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Background The clinical heterogeneity in major depressive disorder (MDD), variable treatment response and conflicting findings limit the ability of genomics towards the discovery of evidence-based diagnosis and treatment regimen. This study attempts to curate all genetic association findings to evaluate potential variants for clinical translation. Methods We systematically reviewed all candidate and genome-wide association studies for both MDD susceptibility and antidepressant response, independently, using MEDLINE, particularly to identify replicated findings. These variants were evaluated for functional consequences using different in silico tools and further estimated their diagnostic predictability by calculating positive predictive values (PPV). Results A total of 217 significantly associated studies comprising 1200 variants across 545 genes and 128 studies including 921 variants across 412 genes were included with MDD susceptibility and antidepressant response, respectively. Although, majority of associations were confirmed by single study, we identified 31 and 18 replicated variants (in atleast 2 studies) for MDD and antidepressant response. Functional annotation of these 31 variants predicted 20% coding variants as deleterious/damaging and 80.6% variants with regulatory effect. Similarly, for the response-related 18 variants revealed 25% coding variant as damaging and 88.2% with substantial regulatory potential. Finally, we could calculate the diagnostic predictability of 19 and 5 variants whose PPV ranges between 0.49 to 0.66 for MDD and 0.36 to 0.66 for response. Conclusions The replicated variants presented in our data are promising for disease diagnosis and improve response outcomes. Though, these quantitative assessment measures are solely directive of available observational evidence, robust homogenous validation studies are required to strengthen these variants for molecular diagnostic application.

Pneumonia, an acute respiratory tract infection, is one of the major causes of mortality worldwide. Depending on the site of acquisition, pneumonia can be community acquired pneumonia (CAP) or nosocomial pneumonia (NP). The risk of pneumonia, is partially driven by host genetics. CYP1A1 is a widely studied pulmonary CYP family gene primarily expressed in peripheral airway epithelium. The CYP1A1 genetic variants, included in this study, alter the gene activity and are known to contribute in lung inflammation, which may cause pneumonia pathogenesis. In this study, we performed a meta-analysis to establish the possible contribution of CYP1A1 gene, and its three variants (rs2606345, rs1048943 and rs4646903) towards the genetic etiology of pneumonia risk. Using PRISMA guidelines, we systematically reviewed and meta-analysed case-control studies, evaluating risk of pneumonia in patients carrying the risk alleles of CYP1A1 variants. Heterogeneity across the studies was evaluated using I2 statistics. Based on heterogeneity, a random-effect (using maximum likelihood) or fixed-effect (using inverse variance) model was applied to estimate the effect size. Pooled odds ratio (OR) was calculated to estimate the overall effect of the risk allele association with pneumonia susceptibility. Egger's regression test and funnel plot were used to assess publication bias. Subgroup analysis was performed based on pneumonia type (CAP and NP), population, as well as age group. A total of ten articles were identified as eligible studies, which included 3049 cases and 2249 healthy controls. The meta-analysis findings revealed CYP1A1 variants, rs2606345 [T vs G; OR = 1.12 (0.75-1.50); p = 0.02; I2 = 84.89%], and rs1048943 [G vs T; OR = 1.19 (0.76-1.61); p = 0.02; I2 = 0.00%] as risk markers whereas rs4646903 showed no statistical significance for susceptibility to pneumonia. On subgroup analysis, both the genetic variants showed significant association with CAP but not with NP. We additionally performed a spatial analysis to identify the key factors possibly explaining the variability across countries in the prevalence of the coronavirus disease 2019 (COVID-19), a viral pneumonia. We observed a significant association between the risk allele of rs2606345 and rs1048943, with a higher COVID-19 prevalence worldwide, providing us important links in understanding the variability in COVID-19 prevalence.

The risk for community acquired pneumonia (CAP) is partially driven by genetics. To identify the CAP-associated genetic risk loci, we performed a meta-analysis of clinically diagnosed CAP (3,310 individuals) with 2,655 healthy controls. The findings revealed CYP1A1 variants (rs2606345, rs4646903, rs1048943) associated with pneumonia. We observed rs2606345 [G vs T; OR=1.49(1.29-1.69); p=0.0001; I2= 15.5%], and rs1048943 [T vs G; OR= 1.31(0.90-1.71); p=0.002; I2=19.3%] as risk markers and rs4646903 [T vs C; OR= 0.79(0.62-0.96); p=0.03; I2=0%] as a protective marker for susceptibility to CAP, when compared with healthy controls. Our meta-analysis showed the presence of CYP1A1 SNP alleles contributing significant risk toward pneumonia susceptibility. Interestingly, we observed a striking difference of allele frequency for rs2606345 (CYP1A1) among Europeans, Africans and Asians which may provide a possible link for observed variations in death due to coronavirus disease 2019 (COVID-19), a viral pneumonia. We report, for the first time, a significant positive correlation for the risk allele (T or A) of rs2606345, with a higher COVID-19 mortality rate worldwide and within a genetically heterogeneous nation like India. Mechanistically, the risk allele ‘A’ (rs2606345) is associated with lower expression of CYP1A1 and presumably leads to reduced capacity for xenobiotic detoxification. We note that ambient air pollution, a powerful inducer of CYP1A1 gene expression, is globally associated with lower, not higher mortality, as would normally be predicted. In conclusion, we find that CYP1A1 alleles are associated with CAP mortality, presumably via altered xenobiotic metabolism. We speculate that gene-environment interactions governing CYP1A1 expression may influence COVID-19 mortality.

