Biology subjects

Gautam, A.

Publications and source records attributed to Gautam, A..

36 records · Page 2Linked to original sources

bioRxiv2022

Penetratin inhibits α-synuclein fibrillation and improves locomotor functions in mice model of Parkinsons disease

Parkinsons disease (PD) is the second most common neurodegenerative disease. The presence of lewy bodies, primarily consisting of -synuclein (-syn) aggregates is one of the common features seen in the substantia nigra region of the brain in PD patients. The disease remains incurable and only symptomatic relief is available. We screened various cell-penetrating peptides and reveal that penetratin is a potent inhibitor of -syn aggregation in-vitro, and significantly improved locomotor coordination in mice models of PD in-vivo. The peptide inhibits -syn aggregation in vitro as well as in yeast, and C.elegans models. We further made a cyclic derivative of penetratin by disulfide coupling of N- and C-terminal cysteine residues. Both penetratin and its cyclized derivative interact with -syn. NMR studies show that both linear as well as cyclic derivative interact at the acidic C-terminal tail of the protein. Similar to penetratin, its cyclic derivative inhibited -syn aggregation in the C.elegans model of Parkinsons disease, and also improved worm motility. Molecular Dynamics studies show that penetratin interacts with -synuclein and prevents its conformational transition from disordered into {beta}-sheet rich structure. The therapeutic efficacy of penetratin was further confirmed in a transgenic mice model of the disease, wherein penetratin treatment over a period of 90 days improved locomotor coordination, and halted disease progression. Overall, the present work provides a potent therapeutic agent that could be further explored in the management of PD.

neuroscience↗

bioRxiv2022

Structural and functional characteristics of SARS-CoV-2 Omicron subvariant BA.2 spike

The Omicron subvariant BA.2 has become the dominant circulating strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in many countries. We have characterized structural, functional and antigenic properties of the full-length BA.2 spike (S) protein and compared replication of the authentic virus in cell culture and animal model with previously prevalent variants. BA.2 S can fuse membranes more efficiently than Omicron BA.1, mainly due to lack of a BA.1-specific mutation that may retard the receptor engagement, but still less efficiently than other variants. Both BA.1 and BA.2 viruses replicated substantially faster in animal lungs than the early G614 (B.1) strain in the absence of pre-existing immunity, possibly explaining the increased transmissibility despite their functionally compromised spikes. As in BA.1, mutations in the BA.2 S remodel its antigenic surfaces leading to strong resistance to neutralizing antibodies. These results suggest that both immune evasion and replicative advantage may contribute to the heightened transmissibility for the Omicron subvariants.

microbiology↗

bioRxiv2022

DeepToA: An Ensemble Deep-Learning Approach to Predicting the Theater of Activity of a Microbiome

MotivationMetagenomics is the study of microbiomes using DNA sequencing. A microbiome consists of an assemblage of microbes that is associated with a "theater of activity" (ToA). To what degree does the taxonomic and functional content of the former depend on the (details of the) latter? More technically, given a taxonomic and/or functional profile estimated from metagenomic sequencing data, how to predict the associated ToA? Here we present a deep learning approach to this question. We use both taxonomic and functional profiles as input. We apply node2vec to embed hierarchical taxonomic profiles into numerical vectors. We then perform dimension reduction using clustering, to address the sparseness of the taxonomic data and thus make it more amenable to deep learning algorithms. Functional features are combined with textual descriptions of protein families or domains. We present an ensemble deep-learning framework DeepToA for predicting the theater of activity" of microbial community, based on taxonomic and functional profiles. We use SHAP (SHapley Additive exPlanations) values to determine which taxonomic and functional features are important for the prediction. ResultsBased on 7,560 metagenomic profiles downloaded from MGnify, classified into ten different theaters of activity, we demonstrate that DeepToA has an accuracy of 98.61%. We show that adding textual information to functional features increases the accuracy. AvailabilityOur approach is available at http://ab.inf.uni-tuebingen.de/software/deeptoa. Contactdaniel.huson@uni-tuebingen.de Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2022

