Dysbiosis in the gut microbiome has been implicated in several diseases including auto-immune diseases, inflammatory diseases, cancers and mental disorders. Keratitis is an inflammatory disease of the eye significantly contributing to corneal blindness in the developing world. It would be worthwhile to investigate the possibility of dysbiosis in the gut microbiome being associated with Keratitis. Here, we have analyzed fungal and bacterial populations in stool samples through high-throughput sequencing of the ITS2 region for fungi and V3-V4 region of 16S rRNA gene for bacteria in healthy controls (HC, n = 31) and patients with fungal keratitis (FK, n = 32). Candida albicans (2 OTUs), Aspergillus (1 OTU) and 3 other denovo-OTUs were enriched in FK samples and an unclassified denovo-OTU was enriched in HC samples. However, the overall abundances of these ‘discriminatory’ OTUs were very low (< 0.001%) and not indicative of significant dysbiosis in the fungal community inhabiting the gut of FK patients. In contrast, the gut bacterial richness and diversity in FK patients was significantly decreased when compared to HC. 52 OTUs were significantly enriched in HC samples whereas only 5 OTUs in FK. The OTUs prominently enriched in HC were identified as Faecalibacterium prausnitzii, Bifidobacterium adolescentis, Lachnospira, Mitsuokella multacida, Bacteroides plebeius, Megasphaera and Lachnospiraceae. In FK samples, 5 OTUs affiliated to Bacteroides fragilis, Dorea, Treponema, Fusobacteriaceae, and Acidimicrobiales were significantly higher in abundance. The functional implications are that Faecalibacterium prausnitzii, an anti-inflammatory bacterium and Megasphaera, Mitsuokella multacida and Lachnospira are butyrate producers, which were enriched in HC patients, whereas Treponema and Bacteroides fragilis, which are pathogenic were abundant in FK patients, playing a potential pro-inflammatory role. Heatmap, PCoA plots and functional profiles further confirm the distinct patterns of gut bacterial composition in FK and HC samples. Our study demonstrates dysbiosis in the gut bacterial microbiomes of FK patients compared to HC. Further, based on inferred functions, it appears that dysbiosis in the gut of FK subjects is strongly associated with the disease phenotype with decrease in abundance of beneficial bacteria and increase in abundance of pro-inflammatory and pathogenic bacteria.

Background Type 2 diabetes (T2D), a multifactorial disease influenced by host genetics and environmental factors, is the most common endocrine disease. Several studies have shown that the gut microbiota as a close-up environmental mediator influences host physiology including metabolism. The aim of the present study is to examine the compositional and functional potential of the gut microbiota across individuals from Denmark and South India with a focus on T2D. Many earlier studies have investigated the microbiome aspects of T2D, and it has also been anticipated that such microbial associations would be dependent on diet and ethnic origin. However, there has been no large scale trans-ethnic microbiome study earlier in this direction aimed at evaluating any “universal” microbiome signature of T2D. Methods 16S ribosomal RNA gene amplicon sequencing was performed on stool samples from 279 Danish and 294 Indian study participants. Any differences between the gut microbiota of both populations were explored using diversity measures and negative binomial Wald tests. Study samples were stratified to discover global and country-specific microbial signatures for T2D and treatment with the anti-hyperglycemic drug, metformin. To identify taxonomical and functional signatures of the gut microbiota for T2D and metformin treatment, we used alpha and beta diversity measures and differential abundances analysis, comparing metformin-naive T2D patients, metformin-treated T2D patients, and normoglycemic individuals. Results Overall, the gut microbial communities of Danes and Indians are compositionally very different. By analyzing the combined study materials, we identify microbial taxonomic and functional signatures for T2D and metformin treatment. T2D patients have an increased relative abundance of two operational taxonomic units (OTUs) from the Lachnospiraceae family, and a decreased abundance of Subdoligranulum and Butyricicoccus. Studying each population per se, we identified T2D-related microbial changes at the taxonomic level within the Danish population only. Alpha diversity indices show that there is no significant difference between normoglycemic individuals and metformin-naive T2D patients, whereas microbial richness is significantly decreased in metformin-treated T2D patients compared to metformin-naive T2D patients and normoglycemic individuals. Enrichment of two OTUs from Bacteroides and depletion of Faecalibacterium constitute a trans-ethnic signature of metformin treatment. Conclusions We demonstrate major compositional differences of the gut microbiota between Danish and South Indian individuals, some of which may relate to differences in ethnicity, lifestyle, and demography. By comparing metformin-naive T2D patients and normoglycemic individuals, we identify T2D-related microbiota changes in the Danish and Indian study samples. In the present trans-ethnic study, we confirm that metformin changes the taxonomic profile and functional potential of the gut microbiota.

