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Abstract Background Vitiligo is an autoimmune disease characterized by loss of pigmentation in the skin. It affects 0.4 to 2% of the global population, but the factors that trigger autoimmunity remain elusive. Previous work on several immune-mediated dermatological disorders has illuminated the substantial roles of the gut microbiome in disease pathogenesis. Here, we examined the gut microbiome composition in a cohort of vitiligo patients and healthy controls from India, including patients with a family history of the disease. Results Our results show significant alterations in the gut microbiome of vitiligo patients compared to healthy controls, affecting taxonomic and functional profiles as well as community structure. We observed a reduction in the abundance of several bacterial taxa commonly associated with a healthy gut microbiome and noted a decrease in the abundance of SCFA (Short Chain Fatty Acids) producing taxa in the vitiligo group. Observation of a higher abundance of genes linked to bacteria-mediated degradation of intestinal mucus suggested a potential compromise of the gut mucus barrier in vitiligo. Functional analysis also revealed a higher abundance of fatty acid and lipid metabolism-related genes in the vitiligo group. Combined analysis with data from a French cohort of vitiligo also led to the identification of common genera differentiating healthy and gut microbiome across populations. Conclusion Our observations, together with available data, strengthen the role of gut microbiome dysbiosis in symptom exacerbation and possibly pathogenesis in vitiligo. The reported microbiome changes also showed similarities with other autoimmune disorders, suggesting common gut microbiome-mediated mechanisms in autoimmune diseases. Further investigation can lead to the exploration of dietary interventions and probiotics for the management of these conditions.

Summary: The invasive character of Eichhornia crassipes (water hyacinth) is a major threat to global biodiversity and ecosystems. To investigate the genomic basis of invasiveness, we performed the genome and transcriptome sequencing of E. crassipes and reported the genome of 1.11 Gbp size with 63,299 coding genes and N50 of 1.98 Mb. We confirmed a recent whole genome duplication event in E. crassipes that resulted in high intraspecific collinearity and significant expansion in gene families. Further, the orthologs gene clustering analysis and comparative evolutionary analysis with 14 other aquatic invasive and non-invasive angiosperm species revealed adaptive evolution in genes associated with plant-pathogen interaction, hormone signaling, abiotic stress tolerance, heavy metals sequestration, photosynthesis, and cell wall biosynthesis with highly expanded gene families, which contributes toward invasive characteristics of the water hyacinth. However, these characteristics also make water hyacinth an excellent candidate for biofuel production, phytoremediation, and other translational applications.

Abstract 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.

Abstract 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.

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.

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.

Abstract 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 .

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.

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.

Chakraborty et al. present the draft genome of turmeric and transcriptome of leaf tissue. Phylogenetic and comparative evolutionary analyses show evolution in genes associated with secondary metabolism, plant phytohormones signaling, and various biotic and abiotic stress tolerance responses.

Abstract 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.

Summary: 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.

Abstract 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.

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.

Abstract Background Yamuna, a major tributary of Ganga, which flows through the national capital region of Delhi, is among the major polluted rivers in India. The accumulation of various effluents, toxic chemicals, heavy metals, and increased organic load in the Yamuna directly affects the organisms that thrive inside or around this river. It also makes it an ideal site for studying the impact of pollution on the river microflora, which are sentinels of the water quality. Results In this study, the microbial community structure and functional diversity of the Yamuna river water was assessed from the New Delhi region. The community structure of Yamuna during pre-monsoon (June) was found to be significantly different from the post-monsoon (November) time, with Acinetobacter being the most abundant genus during June, and Aeromonas during November. The functional characterization revealed the higher abundance of Methyl-accepting chemotaxis protein in the river water, which could be important for the microbial chemosensory adaptation in the environment. A higher abundance of genes related to nitrogen and sulfur metabolism, metal tolerance, and xenobiotic degradation, and complete degradation pathways of aromatic compounds such as toluene, xylene, benzene and phenol were identified. Further, the results showed the presence of a pool of antibiotic resistance genes in the bacterial microbiome in the Yamuna alongside a large number of broad-spectrum antibiotics, such as carbapenemases and metallo-β-lactamases. Efflux mechanism of resistance was found to dominate among these microbes conferring multi-drug resistance. The Principal Coordinate Analysis of the taxonomic composition of the Yamuna River water with publicly available freshwater and sewage datasets revealed significant differences in the two Yamuna samples and a greater resemblance of pre-monsoon Yamuna sample to sewage sample owing to the higher pollution levels in Yamuna in the pre-monsoon time. Conclusion The metagenomic study of the Yamuna river provides the first insights on the bacterial microbiome composition of this large polluted river, and also helps to understand the dynamics in the community structure and functions due to seasonal variations. The presence of antibiotic resistance genes and functional insights on the metabolic potential of a polluted river microbiome are likely to have several applications in health, biotechnology and bioremediation.

Summary: 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.

Summary: 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.

