Demographic, clinical and genetic factors associated with COVID-19 disease susceptibility and mortality in a Kurdish population.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a devastating pandemic that causes disease with a variability in susceptibility and mortality based on variants of various clinical and demographic factors, including particular genes among populations. OBJECTIVES: Determine associations of demographic, clinical, laboratory, and single nucleotide polymorphisms in the ACE2, TMPRSS2, TNF-α, and IFN-γ genes to the incidence of infection and mortality in COVID-19 patients. DESIGN: Prospective cohort study SETTINGS: Various cities in the Kurdistan Region of Iraq. PATIENTS AND METHODS: This prospective cohort study compared laboratory markers (D-dimer, tumor necrosis factor-alpha [TNF-α], interferon-gamma [IFN-γ], C-reactive protein [CRP], lymphocyte and neutrophil counts) between COVID-19 patients and healthy controls. DNA was extracted from blood, and genotypes were done by Sanger sequencing. MAIN OUTCOME MEASURES: Single nucleotide polymorphisms of the ACE2, TMPRSS2, TNF-α, and IFN-γ genes and demographic characteristics and laboratory markers for predicting mortality in COVID-19. SAMPLE SIZE: 203 (153 COVID-19 patients, 50 health control subjects). RESULTS: Forty-eight (31.4%) of the COVID-19 patients died. Age over 40 and comorbidities were risk factors for mortality, but the strongest associations were with serum IFN-γ, the neutrophil-to-lymphocyte ratio (NLR), and serum TNF-α. The AA genotype and A allele of TMPRSS2 rs2070788 decreased while the GA genotype and A allele of TNF-α increased susceptibility to COVID-19. Patients with the GA genotype of TNF-α rs1800629 had shorter survival times (9.9 days) than those carrying the GG genotype (18.3 days) (P<.0001 by log-rank test). The GA genotype versus the GG genotype was associated with higher levels of serum TNF-α. The GA genotype increased mortality rates by up to 3.8 fold. The survival rate for COVID-19 patients carrying the IFN-γ rs2430561 TT genotype (58.5%) was lower than in patients with the TA and AA genotypes (80.3%). The TT genotype increased the risk of death (HR=3.664, P<.0001) and was linked to high serum IFN-γ production. Olfactory dysfunction was a predictor of survival among COVID-19 patients. CONCLUSIONS: Age older than 40, comorbidities, the NLR and particular genotypes for and the IFN-γ and TNF-α genes were risk factors for death. Larger studies in different populations must be conducted to validate the possible role of particular SNPs as genetic markers for disease severity and mortality in COVID-19 disease. LIMITATIONS: Small sample size. CONFLICT OF INTEREST: None.

Analyzing the role of ACE2, AR, MX1 and TMPRSS2 genetic markers for COVID-19 severity.

BACKGROUND: The use of molecular biomarkers for COVID-19 remains unconclusive. The application of a molecular biomarker in combination with clinical ones that could help classifying aggressive patients in first steps of the disease could help clinician and sanitary system a better management of the disease. Here we characterize the role of ACE2, AR, MX1, ERG, ETV5 and TMPRSS2 for trying a better classification of COVID-19 through knowledge of the disease mechanisms. METHODS: A total of 329 blood samples were genotyped in ACE2, MX1 and TMPRSS2. RNA analyses were also performed from 258 available samples using quantitative polymerase chain reaction for genes: ERG, ETV5, AR, MX1, ACE2, and TMPRSS2. Moreover, in silico analysis variant effect predictor, ClinVar, IPA, DAVID, GTEx, STRING and miRDB database was also performed. Clinical and demographic data were recruited from all participants following WHO classification criteria. RESULTS: We confirm the use of ferritin (p < 0.001), D-dimer (p < 0.010), CRP (p < 0.001) and LDH (p < 0.001) as markers for distinguishing mild and severe cohorts. Expression studies showed that MX1 and AR are significantly higher expressed in mild vs severe patients (p < 0.05). ACE2 and TMPRSS2 are involved in the same molecular process of membrane fusion (p = 4.4 × 10-3), acting as proteases (p = 0.047). CONCLUSIONS: In addition to the key role of TMPSRSS2, we reported for the first time that higher expression levels of AR are related with a decreased risk of severe COVID-19 disease in females. Moreover, functional analysis demonstrates that ACE2, MX1 and TMPRSS2 are relevant markers in this disease.