Background: India has been amongst the most affected nations during the SARS CoV2 pandemic, with sparse data on country wide spread of asymptomatic infections and antibody persistence. This longitudinal cohort study was aimed to evaluate SARS CoV2 seropositivity rate as a marker of infection and evaluate temporal persistence of antibodies with neutralization capability and to infer possible risk factors for infection. Methods: Council of Scientific and Industrial Research, India (CSIR) with its more than 40 laboratories and centers in urban and semi urban settings spread across the country piloted the pan country surveillance. 10427 adult individuals working in CSIR laboratories and their family members based on voluntary participation were assessed for antibody presence and stability was analyzed over 6 months utilizing qualitative Elecysys SARS CoV2 specific antibody kit and GENScript cPass SARS CoV2 Neutralization Antibody Detection Kit. Along with demographic information, possible risk factors were evaluated through self to be filled online forms with data acquired on blood group type, occupation type, addiction and habits including smoking and alcohol, diet preferences, medical history and transport type utilized. Symptom history and information on possible contact and compliance with COVID 19 universal precautions was also obtained. Findings:1058 individuals (10.14%) had antibodies against SARS CoV2. A follow up on 346 seropositive individuals after three months revealed stable to higher antibody levels against SARS CoV2 but declining plasma activity for neutralizing SARS CoV2 receptor binding domain and ACE2 interaction. A repeat sampling of 35 individuals, at six months, revealed declining antibody levels while the neutralizing activity remained stable compared to three months. Majority of seropositive individuals (75%) did not recall even one of nine symptoms since March 2020. Fever was the most common symptom with one fourth reporting loss of taste or smell. Significantly associated risks for seropositivity (Odds Ratio, 95% CI, p value) were observed with usage of public transport (1.79, 1.43 to 2∙24, 2.81561x10-6), occupational responsibilities such as security, housekeeping personnel etc. (2.23, 1.92 to 2.59, 6.43969x10-26), non smokers (1.52, 1.16 to 1.99, 0.02) and non vegetarianism (1.67, 1.41 to 1.99, 3.03821x10-8). An iterative regression analysis was confirmatory and led to only modest changes to estimates. Predilections for seropositivity was noted with specific ABO blood groups; O was associated with a lower risk. Interpretation: In a first of its kind study from India, we report the seropositivity in a country wide cohort and identify variable susceptible associations for contacting infection. Serology and Neutralizing Antibody response provides much sought for general insights on the immune response to the virus among Indians and will be an important resource for designing vaccination strategies. Funding: Council of Scientific and Industrial Research, India (CSIR)

With the advent of DNA sequencing technology that led to the human genome project, there was an explosion of big data in the field of biomedical sciences. The successful completion of the human genome project led to the emergence of numerous fields for data-driven discovery like genomics, proteomics, and bioinformatics per se. It was expected that the human genome information would reveal essential insights into the disease biology and revolutionize the field of medical sciences. It has been more than a decade since this important project. However, the impact of fundamental biological discoveries made in the laboratories is not reaching the clinical setting at a pace that was expected. Ever since, scientists have made innumerable attempts to decode the accumulated genetic information but are encountered with new challenges like complex disorders, epigenetics, and multifactorial diseases. So in order to bridge this gap, the focus of biomedical researchers has shifted from trying to decode the human genome in its entirety to “translating” only clinically relevant biological information. The change in focus has redirected the field of biotechnology toward what we call “translational research,” which is the buzzword of the era. By definition, it is “an interdisciplinary branch of the biomedical field supported by three main pillars: bench-side, bedside, and community,” that is, to analyze fundamental biological data with an explicit goal of affecting clinical care and ultimately the community as a whole. Stemming from translational biotechnology came system biology, biological engineering, pharmacogenomics, cheminformatics, molecular medicine, and so on. This chapter begins with a conceptual overview of this field and then introduces some of the key methods for translational research, its different stages implemented in integrative biology, overcoming its challenges with solutions, and the applications of multiomics platforms. Combining such biomedical information from different layers of omics data can ultimately help us in revealing the landscape of molecular mechanisms involved in highly prevalent complex diseases like cancer, cardiovascular, and metabolic diseases.