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Background: Health organizations and countries around the world have found it difficult to control the spread of COVID-19. To minimize the future impact on the UK National Health Service and improve patient care, there is a pressing need to identify individuals who are at a higher risk of being hospitalized because of severe COVID-19. Early targeted work was successful in identifying angiotensin-converting enzyme -2 receptors and type II transmembrane serine protease dependency as drivers of severe infection. Although a targeted approach highlights key pathways, a multiomics approach will provide a clearer and more comprehensive picture of severe COVID-19 etiology and progression. Objective: The COVID-19 Response Study aims to carry out an integrated multiomics analysis to identify biomarkers in blood and saliva that could contribute to host susceptibility to SARS-CoV-2 and the development of severe COVID-19. Methods: The COVID-19 Response Study aims to recruit 1000 people who recovered from SARS-CoV-2 infection in both community and hospital settings on the island of Ireland. This protocol describes the retrospective observational study component carried out in Northern Ireland (NI; Cohort A); the Republic of Ireland cohort will be described separately. For all NI participants (n=519), SARS-CoV-2 infection has been confirmed by reverse transcription -quantitative polymerase chain reaction. A prospective Cohort B of 40 patients is also being followed up at 1, 3, 6, and 12 months postinfection to assess longitudinal symptom frequency and immune response. Data will be sourced from whole blood, saliva samples, and clinical data from the electronic care records, the general health questionnaire, and a 12 -item general health questionnaire mental health survey. Saliva and blood samples were processed to extract DNA and RNA before whole-genome sequencing, RNA sequencing, DNA methylation analysis, microbiome analysis, 16S ribosomal RNA gene sequencing, and proteomic analys, Funding Agencies|Department for the Economy Northern Ireland; Opportunity-Led Research Awards; HSC Research and Development Division, Public Health Agency [COM/5618/20, COM/5631/20]; United Kingdom Research and Innovation

Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features needed for a given task from high-dimensional, heterogeneous data, deep learning (DL) holds great promise in this endeavour. This review paper addresses the main developments of DL algorithms and a set of general topics where DL is decisive, namely, within the SM landscape. It discusses how DL can be applied to SM with an emphasis on the applications to predictive, preventive and precision medicine. Several key challenges have been highlighted including delivering clinical impact and improving interpretability. We used some prototypical examples to highlight the relevance and significance of the adoption of DL in SM, one of them is involving the creation of a model for personalized Parkinson’s disease. The review offers valuable insights and informs the research in DL and SM.

Genetic predisposition has been proposed to be a major determinant in the development of renal complications of diabetes. Among candidate genes examined for susceptibility to diabetic nephropathy, angiotensin-converting enzyme (ACE) gene has been found to be associated with pathogenesis and progression of diabetic nephropathy. However, the role of other renin-angiotensin system (RAS) polymorphisms and their possible interactions with different ACE I/D genotypes are less clearly defined. Recent studies also show that ACE haplotypes may be better predictors to disease susceptibility. Thus, in the present study, we evaluated the association of ACE haplotypes and the interactions of ACE, angiotensinogen (AGT), and angiotensin II receptor type I (AGTR1) gene polymorphisms with DNP in Asian Indians. We genotyped seven variants of the RAS pathway genes (ACE, AGT, and AGTR1) in type 2 diabetic cohorts without nephropathy (DM) and with nephropathy (DNP), using allele-specific oligonucleotide-PCR, and PCR-restriction fragment length polymorphism assays. We studied the interaction of these variants with each other and ACE I/D polymorphism. Frequency of ACE D allele and DD genotype (ACE I/D) was significantly higher in DNP (p <0.005) and was associated with increased risk of nephropathy. The frequency of T allele, MT/TT genotypes (AGT: M235T), and C allele 1166CC genotype (AGTR1: A1166C) was higher and associated with increased risk of DNP (235T, p <0.0001; 235TT/MT, p <0.01; 1166C, p <0.007; 1166CC, p <0.0001). The ACE locus revealed a near doubling in the prevalence of T-D-G risk haplotype (odds ratio, 1.76) in DNP (0.13) compared to DM (0.08; p <0.02). ACE haplotypes carrying the I allele were associated with a lower risk of DNP (C-I-A, p <0.04; C-I-G, p <0.008). ACE ID/DD genotypes in combination with ACE rs4311, rs4343, and AGT rs699 mutant genotypes increased the risk of DNP development fourfold (p <0.01). This study provides the first evidence

Inflammatory cytokine genes have been proposed as good candidate genes for conferring susceptibility to diabetic nephropathy. In the present study, we examined the combined effect of multiple alleles of pro inflammatory cytokine genes for determining the risk of nephropathy in type 2 diabetic patients.

Endothelial dysfunction plays a key role in the pathogenesis of diabetic vascular disease, including diabetic nephropathy. Endothelial-derived nitric oxide synthase (eNOS) gene polymorphisms affect eNOS activity and are associated with endothelial dysfunction. We evaluated the association of the constitutive endothelial nitric oxide synthase gene (eNOS) polymorphisms with type 2 diabetic nephropathy. We genotyped three polymorphisms of eNOS (Two SNPs: -786T > C, 894G > T and one 27-bp repeat polymorphism in Intron 4 (27VNTR)) in type 2 diabetic nephropathy patients (cases: n = 195) and type 2 diabetic without nephropathy (controls: n = 255), using validated PCR-RFLP assays. We measured serum NO levels in these subjects and examined its correlation with diabetic nephropathy and eNOS genotypes. The frequency of CC (-786T > C), TT (894G > T) and aa genotypes (27VNTR) were significantly higher in diabetic nephropathy patients as compared to the diabetes without nephropathy group (CC: P = 0.003, TT: P = 0.03, aa: P <0.0001). These mutant genotypes were found to be associated with higher risk of nephropathy (-786T > C: OR: 5.5, 95%CI: 1.53-19.79; 894G > T: OR: 1.8, 95%CI: 1.03-3.16; Intron 4: OR: 6.23, 95%CI: 2.23-16.31). Haplotype with all the wild alleles (T-b-G) was found to be associated with a decreased risk of nephropathy (OR: 0.68, P = 0.005) and haplotype with all mutant alleles (C-a-T) was associated with higher risk of diabetic nephropathy as compared to diabetes without nephropathy group (OR: 2.6, P = 0.14). No significant linkage disequilibria were observed among the variants in this case-control study. The serum NO levels were observed to be significantly (P <0.05) lower in mutant allele carriers ('C' allele of T-786C SNP and/or 'T' allele of G894T SNP) as compared with the wild-type allele carriers (-786T and/or 894G) within each of the subject groups (with and without nephropathy). These results suggest that the eNOS gene locus is as