Thrombophilia and Immune-Related Genetic Markers in Long COVID.

Aiming to evaluate the role of ten functional polymorphisms in long COVID, involved in major inflammatory, immune response and thrombophilia pathways, a cross-sectional sample composed of 199 long COVID (LC) patients and a cohort composed of 79 COVID-19 patients whose follow-up by over six months did not reveal any evidence of long COVID (NLC) were investigated to detect genetic susceptibility to long COVID. Ten functional polymorphisms located in thrombophilia-related and immune response genes were genotyped by real time PCR. In terms of clinical outcomes, LC patients presented higher prevalence of heart disease as preexistent comorbidity. In general, the proportions of symptoms in acute phase of the disease were higher among LC patients. The genotype AA of the interferon gamma (IFNG) gene was observed in higher frequency among LC patients (60%; p = 0.033). Moreover, the genotype CC of the methylenetetrahydrofolate reductase (MTHFR) gene was also more frequent among LC patients (49%; p = 0.045). Additionally, the frequencies of LC symptoms were higher among carriers of IFNG genotypes AA than among non-AA genotypes (Z = 5.08; p < 0.0001). Two polymorphisms were associated with LC in both inflammatory and thrombophilia pathways, thus reinforcing their role in LC. The higher frequencies of acute phase symptoms among LC and higher frequency of underlying comorbidities might suggest that acute disease severity and the triggering of preexisting condition may play a role in LC development.

Role of Genetic Thrombophilia Markers in Thrombosis Events in Elderly Patients with COVID-19.

Thrombosis is an extremely dangerous complication in elderly patients with COVID-19. Since the first months of the pandemic, anticoagulants have been mandatory in treatment protocols for patients with COVID-19, unless there are serious contraindications. We set out to discover if genetic thrombophilia factors continue to play a triggering role in the occurrence of thrombosis in patients with COVID-19 with prophylactic or therapeutic anticoagulants. We considered the following genetic markers as risk factors for thrombophilia: G1691A in the FV gene, C677T and A1298C in the MTHFR gene, G20210A and C494T in the FII gene, and (-675) 4G/5G in the PAI-I gene. In a cohort of 176 patients, we did not obtain a reliable result indicating a higher risk of thrombotic complications when taking therapeutic doses of anticoagulants in carriers of genetic markers for thrombophilia except the C494T mutation in the FII gene. However, there was still a pronounced tendency to a higher incidence of thrombosis in patients with markers of hereditary thrombophilia, such as FV G1691A and FII G20210A mutations. The presence of the C494T (Thr165Met) allele in the FII gene in this group of patients showed a statistically significant effect of the mutation on the risk of thrombotic complications despite anticoagulant therapy.

Explainable artificial intelligence model for identifying COVID-19 gene biomarkers.

AIM: COVID-19 has revealed the need for fast and reliable methods to assist clinicians in diagnosing the disease. This article presents a model that applies explainable artificial intelligence (XAI) methods based on machine learning techniques on COVID-19 metagenomic next-generation sequencing (mNGS) samples. METHODS: In the data set used in the study, there are 15,979 gene expressions of 234 patients with COVID-19 negative 141 (60.3%) and COVID-19 positive 93 (39.7%). The least absolute shrinkage and selection operator (LASSO) method was applied to select genes associated with COVID-19. Support Vector Machine - Synthetic Minority Oversampling Technique (SVM-SMOTE) method was used to handle the class imbalance problem. Logistics regression (LR), SVM, random forest (RF), and extreme gradient boosting (XGBoost) methods were constructed to predict COVID-19. An explainable approach based on local interpretable model-agnostic explanations (LIME) and SHAPley Additive exPlanations (SHAP) methods was applied to determine COVID-19- associated biomarker candidate genes and improve the final model's interpretability. RESULTS: For the diagnosis of COVID-19, the XGBoost (accuracy: 0.930) model outperformed the RF (accuracy: 0.912), SVM (accuracy: 0.877), and LR (accuracy: 0.912) models. As a result of the SHAP, the three most important genes associated with COVID-19 were IFI27, LGR6, and FAM83A. The results of LIME showed that especially the high level of IFI27 gene expression contributed to increasing the probability of positive class. CONCLUSIONS: The proposed model (XGBoost) was able to predict COVID-19 successfully. The results show that machine learning combined with LIME and SHAP can explain the biomarker prediction for COVID-19 and provide clinicians with an intuitive understanding and interpretability of the impact of risk factors in the model.