Background Recent studies have indicated an association of gut microbiota and microbial metabolites with type 2 diabetes mellitus (T2D). However, large-scale investigation of the gut microbiota of “prediabetic” (PD) subjects has not been reported. Identifying robust gut microbiome signatures of prediabetes and characterizing early prediabetic stages is important for the understanding of disease development and could be crucial in early diagnosis and prevention. Methods The current study performed amplification and sequencing on the variable regions (V1–V5) of the 16S rRNA genes to profile and compare gut microbiota of prediabetic individuals (N = 262) with normoglycemic individuals (N = 275) from two cohorts in India and Denmark. Similarly, fasting serum inflammatory biomarkers were profiled from the study participants. Results After correcting for strong country-specific cohort effect, 16 operational taxonomic units (OTUs) including members from the genera Prevotella9, Phascolarctobacterium, Barnesiella, Flavonifractor, Tyzzerella_4, Bacteroides, Faecalibacterium, and Agathobacter were identified as enriched in normoglycaemic subjects with respect to the subjects with prediabetes using a negative binomial Wald test. We also identified 144 OTUs enriched in the prediabetic subjects, which included members from the genera Megasphaera, Streptococcus, Prevotella9, Alistipes, Mitsuokella, Escherichia/Shigella, Prevotella2, Vibrio, Lactobacillus, Alloprevotella, Rhodococcus, and Klebsiella. Comparative analyses of relative abundance of bacterial taxa revealed that the Streptococcus, Escherichia/Shigella, Prevotella2, Vibrio, and Alloprevotella OTUs exhibited more than fourfold enrichment in the gut microbiota of prediabetic subjects. When considering subjects from the two geographies separately, we were able to identify additional gut microbiome signatures of prediabetes. The study reports a probable association of Megasphaera OTU(s) with impaired glucose tolerance, which is significantly pronounced in Indian subjects. While the overall results confirm a state of proinflammation as early as in prediabetes, the Indian cohort exhibited a characteristic pattern of abundance of inflammatory markers indicating low-grade intestinal inflammation at an overall population level, irrespective of glycemic status. Conclusions The results present trans-ethnic gut microbiome and inflammation signatures associated with prediabetes, in Indian and Danish populations. The identified associations may be explored further as potential early indicators for individuals at risk of dysglycemia.

The SARS-CoV2 mediated Covid-19 pandemic has impacted humankind at an unprecedented scale. While substantial research efforts have focused towards understanding the mechanisms of viral infection and developing vaccines/ therapeutics, factors affecting the susceptibility to SARS-CoV2 infection and manifestation of Covid-19 remain less explored. Given that the Human Leukocyte Antigen (HLA) system is known to vary among ethnic populations, it is likely to affect the recognition of the virus, and in turn, the susceptibility to Covid-19. To understand this, we used bioinformatic tools to probe all SARS-CoV2 peptides which could elicit T-cell response in humans. We also tried to answer the intriguing question of whether these potential epitopes were equally immunogenic across ethnicities, by studying the distribution of HLA alleles among different populations and their share of cognate epitopes. Results indicate that the immune recognition potential of SARS-CoV2 epitopes tend to vary between different ethnic groups. While the South Asians are likely to recognize higher number of CD8-specific epitopes, Europeans are likely to identify higher number of CD4-specific epitopes. We also hypothesize and provide clues that the newer mutations in SARS-CoV2 are unlikely to alter the T-cell mediated immunogenic responses among the studied ethnic populations. The work presented herein is expected to bolster our understanding of the pandemic, by providing insights into differential immunological response of ethnic populations to the virus as well as by gauging the possible effects of mutations in SARS-CoV2 on efficacy of potential epitope-based vaccines through evaluating ∼40000 viral genomes.

Background: Next-generation sequencing (NGS) technologies have enabled probing of microbial diversity in different environmental niches with unprecedented sequencing depth. However, due to read-length limitations of popular NGS technologies, 16S amplicon sequencing-based microbiome studies rely on targeting short stretches of the 16S rRNA gene encompassing a selection of variable (V) regions. In most cases, such a short stretch constitutes a single V-region or a couple of V-regions placed adjacent to each other on the 16S rRNA gene. Given that different V-regions have different resolving ability with respect to various taxonomic groups, selecting the optimal V-region (or a combination thereof) remains a challenge. Methods: The accuracy of taxonomic profiles generated from sequences encompassing 1) individual V-regions, 2) adjacent V-regions, and 3) pairs of non-contiguous V-regions were assessed and compared. Subsequently, the discriminating capability of different V-regions with respect to different taxonomic lineages was assessed. The possibility of using paired-end sequencing protocols to target combinations of non-adjacent V-regions was finally evaluated with respect to the utility of such an experimental design in providing improved taxonomic resolution. Results: Extensive validation with simulated microbiome datasets mimicking different environmental and host-associated microbiome samples suggest that targeting certain combinations of non-contiguously placed V-regions might yield better taxonomic classification accuracy compared to conventional 16S amplicon sequencing targets. This work also puts forward a novel in silico combinatorial strategy that enables creation of consensus taxonomic profiles from experiments targeting multiple pair-wise combinations of V-regions to improve accuracy in taxonomic classification. Conclusion: The study suggests that targeting non-contiguous V-regions with paired-end sequencing can improve 16S rRNA–based taxonomic resolution of microbiomes. Furthermore, employing the novel in silico combinatorial strategy can improve taxonomic classification without any significant additional experimental costs and/or efforts. The empirical observations obtained can potentially serve as a guideline for future 16S microbiome studies, and facilitate researchers in choosing the optimal combination of V-regions for a specific experiment/sampled environment.

