Biology subjects

Singh, N. K.

Publications and source records attributed to Singh, N. K..

35 records · Page 2Linked to original sources

bioRxiv2022

An unbiased characterization of the HLA-E and CD94/NKG2x peptide repertoire reveals peptide ligands that skew NK cell activation

HLA-E is a non-classical class I MHC protein involved in innate and adaptive immune recognition. While recent studies have shown HLA-E can present diverse peptides to NK cells and T cells, the HLA-E and NK receptor peptide repertoire has remained poorly defined, with only a limited number of peptide ligands identified. Here we screen a yeast-displayed peptide library in the context of HLA-E to identify 500 high-confidence unique peptides that bind both HLA-E and CD94/NKG2A or CD94/NKG2C. Utilizing the sequences identified via yeast display selections, we train prediction algorithms and identify human and cytomegalovirus (CMV) proteome-derived, HLA-E-presented peptides capable of binding and signaling through both CD94/NKG2A and CD94/NKG2C. In addition, we identify peptides which selectively activate NKG2C+ NK cells. Taken together, characterization of the HLA-E-binding peptide repertoire and identification of NK activity-modulating peptides present opportunities for studies of NK cell regulation in health and disease, in addition to vaccine and therapeutic design.

immunology↗

bioRxiv2022

Genome-wide association mapping reveals genes underlying population-level metabolome diversity in a fungal crop pathogen

BackgroundFungi produce a wide range of specialized metabolites (SMs) involved in biotic interactions. Pathways for the production of SMs are often encoded in clusters of tightly arranged genes identified as biosynthetic gene clusters. Such gene clusters can undergo horizontal gene transfers between species and rapid evolutionary change within species. The acquisition, rearrangement and deletion of gene clusters can generate significant metabolome diversity. However, the genetic basis underlying variation in SM production remains poorly understood. ResultsHere, we analyzed metabolite production of a large population of the fungal pathogen of wheat, Zymoseptoria tritici. The pathogen causes major yield losses and shows variation in gene clusters. We performed untargeted ultra-high performance liquid chromatography-high resolution mass spectrometry to profile the metabolite diversity among 102 isolates of the same species. We found substantial variation in the abundance of the detected metabolites among isolates. Integrating whole-genome sequencing data, we performed metabolite genome-wide association mapping to identify loci underlying variation in metabolite production (i.e. metabolite-GWAS). We found that significantly associated SNP reside mostly in coding and gene regulatory regions. Associated genes encode mainly transport and catalytic activities. The metabolite-GWAS identified also a polymorphism in the 3UTR region of a virulence gene related to metabolite production and showing expression variation. ConclusionsTaken together, our study provides a significant resource to unravel polymorphism underlying metabolome diversity within a species. Integrating metabolome screens should be feasible for a range of different plant pathogens and help prioritize molecular studies.

microbiology↗

bioRxiv2022

Identification of highly cross-reactive mimotopes for a public T cell response in murine melanoma

While immune checkpoint blockade results in durable responses for some patients, many others have not experienced such benefits. These treatments rely upon reinvigorating specific T cell-antigen interactions. However, it is often unknown what antigens are being recognized by T cells or how to potently induce antigen-specific responses in a broadly applicable manner. Here, we characterized the CD8+ T cell response to a murine model of melanoma following combination immunotherapy to determine the basis of tumor recognition. Sequencing of tumor-infiltrating T cells revealed a repertoire of highly homologous TCR sequences that were particularly expanded in treated mice and which recognized an antigen from an endogenous retrovirus. While vaccination against this peptide failed to raise a protective T cell response in vivo, engineered antigen mimotopes induced a significant expansion of CD8+ T cells cross-reactive to the original antigen. Vaccination with mimotopes resulted in killing of antigen-loaded cells in vivo yet showed modest survival benefit in a prophylactic vaccine paradigm. Together, this work demonstrates the identification of a dominant tumor-associated antigen and generation of mimotopes which can induce robust functional T cell responses that are cross-reactive to the endogenous antigen across multiple individuals.

immunology↗

bioRxiv2022

SARS-CoV-2 Variant Delta Potently Suppresses Innate Immune Response and Evades Interferon-Activated Antiviral Responses