Structural and functional impact by SARS-CoV-2 Omicron spike mutations

The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), bearing an unusually high number of mutations, has become a dominant strain in many countries within several weeks. We report here structural, functional and antigenic properties of its full-length spike (S) protein with a native sequence in comparison with those of previously prevalent variants. Omicron S requires a substantially higher level of host receptor ACE2 for efficient membrane fusion than other variants, possibly explaining its unexpected cellular tropism. Mutations not only remodel the antigenic structure of the N-terminal domain of the S protein, but also alter the surface of the receptor-binding domain in a way not seen in other variants, consistent with its remarkable resistance to neutralizing antibodies. These results suggest that Omicron S has acquired an extraordinary ability to evade host immunity by excessive mutations, which also compromise its fusogenic capability.

biochemistry↗

bioRxiv2021

PLaBAse: A comprehensive web resource for analyzing the plant growth-promoting potential of plant-associated bacteria

Plant-beneficial microorganisms are gaining importance for sustainable plant production and phytosanitary practices. Yet there is a lack of computational approaches targeting bacterial traits associated with plant growth-promotion (PGP), which hinders the in-silico identification, comparison, and selection of phytostimulatory bacterial strains. To address this problem, we have developed the new web resource PLaBAse (v1.01, http://plabase.informatik.uni-tuebingen.de/pb/plabase.php), which provides a number of services, including (i) a database for screening 5,565 plant-associated bacteria (PLaBA-db), (ii) a tool for predicting plant growth-promoting traits (PGPTs) of single bacterial genomes (PGPT-Pred), and (iii) a tool for the prediction of bacterial plant-association by marker gene identification (PIFAR-Pred). The latter was developed by Martinez-Garcia et al. and is now hosted at University of Tuebingen. The PGPT-Pred tool is based on our new PGPT ontology, a literature- and OMICs-curated, comprehensive, and hierarchical collection of [~]6,900 PGPTs that are associated with 6,965,955 protein sequences. To study the distribution of the PGPTs across different environments, we applied it to 70,540 bacterial strains associated with (i) seven different environments (including plants), (iii) five different plant spheres (organs), and (iii) two bacteria-induced plant phenotypes. This analysis revealed that plant-symbiotic bacteria generally have a larger genome size and a higher count of PGPT-annotated protein encoding genes. Obviously, not all reported PGPTs are restricted to -or only enriched in-plant-associated and plant symbiotic bacteria. Some also occur in human- and animal-associated bacteria, perhaps due to the transmission of PGP bacteria (PGPBs) between environments, or because some functions are involved in adaption processes to various environments. Here we provide an easy-to-use approach for screening of PGPTs in bacterial genomes across various phyla and isolation sites, using PLaBA-db, and for standardized annotation, using PGPT-Pred. We believe that this resource will improve our understanding about the entire PGP processes and facilitate the prediction of PGPB as bio-inoculants and for biosafety strategies, so as to help to establish sustainable and targeted bacteria-incorporated plant production systems in the future.

bioinformatics↗

bioRxiv2021

Using AnnoTree to get more assignments, faster, in DIAMOND+MEGAN microbiome analysis

In microbiome analysis, one main approach is to align metagenomic sequencing reads against a protein-reference database such as NCBI-nr, and then to perform taxonomic and functional binning based on the alignments. This approach is embodied, for example, in the standard DIAMOND+MEGAN analysis pipeline, which first aligns reads against NCBI-nr using DIAMOND and then performs taxonomic and functional binning using MEGAN. Here we propose the use of the AnnoTree protein database, rather than NCBI-nr, in such alignment-based analyses to determine the prokaryotic content of metagenomic samples. We demonstrate a 2-fold speedup over the usage of the prokaryotic part of NCBI-nr, and increased assignment rates, in particular, assigning twice as many reads to KEGG. In addition to binning to the NCBI taxonomy, MEGAN now also bins to the GTDB taxonomy. IMPORTANCEThe NCBI-nr database is not explicitly designed for the purpose of microbiome analysis and its increasing size makes its unwieldy and computationally expensive for this purpose. The AnnoTree protein database is only one quarter the size of the full NCBI-nr database and is explicitly designed for metagenomic analysis, and so should be supported by alignment-based pipelines.

