Genome sequencing and functional analysis of a multipurpose medicinal herb Tinospora cordifolia (Giloy).

Tinospora cordifolia (Willd.) Hook.f. & Thomson, also known as Giloy, is among the most important medicinal plants that have numerous therapeutic applications in human health due to the production of a diverse array of secondary metabolites. To gain genomic insights into the medicinal properties of T. cordifolia, the genome sequencing was carried out using 10× Genomics linked read and Nanopore long-read technologies. The draft genome assembly of T. cordifolia was comprised of 1.01 Gbp, which is the genome sequenced from the plant family Menispermaceae. We also performed the genome size estimation for T. cordifolia, which was found to be 1.13 Gbp. The deep sequencing of transcriptome from the leaf tissue was also performed. The genome and transcriptome assemblies were used to construct the gene set, resulting in 17,245 coding gene sequences. Further, the phylogenetic position of T. cordifolia was also positioned as basal eudicot by constructing a genome-wide phylogenetic tree using multiple species. Further, a comprehensive comparative evolutionary analysis of gene families contraction/expansion and multiple signatures of adaptive evolution was performed. The genes involved in benzyl iso-quinoline alkaloid, terpenoid, lignin and flavonoid biosynthesis pathways were found with signatures of adaptive evolution. These evolutionary adaptations in genes provide genomic insights into the presence of diverse medicinal properties of this plant. The genes involved in the common symbiosis signalling pathway associated with endosymbiosis (Arbuscular Mycorrhiza) were found to be adaptively evolved. The genes involved in adventitious root formation, peroxisome biogenesis, biosynthesis of phytohormones, and tolerance against abiotic and biotic stresses were also found to be adaptively evolved in T. cordifolia.

Metagenomic exploration of Andaman region of the Indian Ocean.

Ocean microbiome is crucial for global biogeochemical cycles and primary productivity. Despite numerous studies investigating the global ocean microbiomes, the microbiome composition of the Andaman region of the Indian Ocean remains largely unexplored. While this region harbors pristine biological diversity, the escalating anthropogenic activities along coastal habitats exert an influence on the microbial ecology and impact the aquatic ecosystems. We investigated the microbiome composition in the coastal waters of the Andaman Islands by 16S rRNA gene amplicon and metagenomic shotgun sequencing approaches and compared it with the Tara Oceans Consortium. In the coastal waters of the Andaman Islands, a significantly higher abundance and diversity of Synechococcus species was observed with a higher abundance of photosynthesis pigment-related genes to adapt to variable light conditions and nutrition. In contrast, Prochlorococcus species showed higher abundance in open ocean water samples of the Indian Ocean region, with a relatively limited functional diversity. A higher abundance of antibiotic-resistance genes was also noted in the coastal waters region. We also updated the ocean microbiome gene catalog with 93,172 unique genes from the Andaman coastal water microbiome. This study provides valuable insights into the Indian Ocean microbiome and supplements the global marine microbial ecosystem studies.

Genome sequencing of Syzygium cumini (jamun) reveals adaptive evolution in secondary metabolism pathways associated with its medicinal properties.

Syzygium cumini, also known as jambolan or jamun, is an evergreen tree widely known for its medicinal properties, fruits, and ornamental value. To understand the genomic and evolutionary basis of its medicinal properties, we sequenced S. cumini genome for the first time from the world's largest tree genus Syzygium using Oxford Nanopore and 10x Genomics sequencing technologies. We also sequenced and assembled the transcriptome of S. cumini in this study. The tetraploid and highly heterozygous draft genome of S. cumini had a total size of 709.9 Mbp with 61,195 coding genes. The phylogenetic position of S. cumini was established using a comprehensive genome-wide analysis including species from 18 Eudicot plant orders. The existence of neopolyploidy in S. cumini was evident from the higher number of coding genes and expanded gene families resulting from gene duplication events compared to the other two sequenced species from this genus. Comparative evolutionary analyses showed the adaptive evolution of genes involved in the phenylpropanoid-flavonoid (PF) biosynthesis pathway and other secondary metabolites biosynthesis such as terpenoid and alkaloid in S. cumini, along with genes involved in stress tolerance mechanisms, which was also supported by leaf transcriptome data generated in this study. The adaptive evolution of secondary metabolism pathways is associated with the wide range of pharmacological properties, specifically the anti-diabetic property, of this species conferred by the bioactive compounds that act as nutraceutical agents in modern medicine.