A two-gene marker for the two-tiered innate immune response in COVID-19 patients.

For coronavirus disease 2019 (COVID-19), a pandemic disease characterized by strong immune dysregulation in severe patients, convenient and efficient monitoring of the host immune response is critical. Human hosts respond to viral and bacterial infections in different ways, the former is characterized by the activation of interferon stimulated genes (ISGs) such as IFI27, while the latter is characterized by the activation of anti-bacterial associated genes (ABGs) such as S100A12. This two-tiered innate immune response has not been examined in COVID-19. In this study, the activation patterns of this two-tiered innate immune response represented by IFI27 and S100A12 were explored based on 1421 samples from 17 transcriptome datasets derived from the blood of COVID-19 patients and relevant controls. It was found that IFI27 activation occurred in most of the symptomatic patients and displayed no correlation with disease severity, while S100A12 activation was more restricted to patients under severe and critical conditions with a stepwise activation pattern. In addition, most of the S100A12 activation was accompanied by IFI27 activation. Furthermore, the activation of IFI27 was most pronounced within the first week of symptom onset, but generally waned after 2-3 weeks. On the other hand, the activation of S100A12 displayed no apparent correlation with disease duration and could last for several months in certain patients. These features of the two-tiered innate immune response can further our understanding on the disease mechanism of COVID-19 and may have implications to the clinical triage. Development of a convenient two-gene protocol for the routine serial monitoring of this two-tiered immune response will be a valuable addition to the existing laboratory tests.

A 30-day follow-up study on the prevalence of SARS-COV-2 genetic markers in wastewater from the residence of COVID-19 patient and comparison with clinical positivity.

Wastewater based epidemiology (WBE) is an important tool to fight against COVID-19 as it provides insights into the health status of the targeted population from a small single house to a large municipality in a cost-effective, rapid, and non-invasive way. The implementation of wastewater based surveillance (WBS) could reduce the burden on the public health system, management of pandemics, help to make informed decisions, and protect public health. In this study, a house with COVID-19 patients was targeted for monitoring the prevalence of SARS-CoV-2 genetic markers in wastewater samples (WS) with clinical specimens (CS) for a period of 30 days. RT-qPCR technique was employed to target nonstructural (ORF1ab) and structural-nucleocapsid (N) protein genes of SARS-CoV-2, according to a validated experimental protocol. Physiological, environmental, and biological parameters were also measured following the American Public Health Association (APHA) standard protocols. SARS-CoV-2 viral shedding in wastewater peaked when the highest number of COVID-19 cases were clinically diagnosed. Throughout the study period, 7450 to 23,000 gene copies/1000 mL were detected, where we identified 47 % (57/120) positive samples from WS and 35 % (128/360) from CS. When the COVID-19 patient number was the lowest (2), the highest CT value (39.4; i.e., lowest copy number) was identified from WS. On the other hand, when the COVID-19 patients were the highest (6), the lowest CT value (25.2 i.e., highest copy numbers) was obtained from WS. An advance signal of increased SARS-CoV-2 viral load from the COVID-19 patient was found in WS earlier than in the CS. Using customized primer sets in a traditional PCR approach, we confirmed that all SARS-CoV-2 variants identified in both CS and WS were Delta variants (B.1.617.2). To our knowledge, this is the first follow-up study to determine a temporal relationship between COVID-19 patients and their discharge of SARS-CoV-2 RNA genetic markers in wastewater from a single house including all family members for clinical sampling from a developing country (Bangladesh), where a proper sewage system is lacking. The salient findings of the study indicate that monitoring the genetic markers of the SARS-CoV-2 virus in wastewater could identify COVID-19 cases, which reduces the burden on the public health system during COVID-19 pandemics.