bioinformatics↗

bioRxiv2021

Interactive Analysis of Biosurfactants in Fruit-Waste Fermentation Samples using BioSurfDB and MEGAN

Microbial biosurfactants are of major interest due to their multifunctional properties, biodegradable nature and low toxicity. Agroindustrial waste, such as fruit waste, can be used as substrates for producing bacteria. In this study, six samples of fruit waste, from oranges, mangoes and mixed fruits, were self-fermented, and then subjected to short-read metagenomic sequencing, so as to allow assessment of the potential of the associated microbiota for biosurfactant production. Taxonomic analysis using the DIAMOND+MEGAN analysis pipeline shows that all six samples are dominated by Proteobacteria, in particular, a common core consisting of the genera Klebsiella, Enterobacter, Stenotrophomonas, Acinetobacter and Escherichia. To support the interactive visualization and exploration of the surfactant-related genes in such samples, we have integrated the BiosurfDB classification into MEGAN and make this available. Functional analysis indicates high similarity among samples and that a significant number of reads map to genes that are involved in the biosynthesis of lipopeptide-class biosurfactants. Gene-centric analysis reveals Klebsiella as the main assignment for genes related to putisolvins biosynthesis. This suggests that fruit waste is a promising substrate for fermentative processes because the associated microbiota may be able to produce biosurfactants that are potentially useful for the agricultural, chemical, food and pharmaceutical industries.

bioinformatics↗

bioRxiv2021

Differential antibody dynamics to SARS-CoV-2 infection and vaccination

Optimal immune responses furnish long-lasting (durable) antibodies protective across dynamically mutating viral variants (broad). To assess robustness of mRNA vaccine-induced immunity, we compared antibody durability and breadth after SARS-CoV-2 infection and vaccination. While vaccination delivered robust initial virus-specific antibodies with some cross-variant coverage, pre-variant SARS-CoV-2 infection-induced antibodies, while modest in magnitude, showed highly stable long-term antibody dynamics. Vaccination after infection induced maximal antibody magnitudes with enhanced longitudinal stability while infection-naive vaccinee antibodies fell with time to post-infection-alone levels. The composition of antibody neutralizing activity to variant relative to original virus also differed between groups, with infection-induced antibodies demonstrating greater relative breadth. Differential antibody durability trajectories favored COVID-19-recovered subjects with dual memory B cell features of greater early antibody somatic mutation and cross-coronavirus reactivity. By illuminating an infection-mediated antibody breadth advantage and an anti-SARS-CoV-2 antibody durability-enhancing function conferred by recalled immunity, these findings may serve as guides for ongoing vaccine strategy improvement.

immunology↗

bioRxiv2021

Membrane fusion and immune evasion by the spike protein of SARS-CoV-2 Delta variant

The Delta variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has outcompeted previously prevalent variants and become a dominant strain worldwide. We report here structure, function and antigenicity of its full-length spike (S) trimer in comparison with those of other variants, including Gamma, Kappa, and previously characterized Alpha and Beta. Delta S can fuse membranes more efficiently at low levels of cellular receptor ACE2 and its pseudotyped viruses infect target cells substantially faster than all other variants tested, possibly accounting for its heightened transmissibility. Mutations of each variant rearrange the antigenic surface of the N-terminal domain of the S protein in a unique way, but only cause local changes in the receptor-binding domain, consistent with greater resistance particular to neutralizing antibodies. These results advance our molecular understanding of distinct properties of these viruses and may guide intervention strategies.

biochemistry↗

bioRxiv2021

Discovery of nanobodies against SARS-CoV-2 and an uncommon neutralizing mechanism