Sexual selection is a well-known biological process, yet the genomic basis and patterns of sexual selection are not fully understood. The extravagant ornamental plumage of peacock (Pavo cristatus) was instrumental in shaping Charles Darwin's theory of sexual selection and is considered to be an honest signal of its immunocompetence. Here, we used the recently generated draft genome sequence of peafowl (Pavo cristatus) and carried out a comparative analysis across 11 bird genomes that encompass a range of sexual selection and also had high-quality genomic and phenotypic data publically available to study the genomic basis of sexual selection. We found that varying degree of purifying selection was the predominant mechanism of action for sexual selection at the genome-wide scale and observed that sexual selection mostly influences genes regulating gene expression and protein processing. Specifically, the genome-wide phylogenetically corrected regression analysis supported the continuous or ongoing model of sexual selection. Genes involved in nucleic acid binding and gene expression regulation, including a specific regulator of sex-determination known as TRA2A to be under positive selection in the species with high post-copulatory sexual selection manifested as high sperm competition. We also detected specific feather-related and immune-related gene-pairs evolving under similar selection pressures across the 11 species, including peacock (Pavo cristatus), which is consistent with the Hamilton-Zuk hypothesis. The comparative genomics analysis of 11 avian taxa has provided new insights on the molecular underpinnings of sexual selection and identifies specific genomic regions for future in-depth analysis.

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.

ABSTRACT Recently, dysbiosis in the human gut microbiome and shifts in the relative abundances of several bacterial species have been recognized as important factors in colorectal cancer (CRC). However, these studies have been carried out mainly in developed countries where CRC has a high incidence, and it is unclear whether the host-microbiome relationships deduced from these studies can be generalized to the global population. To test if the documented associations between the microbiome and CRC are conserved in a distinct context, we performed metagenomic and metabolomic association studies on fecal samples from 30 CRC patients and 30 healthy controls from two different locations in India, followed by a comparison of CRC data available from other populations. We confirmed the association of Bacteroides and other bacterial taxa with CRC that have been previously reported in other studies. However, the association of CRC with Flavonifractor plautii in Indian patients emerged as a novel finding. The plausible role of F. plautii appears to be linked with the degradation of beneficial anticarcinogenic flavonoids, which was also found to be significantly correlated with the enzymes and modules involved in flavonoid degradation within Indian CRC samples. Thus, we hypothesize that the degradation of beneficial flavonoids might be playing a role in cancer progression within this Indian cohort. We also identified 20 potential microbial taxonomic markers and 33 potential microbial gene markers that discriminate the Indian CRC from healthy microbiomes with high accuracy based on machine learning approaches. IMPORTANCE This study provides novel insights on the CRC-associated microbiome of a unique cohort in India, reveals the potential role of a new bacterium in CRC, and identifies cohort-specific biomarkers, which can potentially be used in noninvasive diagnosis of CRC. The study gains additional significance, as India is among the countries with a very low incidence of CRC, and the diet and lifestyle in India have been associated with a distinct gut microbiome in healthy Indians compared to other global populations. Thus, in this study, we hypothesize a unique relationship between CRC and the gut microbiome in an Indian population.

The increase in greenhouse gases with high global warming potential such as methane is a matter of concern and requires multifaceted efforts to reduce its emission and increase its mitigation from the environment. Microbes such as methanotrophs can assist in methane mitigation. To understand the metabolic capabilities of methanotrophs, a complete genome-scale metabolic model (GSMM) of an obligate methanotroph, Methylococcus capsulatus str. Bath was reconstructed. The model contains 535 genes, 899 reactions and 865 metabolites and is named iMC535. The predictive potential of the model was validated using previously-reported experimental data. The model predicted the Entner–Duodoroff pathway to be essential for the growth of this bacterium, whereas the Embden–Meyerhof–Parnas pathway was found non-essential. The performance of the model was simulated on various carbon and nitrogen sources and found that M. capsulatus can grow on amino acids. The analysis of network topology of the model identified that six amino acids were in the top-ranked metabolic hubs. Using flux balance analysis, 29% of the metabolic genes were predicted to be essential, and 76 double knockout combinations involving 92 unique genes were predicted to be lethal. In conclusion, we have reconstructed a GSMM of a methanotroph Methylococcus capsulatus str. Bath. This is the first high quality GSMM of a Methylococcus strain which can serve as an important resource for further strain-specific models of the Methylococcus genus, as well as identifying the biotechnological potential of M. capsulatus Bath.

Several scalp microbiome studies from different populations have revealed the association of dandruff with bacterial and fungal dysbiosis. However, the functional role of scalp microbiota in scalp disorders and health remains scarcely explored. Here, we examined the bacterial and fungal diversity of the scalp microbiome and their potential functional role in the healthy and dandruff scalp of 140 Indian women. Propionibacterium acnes and Staphylococcus epidermidis emerged as the core bacterial species, where the former was associated with a healthy scalp and the latter with dandruff scalp. Along with the commonly occurring Malassezia species (M. restricta and M. globosa) on the scalp, a strikingly high association of dandruff with yet uncharacterized Malassezia species was observed in the core mycobiome. Functional analysis showed that the fungal microbiome was enriched in pathways majorly implicated in cell-host adhesion in the dandruff scalp, while the bacterial microbiome showed a conspicuous enrichment of pathways related to the synthesis and metabolism of amino acids, biotin, and other B-vitamins, which are reported as essential nutrients for hair growth. A systematic measurement of scalp clinical and physiological parameters was also carried out, which showed significant correlations with the microbiome and their associated functional pathways. The results point toward a new potential role of bacterial commensals in maintaining the scalp nutrient homoeostasis and highlights an important and yet unknown role of the scalp microbiome, similar to the gut microbiome. This study, therefore, provides new perspectives on the better understanding of the pathophysiology of dandruff.

