a sample of the antigen immobilised at a discrete location or reaction site on a solid support. Solid supports include but are not limited to filters, membranes, beads (for example magnetic or fluorophore- labelled beads), small plates, silicon wafers, glass, metal, plastic, chips, mass spectrometry targets or matrices. In some embodiments, the solid support is a bead. In some embodiments, the bead is a microsphere.

For embodiments wherein autoantibodies that specifically bind to multiple antigens are being detected, the antigens may be coupled to multiple different solid supports and then arranged onto an array. The array may be in the form of a "protein array", wherein a protein array refers to the systematic arrangement of antigens on a solid support, wherein the antigens are proteins or peptides or parts thereof. Protein arrays or “microarrays” may be used to perform multiple assays for autoantibodies of different specificity on a single sample in parallel. This can be done using arrays comprising multiple antigens or sets of antigens.

In certain embodiments, and depending on the precise nature of the assay in which it will be used, the antigen may comprise a naturally occurring protein, or fragment thereof, linked to one or more further molecules which impart some desirable characteristic not naturally present in the protein. For example, the protein or fragment may be conjugated to a revealing label, such as for example a fluorescent label, coloured label, luminescent label, radiolabel or heavy metal such as colloidal gold. In other embodiments the protein or fragment may be expressed as a recombinantly produced fusion protein. By way of example, fusion proteins may include a tag peptide at the N- or C- terminus to assist in purification of the recombinantly expressed antigen.

The level of any given autoantibody biomarker in the patient sample may be determined by measuring the degree of binding between the autoantibody present in the sample and the antigen. Binding between autoantibody and antigen can be visualized, for example, by means of fluorescence labelling, biotinylation, radio-isotope labelling or colloid gold or latex particle labelling. Suitable techniques are known to those skilled in the art and may be employed in the methods of the invention. Bound autoantibodies may be detected with the aid of secondary antibodies, which are labelled using commercially available reporter molecules (for example Cy, Alexa, Dyomics, FITC or similar fluorescent dyes, colloidal gold or latex particles), or with reporter enzymes, such as alkaline phosphatase, horseradish peroxidase, etc. and the corresponding colorimetric, fluorescent or chemoluminescent substrates. A read-out can be determined, for example by means of a microarray laser scanner, a CCD camera or visually.

In a most preferred embodiment the immunoassay used to detect autoantibodies in accordance with the invention is an ELISA. ELISAs are generally well known in the art. In a typical indirect ELISA an antigen having specificity for the autoantibodies under test is immobilised on a solid surface (e.g. the wells of a standard microtiter assay plate, or the surface of a microbead or a microarray) and a sample to be tested for the presence of autoantibodies is brought into contact with the immobilised antigen. Any autoantibodies of the desired specificity present in the sample will bind to the immobilised antigen. The bound antigen / autoantibody complexes may then be detected using any suitable method. In one preferred embodiment a labelled secondary anti- human immunoglobulin antibody, which specifically recognises an epitope common to one or more classes of human immunoglobulins, is used to detect the antigen / autoantibody complexes. Typically the secondary antibody will be anti-lgG or anti-lgM. The secondary antibody is usually labelled with a detectable marker, typically an enzyme marker such as, for example, peroxidase or alkaline phosphatase, allowing quantitative detection by the addition of a substrate for the enzyme which generates a detectable product, for example a coloured, chemiluminescent or fluorescent product. Other types of detectable labels known in the art may be used with equivalent effect.

In a further step of the methods of the invention, the level or levels of autoantibody biomarkers determined in the patient sample are compared with pre-determined cut-off values for autoantibodies specifically binding to the same antigens. The pre-determined cut-off value may be different for different autoantibodies. The pre-determined cut-off value will have been calculated or may be calculated based on the analysis of a control cohort. In particular, the pre- determined cut-off for any given autoantibody biomarker will typically be the average level of autoantibodies determined in a control cohort.

Typically the “control cohort” may be a control cohort of healthy individuals. The pre-determined cut-off values against which the autoantibody levels are compared, in accordance with these aspects of the invention, will have been calculated or may be calculated based on the analysis of healthy cohorts of mammalian subjects, preferably human subjects. The pre-determined cut-off value may be different for different autoantibodies. As reported herein, the autoantibody biomarkers used in the methods of these aspects of the invention are increased in COVID-19 patients as compared with healthy controls (see Table 3). As such, these autoantibodies can be analysed in samples obtained from mammalian subjects and the levels compared with pre- determined cut-off values determined for healthy cohorts of subjects. The “healthy cohort” from which the pre-determined cut-off value is calculated for any given autoantibody may be any reasonably-sized cohort of healthy subjects, for example at least 50 subjects, at least 100 subjects, at least 200 subjects, at least 500 subjects. The pre-determined cut-off value against which the autoantibodies of the test sample are compared in accordance with the methods of the invention may be pre-determined based upon a particular healthy cohort matched to the subject under test. For example, the pre-determined cut-off value for autoantibodies binding to any given antigen may be determined on the basis of a healthy cohort matched for any one of the following criteria with the subject under test: age, gender, ethnic origin. The pre-determined cut-off value for any given autoantibody will typically be the average level of autoantibodies calculated for the healthy cohort of mammalian subjects.

Alternatively, the pre-determined cut-off value will have been calculated or may be calculated based on the analysis of a control cohort of COVID-19 patients. In particular, the pre-determined cut-off for any given autoantibody biomarker may be the average level of autoantibodies determined in a control cohort of COVID-19 patients. The pre-determined cut-off value against which the autoantibodies of the COVID-19 patient sample are compared in accordance with the methods of the invention may be pre-determined based upon a particular control cohort of COVID-19 patients matched to the patient under test. Thus, in certain embodiments, the control cohort may be a cohort of COVID-19 patients matched for any one of the following criteria with the patient under test: type of symptom/disease; disease severity, age; gender; use of pre- existing treatment. The control cohort of COVID-19 patients from which the pre-determined cut- off value is calculated for any given antigen may be any reasonably-sized cohort of COVID-19 patients, for example at least 50 patients, at least 100 patients, at least 200 patients, at least 500 patients.