SARS-CoV-2 and its variants continue to threaten public health. The virus recognizes the host cell by attaching its Spike receptor-binding domain (RBD) to the host receptor ACE2. Therefore, RBD is a primary target for neutralizing antibodies and vaccines. Here we report the isolation, and biological and structural characterization of two single-chain antibodies (nanobodies, DL4 and DL28) from RBD-immunized alpaca. Both nanobodies bind Spike with affinities that exceeded the detection limit (picomolar) of the biolayer interferometry assay and neutralize the original SARS-CoV- 2 strain with IC50 of 0.086 g mL-1 (DL4) and 0.385 g mL-1 (DL28). DL4 and a more potent, rationally designed mutant, neutralizes the Alpha variant as potently as the original strain but only displays marginal activity against the Beta variant. By contrast, the neutralizing activity of DL28, when in the Fc-fused divalent form, was less affected by the mutations in the Beta variant (IC50 of 0.414 g mL-1 for Alpha, 1.060 g mL-1 for Beta). Crystal structure studies reveal that DL4 blocks ACE2-binding by direct competition, while DL28 neutralizes SARS-CoV-2 by an uncommon mechanism through which DL28 distorts the receptor-binding motif in RBD and hence prevents ACE2-binding. Our work provides two neutralizing nanobodies for potential therapeutic development and reveals an uncommon mechanism to design and screen novel neutralizing antibodies against SARS-CoV-2.

biochemistry↗

bioRxiv2021

Structural basis for enhanced infectivity and immune evasion of SARS-CoV-2 variants

Several fast-spreading variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have become the dominant circulating strains that continue to fuel the COVID-19 pandemic despite intensive vaccination efforts throughout the world. We report here cryo-EM structures of the full-length spike (S) trimers of the B.1.1.7 and B.1.351 variants, as well as their biochemical and antigenic properties. Mutations in the B.1.1.7 protein increase the accessibility of its receptor binding domain and also the binding affinity for receptor angiotensin-converting enzyme 2 (ACE2). The enhanced receptor engagement can account for the increased transmissibility and risk of mortality as the variant may begin to infect efficiently infect additional cell types expressing low levels of ACE2. The B.1.351 variant has evolved to reshape antigenic surfaces of the major neutralizing sites on the S protein, rendering complete resistance to some potent neutralizing antibodies. These findings provide structural details on how the wide spread of SARS-CoV-2 enables rapid evolution to enhance viral fitness and immune evasion. They may guide intervention strategies to control the pandemic.

biochemistry↗

bioRxiv2021

Memory B cell repertoire for recognition of evolving SARS-CoV-2 spike

Memory B cell reserves can generate protective antibodies against repeated SARS-CoV-2 infections, but with an unknown reach from original infection to antigenically drifted variants. We charted memory B cell receptor-encoded monoclonal antibodies (mAbs) from 19 COVID-19 convalescent subjects against SARS-CoV-2 spike (S) and found 7 major mAb competition groups against epitopes recurrently targeted across individuals. Inclusion of published and newly determined structures of mAb-S complexes identified corresponding epitopic regions. Group assignment correlated with cross-CoV-reactivity breadth, neutralization potency, and convergent antibody signatures. mAbs that competed for binding the original S isolate bound differentially to S variants, suggesting the protective importance of otherwise-redundant recognition. The results furnish a global atlas of the S-specific memory B cell repertoire and illustrate properties conferring robustness against emerging SARS-CoV-2 variants.

immunology↗

bioRxiv2019

Genomic Influences on Self-Reported Childhood Maltreatment

Childhood maltreatment is highly prevalent and serves as a risk factor for mental and physical disorders. Self-reported childhood maltreatment appears heritable, but the specific genetic influences on this phenotype are largely unknown. The aims of this study were to 1) identify genetic variation associated with reported childhood maltreatment, 2) calculate the relevant SNP-based heritability estimates, and 3) quantify the genetic overlap of reported childhood maltreatment with mental and physical health-related phenotypes. Genome-wide association analysis for childhood maltreatment was undertaken, using a discovery sample from the UK Biobank (UKBB) (n=124,000) and a replication sample from the Psychiatric Genomics Consortium-posttraumatic stress disorder working group (PGC-PTSD) (n=26,290). Heritability estimations for childhood maltreatment and genetic correlations with mental/physical health traits were calculated using linkage disequilibrium score regression (LDSR). Two genome-wide significant loci associated with childhood maltreatment, located on chromosomes 3p13 (rs142346759, beta=0.015, p=4.35x10-8, FOXP1) and 7q31.1 (rs10262462, beta=-0.016, p=3.24x10-8, FOXP2), were identified in the discovery dataset but were not replicated in the PGC-PTSD sample. SNP-based heritability for childhood maltreatment was estimated to be [~]6%. Childhood maltreatment was most significantly genetically correlated with depressive symptoms (rg=0.70, p=4.65x10-40). This is the first large-scale genetic study to identify specific variants associated with self-reported childhood maltreatment. FOXP genes could influence traits such as depression and thereby be relevant to childhood maltreatment. Alternatively, these variants may be associated with a greater likelihood of reporting maltreatment. A clearer understanding of the genetic relationships of childhood maltreatment, including particular abuse subtypes, with various psychiatric disorders, may ultimately be useful in in developing targeted treatment and prevention strategies.

