Biology subjects

Singh, H.

Publications and source records attributed to Singh, H..

At least 91 records · Page 5Linked to original sources

bioRxiv2021

The aging lung mucosa: A proteomics study

The elderly population is at increased risk of acute and chronic respiratory infections and other pulmonary diseases, and it is estimated that this population will double in the next 30 years. Biochemical changes in the lung alveolar mucosa and lung cells alter local immune response as we age, creating opportunities for invading pathogens to establish successful infections. Indeed, the lungs of the elderly are a pro-inflammatory, pro-oxidative, dysregulated environment but this environment has remained understudied. We performed a comprehensive, quantitative proteomic profile of the lung mucosa in the elderly, developing insight into the molecular fingerprints, pathways, and regulatory networks that characterize the lung in old age. We identified neutrophils in the lungs of elderly individuals as possible contributors to dysregulated lung tissue environment. This study establishes a baseline for future investigations to develop strategies to mitigate susceptibility to respiratory infections in the elderly.

systems biology↗

bioRxiv2021

PRMT5 promotes symmetric dimethylation of RNA processing proteins and modulates activated T cell alternative splicing and Ca2+/NFAT signaling

Protein Arginine Methyltransferase (PRMT) 5 is the major type 2 methyltransferase catalyzing symmetric dimethylation (SDM) of arginine. PRMT5 inhibition or deletion in CD4 Th cells reduces TcR engagement-induced IL-2 production and Th cell expansion and confers protection against experimental autoimmune encephalomyelitis (EAE), the animal model of Multiple Sclerosis. However, the mechanisms by which PRMT5 modulates T helper (Th) cell proliferation are still not completely understood and neither are the methylation targets in T cells. In this manuscript, we uncover the role of PRMT5 on alternative splicing (AS) in activated T cells and identify several targets of PRMT5 SDM involved in splicing. In addition, we find a possible link between PRMT5 mediated AS of Trpm4 (Transient Receptor Potential Cation Channel Subfamily M Member 4) and TcR/NFAT signaling/IL-2 production. This understanding may guide development of drugs targeting these processes to benefit patients with T cell-mediated diseases.

immunology↗

bioRxiv2021

A cost-effective and efficient approach for generating and assembling reagents for conducting real-time PCR

Real-time PCR is a widely used technique for quantification of gene expression. However, commercially available kits for real-time PCR are very expensive. The ongoing coronavirus pandemic has severely hampered the economy in a number of developing countries, resulting in a reduction in available research funding. The fallout of this will result in limiting educational institutes and small enterprises from using cutting edge biological techniques such as real-time PCR. Here, we report a cost-effective approach for preparing and assembling cDNA synthesis and real-time PCR mastermixes with similar efficiencies as commercially available kits. Our results thus demonstrate an alternative to commercially available kits.

molecular biology↗

bioRxiv2021

Identifying Protein Complexes in Protein-protein Interaction Data using Graph Convolution Network

Protein complexes are groups of two or more polypeptide chains that join together to build noncovalent networks of protein interactions. A number of means of computing the ways in which protein complexes and their members can be identified from these interaction networks have been created. While most of the existing methods identify protein complexes from the protein-protein interaction networks (PPIs) at a fairly decent level, the applicability of advanced graph network methods has not yet been adequately investigated. In this paper, we proposed various graph convolutional networks (GCNs) methods to improve the detection of the protein functional complexes. We first formulated the protein complex detection problem as a node classification problem. Second, the Neural Overlapping Community Detection (NOCD) model was applied to cluster the nodes (proteins) using a complex affiliation matrix. A representation learning approach, which combines the multi-class GCN feature extractor (to obtain the features of the nodes) and the mean shift clustering algorithm (to perform clustering), is also presented. We have also improved the efficiency of the multi-class GCN network to reduce space and time complexities by converting the dense-dense matrix operations into dense-spares or sparse-sparse matrix operations. This proposed solution significantly improves the scalability of the existing GCN network. Finally, we apply clustering aggregation to find the best protein complexes. A grid search was performed on various detected complexes obtained by applying three well-known protein detection methods namely ClusterONE, CMC, and PEWCC with the help of the Meta-Clustering Algorithm (MCLA) and Hybrid Bipartite Graph Formulation (HBGF) algorithm. The proposed GCN-based methods were tested on various publicly available datasets and provided significantly better performance than the previous state-of-the-art methods. The code and data used in this study are available from https://github.com/Analystharsh/GCN_complex_detection

bioinformatics↗

bioRxiv2021

CausER - a framework for inferring causal latent factors using multi-omic human datasets

High-dimensional cellular and molecular profiling of human samples highlights the need for analytical approaches that can integrate multi-omic datasets to generate predictive biomarkers and prioritized causal inferences. Current methods are limited by high dimensionality of the combined datasets, the differences in their data distributions and their integration to infer causal relationships. Here we present Essential Regression (ER), an interpretable machine learning approach for high-dimensional multi-omic datasets, that addresses these problems by identifying latent factors and their likely cause-effect relationships with the system-wide outcome/properties of interest. ER is a novel data-distribution-free latent-factor regression model that integrates multi-omic datasets and identifies latent factors significantly associated with an outcome. ER outperforms a range of state-of-the-art methods in terms of prediction performance on simulated datasets. ER can be coupled with probabilistic graphical modeling thereby strengthening the causal inferences. ER generates novel cellular and molecular predictions, using multi-omic human systems immunology datasets, pertaining to immunosenescence and immune dysregulation.