The unique ornamental features and extreme sexual traits of Peacock have always intrigued scientists and naturalists for centuries. However, the genomic basis of these phenotypes are yet unknown. Here, we report the first genome sequence and comparative analysis of peacock with the high quality genomes of chicken, turkey, duck, flycatcher and zebra finch. Genes involved in early developmental pathways including TGF-β, BMP, and Wnt signaling, which have been shown to be involved in feather patterning, bone morphogenesis, and skeletal muscle development, revealed signs of adaptive evolution and provided useful clues on the phenotypes of peacock. Innate and adaptive immune genes involved in complement system and T-cell response also showed signs of adaptive evolution in peacock suggesting their possible role in building a robust immune system which is consistent with the predictions of the Hamilton–Zuk hypothesis. This study provides novel genomic and evolutionary insights into the molecular understanding toward the phenotypic evolution of Indian peacock.

The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93%) and Matthews's correlation coefficient (0.84). The performances of all the three models were comparable (Matthews's correlation coefficient = 0.84–0.87) on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better (R2 = 0.84) than the multi-linear regression (MLR) and partial least square regression (PLSR) models, whereas, the partial least squares based regression model for the prediction of permeability (caco-2) performed better (R2 = 0.68) in comparison to the random forest and MLR based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity, solubility, and permeability of small molecules.

IL-17 cytokines are pro-inflammatory cytokines and are crucial in host defense against various microbes. Induction of these cytokines by microbial antigens has been investigated in the case of ischemic brain injury, gingivitis, candidiasis, autoimmune myocarditis, etc. In this study, we have investigated the ability of amino acid sequence of antigens to induce IL-17 response using machine-learning approaches. A total of 338 IL-17-inducing and 984 IL-17 non-inducing peptides were retrieved from Immune Epitope Database. 80% of the data were randomly selected as training dataset and rest 20% as validation dataset. To predict the IL-17-inducing ability of peptides/protein antigens, different sequence-based machine-learning models were developed. The performance of support vector machine (SVM) and random forest (RF) was compared with different parameters to predict IL-17-inducing epitopes (IIEs). The dipeptide composition-based SVM-model displayed an accuracy of 82.4% with Matthews correlation coefficient = 0.62 at polynomial (t = 1) kernel on 10-fold cross-validation and outperformed RF. Amino acid residues Leu, Ser, Arg, Asn, and Phe and dipeptides LL, SL, LK, IL, LI, NL, LR, FK, SF, and LE are abundant in IIEs. The present tool helps in the identification of IIEs using machine-learning approaches. The induction of IL-17 plays an important role in several inflammatory diseases, and identification of such epitopes would be of great help to the immunologists. It is freely available at http://metagenomics.iiserb.ac.in/IL17eScan/ and http://metabiosys.iiserb.ac.in/IL17eScan/.

Background In light of the rapid decrease in fossils fuel reserves and an increasing demand for energy, novel methods are required to explore alternative biofuel production processes to alleviate these pressures. A wide variety of molecules which can either be used as biofuels or as biofuel precursors are produced using microbial enzymes. However, the common challenges in the industrial implementation of enzyme catalysis for biofuel production are the unavailability of a comprehensive biofuel enzyme resource, low efficiency of known enzymes, and limited availability of enzymes which can function under extreme conditions in the industrial processes. Methods We have developed a comprehensive database of known enzymes with proven or potential applications in biofuel production through text mining of PubMed abstracts and other publicly available information. A total of 131 enzymes with a role in biofuel production were identified and classified into six enzyme classes and four broad application categories namely ‘Alcohol production’, ‘Biodiesel production’, ‘Fuel Cell’ and ‘Alternate biofuels’. A prediction tool ‘Benz’ was developed to identify and classify novel homologues of the known biofuel enzyme sequences from sequenced genomes and metagenomes. ‘Benz’ employs a hybrid approach incorporating HMMER 3.0 and RAPSearch2 programs to provide high accuracy and high speed for prediction. Results Using the Benz tool, 153,754 novel homologues of biofuel enzymes were identified from 23 diverse metagenomic sources. The comprehensive data of curated biofuel enzymes, their novel homologs identified from diverse metagenomes, and the hybrid prediction tool Benz are presented as a web server which can be used for the prediction of biofuel enzymes from genomic and metagenomic datasets. The database and the Benz tool is publicly available at http://metabiosys.iiserb.ac.in/biofueldb& http://metagenomics.iiserb.ac.in/biofueldb.