Spatial and biological oceanographic insights into the massive fish-killing bloom of the haptophyte Chrysochromulina leadbeateri in northern Norway.

A bloom of the fish-killing haptophyte Chrysochromulina leadbeateri in northern Norway during May and June 2019 was the most harmful algal event ever recorded in the region, causing massive mortalities of farmed salmon. Accordingly, oceanographic and biodiversity aspects of the bloom were studied in unprecedented detail, based on metabarcoding and physico-chemical and biotic factors related with the dynamics and distribution of the bloom. Light- and electron-microscopical observations of nanoplankton samples from diverse locations confirmed that C. leadbeateri was dominant in the bloom and the primary cause of associated fish mortalities. Cell counts by light microscopy and flow cytometry were obtained throughout the regional bloom within and adjacent to five fjord systems. Metabarcoding sequences of the V4 region of the 18S rRNA gene from field material collected during the bloom and a cultured isolate from offshore of Tromsøy island confirmed the species identification. Sequences from three genetic markers (18S, 28S rRNA gene and ITS region) verified the close if not identical genetic similarity to C. leadbeateri from a previous massive fish-killing bloom in 1991 in northern Norway. The distribution and cell abundance of C. leadbeateri and related Chrysochromulina species in the recent incident were tracked by integrating observations from metabarcoding sequences of the V4 region of the 18S rRNA gene. Metabarcoding revealed at least 14 distinct Chrysochromulina variants, including putative cryptic species. C. leadbeateri was by far the most abundant of these species, but with high intraspecific genetic variability. Highest cell abundance of up to 2.7 × 107 cells L - 1 of C. leadbeateri was found in Balsfjorden; the high cell densities were associated with stratification near the pycnocline (at ca. 12 m depth) within the fjord. The cell abundance of C. leadbeateri showed positive correlations with temperature, negative correlation with salinity, and a slightly positive correlation with ambient phosphate and nitrate concentrations. The spatio-temporal succession of the C. leadbeateri bloom suggests independent initiation from existing pre-bloom populations in local zones, perhaps sustained and supplemented over time by northeastward advection of the bloom from the fjords.

Rapid Identification of Drug Resistance and Phylogeny in M. tuberculosis, Directly from Sputum Samples.

Tuberculosis (TB) remains one of the most important infectious diseases globally. Establishing a resistance profile from the initial TB diagnosis is a priority. Rapid molecular tests evaluate only the most common genetic variants responsible for resistance to certain drugs, and Whole Genome Sequencing (WGS) needs culture prior to next-generation sequencing (NGS), limiting their clinical value. Targeted sequencing (TS) from clinical samples avoids these drawbacks, providing a signature of genetic markers that can be associated with drug resistance and phylogeny. In this study, a proof-of-concept protocol was developed for detecting genomic variants associated with drug resistance and for the phylogenetic classification of Mycobacterium Tuberculosis (Mtb) in sputum samples. Initially, a set of Mtb reference strains from the WHO were sequenced (WGS and TS). The results from the protocol agreed >95% with WHO reported data and phenotypic drug susceptibility testing (pDST). Lineage genetics results were 100% concordant with those derived from WGS. After that, the TS protocol was applied to sputum samples from TB patients to detect resistance to first- and second-line drugs and derive phylogeny. The accuracy was >90% for all evaluated drugs, except Eto/Pto (77.8%), and 100% were phylogenetically classified. The results indicate that the described protocol, which affords the complete drug resistance profile and phylogeny of Mtb from sputum, could be useful in the clinical area, advancing toward more personalized and more effective treatments in the near future. IMPORTANCE The COVID-19 pandemic negatively affected the progress in accessing essential Tuberculosis (TB) services and reducing the burden of TB disease, resulting in a decreased detection of new cases and increased deaths. Generating molecular diagnostic tests with faster results without losing reliability is considered a priority. Specifically, developing an antimicrobial resistance profile from the initial stages of TB diagnosis is essential to ensure appropriate treatment. Currently available rapid molecular tests evaluate only the most common genetic variants responsible for resistance to certain drugs, limiting their clinical value. In this work, targeted sequencing on sputum samples from TB patients was used to identify Mycobacterium tuberculosis mutations in genes associated with drug resistance and to derive a phylogeny of the infecting strain. This protocol constitutes a proof-of-concept toward the goal of helping clinicians select a timely and appropriate treatment by providing them with actionable information beyond current molecular approaches.