Application of artificial intelligence approaches to predict the metabolism of xenobiotic molecules by human gut microbiome.

A highly complex, diverse, and dense community of more than 1,000 different gut bacterial species constitutes the human gut microbiome that harbours vast metabolic capabilities encoded by more than 300,000 bacterial enzymes to metabolise complex polysaccharides, orally administered drugs/xenobiotics, nutraceuticals, or prebiotics. One of the implications of gut microbiome mediated biotransformation is the metabolism of xenobiotics such as medicinal drugs, which lead to alteration in their pharmacological properties, loss of drug efficacy, bioavailability, may generate toxic byproducts and sometimes also help in conversion of a prodrug into its active metabolite. Given the diversity of gut microbiome and the complex interplay of the metabolic enzymes and their diverse substrates, the traditional experimental methods have limited ability to identify the gut bacterial species involved in such biotransformation, and to study the bacterial species-metabolite interactions in gut. In this scenario, computational approaches such as machine learning-based tools presents unprecedented opportunities and ability to predict the gut bacteria and enzymes that can potentially metabolise a candidate drug. Here, we have reviewed the need to identify the gut microbiome-based metabolism of xenobiotics and have provided comprehensive information on the available methods, tools, and databases to address it along with their scope and limitations.

Genome of Phyllanthus emblica: the medicinal plant Amla with super antioxidant properties.

Phyllanthus emblica or Indian gooseberry, commonly known as amla, is an important medicinal horticultural plant used in traditional and modern medicines. It bears stone fruits with immense antioxidant properties due to being one of the richest natural sources of vitamin C and numerous flavonoids. This study presents the first genome sequencing of this species performed using 10x Genomics and Oxford Nanopore Technology. The draft genome assembly was 519 Mbp in size and consisted of 4,384 contigs, N50 of 597 Kbp, 98.4% BUSCO score, and 37,858 coding sequences. This study also reports the genome-wide phylogeny of this species with 26 other plant species that resolved the phylogenetic position of P. emblica. The presence of three ascorbate biosynthesis pathways including L-galactose, galacturonate, and myo-inositol pathways was confirmed in this genome. A comprehensive comparative evolutionary genomic analysis including gene family expansion/contraction and identification of multiple signatures of adaptive evolution provided evolutionary insights into ascorbate and flavonoid biosynthesis pathways and stone fruit formation through lignin biosynthesis. The availability of this genome will be beneficial for its horticultural, medicinal, dietary, and cosmetic applications and will also help in comparative genomics analysis studies.

High-quality genome assemblies provide clues on the evolutionary advantage of blue peafowl over green peafowl.

An intriguing example of differential adaptability is the case of two Asian peafowl species, Pavo cristatus (blue peafowl) and Pavo muticus (green peafowl), where the former has a "Least Concern" conservation status and the latter is an "Endangered" species. To understand the genetic basis of this differential adaptability of the two peafowl species, a comparative analysis of these species is much needed to gain the genomic and evolutionary insights. Thus, we constructed a high-quality genome assembly of blue peafowl with an N50 value of 84.81 Mb (pseudochromosome-level assembly), and a high-confidence coding gene set to perform the genomic and evolutionary analyses of blue and green peafowls with 49 other avian species. The analyses revealed adaptive evolution of genes related to neuronal development, immunity, and skeletal muscle development in these peafowl species. Major genes related to axon guidance such as NEO1 and UNC5, semaphorin (SEMA), and ephrin receptor showed adaptive evolution in peafowl species. However, blue peafowl showed the presence of 42% more coding genes compared to the green peafowl along with a higher number of species-specific gene clusters, segmental duplicated genes and expanded gene families, and comparatively higher evolution in neuronal and developmental pathways. Blue peafowl also showed longer branch length compared to green peafowl in the species phylogenetic tree. These genomic insights obtained from the high-quality genome assembly of P. cristatus constructed in this study provide new clues on the superior adaptability of the blue peafowl over green peafowl despite having a recent species divergence time.