genomics↗

bioRxiv2018

Mechanistic insights on metabolic dysfunction in PTSD: Role of glucocorticoid receptor sensitivity and energy deficit

PTSD is associated with metabolic comorbidities; however it is not clear how the neuroendocrine disturbances affect metabolism. To analyze this we employed a systems biological approach using an integrated mathematical model of metabolism, HPA axis and inflammation. We combined the metabolomics, neuroendocrine, clinical lab and cytokine data from combat-exposed veterans with and without PTSD, to characterize the differences in regulatory effects. We used the pattern of fold change in metabolites representing pathway level differences as reference for metabolic control analysis (MCA) using the model. MCA revealed parameters constituting the HPA axis, inflammation and GPCR pathway that yielded metabolic dysfunction consistent with PTSD. To support this, we performed causal analysis between regulatory components and the significantly different metabolites in our sample. Causal inference revealed that the changes in glucocorticoid receptor sensitivity were mechanistically associated with metabolic dysfunction and the effects were jointly mediated by insulin resistance, inflammation, oxidative stress and energy deficit.

systems biology↗

bioRxiv2018

A web bench for analysis and prediction of oncological status from proteomics data of urine samples

Urine-based cancer biomarkers offer numerous advantages over the other biomarkers and play a crucial role in cancer management. In this study, an attempt has been made to develop proteomics-based prediction models to discriminate patients of oncological disorders related to urinary tract and healthy controls from their urine samples. The dataset used in this study was obtained from human urinary peptide database that contains urine proteomics data of 1525 oncological and 1503 healthy controls with the spectral intensity of 5605 peptides. First, we identified peptide spectra using various feature selection techniques, which display different intensity and occurrence in oncological samples and healthy controls. Based on selected 173 peptide-based biomarkers, we developed models for predicting oncological samples and achieved maximum accuracy of 91.94% with 0.84 MCC. Prediction models were also developed based on spectral intensities with known peptide sequences. We also quantitated the amount of protein in a sample based on intensities of its fragments/peptides and developed prediction models based on protein expression. It was observed that certain proteins and their peptides such as fragments of collagen protein are more abundant in oncological samples. Based on this study, we also developed a web bench, CancerUBM, for mining proteomics data, which is freely available at http://webs.iiitd.edu.in/raghava/cancerubm/.