Computational identification of host genomic biomarkers highlighting their functions, pathways and regulators that influence SARS-CoV-2 infections and drug repurposing.

The pandemic threat of COVID-19 has severely destroyed human life as well as the economy around the world. Although, the vaccination has reduced the outspread, but people are still suffering due to the unstable RNA sequence patterns of SARS-CoV-2 which demands supplementary drugs. To explore novel drug target proteins, in this study, a transcriptomics RNA-Seq data generated from SARS-CoV-2 infection and control samples were analyzed. We identified 109 differentially expressed genes (DEGs) that were utilized to identify 10 hub-genes/proteins (TLR2, USP53, GUCY1A2, SNRPD2, NEDD9, IGF2, CXCL2, KLF6, PAG1 and ZFP36) by the protein-protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of hub-DEGs revealed some important functions and signaling pathways that are significantly associated with SARS-CoV-2 infections. The interaction network analysis identified 5 TFs proteins and 6 miRNAs as the key regulators of hub-DEGs. Considering 10 hub-proteins and 5 key TFs-proteins as drug target receptors, we performed their docking analysis with the SARS-CoV-2 3CL protease-guided top listed 90 FDA approved drugs. We found Torin-2, Rapamycin, Radotinib, Ivermectin, Thiostrepton, Tacrolimus and Daclatasvir as the top ranked seven candidate drugs. We investigated their resistance performance against the already published COVID-19 causing top-ranked 11 independent and 8 protonated receptor proteins by molecular docking analysis and found their strong binding affinities, which indicates that the proposed drugs are effective against the state-of-the-arts alternatives independent receptor proteins also. Finally, we investigated the stability of top three drugs (Torin-2, Rapamycin and Radotinib) by using 100 ns MD-based MM-PBSA simulations with the two top-ranked proposed receptors (TLR2, USP53) and independent receptors (IRF7, STAT1), and observed their stable performance. Therefore, the proposed drugs might play a vital role for the treatment against different variants of SARS-CoV-2 infections.

Exploring the ethics of genetic prioritisation for COVID-19 vaccines.

There is evidence to suggest that host genomic factors may account for disease response variability in COVID-19 infection. In this paper, we consider if and how host genomics should influence decisions about vaccine allocation. Three potential host genetic factors are explored: vulnerability to infection, resistance to infection, and increased infectivity. We argue for the prioritisation of the genetically vulnerable in vaccination schemes, and evaluate the potential for ethical de-prioritisation of individuals with genetic markers for resistance. Lastly, we discuss ethical prioritisation of individuals with genetic markers for increased infectivity (those more likely to spread COVID-19).

Longitudinal monitoring of SARS-CoV-2 in wastewater using viral genetic markers and the estimation of unconfirmed COVID-19 cases.

In this study, wastewater-based surveillance was carried out to establish the correlation between SARS-CoV-2 viral RNA concentrations in wastewater and the incidence of corona virus disease 2019 (COVID-19) from clinical testing. The influent wastewater of three major water reclamation facilities (WRFs) in Northern Nevada, serving a population of 390,750, was monitored for SARS-CoV-2 viral RNA gene markers, N1 and N2, from June 2020 through September 2021. A total of 614 samples were collected and analyzed. The SARS-CoV-2 concentrations in wastewater were observed to peak twice during the study period. A moderate correlation trend between coronavirus disease 2019 (COVID-19) incidence data from clinical testing and SARS-CoV-2 viral RNA concentrations in wastewater was observed (Spearman r = 0.533). This correlation improved when using weekly average SARS-CoV-2 marker concentrations of wastewater and clinical case data (Spearman r = 0.790), presumably by mitigating the inherent variability of the environmental dataset and the effects of clinical testing artifacts (e.g., reporting lags). The research also demonstrated the value of wastewater-based surveillance as an early warning signal for early detection of trends in COVID-19 incidence. This was accomplished by identifying that the reported clinical cases had a stronger correlation to SARS-CoV-2 wastewater monitoring data when they were estimated to lag 7-days behind the wastewater data. The results aided local decision makers in developing strategies to manage COVID-19 in the region and provide a framework for how wastewater-based surveillance can be applied across localities to enhance the public health monitoring of the ongoing pandemic.