Once the level of autoantibodies in the patient sample has been compared with the pre- determined cut-off value for autoantibodies specifically binding to the same target antigen, an assessment is made as to whether the level of autoantibodies in the patient sample is higher, than the predetermined cut-off value. As reported herein, this comparison allows a decision to be made as to whether or not the patient is selected for treatment.

For embodiments wherein the autoantibody is assessed as “higher” than the pre-determined cut- off value, a threshold may be applied. For example, a threshold may be applied such that the autoantibodies in the patient sample must be at least 1 .5 fold higher or lower, at least 2 fold higher or lower, at least 2.5 fold higher or lower than the pre-determined cut-off value for the patient to be selected for treatment. A threshold may be applied such that the autoantibodies in the patient sample must be at least 10%, at least 20%, at least 50% higher than the pre- determined cut-off value for the patient to be selected for treatment.

In some embodiments wherein the method involves determining the levels of autoantibodies binding to multiple antigens, the patient may be selected for treatment if the levels of autoantibodies specifically binding to each antigen tested are higher than the pre-determined cut- off values for autoantibodies specifically binding to the corresponding antigens.

EXAMPLES

The invention will now be further understood with reference to the following non-limiting examples. The use of these and other examples anywhere in the specification is illustrative only and in no way limits the scope and meaning of the invention or of any exemplified term. Likewise, the invention is not limited to any particular preferred embodiments described here. Indeed, many modifications and variations of the invention may be apparent to those skilled in the art upon reading this specification, and such variations can be made without departing from the invention in spirit or in scope. The invention is therefore to be limited only by the terms of the appended claims along with the full scope of equivalents to which those claims are entitled.

EXAMPLE 1 :

Production of recombinant autoantiaens

Recombinant antigens were produced in Escherichia coli. Five cDNA libraries originating from different human tissues (fetal brain, colon, lung, liver, CD4-induced and non-induced T cells) were used for the recombinant production of human antigens. All of these cDNA libraries were oligo(dT)-primed, containing the coding region for an N-terminally located hexa-histidine-tag and were under transcriptional control of the lactose inducible promoter (from E. coli). Sequence integrity of the cDNA libraries was confirmed by 5’ DNA sequencing. Additionally, expression clones representing the full-length sequence derived from the human ORFeome collection were included. Individual antigens were designed in silico, synthesized chemically (Life Technologies, Carlsbad, USA) and cloned into the expression vector pQE30-NST fused to the coding region for the N-terminal-located His6-tag. Recombinant gene expression was performed in E. coli SCSI cells carrying plasmid pSE111 for improved expression of human genes. A subset was expressed with a BirA and hexa-histidine-tag in E. coli BL21 (Macdonald et al., 2012). Cells were cultivated in 200 ml auto-induction medium (Overnight Express auto-induction medium, Merck, Darmstadt, Germany) overnight and harvested by centrifugation. Bacterial pellets were lysed by resuspension in 15 ml lysis buffer (6 M guanidinium-HCI, 0.1 M NaFhPO^ 0.01 M Tris-HCI, pH 8.0).

Soluble proteins were affinity-purified after binding to Protino® Ni-IDA 1000 Funnel Column (Macherey-Nagel, Diiren, Germany). Columns were washed with 8 ml washing buffer (8 M urea, 0.1 M NaH₂P0₄, 0.01 M Tris-HCI, pH 6.3). Proteins were eluted in 3 ml elution buffer (6 M urea, 0.1 M NaH₂P0₄, 0.01 M Tris-HCI, 0.5 % (w/v) trehalose pH 4.5). Each protein preparation was transferred into 2D-barcoded tubes, lyophilized and stored at -20°C.

Selection of antigens and design of the COVID-19 screen

A bead-based array was designed to screen for autoantibodies binding to proteins playing a role in autoimmune signaling pathways. In total, 97 potential antigens (Table 1 ) were selected for screening of COVID-19 patient samples to be compared with health controls collected prior to the SARS-CoV-2 outbreak. Table 1. Antigens selected for screening of COVID-19 patient samples

No

1

2

3

4

5

6

8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

The Gene ID and Gene Symbol can be found on the NCBI website available at www.ncbi.nlm.nih.gov. More information can be found by accessing the NCBI website and entering the Gene ID or Gene Symbol, for instance.

Coupling of antigens to beads

For the production of bead-based arrays (BBA), the proteins were coupled to magnetic carboxylated color-coded beads (MagPlex™ microspheres, Luminex Corporation, Austin, TX, USA). The manufacturer’s protocol for coupling proteins to MagPlex™ microspheres was adapted to use liquid handling systems. A semi-automated coupling procedure of one NSL beadset encompassed 96 single, separate coupling reactions, which were carried out in a 96- well plate. For each single coupling reaction, up to 12.5 pg antigen and 8.8 c 10⁵ MagPlex™ beads of one color region (ID) were used. All liquid handling steps were carried out by either an eight-channel pipetting system (Starlet, Hamilton Robotics, Bonaduz, Switzerland) or a 96- channel pipetting system (Evo Freedom 150, Tecan, Mannderdorf, Switzerland). For semi- automated coupling, antigens were dissolved in H2O, and aliquots of 60 mI were transferred from 2D barcode tubes to 96-well plates. MagPlex™ microspheres were homogeneously resuspended and each bead ID was transferred in one well of a 96-well plate. The 96-well plates containing the microspheres were placed on a magnetic separator (LifeSep™, Dexter Magnetic Technologies Inc., Elk Grove Village, USA) to sediment the beads for washing steps and on a microtiter plate shaker (MTS2/4, IKA) to facilitate permanent mixing for incubation steps.