Delta variant of SARS-CoV-2 has caused more severe infections than its previous variants. We studied the host innate immune response to Delta, Alpha and two earlier variants to map the evolution of the recent ones. Our biochemical and transcriptomic studies reveal that Alpha and Delta have progressively evolved over the ancestral variants by silencing innate immune response, thereby limiting cytokine and chemokine production. Though Alpha silenced RLR pathway just as Delta, it failed to persistently silence the innate immune response unlike Delta. Both Alpha and Delta have evolved to resist IFN treatment while they are still susceptible to RLR activation, further highlighting the importance of RLR-mediated, IFN-independent mechanisms in restricting SARS-CoV-2. Our studies reveal that SARS-CoV-2 Delta has integrated multiple mechanisms to silence host innate immune response and evade IFN response. Deltas silent replication and sustained suppression of host innate immune response, possibly resulting in delayed or reduced intervention by the adaptive immune response, could potentially contribute to the severe symptoms and poor recovery index associated with it.

immunology↗

bioRxiv2022

Machine learning algorithm to characterize antimicrobial resistance associated with the International Space Station surface microbiome

BackgroundAntimicrobial Resistance (AMR) has a detrimental impact on human health on Earth and it is equally concerning in other environments such as space due to microgravity, radiation and confinement, especially for long-distance space travel. The International Space Station (ISS) is ideal for investigating microbial diversity and virulence. The shotgun metagenomics data of the ISS generated during the Microbial Tracking - 1 (MT-1) project and resulting metagenome-assembled genomes (MAGs) across three flights in eight different locations during 12 months were used in this study. The objective of this study was to identify the AMR genes associated with whole genomes of 227 cultivable strains, 21 shotgun metagenome sequences, and 24 MAGs retrieved from the ISS environmental samples that were treated with propidium monoazide (PMA; viable microbes). ResultsWe have analyzed the data using a deep learning model, allowing us to go beyond traditional cut-offs based only on high DNA sequence similarity and extending the catalog of AMR genes. Our results in PMA treated samples revealed AMR dominance in the last flight for Kalamiella piersonii, a bacteria related to urinary tract infection in humans. The analysis of 227 pure strains isolated from the MT-1 project revealed hundreds of antibiotic resistance genes from many isolates, including two top-ranking species that corresponded to strains of Enterobacter bugandensis and Bacillus cereus. Computational predictions were experimentally validated by antibiotic resistance profiles in these two species, showing a high degree of concordance. Specifically, disc assay data confirmed the high resistance of these two pathogens to various beta-lactam antibiotics. ConclusionOverall, our computational predictions and validation analyses demonstrate the advantages of machine learning to uncover concealed AMR determinants in metagenomics datasets, expanding the understanding of the ISS environmental microbiomes and their pathogenic potential in humans.

bioinformatics↗

bioRxiv2021

Metabolic modeling of the International Space Station microbiome reveals key microbial interactions

BackgroundRecent studies have provided insights into the persistence and succession of microbes aboard the International Space Station (ISS), notably the dominance of Klebsiella pneumoniae. However, the interactions between the various microbes aboard the ISS, and how it shapes the microbiome remain to be clearly understood. In this study, we apply a computational approach to predict possible metabolic interactions in the ISS microbiome and shed further light on its organization. ResultsThrough a combination of a systems-based graph-theoretical approach, and a constraint-based community metabolic modelling approach, we demonstrated several key interactions in the ISS microbiome. These complementary approaches provided insights into the metabolic interactions and dependencies present amongst various microbes in a community, highlighting key interactions and keystone species. Our results showed that the presence of K. pneumoniae is beneficial to many other microorganisms it coexists with, notably those from the Pantoea genus. Species belonging to the Enterobacteriaceae family were often found to be the most beneficial for the survival of other microorganisms in the ISS microbiome. However, K. pneumoniae was found to exhibit parasitic and amensalistic interactions with Aspergillus and Penicillium species, respectively. To prove this metabolic prediction, K. pneumoniae and Aspergillus fumigatus were co-cultured under normal and simulated microgravity, where K. pneumoniae cells showed parasitic characteristics to the fungus. The electron micrography revealed that the presence of K. pneumoniae compromised the morphology of fungal conidia and its biofilm biofilm-forming structures. ConclusionsOur study underscores the importance of K. pneumoniae in the ISS, and its potential contribution to the survival (mutualism) and eradication (parasitism) of other microbes, including potential pathogens. This integrated modelling approach, combined with experiments, demonstrates immense potential for understanding the organization of other such microbiomes, unravelling key organisms and their interdependencies.

