Multi-Omics Analysis Demonstrates the Critical Role of Non-Ethanolic Components of Alcoholic Beverages in the Host Microbiome and Metabolome: A Human- and Animal-Based Study.

It is known that alcoholic beverages alter the human gut microbiome. This study focused on the potential impact of non-ethanolic ingredients in whisky on the gut bacteriome. A pilot study was carried out on 15 whisky drinkers, 5 rice beer drinkers, and 9 non-drinkers to determine the effect of alcoholic beverages on the host microbiome and metabolome. Additionally, a mouse model was used to assess the differential impact of three whisky brands (each with an equal ethanol concentration). The results indicate that the non-ethanolic components have an impact on the gut microbiome, as well as on the metabolites in blood and feces. The amount of Prevotella copri, a typical core Indian gut bacterium, decreased in both the human and mouse groups of whisky type 1, but an increase in abundance of Helicobacteriaceae (p = 0.01) was noticed in both groups. Additionally, the alcohol-treated cohorts had lower levels of short-chain fatty acids (SCFAs), specifically butyric acid, and higher amounts of lipids and stress marker IL1-ß than the untreated groups (p = 0.04-0.01). Furthermore, two compounds, ethanal/acetaldehyde (found in all the whisky samples) and arabitol (unique to whisky type 1), were tested in the mice. Similar to the human subjects, the whisky type 1 treated mouse cohort and the arabitol-treated group showed decreased levels of Prevotella copri (p = 0.01) in their gut. The results showed that non-ethanolic compounds have a significant impact on host gut bacterial diversity and metabolite composition, which has a further vital impact on host health. Our work further emphasizes the need to study the impact of non-ethanolic ingredients of alcoholic beverages on host health.

Seroepidemiological and genomic investigation of SARS-CoV-2 spread in North East region of India.

PURPOSE: Seroepidemiology and genomic surveillance are valuable tools to investigate infection transmission during a pandemic. North East (NE) India is a strategically important region being the gateway connecting the country with Southeast Asia. Here, we examined the spread of SARS-CoV-2 in NE India during the first and second waves of COVID-19 using serological and whole genome sequencing approaches. METHODS: qRT-PCR analysis was performed on a selected population (n â€‹= â€‹16,295) from June 2020 to July 2021, and metadata was collected. Immunoassays were studied (n â€‹= â€‹2026) at three-time points (August 2020, February 2021, and June 2021) and in a cohort (n â€‹= â€‹35) for a year. SARS-CoV-2 whole genomes (n â€‹= â€‹914) were sequenced and analyzed with those obtained from the databases. RESULTS: Test positivity rates (TPR) in the first and second waves were 6.34% and 6.64% in Assam, respectively, and a similar pattern was observed in other NE states. Seropositivity in the three time points was 10.63%, 40.3%, and 46.33%, respectively, and neutralizing antibody prevalence was 90.91%, 52.14%, and 69.30%, respectively. Persistence of pan-IgG-N SARS-CoV-2 antibody for over a year was observed among three subjects in the cohort group. Normal variants dominated the first wave, while B.1.617.2 and AY-sublineages dominated the second wave in the region. The prevalence of the variants co-related well with high TPR and seropositivity rate in the region and identified mostly among vaccinated individuals. CONCLUSION: The COVID-19 first wave in the region witnessed low transmission with the evolution of diverse variants. Seropositivity increased during the study period with over half of the individuals carrying neutralizing antibodies against SARS-CoV-2. High infection and seroprevalence in NE India during the second wave were associated with the dominant emergence of variants of concern.

A machine learning-based approach to determine infection status in recipients of BBV152 whole virion inactivated SARS-CoV-2 vaccine for serological surveys

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the effectiveness of interventions. Asymptomatic breakthrough infections have been a major problem during the ongoing surge of Delta variant globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines used in the higher-income regions. Here, we show for the first time how statistical and machine learning (ML) approaches can discriminate SARS-CoV-2 infection from immune response to an inactivated whole virion vaccine (BBV152, Covaxin, India), thereby permitting real-world vaccine effectiveness assessments from cohort-based serosurveys in Asia and Africa where such vaccines are commonly used. Briefly, we accessed serial data on Anti-S and Anti-NC antibody concentration values, along with age, sex, number of doses, and number of days since the last vaccine dose for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine (SVM) model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, 724 were classified as infected. Since the vaccine contains wild-type virus and the antibodies induced will neutralize wild type much better than Delta variant, we determined the relative ability of a random subset of such samples to neutralize Delta versus wild type strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, Delta variant, was neutralized more effectively than the wild type, which cannot happen without infection. The fraction rose to 71.8% (28 of 39) in subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period.

Insights from a Pan India Sero-Epidemiological survey (Phenome-India Cohort) for SARS-CoV2

To understand the spread of SARS-CoV2, in August and September 2020, the Council of Scientific and Industrial Research (India), conducted a sero-survey across its constituent laboratories and centers across India. Of 10,427 volunteers, 1058 (10.14%) tested positive for SARS CoV2 anti-nucleocapsid (anti-NC) antibodies; 95% with surrogate neutralization activity. Three-fourth recalled no symptoms. Repeat serology tests at 3 (n=346) and 6 (n=35) months confirmed stability of antibody response and neutralization potential. Local sero-positivity was higher in densely populated cities and was inversely correlated with a 30 day change in regional test positivity rates (TPR). Regional seropositivity above 10% was associated with declining TPR. Personal factors associated with higher odds of sero-positivity were high-exposure work (Odds Ratio, 95% CI, p value; 2{middle dot}23, 1{middle dot}92-2{middle dot}59, 6{middle dot}5E-26), use of public transport (1{middle dot}79, 1{middle dot}43-2{middle dot}24, 2{middle dot}8E-06), not smoking (1{middle dot}52, 1{middle dot}16-1{middle dot}99, 0{middle dot}02), non-vegetarian diet (1{middle dot}67, 1{middle dot}41-1{middle dot}99, 3{middle dot}0E-08), and B blood group (1{middle dot}36,1{middle dot}15-1{middle dot}61, 0{middle dot}001). Impact StatementWidespread asymptomatic and undetected SARS-CoV2 infection affected more than a 100 million Indians by September 2020. Declining new cases thereafter may be due to persisting humoral immunity amongst sub-communities with high exposure. FundingCouncil of Scientific and Industrial Research, India (CSIR)