For coupling, the microspheres were washed three times with activation buffer (100 mM NaH₂PO₄, pH 6.2) and resuspended in 120 μI activation buffer. To obtain reactive sulfo-NHS- ester intermediates, 15 mI 1 -ethly-3-(3-dimethlyaminopropyl) carbodiimide (50 mg/ml) and 15 mI N-hydroxy-succinimide (50 mg/ml) were applied to microspheres. After 20 minutes incubation (900 rpm, room temperature (RT)) the microspheres were washed three times with coupling buffer (50 mM MES, pH 5.0) and resuspended in 65 mI coupling buffer. Immediately, 60 mI antigen solution was added to reactive microspheres and coupling took place over 120 minutes under permanent mixing (900 rpm, RT). After three wash cycles using washing buffer (PBS, 0.1 % Tween20) coupled beads were resuspended in blocking buffer (PBS, 1 % BSA, 0.05 % ProClin300), incubated for 20 minutes (900 rpm, RT) and then transferred to be maintained at 4- 8 °C for 12-72 h.

Finally, a multiplex BBA was produced by pooling 96 antigen-coupled beads.

Incubation of serum samples with antigen-coupled beads

Serum samples were transferred to 2D barcode tubes and a 1 :100 serum dilution was prepared with assay buffer (PBS, 0.5 % BSA, 10 % E. coli lysate, 50 % Low-Cross buffer (Candor Technologies, Nurnberg, Germany)) in 96-well plates. The serum dilutions were first incubated for 20 minutes to neutralize any human IgG eventually directed against E. coli proteins. The BBA was sonicated for 5 minutes and the bead mix was distributed in 96-well plates. After three wash cycles with washing buffer (PBS, 0.05 % Tween20) serum dilutions (50 mI) were added to the bead mix and incubated for 22 h (900 rpm, 4-8°C). Supernatants were removed from the beads by three wash cycles, and secondary R-phycoerythrin-labeled antibody (5 μg/ml, goat anti- human, Dianova, Hamburg, Germany) was added for a final incubation of 45 minutes (900 rpm, RT). The beads were washed three times with washing buffer (PBS, 0.1 % Tween20) and resuspended in 100 mI sheath fluid (Luminex Corporation). Subsequently, beads were analyzed in a FlexMap3D device for fluorescent signal readout (DD gate 7.500-15.000; sample size: 80 mI; 1000 events per bead ID; timeout 60 sec). The binding events were displayed as median fluorescence intensity (MFI). Measurements were disregarded when low numbers of bead events (<30 beads) were counted per bead ID.

Statistical analysis

A filtering approach based on statistical test was applied to identify autoantibody biomarkers associated with a COVID-19 disease. The filtering approach included a variance stabilized version of the T-test called Significance Analysis of Microarrays (SAM) (Tusher et al., 2001 ) and an adapted version of a robust quantile based rank test initially invented by Wilcox et. al, 2014. The strength of differences between the two test groups was estimated via SAM D-Score as well as the number of significant quantiles and the corresponding maximal span from the quantile test. Furthermore, receiver-operating characteristics were calculated to provide partial (sensitivity fixed at a value of 90%) area under the curve (pAUC) values for each antigen in combination of its sensitivity at 90% specificity.

Collection of serum samples from patients with COVID-19 Table 2: Serum samples

Characterization of the autoantibodv response in COVID-19 patients

Figure 1 shows the heat map for the 22 antigens that were determined to have elevated levels of autoantibodies in COVID-19 patient samples compared to healthy controls.

Table 3. The statistical test results for the markers showing an elevated levels of the autoantibodies in the COVID-19 samples

The markers were selected from a panel of 97 proteins designed for the analysis of autoantibodies in COVID-19. The panel includes cytokines, lung-specific proteins, interaction partners of COVID-19 proteins, proteins with homologies to COVID-19 proteins, as well as relevant autoantibody targets in autoimmune diseases. The included proteins and their respective disease associations are listed in Table 1 .

Panels of markers

From the set referred to our best selected markers (Table 3), panels can be computed. The methodology used for panel computation is to select a number from two to all of our biomarkers and subjecting them to a method as logistic regression, or decision tree (C4.5), or random forests, or a support vector machine, or a PLS-DA computation, or a linear regression, or a logical rule combining the markers after applying a cut point. Figure 2 illustrates how all 22 markers as selected in table 3 can be combined to a full panel in a logistic regression analysis increasing predictive performance. Figure 3 complements the value of panel summarizing ROC area under the curve values in a histogram for all combinations of markers in panels of three. EXAMPLE 2:

Study design and participants

A total of 6318 active health care workers (HCW) were enrolled from Los Angeles County, California to discover a cohort of workers who had COVID-19. . For all participants, EDTA plasma specimens were transported within 1 hour of phlebotomy to the Cedars-Sinai Department of Pathology and Laboratory Medicine and underwent serology testing using the Abbott Diagnostics SARS-CoV-2 IgG chemiluminescent microparticle immunoassay (Abbott Diagnostics, Abbott Park, Illinois) against the nucleocapsid (N) antigen of the SARS-CoV-2 virus. For the current study, 177 participants who were IgG positive and had completed electronic survey forms via Research Electronic Data capture (REDCap) were included. Survey forms collected data on pre-existing traits, self-reported symptoms, and medical history characteristics (Table 4). All study participants provided written informed consent and all study protocols were approved by the Cedars-Sinai Medical Center institutional review board. Age and sex match pre- pandemic healthy serum samples (HC) were purchased from the Bavarian Red Cross (Wiesentheid, Germany) with ethical approval from the Bayerische Landesaerztekammer (Study No. 01/09) (. Additionally, to serve as a positive comparator group, SLE samples were obtained from BiolVT (West Sussex, United Kingdom).