systems biology↗

bioRxiv2021

Host transcriptional response to SARS-CoV-2 infection in COVID-19 patients

BackgroundOne of the most perplexing aspects of infection with the SARS-CoV-2 virus has been the variable response elicited in its human hosts. Investigating the transcriptional changes in individuals affected by COVID-19 can help understand and predict the degree of illness and guide clinical outcomes in diverse backgrounds. MethodsAnalysis of host transcriptome variations via RNA sequencing from naso/oropharyngeal swabs of COVID-19 patients. ResultsWe report strong upregulation of the innate immune response, especially type I interferon pathway, upon SARS-CoV-2 infection. Upregulated genes were subjected to a comparative meta-analysis using global datasets to identify a common network of interferon stimulated and viral response genes that mediate the host response and resolution of infection. A large proportion of mis-regulated genes showed a reduction in expression level, suggesting an overall decrease in host mRNA production. Significantly downregulated genes included those encoding olfactory, taste and neuro-sensory receptors. Many pro-inflammatory markers and cytokines were also downregulated or remained unchanged in the COVID-19 patients. Finally, a large number of non-coding RNAs were identified as down-regulated, with a few of the lncRNAs associated with functional roles in directing the response to viral infection. ConclusionsSARS-CoV-2 infection results in the robust activation of the bodys innate immunity. Reduction of gene expression is well correlated with the clinical manifestations and symptoms of COVID-19 such as the loss of smell and taste, and myocardial and neurological complications. This study provides a critical dataset of genes that will enhance our understanding of the nature and prognosis of COVID-19.

bioinformatics↗

bioRxiv2021

Rapid sequence evolution driven by transposable elements at a virulence locus in a fungal wheat pathogen

Background: Plant pathogens cause substantial crop losses in agriculture production and threaten food security. Plants evolved the ability to recognize virulence factors and pathogens have repeatedly escaped recognition due rapid evolutionary change at pathogen virulence loci (i.e. effector genes). The presence of transposable elements (TEs) in close physical proximity of effector genes can have important consequences for gene regulation and sequence evolution. Species-wide investigations of effector gene loci remain rare hindering our ability to predict pathogen evolvability. Results: Here, we performed genome-wide association studies (GWAS) on a highly polymorphic mapping population of 120 isolates of Zymoseptoria tritici, the most damaging pathogen of wheat in Europe. We identified a major locus underlying significant variation in reproductive success of the pathogen and damage caused on the wheat cultivar Claro. The most strongly associated locus is intergenic and flanked by genes encoding a predicted effector and a serine type protease, respectively. The center of the locus contained a highly dynamic region consisting of multiple families of TEs. Based on a large global collection of assembled genomes, we show that the virulence locus has undergone substantial recent sequence evolution. Large insertion and deletion events generated length variation between the flanking genes by a factor of seven (5-35 kb). The locus showed also strong signatures of genomic defenses against TEs (i.e. RIP) contributing to the rapid diversification of the locus. Conclusions: In conjunction, our work highlights the power of combining GWAS and population-scale genome analyses to investigate major effect loci in pathogens.

genomics↗

bioRxiv2020

Genetic Evidence for Selective Transfer of Microbes Between the International Space Station and an Astronaut

Microbial transfer from the environment can influence a persons health, but relevant studies often have confounding variables and short durations. Here, we used the unique environment of the International Space Station (ISS) to track movement of microbes between an astronauts commensal microbiomes and their environment. We identified several microbial taxa, including Serratia proteamaculans and Rickettsia australis which appear to have been transferred from the ISS to the commensal microbiomes of the astronaut. Strains were matched at the SNP and haplotype-level, and notably some strains persisted even after the astronauts return to Earth. Some transferred taxa correspond to secondary strains in the ISS environment, suggesting that transfer may be mediated by evolutionary selection. Finally, we show evidence that the T-Cell repertoire of the astronaut changes to become more specific to environmental taxa, suggesting that continual microbial and immune monitoring can help guide spaceflight mission planning, health monitoring, and habitat design.

microbiology↗

bioRxiv2020

The complex genomic basis of rapid convergent adaptation to pesticides across continents in a fungal plant pathogen

Convergent evolution leads to identical phenotypic traits in different species or populations. Convergence can be driven by standing variation allowing selection to favor identical alleles in parallel or the same mutations can arise independently. However, the molecular basis of such convergent adaptation remains often poorly resolved. Pesticide resistance in agricultural ecosystems is a hallmark of convergence in phenotypic traits. Here, we analyze the major fungal pathogen Zymoseptoria tritici causing serious losses on wheat and with parallel fungicide resistance emergence across continents. We sampled three population pairs each from a different continent spanning periods early and late in the application of fungicides. To identify causal loci for resistance, we combined knowledge from molecular genetics work and performed genome-wide association studies (GWAS) on a global set of isolates. We discovered yet unknown factors in azole resistance including membrane stability functions. We found strong support for the hotspot model of resistance evolution with parallel changes in a small set of loci but additional loci showed more population-specific allele frequency changes. Genome-wide scans of selection showed that half of all known resistance loci were overlapping a selective sweep region. Hence, the application of fungicides was one of the major selective agents acting on the pathogen over the past decades. Furthermore, loci identified through GWAS showed the highest overlap with selective sweep regions underlining the importance to map phenotypic trait variation in evolving populations. Our population genomic analyses showed that both de novo mutations and gene flow likely contributed to the parallel emergence of resistance.