Cell-Free DNA: Potential Application in COVID-19 Diagnostics and Management.

WHO has declared COVID-19 as a worldwide, public health emergency. The elderly, pregnant women, and people with associated co-morbidities, including pulmonary disease, heart failure, diabetes, and cancer are the most predisposed population groups to infection. Cell-free DNA is a very commonly applied marker, which is elevated in various pathological conditions. However, it has a much higher sensitivity than standard biochemical markers. cfDNA appears to be an effective marker of COVID-19 complications, and also serves as a marker of certain underlying health conditions and risk factors of severe illness during COVID-19 infection. We aimed to present the possible mechanisms and sources of cfDNA released during moderate and severe infections. Moreover, we attempt to verify how efficiently cfDNA increase could be applied in COVID-19 risk assessment and how it corresponds with epidemiological data.

Integration of gene co-expression analysis and multi-class SVM specifies the functional players involved in determining the fate of HTLV-1 infection toward the development of cancer (ATLL) or neurological disorder (HAM/TSP).

Human T-cell Leukemia Virus type-1 (HTLV-1) is an oncovirus that may cause two main life-threatening diseases including a cancer type named Adult T-cell Leukemia/Lymphoma (ATLL) and a neurological and immune disturbance known as HTLV-1 Associated Myelopathy/Tropical Spastic Paraparesis (HAM/TSP). However, a large number of the infected subjects remain as asymptomatic carriers (ACs). There is no comprehensive study that determines which dysregulated genes differentiate the pathogenesis routes toward ATLL or HAM/TSP. Therefore, two main algorithms including weighted gene co-expression analysis (WGCNA) and multi-class support vector machines (SVM) were utilized to find major gene players in each condition. WGCNA was used to find the highly co-regulated genes and multi-class SVM was employed to identify the most important classifier genes. The identified modules from WGCNA were validated in the external datasets. Furthermore, to find specific modules for ATLL and HAM/TSP, the non-preserved modules in another condition were found. In the next step, a model was constructed by multi-class SVM. The results revealed 467, 3249, and 716 classifiers for ACs, ATLL, and HAM/TSP, respectively. Eventually, the common genes between the WGCNA results and classifier genes resulted from multi-class SVM that also determined as differentially expressed genes, were identified. Through these step-wise analyses, PAIP1, BCAS2, COPS2, CTNNB1, FASLG, GTPBP1, HNRNPA1, RBBP6, TOP1, SLC9A1, JMY, PABPC3, and PBX1 were found as the possible critical genes involved in the progression of ATLL. Moreover, FBXO9, ZNF526, ERCC8, WDR5, and XRCC3 were identified as the conceivable major involved genes in the development of HAM/TSP. These genes can be proposed as specific biomarker candidates and therapeutic targets for each disease.

Reference Gene Selection for Gene Expression Analyses in Mouse Models of Acute Lung Injury.