Luminex autoantibodv array and autoantiaens design

A total of 91 protein antigens were selected for this study, with specific antigens prioritized based on their previously reported associations with SLE and viral infection. The panel includes cytokines, lung-specific proteins, protein partners that are known to interact with COVID-19 proteins, proteins with high amino acid homologies to COVID-19 proteins, as well as known auto- antigens implicated in systemic sclerosis (SSc), rheumatoid arthritis (RA), Sjogren’s syndrome (SjS), mixed connective tissue disease (MCTD) and idiopathic inflammatory myopathies (IIM) (Table 5). The following antigens were purchased from Diarect AG (Freiburg, Germany): U1 - snRNP68/70 kDa (SNRNP70), U1 -snRNP A (SNRPA), U1 -snRNP C (SNRPC), U1-RNP B/B' (SNRPB), SmD3 (SNRPD3), ribosomal phosphoprotein P0 (RPLPO), ribosomal phosphoprotein P1 (RPLP1), DNA topoisomerase I (TOP1 , Scl-70), SSA/Ro52 (TRIM21 ), myeloperoxidase (MPO), SSB/La, and PDC-E2 (DLAT). Other antigens were produced in-house using E.coli SCSI carrying plasmid pSE111 , which contains an N-terminally located hexa-histidine-tag. A subset was expressed with a BirA and hexa-histidine-tag in E.coli BL21 . Antigens were affinity-purified under denaturing conditions using Protino® Ni-IDA 1000 funnel columns (Macherey-Nagel, Diiren, Germany). The selected analytical targets can be categorized by functional protein families or their previously reported associations with immune-relevant clinical disease states (Table 5).

The SeroTag® AABs workflow was designed to profile the AABs reactivity using Luminex color- coded beads technology. Briefly, for each coupling reaction up to 12.5 pg antigen and 8.8 x 105 MagPlex™ beads per color were used. Plasma or serum samples from all subjects were diluted 1 :100 in assay buffer (PBS, 0.5% BSA, 50% Low-Cross buffer (Candor Biosciences, Wangen, Germany). Diluted samples and beads were mixed and incubated at 4-8 °C in the dark. The bound AABs were detected by addition of an anti-lgG, specific detection antibody, conjugated to the reporter dye phycoerythrin (PE). The Luminex FlexMAP3D analyzer identified and quantified each antigen-AABs reaction based on bead color and median fluorescence intensity (MFI). The background reactivity of the screen was determined using all healthy control samples. The 0.1 quantile of MFI values of all samples was calculated (<500 MFI). Median intra and inter-plate coefficient of variation (CV) was calculated by measuring reference samples in triplicate on all and each assay plates. The cut off applied for the median intra- and inter-plate CV was <30%. Matched analysis of samples from other convalescent subjects showed no differences in AAB values. Table 5. Composition of Antigen Array.

Statistical analyses

Data processing and analysis were performed using R v3.5.1 and KNIME 2.12 (https://www.knime.org/). Among the pre-pandemic non-SLE control group (HCs), a mean and standard deviation (STD) was calculated for the MFI for each of the 91 antibodies assayed. An individual was classified as having an increased AAB level if the MFI for that given AAB was at least 3 SD above the mean level in the pre-pandemic HC group. Frequencies of AABs among the groups were compared using Chi-square test. Multivariate logistic and Poisson linear regression was used to determine the odds of possessing an AAB and the number of AABs respectively. In these multivariate analyses, predictors included the different groups (SLE, HCWs relative to the HC individuals), age, gender, race, self-reported symptoms, and medical history characteristics.

Results: Cohort characteristics and symptomatology

Samples from the primary cohort of 177 previously SARS-CoV-2 infected HCW individuals in addition to the comparator cohorts comprising 54 HCs blood donors and 6 SLE patients were analysed. Twenty-one symptoms were scored based on their frequency of report by participants of the HCW survey. Overall, 13% (23/177) of the HCWs reported no symptoms, 36% (64/177) reported a mild overall symptom burden (i.e. having experienced up to 7 distinct symptom types), and 51% (90/177) reported a more than mild overall symptom burden (>7 symptoms). In the mild and the more than mild symptom burden groups, 32.8% and 51 .1% had PCR confirmed COVID- 19. The most frequently reported symptoms were fatigue (66%) and headache (62%), followed by muscle aches (57.1%), dry cough (56.5%) and nasal congestion (52.5%). The time interval between SARS-CoV-2 PCR diagnosis and IgG Abbott test acquisition did not differ between HCWs with or without AABs (data not shown).

Diversity of laG autoantibodv reactivity

There was significant increase in IgG reactivity against 12 out of 91 antigens including the CHD3, CTLA4, HARS, IFNA4, INS, MIF, MX1 , RNF41 , S100A9, SRP19, TROVE2, VEGFA when compare to HCs (Table 6). Although, the titer of anti-C3 (p=0.08), IL-6(p=0.07), ELANE (p=0.06), SMD3 (p=0.22), SP100 (p=0.22), SNRPD1 (p=0.12), and TOP1 (p=0.12) was also elevated in HWC compare to HCs, the magnitude of elevation for each individual AAB did not meet statistical significance. However, there did emerge distinct clusters of AABs that featured landmark characteristics. For instance, we identified a “virus infection” cluster that was characterized by reactivity of a preponderance of AABs against immune defense proteins (MX1 , TOP1 , SP100, SNRPD1 , SNRPC, SPP1). The next largest cluster was composed of AABs reactivities against TROVE2, DLST, IFNA4, HARS, C3, CTLA4 and SMD3. The antigens collected under these cluster exhibit the AABs associated with the autoimmune diseases, like SLE. Remarkably, in a third cluster, we also identified novel autoantibody targets implicated in organ- specific chronic inflammation, such as the thyroid in Graves' disease (VEGFA, PTPRN, IGF1 R, TG) and the islet cell in type I diabetes (INS, S100A9, ICA1).