evolutionary biology↗

bioRxiv2020

Comparative analysis of chloroplast genomes indicated different origin for Indian Tea (Camellia assamica) cv TV-1 as compared to Chinese tea

Tea is an important plantation crop of some Asian and African countries. Based upon the morphological characteristics, tea is classified botanically into 2 main types i.e. Assam and China, which are morphologically very distinct. Further, they are so easily pollinated among themselves, that a third category, Cambod type is also described. Although the general consensus of origin of tea is India, Burma and China joining area, yet specific origin of China and Assam tea are not yet clear. In the present study, we made an attempt to understand the origin of Indian tea through the comparative analysis of different chloroplast (cp) genomes under the Camellia genus. Cp genome based phylogenetic analysis indicated that Indian Assam Tea, TV-1 formed a different group from that of China tea, indicating that TV-1 might have undergone different domestication and hence owe different origin. The simple sequence repeats (SSRs) analysis and codon usage distribution pattern also supported the clustering order in the cp genome based phylogenetic tree.

genomics↗

bioRxiv2020

Population-level deep sequencing reveals the interplay of clonal and sexual reproduction in the fungal wheat pathogen Zymoseptoria tritici

Pathogens can rapidly surmount crop resistance challenging global food security. On annual crops, pathogens must re-infect from environmental sources at the beginning of every growing season. Fungal pathogens evolved mixed reproductive strategies to cope with the distinct life cycle challenges of colonizing plants, spreading within fields and ultimately producing propagules for survival in winter. However, how genotypic diversity evolves over this period remains largely unknown. Here, we performed a deep hierarchical sampling in a single experimental wheat field infected by the major fungal pathogen Zymoseptoria tritici. We analyzed whole genome sequences of 177 isolates collected from twelve distinct cultivars replicated in space at three time points of the growing season. The field population was highly diverse with 37 SNPs per kilobase and a linkage disequilibrium decay within 200-700 bp. We found that ~20% of the individual isolates were grouping into 15 clonal groups. Pairs of clones were disproportionally found at short distance (<5m) but a low degree of dispersal occurred also at the scale of the entire field consistent with a predominant leaf-to-leaf dispersal. We found no association of wheat cultivars and clonal genotypes with the exception of one cultivar. Surprisingly, levels of clonality did not increase over time in the field although reproduction is thought to be exclusively asexual during the growing season. Our study shows that the pathogen establishes vast and stable gene pools in single fields over the growing season. Monitoring short-term evolutionary changes in crop pathogens will inform more durable strategies to contain diseases.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genomics↗

bioRxiv2020

Patterns of gene-body-methylation conservation and its divergent association with gene expression in Pigeonpea and Soybean

Pigeonpea and Soybean are closely related agronomically important legumes that diverged [~]23 million years ago followed by a whole genome duplication event in Soybean. However, the impact of divergence on their epigenomes is not known. Comparative epigenomics is a powerful tool to understand shaping of epigenomes and its functional consequences during evolution. In this study, we investigated the conservation and divergence of Gene body methylation (GbM) and gene-expression patterns between the two species. We compared their genome-wide DNA methylation landscape at single-base resolution and studied the influence of GbM on duplicate gene retention. Our results indicate that during the divergence of Pigeonpea and Soybean, the effects of methylation on gene expression evolved in a heterogeneous manner. GbM features-slow evolution rate and increased length remained conserved in Pigeonpea and Soybean. We also found that contrary to high CG-methylation conservation, non-CG methylated genes remained diverged during evolution due to transposons insertion in adjacent sequences. The methylation differences in paralogs were associated with expression divergence. Notably, we found that variably methylated marks are less likely to target evolutionary conserved sequences. Finally, our findings identify conservation of nitrogen-related genes during evolution, which are promising candidates for epi-alleles for crop improvement.

genomics↗

bioRxiv2019

Molecular characterization and differential expression reveal functional divergence of stress-responsive enzymes in C4 panicoid models, Setaria italica and Setaria viridis