qRT-PCR still remains the most widely used method for quantifying gene expression levels, although newer technologies such as next generation sequencing are becoming increasingly popular. A critical, yet often underappreciated, problem when analysing qRT-PCR data is the selection of suitable reference genes. This problem is compounded in situations where up to 25% of all genes may change (e.g., due to leukocyte invasion), as is typically the case in ARDS. Here, we examined 11 widely used reference genes for their suitability in commonly used models of acute lung injury (ALI): ventilator-induced lung injury (VILI), in vivo and ex vivo, lipopolysaccharide plus mechanical ventilation (MV), and hydrochloric acid plus MV. The stability of reference gene expression was determined using the NormFinder, BestKeeper, and geNorm algorithms. We then proceeded with the geNorm results because this is the only algorithm that provides the number of reference genes required to achieve normalisation. We chose interleukin-6 (Il-6) and C-X-C motif ligand 1 (Cxcl-1) as the genes of interest to analyse and demonstrate the impact of inappropriate normalisation. Reference gene stability differed between the ALI models and even within the subgroup of VILI models, no common reference gene index (RGI) could be determined. NormFinder, BestKeeper, and geNorm produced slightly different, but comparable results. Inappropriate normalisation of Il-6 and Cxcl1 gene expression resulted in significant misinterpretation in all four ALI settings. In conclusion, choosing an inappropriate normalisation strategy can introduce different kinds of bias such as gain or loss as well as under- or overestimation of effects, affecting the interpretation of gene expression data.

Molecular Research in Cardiovascular Disease.

Cardiovascular diseases have attracted our full attention not only because they are the main cause of mortality and morbidity in many countries but also because the therapy for and cure of these maladies are among the major challenges of the medicine in the 21st century [...].

An epigenome-wide DNA methylation study of patients with COVID-19.

In the early 2000s, emerging SARS-CoV-2, which is highly pathogenic, posed a great threat to public health. During COVID-19, epigenetic regulation is deemed to be an important part of the pathophysiology and illness severity. Using the Illumina Infinium Methylation EPIC BeadChip (850 K), we investigated genome-wide differences in DNA methylation between healthy subjects and COVID-19 patients with different disease severities. We conducted a combined analysis and selected 35 "marker" genes that could indicate a SARS-CoV-2 infection, including 12 (ATHL1, CHN2, CHST15, CPLX2, CRHR2, DCAKD, GNAI2, HECW1, HYAL1, MIR510, PDE11A, and SMG6) situated in the promoter region. The functions and pathways of differentially methylated genes were enriched in biological processes, signal transduction, and the immune system. In the "Severe versus Mild" group, differentially methylated genes, after eliminating duplicates, were used for PPI analyses. The four hub genes (GNG7, GNAS, PRKCZ, and PRKAG2) that had the highest degree of nodes were identified and among them, GNG7 and GNAS genes expressions were also downregulated in the severe group in sequencing results. Above all, the results suggest that GNG7 and GNAS may play a non-ignorable role in the progression of COVID-19. In conclusion, the identified key genes and related pathways in the current study can be used to study the molecular mechanisms of COVID-19 and may provide possibilities for specific treatments.

AI-guided discovery of the invariant host response to viral pandemics.

BACKGROUND: Coronavirus Disease 2019 (Covid-19) continues to challenge the limits of our knowledge and our healthcare system. Here we sought to define the host immune response, a.k.a, the "cytokine storm" that has been implicated in fatal COVID-19 using an AI-based approach. METHOD: Over 45,000 transcriptomic datasets of viral pandemics were analyzed to extract a 166-gene signature using ACE2 as a 'seed' gene; ACE2 was rationalized because it encodes the receptor that facilitates the entry of SARS-CoV-2 (the virus that causes COVID-19) into host cells. An AI-based approach was used to explore the utility of the signature in navigating the uncharted territory of Covid-19, setting therapeutic goals, and finding therapeutic solutions. FINDINGS: The 166-gene signature was surprisingly conserved across all viral pandemics, including COVID-19, and a subset of 20-genes classified disease severity, inspiring the nomenclatures ViP and severe-ViP signatures, respectively. The ViP signatures pinpointed a paradoxical phenomenon wherein lung epithelial and myeloid cells mount an IL15 cytokine storm, and epithelial and NK cell senescence and apoptosis determine severity/fatality. Precise therapeutic goals could be formulated; these goals were met in high-dose SARS-CoV-2-challenged hamsters using either neutralizing antibodies that abrogate SARS-CoV-2•ACE2 engagement or a directly acting antiviral agent, EIDD-2801. IL15/IL15RA were elevated in the lungs of patients with fatal disease, and plasma levels of the cytokine prognosticated disease severity. INTERPRETATION: The ViP signatures provide a quantitative and qualitative framework for titrating the immune response in viral pandemics and may serve as a powerful unbiased tool to rapidly assess disease severity and vet candidate drugs. FUNDING: This work was supported by the National Institutes for Health (NIH) [grants CA151673 and GM138385 (to DS) and AI141630 (to P.G), DK107585-05S1 (SD) and AI155696 (to P.G, D.S and S.D), U19-AI142742 (to S. C, CCHI: Cooperative Centers for Human Immunology)]; Research Grants Program Office (RGPO) from the University of California Office of the President (UCOP) (R00RG2628 & R00RG2642 to P.G, D.S and S.D); the UC San Diego Sanford Stem Cell Clinical Center (to P.G, D.S and S.D); LJI Institutional Funds (to S.C); the VA San Diego Healthcare System Institutional funds (to L.C.A). GDK was supported through The American Association of Immunologists Intersect Fellowship Program for Computational Scientists and Immunologists. ONE SENTENCE SUMMARY: The host immune response in COVID-19.