In multivariable analyses of the primary HCW cohort, we examined pre-existing clinical and symptom traits in association with detected presence (AABs”+”) versus absence (ABBs”-“) of AABs defined based on the MIF parameter. The presence of AAB reactivity against CHD3 was associated with reported skin changes (AABs”-“ (5.3%) vs. AABs“+” (19.2%). Similarly, anti- SRP19 AABs was associated with shortness of breath symptom (AABs (28.3%) vs AABs“+”

(56%)), while anti-INS AABs was associated with reported fever (AABs (47.7%) vs. AABs“+”

(20.8%)) and early occurring symptoms (AABs”-“ (32%) vs. AABs“+” (66.7%)); anti-IFNA4 AABs were increased in association with reported diarrhoea (AABs”-“ (26.9%) vs. AABs“+” (52.4%)). IgG reactivity against CTA4 was detected more frequently in males (56.6%) than in females (31 .8%). There were no significant differences in the age, sex, race, and severity of the symptoms between HCWs who were positive or negative for the remaining AABs. Table 6. Frequency of autoantibodies in HCWs compared to HCs (non-SLE pre-pandemic samples) - number and percentage of total in brackets -bold are those autoantibodies with a p value less than 0.05

Discussion

To address whether SARS-CoV2 patients demonstrate the existence of individual specific autoantibody profiles that might be associated to autoimmune diseases or might be merely part of a naturally occurring stochastic repertoire we used multiplex Luminex assay with 91 antigens and compare our cohort to SLE positive and healthy individuals, who were not expose to SARS- CoV2. According to our knowledge, we evaluated the largest number of the protein antigens to characterize the prevalence and heterogeneity of the AABs signature in SARS-CoV-2 convalescent HCWs. We deliberately examined autoimmune reactivity to SARS-CoV-2 in the absence of extreme clinical disease to acknowledge the existence of AABs even among those who had mild-to-moderate or no symptoms during their illness, as a hallmark of ongoing long- COVID syndrome. We hypothesize that the group of seropositive but healthy individuals will develop autoimmune diseases, and the presence of AABs is a prognostic marker that precedes clinical manifestation. EXAMPLE 3:

Sex-specific differences

Amidst the millions of individuals affected directly by the pandemic, pronounced sex differences in the susceptibility and response to SARS-CoV-2 infection have been unremitting and remain poorly understood. Emerging evidence has highlighted in the potential importance of autoimmune activation in modulating not only the acute response but recovery trajectories following SARS-CoV-2 exposure. Given that assessments of immune-inflammatory activity can be sex-biased in the setting of severe COVID-19 illness, we deliberately examined sex-specific autoimmune reactivity to SARS-CoV-2 in the absence of extreme clinical disease. Using an array to detect 91 autoantigens previously linked to a range of classic autoimmune diseases (Table 5), we found a diversity of autoantibody responses that exhibited sex-specific patterns of frequency distribution as well as associations with symptomatology and variations in symptom burden.

In age-adjusted regression analyses, we examined the association of sex (female versus male) with measured plasma levels for each of the 91 autoantibodies assayed. Across the entire cohort (Table 4), the majority of assayed AABs were associated with male sex and the minority with female sex but the sex-specific frequency and magnitudes of association varied by symptom burden. Among asymptomatic individuals, the breadth and magnitude of AAB reactivity was much more pronounced in females compared to males. Notably, AABs to cytokine and chemokine antigens (IL6 and CSF2) involved in immune defence, together with lung specific proteins (gastrin release peptide (GRP) and serpin family B member 3 (SERPINB3)), were predominantly elevated in asymptomatic females. By contrast, thyroid stimulating hormone receptor (TSHR) and lysine demethylase 6B (KDMA6B), which are known primary antigens in autoimmune diseases, were highly expressed in asymptomatic males.

In age-adjusted regression analyses, we examined the sex-specific associations of distinct autoantibody levels with symptomatology. In males, among the significantly associated AABs, small nuclear ribonucleoprotein polypeptides B (SNRPB), chromodomain helicase DNA binding protein 4 (CHD4) and chromogranin A (CHGA) were the most frequently associated with the distinct symptoms of productive cough and nasal congestion. In females, the AABs to dihydrolipoamide branched chain transacylase E2 (DBT) and ROS proto-oncogene 1 , receptor tyrosine kinase (ROS1 ), were the most frequently elevated in relation to dry cough and loss of appetite. Overall profiles of AAB reactivity in relation to symptoms indicated the most frequent and significantly associated AABs in males appeared to follow an SLE-related pattern, whereas AAB profiles in females were more suggestive of poly-autoimmunity. To further investigate the AAB sub-groups and select potential discriminatory symptoms, we applied hierarchical clustering analyses to identify similar magnitudes and directions of associations across AABs. The results of sex-specific two-dimensional clustering of symptoms variables in relation to AABs are shown in Figure 26. In males, the initially identified cluster included the symptoms of muscle aches and fatigue (cluster 1), with diarrhoea and loss of appetite clustered next (cluster 2), and sneezing, runny nose, and nasal congestion also clustered together (cluster 3). In females, there were also three major clusters identified: dry cough, chills, and loss of appetite (cluster 1 ); sore throat, nausea, nasal congestion, and fever (cluster 2); and smell/taste change and shortness of breath (cluster 3). In males, clustering of AABs including C3 with TG antigens (cluster 1 ) and AABs to antigens representing LYZ and IFNA6 protein (cluster 2). In females, we found 5 clusters in total: ECE1 and HARS (cluster 1); SMD3, UBTF, and TOP1 (cluster 2); IL10 and S100A9 (cluster 3); HIST1 H4A, MX1 , and EXOSC10 (cluster 4); and RPLP2 and TG (cluster 5).