bioinformatics↗

bioRxiv2018

Altered Fecal Microbiota and Urine Metabolome as Signatures of Soman Poisoning

The experimental pathophysiology of organophosphorus (OP) chemical exposure has been extensively reported. Here, we describe an altered fecal microbiota and urine metabolome that follows intoxication with soman, a lipophilic G class chemical warfare nerve agent. Non-anaesthetized Sprague-Dawley male rats were subcutaneously administered soman at 0.8 - 1.0 of the median lethal dose (LD50) and evaluated for signs of toxicity. Animals were stratified based on seizing activity to evaluate effects of soman exposure on fecal bacterial biota and urine metabolites. Soman exposure reshaped fecal bacterial biota by preferentially expanding Facklamia, Agrobacterium, Bilophila, Enterobacter, and Morganella genera of the Firmicutes and Proteobacteria phyla, some of which are known to hydrolyze OPs. However, analogous changes were not observed in the bacterial biota of the ileum, which remained the same irrespective of dose or seizing status of animals after exposure. Interestingly, when considering just the seizing status of animals, we found that the urine metabolome was markedly altered. Leukotriene C4, kynurenic acid, 5-hydroxyindoleacetic acid, norepinephrine, and aldosterone were excreted at much higher rates at 72 hrs in seizing animals, consistent with early multi-organ involvement during soman poisoning. However, at 75 days post soman exposure, bacterial biota stabilized and no differences were observed. These findings demonstrate the feasibility of using the dysbiosis of fecal bacterial biota in combination with urine metabolome alterations as forensic evidence for OP exposure temporally.\n\nImportanceThe paucity of assays to determine physiologically relevant OP exposure presents an opportunity to explore the use bacterial sentinels in combination with urine to assess changes in the exposed host. Recent advances in technologies and computational approaches have enabled researches to survey large community level changes of gut bacterial biota and metabolomic changes in various biospecimens. Here, we profile combined changes in bacterial biota and urine metabolome due to chemical warfare OP exposure. The significance of our work is to reveal that monitoring bacterial biota and urine metabolites as surrogates of OP exposure in biospecimens suitable for existing clinical laboratory workflows is plausible without the need for the development of new technology, invasive procedures, or complicated analytical approaches. The larger value of such an approach is that any setting with a moderate clinical chemistry and microbiology capability can determine pre-symptomatic exposure to enhance current triage standards in case of mass exposures, refugee movements, humanitarian missions, and training settings once an algorithm has been validated. In the event of \"potential\" exposures by time or distance, this assay can be further developed to estimate affected radius or time dimension for health monitoring and treatment interventions.

physiology↗

bioRxiv2017

SEA: The Small RNA Expression Atlas

We present the Small RNA Expression Atlas (SEA), a web application that allows for the interactive querying, visualization, and analysis of known and novel small RNAs across ten organisms. It contains sRNA and pathogen expression information for over 4,200 published samples with standardized search terms and ontologies. In addition, SEA allows for the interactive visualization and re-analysis of 879 differential expression and 514 classification comparisons. SEAs user model enables sRNA researchers to compare and re-analyze user-specific and published datasets, highlighting common and distinct sRNA expression patterns.\n\nWe provide evidence for SEAs fidelity by (i) generating a set of 591 tissue specific miRNAs across 30 tissues, (ii) finding known and novel bacterial and viral infections across diseases, and (iii) determining a Parkinsons disease-specific blood biomarker signature using novel data.\n\nWe believe that SEAs simple semantic search interface, the flexible interactive reports, and the user model with rich analysis capabilities will enable researchers to better understand the potential function and diagnostic value of sRNAs or pathogens across tissues, diseases, and organisms.\n\nAvailability and ImplementationSEA is implemented in Java, J2EE, spring, Django, html5, css3, JavaScript, Bootstrap, Vue.js, D3, mongodb and neo4j. It is freely available at http://sea.ims.bio/.

bioinformatics↗

bioRxiv2017

Oasis2.0: improved online analysis of small RNA-seq data

Oasis 2 is a new main release of the Oasis web application for the detection, differential expression, and classification of small RNAs in deep sequencing data. Compared to its predecessor Oasis, Oasis 2 features a novel and speed-optimized sRNA detection module that supports the identification of small RNAs in any organism with higher accuracy. Next to the improved detection of small RNAs in a target organism, the software now also recognizes potential cross-species miRNAs and viral and bacterial sRNAs in infected samples. In addition, novel miRNAs can now be queried and visualized interactively, providing essential information for over 700 high-quality miRNA predictions across 14 organisms. Robust biomarker signatures can now be obtained using the novel enhanced classification module. Oasis 2 enables biologists and medical researchers to rapidly analyze and query small RNA deep sequencing data with improved precision, recall, and speed, in an interactive and user-friendly environment.\n\nAvailability and Implementation: Oasis 2 is implemented in Java, J2EE, mysql, Python, R, PHP and JavaScript. It is freely available at http://oasis.dzne.de

bioinformatics↗