GutBug: A Tool for Prediction of Human Gut Bacteria Mediated Biotransformation of Biotic and Xenobiotic Molecules Using Machine Learning.

Dietary components and bioactive molecules present in functional foods and nutraceuticals provide various beneficial effects including modulation of host gut microbiome. These metabolites along with orally administered drugs can be potentially bio-transformed by gut microbiome, which can alter their bioavailability and intended biological or pharmacological activity resulting in individual or population-specific variation in drug and dietary responses. Experimental determination of microbiome-mediated metabolism of orally ingested molecules is difficult due to the enormous diversity and complexity of the gut microbiome. To address this problem, we developed "GutBug", a web-based resource that predicts all possible bacterial metabolic enzymes that can potentially biotransform xenobiotics and biotic molecules using a combination of machine learning, neural networks and chemoinformatic methods. Using 3,457 enzyme substrates for training and a curated database of 363,872 enzymes from ∼700 gut bacterial strains, GutBug can predict complete EC number of the bacterial enzymes involved in a biotransformation reaction of the given molecule along with the reaction centres with accuracies between 0.78 and 0.97 across different reaction classes. Validation of GutBug's performance using 27 molecules known to be biotransformed by human gut bacteria, including complex polysaccharides, flavonoids, and oral drugs further attests to GutBug's accuracy and utility. Thus, GutBug enhances our understanding of various metabolite-gut bacterial interactions and their resultant effects on the human host health across populations, which will find enormous applications in diet design and intervention, identification and administration of new prebiotics, development of nutraceutical products, and improvements in drug designing. GutBug is available at https://metabiosys.iiserb.ac.in/gutbug.

Genome sequencing and de novo and reference-based genome assemblies of Bos indicus breeds.

BACKGROUND: Indian cattle breeds (Bos indicus) are known for their remarkable adaptability to hot and humid climates, higher nutritious quality of milk, better disease tolerance, and greater ability to perform in poor feed compared to taurine cattle (Bos taurus). Distinct phenotypic differences are observed among the B. indicus breeds; however, the whole genome sequences were unavailable for these indigenous breeds. OBJECTIVE: We aimed to perform whole genome sequencing to construct the draft genome assemblies of four B. indicus breeds; Ongole, Kasargod Dwarf, Kasargod Kapila, and Vechur (the smallest cattle of the world). METHODS: We sequenced the whole genomes using Illumina short-read technology, and constructed de novo and reference-based genome assemblies of these native B. indicus breeds for the first time. RESULTS: The draft de novo genome assemblies of B. indicus breeds ranged from 1.98 to 3.42 Gbp. We also constructed the mitochondrial genome assemblies (~ 16.3 Kbp), and yet unavailable 18S rRNA marker gene sequences of these B. indicus breeds. The genome assemblies helped to identify the bovine genes related to distinct phenotypic characteristics and other biological processes for this species compared to B. taurus, which are plausibly responsible for providing better adaptive traits. We also identified the genes that showed sequence variation in dwarf and non-dwarf breeds of B. indicus compared to B. taurus. CONCLUSIONS: The genome assemblies of these Indian cattle breeds, the 18S rRNA marker genes, and identification of the distinct genes in B. indicus breeds compared to B. taurus will help in future studies on these cattle species.

Exploring variation in the fecal microbial communities of Kasaragod Dwarf and Holstein crossbred cattle.

The gut microbiota and its impact on health and nutrition in animals, including cattle has been of intense interest in recent times. Cattle, in particular indigenous varieties like Kasaragod Dwarf cow, have not received the due consideration given to other non-native cattle breeds, and the composition of their fecal microbiome is yet to be established. This study applied 16S rRNA high-throughput sequencing of fecal samples and compared the Kasaragod Dwarf with the highly prevalent Holstein crossbred cattle. Variation in their microbial composition was confirmed by marker gene-based taxonomic analysis. Principle Coordinate Analysis (PCoA) showed the distinct microbial architecture of the two cattle types. While the two cattle types possess unique signature taxa, in Kasaragod Dwarf cattle, many of the identified genera, including Anaerovibrio, Succinivibrio, Roseburia, Coprococcus, Paludibacter, Sutterella, Coprobacillus, and Ruminobacter, have previously been shown to be present in higher abundance in animals with higher feed efficiency. This is the first report of Kasaragod Dwarf cattle fecal microbiome profiling. Our findings highlight the predominance of specific taxa potentially associated with different fermentation products and feed efficiency phenotypes in Kasaragod Dwarf cattle compared to Holstein crossbred cattle.