Discussion

In this study, comprehensive profiling of autoantibody activation in over 170 healthcare workers with prior SARS-CoV-2 infection revealed several important sex-specific findings of interest. First, a surprisingly large number of the diverse autoantibodies assayed were differentially activated in males compared to females. Among previously infected individuals who were asymptomatic, the breadth of autoantibody response was more prominent in women than in men; by contrast, among previously infected individuals who experienced at least a mild burden of symptoms, the extent of antibody response was far more pronounced in men. Second, we found that the autoantibody response to symptom clusters were also sex-specific, with certain autoantibody- symptom associations seen more prominently in men compared to women, across the range of symptom burden. Finally, we observed these sex-specific autoantibody associations up to 6 months following symptomatology, indicating that SAFtS-CoV-2 triggers a complement of autoantibody responses that persists over time - in a sex-specific manner and irrespective of illness severity.

Our results reveal a remarkable sex-specific prevalence and selectivity of the autoantibody response to SAFtS-CoV-2. Intriguingly, a distinct set of AABs to 59 antigens were highly correlated with reported symptoms in the male population, while another set of AABs to only 38 antigens were associated with symptoms in females (Figure 26). Notably, in males, we observed AABs associated with symptoms at a high frequency (>6 symptoms) as well as at a moderate frequency (>4 symptoms). The high frequency associated AABs included SNRPB, a ribonucleoprotein that is widely prevalent in human SLE (12). The moderate frequency associated AABs included MOV10, CHGA, CHD4, HIST1 H4A, ACE2, IFNA6, LYZ, RNF41 . In females, we observed an overall lower frequency of significant symptoms associated AABs when compared to males. The 3 most prominent symptoms in females were associated with AABs to DBT and ROS1.

The present invention is not to be limited in scope by the specific embodiments described herein. Indeed, various modifications of the invention in addition to those described herein will become apparent to those skilled in the art from the foregoing description and accompanying figures. Moreover, all aspects and embodiments of the invention described herein are considered to be broadly applicable and combinable with any and all other consistent embodiments, including those taken from other aspects of the invention (including in isolation) as appropriate. Various publications and patent applications are cited herein, the disclosures of which are incorporated by reference in their entireties.

Claims

1 . A method of selecting a COVID-19 patient for treatment, the method comprising:

(a) determining in a sample obtained from the patient the level(s) of autoantibodies specifically binding to one or more antigens selected from MPO, APOH, CADM3, IL-6, EXOSC10, INS, MIF, MX1 , NCL, SLC30A8, SRP19, ELANE, SMD3, VEGFA, TOP1 , SSB, SOX13, TPO, SNRPD1 , DBT, CTLA4, CHD3, HARS, IFNA4, RNF41 , S100A9, and TROVE2; and

2. The method of claim 1 , comprising (a) determining the levels of autoantibodies specifically binding to two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine of more, or ten or more of the antigens.

3. The method of claim 2, comprising (a) determining the levels of autoantibodies specifically binding to three or more of the antigens.

4. The method of any of claims 1-3, comprising (a) determining the level(s) of autoantibodies specifically binding to one or more, or all, of the antigens selected from MPO, APOH, CADM3, IL- 6, EXOSC10, INS, MIF, MX1 , NCL, SLC30A8, SRP19, ELANE, SMD3, VEGFA, TOP1 , SSB, SOX13, TPO, SNRPD1 , DBT, CTLA4, and CHD3.

5. The method of any of claims 1-3, comprising (a) determining the level(s) of autoantibodies specifically binding to one or more, or all, of the antigens selected from INS, MIF, MX1 , SPR19, VEGFA, CTL4A, CHD3, HARS, IFNA4, RNF41 , S100A9, and TROVE2.

6. The method of any of claims 4-5, comprising (a) determining the level(s) of autoantibodies specifically binding to one or more, or all, of the antigens selected from INS, MIF, MX1 , SPR19, VEGFA, CTL4A, and CHD3.

7. The method of any of claims 1-6, comprising (a) determining the level(s) of autoantibodies specifically binding to one or more, or all, of the antigens selected from:

(i) MX1 , SNRPD1 , TOP1 , SP100, SNRPC and SPP1 ;

(ii) TROVE2, IFNA4, HARS, CTLA4, SMD3, DLST, and C3;

(iii) VEGFA, PTPRN, IGF1 R and TG;

(iv) INS, S100A9, and ICA1 ; or (v) any combination of (i)-(iv).

8. The method of any of claims 1-7, wherein the levels of autoantibodies in the patient sample is determined by contacting the sample with a panel or array of the antigens immobilized onto a solid support.

9. The method of any of claims 1-8, wherein the predetermined cut-off value for autoantibodies is the average level of autoantibodies specifically binding to the antigen determined for a control cohort.

10. The method of any of claims 1 -9, wherein the patient sample is a serum sample.

11 . The method of any of claims 1 -10, wherein the COVID-19 patient has or is at risk of developing one or more of the following disorders: acute respiratory distress syndrome (ARDS), septic shock, refractory metabolic acidosis, multiple organ failure, diabetes, vasculitis, myositis, systemic sclerosis, multiple sclerosis, chronic pulmonary diseases, systemic lupus erythematosus, Guillain Barre syndrome, rheumatoid arthritis, Sjogren’s syndrome, mixed connective tissue disease, idiopathic inflammatory myopathies, and coagulation disorders including but not limited to venous thromboembolism, anti-phospholipid syndrome and catastrophic anti-phospholipid syndrome.