Comprehensive comparative genomic and microsatellite analysis of SARS, MERS, BAT-SARS, and COVID-19 coronaviruses.

The coronavirus disease 2019 (COVID-19) pandemic has spread around the globe very rapidly. Previously, the evolution pattern and similarity among the COVID-19 causative organism severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and causative organisms of other similar infections have been determined using a single type of genetic marker in different studies. Herein, the SARS-CoV-2 and related ß coronaviruses Middle East respiratory syndrome coronavirus (MERS-CoV), SARS-CoV,  bat coronavirus (BAT-CoV) were comprehensively analyzed using a custom-built pipeline that employed phylogenetic approaches based on multiple types of genetic markers including the whole genome sequences, mutations in nucleotide sequences, mutations in protein sequences, and microsatellites. The whole-genome sequence-based phylogeny revealed that the strains of SARS-CoV-2 are more similar to the BAT-CoV strains. The mutational analysis showed that on average MERS-CoV and BAT-CoV genomes differed at 134.21 and 136.72 sites, respectively, whereas the SARS-CoV genome differed at 26.64 sites from the reference genome of SARS-CoV-2. Furthermore, the microsatellite analysis highlighted a relatively higher number of average microsatellites for MERS-CoV and SARS-CoV-2 (106.8 and 107, respectively), and a lower number for SARS-CoV and BAT-CoV (95.8 and 98.5, respectively). Collectively, the analysis of multiple genetic markers of selected ß viral genomes revealed that the newly born SARS-COV-2 is closely related to BAT-CoV, whereas, MERS-CoV is more distinct from the SARS-CoV-2 than BAT-CoV and SARS-CoV.

A single transcript for the prognosis of disease severity in COVID-19 patients.

With many countries strapped for medical resources due to the COVID-19 pandemic, it is highly desirable to allocate the precious resources to those who need them the most. Several markers have been found to be associated with the disease severity in COVID-19 patients. However, the established markers only display modest prognostic power individually and better markers are urgently needed. The aim of this study is to investigate the potential of S100A12, a prominent marker gene for bacterial infection, in the prognosis of disease severity in COVID-19 patients. To ensure the robustness of the association, a total of 1695 samples from 14 independent transcriptome datasets on sepsis, influenza infection and COVID-19 infection were examined. First, it was demonstrated that S100A12 was a marker for sepsis and severity of sepsis. Then, S100A12 was found to be a marker for severe influenza infection, and there was an upward trend of S100A12 expression as the severity level of influenza infection increased. As for COVID-19 infection, it was found that S100A12 expression was elevated in patients with severe and critical COVID-19 infection. More importantly, S100A12 expression at hospital admission was robustly correlated with future quantitative indexes of disease severity and outcome in COVID-19 patients, superior to established prognostic markers including CRP, PCT, d-dimer, ferritin, LDH and fibrinogen. Thus, S100A12 is a valuable novel prognostic marker for COVID-19 severity and deserves more attention.