MP4: a machine learning based classification tool for prediction and functional annotation of pathogenic proteins from metagenomic and genomic datasets.

Bacteria can exceptionally evolve and develop pathogenic features making it crucial to determine novel pathogenic proteins for specific therapeutic interventions. Therefore, we have developed a machine-learning tool that predicts and functionally classifies pathogenic proteins into their respective pathogenic classes. Through construction of pathogenic proteins database and optimization of ML algorithms, Support Vector Machine was selected for the model construction. The developed SVM classifier yielded an accuracy of 81.72% on the blind-dataset and classified the proteins into three classes: Non-pathogenic proteins (Class-1), Antibiotic Resistance Proteins and Toxins (Class-2), and Secretory System Associated and capsular proteins (Class-3). The classifier provided an accuracy of 79% on real dataset-1, and 72% on real dataset-2. Based on the probability of prediction, users can estimate the pathogenicity and annotation of proteins under scrutiny. Tool will provide accurate prediction of pathogenic proteins in genomic and metagenomic datasets providing leads for experimental validations. Tool is available at: http://metagenomics.iiserb.ac.in/mp4 .

Genome sequencing and comparative analysis of Ficus benghalensis and Ficus religiosa species reveal evolutionary mechanisms of longevity.

Ficus benghalensis and Ficus religiosa are large woody trees well known for their long lifespan, ecological and traditional significance, and medicinal properties. To understand the genomic and evolutionary aspects of these characteristics, the whole genomes of these Ficus species were sequenced using 10x Genomics linked reads and Oxford Nanopore long reads. The draft genomes of F. benghalensis and F. religiosa comprised of 392.89 Mbp and 332.97 Mbp, respectively. We established the genome-wide phylogenetic positions of the two Ficus species with respect to 50 other Angiosperm species. Comparative evolutionary analyses with other phylogenetically closer Eudicot species revealed adaptive evolution in genes involved in key cellular mechanisms associated with prolonged survival including phytohormones signaling, senescence, disease resistance, and abiotic stress tolerance, which provide genomic insights into the mechanisms conferring longevity and suggest that longevity is a multifaceted phenomenon. This study also provides clues on the existence of CAM pathway in these Ficus species.

Assessing the Effect of Smokeless Tobacco Consumption on Oral Microbiome in Healthy and Oral Cancer Patients.

Oral cancer is a globally widespread cancer that features among the three most prevalent cancers in India. The risk of oral cancer is elevated by factors such as tobacco consumption, betel-quid chewing, excessive alcohol consumption, unhygienic oral condition, sustained viral infections, and also due to dysbiosis in microbiome composition of the oral cavity. Here, we performed an oral microbiome study of healthy and oral cancer patients to decipher the microbial dysbiosis due to the consumption of smokeless-tobacco-based products and also revealed the tobacco-associated microbiome. The analysis of 196 oral microbiome samples from three different oral sites of 32 healthy and 34 oral squamous cell carcinoma (OSCC) patients indicated health status, site of sampling, and smokeless tobacco consumption as significant covariates associated with oral microbiome composition. Significant similarity in oral microbiome composition of smokeless-tobacco-consuming healthy samples and OSCC samples inferred the possible role of smokeless tobacco consumption in increasing inflammation-associated species in oral microbiome. Significantly higher abundance of Streptococcus was found to adequately discriminate smokeless-tobacco-non-consuming healthy samples from smokeless-tobacco-consuming healthy samples and contralateral healthy site of OSCC samples from the tumor site of OSCC samples. Comparative analysis of oral microbiome from another OSCC cohort also confirmed Streptococcus as a potential marker for healthy oral microbiome. Gram-negative microbial genera such as Prevotella, Capnocytophaga, and Fusobacterium were found to be differentially abundant in OSCC-associated microbiomes and can be considered as potential microbiome marker genera for oral cancer. Association with lipopolysaccharide (LPS) biosynthesis pathway further confirms the differential abundance of Gram-negative marker genera in OSCC microbiomes.