12. The method of claim 11 , wherein the COVID-19 patient has or is at risk of developing ARDS, optionally wherein the treatment is an anti-severe acute respiratory syndrome monoclonal antibody such as bamlanivimab, casirivimab, imdevimab , etesevimab, or any combination thereof.

13. The method of claim 11 , wherein the COVID-19 patient has or is at risk of developing diabetes, and the method comprises (a) determining the level(s) of autoantibodies specifically binding to INS, SOX13, and/or SLC30A8, optionally wherein the treatment inhibits or decreases the levels of INS, SOX13, and/or SLC30A8, further optionally wherein the treatment alleviates one or more symptoms associated with diabetes.

14. The method of claim 11 , wherein the COVID-19 patient has or is at risk of developing systemic lupus erythematosus, and the method comprises (a) determining the level(s) of autoantibodies specifically binding to IL6, SNRPD1 and/or MX1 , optionally wherein the treatment inhibits or decreases the levels of IL6, SNRPD1 and/or MX1 , further optionally wherein the treatment alleviates one or more symptoms associated with systemic lupus erythematosus.

15. The method of claim 11 , wherein the COVID-19 patient has or is at risk of developing a coagulation disorder, and the method comprises (a) determining the level(s) of autoantibodies specifically binding to APOH and/or VEGFA, optionally wherein the treatment inhibits or decreases the levels of APOH and/or VEGFA, optionally wherein the treatment alleviates one or more symptoms associated with the coagulation disorder, further optionally wherein the treatment is heparin.

16. The method of claim 11 , wherein the COVID-19 patient has or is at risk of developing vasculitis, and the method comprises (a) determining the level(s) of autoantibodies specifically binding to MPO and/or ELANE, optionally wherein the treatment inhibits or decreases the levels of MPO and/or ELANE, further optionally wherein the treatment alleviates one or more symptoms associated with vasculitis.

17. The method of claim 11 , wherein the COVID-19 patient has or is at risk of developing myositis or systemic sclerosis, and the method comprises (a) determining the level of autoantibodies specifically binding to EXOSC10, optionally wherein the treatment inhibits or decreases the levels of EXOSC10, further optionally wherein the treatment alleviates one or more symptoms associated with systemic sclerosis.

18. The method of any of claims 1 -17, wherein the COVID-19 patient is asymptomatic.

19. The method of any of claims 1 -17, wherein the COVID-19 patient exhibits or is at risk of developing one or more symptoms selected from the following: pneumonia, breathing difficulties, dyspnea, pleuritic chest pain, low blood oxygen levels, hemoptysis, syncope, high blood pressure, pulmonary embolism, deep vein thrombosis, muscle weakness, joint pain, inflammation, chills, conjunctivitis, dry cough, productive cough, diarrhoea or other gastrointestinal symptoms, fatigue, fever, headache, loss of appetite, muscle aches, nasal congestion, nausea, vomiting, runny nose, shortness of breath, skin changes, dermatomyositis, loss of smell and/or taste, sneezing, sore throat, and stroke symptoms.

20. The method of claim 19, wherein the COVID-19 patient exhibits or is at risk of developing dermatomyositis, and the method comprises (a) determining the level of autoantibodies specifically binding to CHD3 and/or MDA5, optionally wherein the treatment inhibits or decreases the levels of CHD3 and/or MDA5, further optionally wherein the treatment alleviates the dermatomyositis.

21 . The method of claim 19, wherein the COVID-19 patient exhibits or is at risk of developing shortness of breath, and the method comprises (a) determining the level of autoantibodies specifically binding to SRP19, optionally wherein the treatment inhibits or decreases the levels of SRP19, further optionally wherein the treatment alleviates the shortness of breath.

22. The method of claim 19, wherein the COVID-19 patient exhibits or is at risk of developing diarrhoea, and the method comprises (a) determining the level of autoantibodies specifically binding to IFNA4, optionally wherein the treatment inhibits or decreases the levels of IFNA4, further optionally wherein the treatment alleviates the diarrhoea.

23. The method of claim 19, wherein the COVID-19 patient exhibits or is at risk of developing flu- like symptoms, and the method comprises (a) determining the level of autoantibodies specifically binding to APOH, IL17A and/or SNRPB, optionally wherein the treatment inhibits or decreases the levels of APOH, IL17A and/or SNRPB, further optionally wherein the treatment alleviates the flu-like symptoms.

24. The method of claim 19, wherein the COVID-19 patient exhibits or is at risk of developing gastrointestinal symptoms, and the method comprises (a) determining the level of autoantibodies specifically binding to NCL and/or GRP, optionally wherein the treatment inhibits or decreases the levels of NCL and/or GRP, further optionally wherein the treatment alleviates the gastrointestinal symptoms.

25. The method of claim 19, wherein the COVID-19 patient exhibits or is at risk of developing chest pain, and the method further comprises determining the level of autoantibodies specifically binding to SNRPB and/or GRP, optionally wherein the treatment inhibits or decreases the levels of SNRPB and/or GRP, further optionally wherein the treatment alleviates the chest pain.