Stress-responsive genes regulate the morpho-physiological as well as molecular responses of plants to environmental cues. In addition to known genes, there are several unknown genes underlying stress-responsive machinery. One such machinery is the sophisticated biochemical carbon-concentrating mechanism of the C4 photosynthetic pathway that enables the plants to survive in high temperatures, high light intensities and drought conditions. Despite the importance of C4 photosynthesis, no comprehensive study has been performed to identify and characterize the key enzymes involved in this process among sequenced Poaceae genomes. In the present study, five major classes of enzymes that are reported to play roles in C4 biochemical carbon-concentrating mechanism were identified in sequenced Poaceae genomes with emphasis on the model crops, Setaria italica and S. viridis. Further analysis revealed that segmental and tandem duplications have contributed to the expansion of these gene families. Comparative genome mapping and molecular dating provided insights into their duplication and divergence in the course of evolution. Expression profiling of candidate genes in contrasting S. italica cultivars subjected to abiotic stresses and hormone treatments showed distinct stress-specific upregulation of SiCaH1, Si{beta}CaH5, SiPEPC2, SiPPDK2, SiMDH8 and SiNADP-ME5 in the tolerant cultivar. Altogether, the study highlights key stress-responsive genes that could serve as potential candidates for elucidating their precise roles in stress tolerance. Key messageComprehensive analysis of stress-responsive gene families in C4 model plants, Setaria italica and S. viridis identified SiCaH1, SiPEPC2, SiPPDK2, SiMDH8 and SiNADP-ME5 as potential candidates for engineering abiotic stress tolerance.

plant biology↗

bioRxiv2019

Draft genome sequence of a popular Indian tea genotype TV-1

Camellia assamica L. (O). Kuntze. var. TV-1 from Assam, India is one of the most important tea cultivar of Indian Tea Industry which botanically belongs to Assam type and has distinct morphological as well as chemical constituent in leaf than China type of tea. We here present the first draft genome sequence of TV-1, assembled in 2.93 Gb size comprising of 14,824 scaffolds and covering 97.66% of the predicted genome size (3.0 Gb) as determined by flow cytometry. The N50 value, longest and average contig size for this draft genome was found to be as 538.96 Kb, 3.64 Mb and 197.99 Kb, respectively. There were 495,747 SSRs (Di- to Hexa-mers) in the genome and the repeat contents accounted for 71.87% of the genome. About 94.40% genome completeness was predicted with BUSCO, maximum among the published and reported tea genomes. The clustering of the assembled genome with Hi-C data resulted in 15 clusters ranging from the size of 631.46 Mb to 14.39 Mb and accounting 99.32% of assembled genome. This data will act as the starting point for unravelling the important genes and differentiating Indian tea from other commercially available tea varieties.

plant biology↗

bioRxiv2018

Analysis of genetic control and QTL mapping of essential wheat grain quality traits in a recombinant inbred population

Wheat cultivars are genetically crossed for improving end use quality for apt traits as per need of baking industry and broad consumers preferences. The processing and baking qualities of bread wheat underlie into genetic make-up of a variety and influence by environmental factors and their interactions. WL711 and C306 derived recombinant inbred lines (RILs) population of 206 was used for phenotyping of quality related traits in three different environmental conditions. The genetic analysis of quality traits showed considerable variation for measurable quality traits with normal distribution and transgressive segregation across the years. From the 206 RIL, few RILs found to be superior to those of the parental cultivars for key quality traitsindicating their potential usefor improvement of end use quality and also suggestingprobability of finding new alleles and allelic combinations from the RIL population. A genetic linkage map including 346 markers was constructed withtotal map distance of 4526.8cM andinterval distance between adjacent markersof 12.9cM. Mapping analysis identified 38 putative QTLs for 13 quality related traits with QTLs explaining 7.9% - 16.8% phenotypic variation spanning over 14 chromosomes i.e. 1A, 1B, 1D, 2A, 2D, 3B, 3D, 4A, 4B, 4D, 5D, 6A, 7A and 7B. Major novel QTLs regions for quality traits have been identified on several chromosome in studied RIL population posing their potential role in marker assisted selection for better bread making quality after validation.

genetics↗

bioRxiv2017

MetaRiPPquest: A Peptidogenomics Approach for the Discovery of Ribosomally Synthesized and Post-translationally Modified Peptides

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are an important class of natural products that include many antibiotics and a variety of other bioactive compounds. While recent breakthroughs in RiPP discovery raised the challenge of developing new algorithms for their analysis, peptidogenomic-based identification of RiPPs by combining genome/metagenome mining with analysis of tandem mass spectra remains an open problem. We present here MetaRiPPquest, a software tool for addressing this challenge that is compatible with large-scale screening platforms for natural product discovery. After searching millions of spectra in the Global Natural Products Social (GNPS) molecular networking infrastructure against just six genomic and metagenomic datasets, MetaRiPPquest identified 27 known and discovered 5 novel RiPP natural products.

bioinformatics↗