Genome sequencing of turmeric provides evolutionary insights into its medicinal properties.

Curcuma longa, or turmeric, is traditionally known for its immense medicinal properties and has diverse therapeutic applications. However, the absence of a reference genome sequence is a limiting factor in understanding the genomic basis of the origin of its medicinal properties. In this study, we present the draft genome sequence of C. longa, belonging to Zingiberaceae plant family, constructed using 10x Genomics linked reads and Oxford Nanopore long reads. For comprehensive gene set prediction and for insights into its gene expression, transcriptome sequencing of leaf tissue was also performed. The draft genome assembly had a size of 1.02 Gbp with ~70% repetitive sequences, and contained 50,401 coding gene sequences. The phylogenetic position of C. longa was resolved through a comprehensive genome-wide analysis including 16 other plant species. Using 5,388 orthogroups, the comparative evolutionary analysis performed across 17 species including C. longa revealed evolution in genes associated with secondary metabolism, plant phytohormones signaling, and various biotic and abiotic stress tolerance responses. These mechanisms are crucial for perennial and rhizomatous plants such as C. longa for defense and environmental stress tolerance via production of secondary metabolites, which are associated with the wide range of medicinal properties in C. longa.

Western and non-western gut microbiomes reveal new roles of Prevotella in carbohydrate metabolism and mouth-gut axis.

The abundance and diversity of host-associated Prevotella species have a profound impact on human health. To investigate the composition, diversity, and functional roles of Prevotella in the human gut, a population-wide analysis was carried out on 586 healthy samples from western and non-western populations including the largest Indian cohort comprising of 200 samples, and 189 Inflammatory Bowel Disease samples from western populations. A higher abundance and diversity of Prevotella copri species enriched in complex plant polysaccharides metabolizing enzymes, particularly pullulanase containing polysaccharide-utilization-loci (PUL), were found in Indian and non-western populations. A higher diversity of oral inflammations-associated Prevotella species and an enrichment of virulence factors and antibiotic resistance genes in the gut microbiome of western populations speculates an existence of a mouth-gut axis. The study revealed the landscape of Prevotella composition in the human gut microbiome and its impact on health in western and non-western populations.

Longitudinal study of the scalp microbiome suggests coconut oil to enrich healthy scalp commensals.

Dandruff is a recurrent chronic scalp disorder, affecting majority of the population worldwide. Recently a metagenomic study of the Indian scalp microbiome described an imperative role of bacterial commensals in providing essential vitamins and amino acids to the scalp. Coconut oil and its formulations are commonly applied on the scalp in several parts of the world to maintain scalp health. Thus, in this study we examined the effect of topical application of coconut oil on the scalp microbiome (bacterial and fungal) at the taxonomic and functional levels and their correlation with scalp physiological parameters. A 16-weeks-long time-course study was performed including 12-weeks of treatment and 4-weeks of relapse phase on a cohort of 140 (70 healthy and 70 dandruff) Indian women, resulting in ~ 900 metagenomic samples. After the treatment phase, an increase in the abundance of Cutibacterium acnes and Malassezia globosa in dandruff scalp was observed, which were negatively correlated to dandruff parameters. At the functional level, an enrichment of healthy scalp-related bacterial pathways, such as biotin metabolism and decrease in the fungal pathogenesis pathways was observed. The study provides novel insights on the effect of coconut oil in maintaining a healthy scalp and in modulating the scalp microbiome.

The genome sequence of Aloe vera reveals adaptive evolution of drought tolerance mechanisms.

Aloe vera is a species from Asphodelaceae family having characteristics like drought resistance and numerous medicinal properties. However, the genetic basis of these phenotypes is yet unknown primarily due to unavailability of its genome sequence. Thus, we report the first Aloe vera genome sequence comprising of 12.93 Gbp and harboring 86,177 protein-coding genes. It is the first genome from Asphodelaceae family and the largest angiosperm genome sequenced and assembled till date. We also report the first genome-wide phylogeny of monocots including Aloe vera to resolve its phylogenetic position. The comprehensive comparative analysis of Aloe vera with other available high-quality monocot genomes revealed adaptive evolution in several genes of drought stress response, CAM pathway, and circadian rhythm and positive selection in DNA damage response genes in Aloe vera. This study provides clues on the genetic basis of evolution of drought stress tolerance capabilities of Aloe vera.