26. A method of predicting the risk of a COVID-19 patient developing diabetes, the method comprising:

27. A method of predicting the risk of a COVID-19 patient developing systemic lupus erythematosus, the method comprising: (a) determining in a sample obtained from the patient the level(s) of autoantibodies specifically binding to one or more antigens selected from IL-6, SNRPD1 and MX1 ; and

28. A method of predicting the risk of a COVID-19 patient developing a coagulation disorders including but not limited to venous thromboembolism, anti-phospholipid syndrome and catastrophic anti-phospholipid syndrome, the method comprising:

29. A method of predicting the risk of a COVID-19 patient developing vasculitis, the method comprising:

30. A method of predicting the risk of a COVID-19 patient developing myositis or systemic sclerosis, the method comprising:

31 . A method of selecting a COVID-19 patient for treatment, wherein the patient is male, the method comprising:

32. The method of claim 31 , comprising (a) determining the levels of autoantibodies specifically binding to two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine of more, or ten or more of the antigens.

33. The method of any one of claims 31 -32, comprising (a) determining the level(s) of autoantibodies specifically binding to one or more, or all, of the antigens selected from:

(i) SNRPB, MOV10, CHGA, CHD4, HIST1 H4A, ACE2, IFNA6, LYZ, and RNF41 ;

(ii) C3 and TG;

(iii) LYZ and IFNA6;

(iv) SNRPB, CHD4 and CHGA; or

(v) any combination of (i)-(v).

34. A method of selecting a COVID-19 patient for treatment, wherein the patient is female, the method comprising:

TOP1 , RNF41 , S100A9, TPO, MX1 , EXOSC10, DBT, MOV10, SP100, PTPRN, CHGA, S100A8, SNRPC, ECE1 , CENPB, PRTN3, SRP54, CHD4, TRIM33, TGFB1 , UBTF, AQP4, ROS1 , GAD65, IL10, SET, HIST1 H4A, IFNA2, IGF1 R, C3, RPLP2, TG; and

35. The method of claim 34, comprising (a) determining the levels of autoantibodies specifically binding to two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine of more, or ten or more of the antigens.

36. The method of any one of claims 34-35, comprising (a) determining the level(s) of autoantibodies specifically binding to one or more, or all, of the antigens selected from:

(i) ECE1 and HARS;

(ii) SMD3, UBTF and TOP1 ;

(iii) IL10 and S100A9;

(iv) HIST1 H4A, MX1 and EXOSC10;

(v) RTLP2 and TG;

(vi) DBT and ROS1 ; or

(vii) any combination of (i)-(vi).

37. A kit for the detection of autoantibodies in a test sample obtained from a COVID-19 patient, the kit comprising:

(a) one or more antigens selected from MPO, APOH, CADM3, IL-6, EXOSC10, INS, MIF, MX1 , NCL, SLC30A8, SRP19, ELANE, SMD3, VEGFA, TOP1 , SSB, SOX13, TPO, SNRPD1 , DBT, CTLA4, CHD3, HARS, IFNA4, RNF41 , S100A9, and TROVE2; and

38. The kit of claim 39, further comprising:

(c) means for contacting the one or more antigens with the test sample obtained from the COVID-19 patient.

39. The kit of claim 38, wherein the means for contacting the one or more antigens with the test sample comprises the one or more antigens immobilised on a chip, slide, plate, wells of a microtitre plate, bead, membrane or nanoparticple.

40. The kit of any of claims 37-39, wherein the kit comprises two or more, three or more, four or more, five or more, six or more, seven of more, eight or more, nine or more, ten or more, or all of the antigens.

41 . The kit of claim 40, where the antigens are present in a panel of two or more distinct antigens.

42. The kit of any of claims 37-41 , wherein the kit comprises:

(a) one or more, or all, of the antigens selected from MPO, APOH, CADM3, IL-6, EXOSC10, INS, MIF, MX1 , NCL, SLC30A8, SRP19, ELANE, SMD3, VEGFA, TOP1 , SSB, SOX13, TPO,

SNRPD1 , DBT, CTLA4, and CHD3.

43. The kit of any of claims 37-41 , where the kit comprises (a) one or more, or all, of the antigens selected from INS, MIF, MX1 , SPR19, VEGFA, CTL4A, CHD3, HARS, IFNA4, RNF41 , S100A9, and TROVE2.

44. The kit of any of claims 42-43, where the kit comprises (a) one or more, or all, of the antigens selected from INS, MIF, MX1 , SPR19, VEGFA, CTL4A, and CHD3.

45. The kit of any of claims 37-44, wherein the test sample is a serum sample.

46. A kit for the detection of autoantibodies in a test sample obtained from a COVID-19 patient, the kit comprising:

47. The kit of claim 46, wherein the kit comprises (a) one or more, or all, of the antigens selected from:

(i) SNRPB, MOV10, CHGA, CHD4, HIST1 H4A, ACE2, IFNA6, LYZ, and RNF41 ;

(ii) C3 and TG;

(iii) LYZ and IFNA6;

(iv) SNRPB, CHD4 and CHGA; or

(v) any combination of (i)-(iv).

48. A kit for the detection of autoantibodies in a test sample obtained from a COVID-19 patient, the kit comprising:

(a) one or more antigens selected from INS, ELANE, HARS, SOX13, CHD3, SMD3, TOP1 , RNF41 , S100A9, TPO, MX1 , EXOSC10, DBT, MOV10, SP100, PTPRN, CHGA, S100A8, SNRPC, ECE1 , CENPB, PRTN3, SRP54, CHD4, TRIM33, TGFB1 , UBTF, AQP4, R0S1 , GAD65, IL10, SET, HIST1 H4A, IFNA2, IGF1 R, C3, RPLP2, TG; and

49. The kit of claim 48, wherein the kit comprises (a) one or more, or all, of the antigens selected from:

(i) ECE1 and HARS;

(ii) SMD3, UBTF and TOP1 ; (iii) IL10 and S100A9;

(iv) HIST1 H4A, MX1 and EXOSC10;

(v) RTLP2 and TG;

(vi) DBT and ROS1 ; or

(vii) any combination of (i)-(vi).