Chrysin and 7-hydroxy flavone were prepared by Baker-Venkatraman rearrangement followed by esterification at 7th position and replacement of ester with acetamide linking to different heterocyclic moieties synthesized 13a-g and 14a-g series of flavones analogues. These were screened against COX-2 and COX-1 enzymes for inhibition by in vitro assay and COX-2 for in silico docking studies. The compound 14a was found to be most active with IC 50 of 3.11 lM concentration, with highest binding energy of -12.4 kcal/mole and 77.2 and 80.5 % inhibition at 3 and 5 h post-carrageenan induced in paw oedema.

Background: SARS-COV-2 is an enveloped RNA virus that is responsible for the global pandemic COVID-19. The virus is reported to cause dysbiosis of the Human Nasopharyngeal microbiota, consequently regulating the host immunity and infection pathophysiology. The compositional change in microbial diversity due to the virus has been reported by independent authors in smaller cohorts and different geographical regions, with a few correlating with fungal and bacterial co-infections. Here, we study for the first time, the nasopharyngeal microbial diversity in the COVID-19 patients, across the three waves in India and explore its correlation with the causative virus variant (and/or the severity of symptoms, if any). Methods: We profiled the nasopharyngeal microbiota of 589 Indian subjects, across the three waves (First; n=181, Second; n=217, Third; n=191), which were further categorized as COVID-19 positives and COVID-19 negatives. These respective groups were further divided into subgroups based on the symptoms as Asymptomatic and Symptomatic. The nasopharyngeal swabs were collected from subjects providing samples for diagnostics purposes at the Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India. Using high throughput 16S rRNA gene amplicon-based sequencing, we sequenced and profiled the nasopharyngeal DNA microbiome prior to subjecting them to diversity, composition and network analyses. Results: Patients infected with SARS-COV-2 showed a reduced microbial alpha diversity compared to the COVID-19 negatives, in a wave-dependent manner, as implicated by measuring the alpha diversity indices. Furthermore, the compositional change in the community was found to be significantly associated with the viral load as well as the severity of the symptoms observed in the patients. Preliminary taxonomic analysis indicated that, overall, Firmicutes, Proteobacteria, and Actinobacteriota were amongst the dominating Phyla, while Staphylococcaceae and Corynebacteriaceae were the most abundant Families. Also, the microbiota signatures of the first and third wave were more similar to each other at the phylum level compared to the second wave. However, the abundance of microbes varied greatly between the major groups i.e COVID-19 positives and the negatives at the family level, in the respective waves. A similar observation was made where both the commensals and pathobionts differed in abundance between the patient subgroups. Interestingly, the change in microbial network architecture from first to second wave was driven by opportunistic pathogens such as Paenibacillus, Peptostreptococcus, and Solobacterium while Leptotrichia and Actinomyces were noted to be taxonomic groups driving the changes during the third wave when compared to the second wave. Conclusion: In the Indian cohort examined, SARS-COV-2 infection perturbs the nasopharyngeal microbiome, resulting in lower & varied diversity in the niche, irrespective of the virus variant (& thus, the COVID wave) and the disease severity. Whether these changes assist in COVID-19 disease onset & progression, would be interesting to explore in the future.

Natural language processing (NLP) algorithms process linguistic data in order to discover the associated word semantics and develop models that can describe or even predict the latent meanings of the data. The applications of NLP become multi-fold while dealing with dynamic or temporally evolving datasets (e.g., historical literature). Biological datasets of genome-sequences are interesting since they are sequential as well as dynamic. Here we describe how SARS-CoV-2 genomes and mutations thereof can be processed using fundamental algorithms in NLP to reveal the characteristics and evolution of the virus. We demonstrate applicability of NLP in not only probing the temporal mutational signatures through dynamic topic modelling, but also in tracing the mutation-associations through tracing of semantic drift in genomic mutation records. Our approach also yields promising results in unfolding the mutational relevance to patient health status, thereby identifying putative signatures linked to known/highly speculated mutations of concern.