SkinBug: an artificial intelligence approach to predict human skin microbiome-mediated metabolism of biotics and xenobiotics.

In addition to being pivotal for the host health, the skin microbiome possesses a large reservoir of metabolic enzymes, which can metabolize molecules (cosmetics, medicines, pollutants, etc.) that form a major part of the skin exposome. Therefore, to predict the complete metabolism of any molecule by skin microbiome, a curated database of metabolic enzymes (1,094,153), reactions, and substrates from ∼900 bacterial species from 19 different skin sites were used to develop "SkinBug." It integrates machine learning, neural networks, and chemoinformatics methods, and displays a multiclass multilabel accuracy of up to 82.4% and binary accuracy of up to 90.0%. SkinBug predicts all possible metabolic reactions and associated enzymes, reaction centers, skin microbiome species harboring the enzyme, and the respective skin sites. Thus, SkinBug will be an indispensable tool to predict xenobiotic/biotic metabolism by skin microbiome and will find applications in exposome and microbiome studies, dermatology, and skin cancer research.

Emergency Department Well-being Initiatives During the COVID-19 Pandemic: An After-action Review.

The COVID-19 pandemic has significantly impacted the well-being of our health care professionals, particularly frontline providers in the emergency department (ED). Our ED, located in New York City, was severely affected, exposing the staff to a combination of unique stressors. Our ED Wellness Committee responded by implementing various initiatives focusing on the physical, mental, and social needs of our providers to support them through this difficult time. The initiatives we describe offer a framework that may help other departments understand the importance of provider well-being during a pandemic.

The Gene Catalog and Comparative Analysis of Gut Microbiome of Big Cats Provide New Insights on Panthera Species.

Majority of metagenomic studies in the last decade have focused on revealing the gut microbiomes of humans, rodents, and ruminants; however, the gut microbiome and genic information (gene catalog) of large felids such as Panthera species are largely unknown to date. In this study, the gut bacterial, fungal, and viral metagenomic composition was assessed from three Panthera species (lion, leopard, and tiger) of Indian origin, which were consuming the same diet and belonged to the same geographical location. A non-redundant bacterial gene catalog of the Panthera gut consisting of 1,507,035 putative genes was constructed from 27 Panthera individuals, which revealed a higher abundance of purine metabolism genes correlating with their purine-rich dietary intake. Analysis with Carbohydrate Active enZyme (CAZy) and MEROPS databases identified enrichment of glycoside hydrolases (GHs), glycoside-transferases, and collagenases in the gut, which are important for nutrient acquisition from animal biomass. The bacterial, fungal, and viral community analysis provided the first comprehensive insights into the Panthera-specific microbial community. The Panthera gene catalog and the largest comparative study of the gut bacterial composition of 68 individuals of Carnivora species from different geographical locations and diet underscore the role of diet and geography in shaping the Panthera gut microbiome, which is significant for the health and conservation management of these highly endangered species.

Molib: A machine learning based classification tool for the prediction of biofilm inhibitory molecules.

Identification of biofilm inhibitory small molecules appears promising for therapeutic intervention against biofilm-forming bacteria. However, the experimental identification of such molecules is a time-consuming task, and thus, the computational approaches emerge as promising alternatives. We developed the 'Molib' tool to predict the biofilm inhibitory activity of small molecules. We curated a training dataset of biofilm inhibitory molecules, and the structural and chemical features were used for feature selection, followed by algorithms optimization and building of machine learning-based classification models. On five-fold cross validation, Random Forest-based descriptor, fingerprint and hybrid classification models showed accuracies of 0.93, 0.88 and 0.90, respectively. The performances of all models were evaluated on two different validation datasets including biofilm inhibitory and non-inhibitory molecules, attesting to its accuracy (≥ 0.90). The Molib web server would serve as a highly useful and reliable tool for the prediction of biofilm inhibitory activity of small molecules.