systems biology↗

bioRxiv2021

Auxin-responsive (phospho)proteome analysis reveals regulation of cell cycle and ethylene signaling during rice crown root development

The rice root system, which primarily consists of adventitious/crown roots (AR/CR) developed from the coleoptile base, is an excellent model system for studying shoot-to-root trans-differentiation process. We reveal global changes in protein and metabolite abundance, and protein phosphorylation in response to an auxin stimulus during CR development. Global proteome and metabolome analyses of developing crown root primordia (CRP) and emerged CRs uncovered that the biological processes associated with chromatin conformational change, gene expression, and cell cycle were translationally regulated by auxin signaling. Spatial gene expression pattern analysis of differentially abundant proteins disclosed their stage-specific dynamic expression pattern during CRP development. Further, our tempo-spatial gene expression and functional analyses revealed that auxin creates a regulatory feedback module during CRP development and activates ethylene biosynthesis exclusively during CRP initiation. Ethylene signaling promotes CR formation by repressing the cytokinin response regulator, OsRR2. Additionally, the (phospho)proteome analysis identified differential phosphorylation of the Cyclin-dependent kinase G-2 (OsCDKG;2), and cell wall proteins, in response to auxin signaling, suggesting that auxin-dependent phosphorylation may be required for cell cycle activation, and cell wall synthesis during root organogenesis. Thus, our study provides evidence for the translational and post-translational regulation during CRP trans-differentiation downstream of the auxin signaling pathway. HighlightGlobal (phospho)proteome and metabolic profiling of rice CRP and CRs uncover differential proteins and metabolites associated with gene expression, cell cycle, ethylene signaling and cell wall synthesis during CR development.

plant biology↗

bioRxiv2021

Understanding distribution and occupancy of Himalayan Monal in Uttarkashi district, Uttarakhand

The Himalayan Monal is a conservation priority species in its entire distribution range. Its population is declining in many areas due to various anthropogenic threats. The information on species distribution and its abundance is lacking in many areas which are vital for conservation and management planning. Hence, through the present study, we have assessed the abundance and occupancy of Himalayan monal in Uttarkashi district (Uttarakhand). We used camera traps and conventional sign surveys for documenting the species during 2018-2019. We installed a total of 69 camera traps (2819 trap nights) and surveyed 54 trails (650 km) which represents entire habitat and topographic variability of the landscape. The occupancy and detection probability was modelled using the habitat variables. The top model showed that occupancy probability of Himalayan monal was positively influenced by the slope ({beta} =27.52 {+/-}16.25) and negatively influenced by Reserve Forest (RF) ({beta}= -8.14 SE {+/-} 4.99). The observed naive occupancy of Himalayan Monal was 0.69 in the study area, which was slightly lower than the estimated occupancy. However, in the null model, the site occupancy estimated was found to be 0.82{+/-}0.08 and with detection probability 0.23{+/-}0.03. The overall abundance of monal was estimated about 171.58 {+/-}10.2 in the study area with an average density of 0.62/ km2. The activity pattern analysis indicates that monal remains very active between 6.00 hrs -12.00 hrs and relatively less active during mid-day when humans are most active 11.30 hrs-16.30 hrs. The present study is a first attempt to estimate occupancy and abundance using camera traps as well as sign survey for the species primarily from non-Protected Area (PA). We found that Himalayan monal is abundant outside the PAs, which is a good indication for its long-term viability and also identified areas for conservation and management prioritization in Uttarkashi.

ecology↗

bioRxiv2021

Bifurcated monocyte states are predictive of mortality in severe COVID-19

Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 infection presents with varied clinical manifestations1, ranging from mild symptoms to acute respiratory distress syndrome (ARDS) with high mortality2,3. Despite extensive analyses, there remains an urgent need to delineate immune cell states that contribute to mortality in severe COVID-19. We performed high-dimensional cellular and molecular profiling of blood and respiratory samples from critically ill COVID-19 patients to define immune cell genomic states that are predictive of outcome in severe COVID-19 disease. Critically ill patients admitted to the intensive care unit (ICU) manifested increased frequencies of inflammatory monocytes and plasmablasts that were also associated with ARDS not due to COVID-19. Single-cell RNAseq (scRNAseq)-based deconvolution of genomic states of peripheral immune cells revealed distinct gene modules that were associated with COVID-19 outcome. Notably, monocytes exhibited bifurcated genomic states, with expression of a cytokine gene module exemplified by CCL4 (MIP-1{beta}) associated with survival and an interferon signaling module associated with death. These gene modules were correlated with higher levels of MIP-1{beta} and CXCL10 levels in plasma, respectively. Monocytes expressing genes reflective of these divergent modules were also detectable in endotracheal aspirates. Machine learning algorithms identified the distinctive monocyte modules as part of a multivariate peripheral immune system state that was predictive of COVID-19 mortality. Follow-up analysis of the monocyte modules on ICU day 5 was consistent with bifurcated states that correlated with distinct inflammatory cytokines. Our data suggests a pivotal role for monocytes and their specific inflammatory genomic states in contributing to mortality in life-threatening COVID-19 disease and may facilitate discovery of new diagnostics and therapeutics.

immunology↗

bioRxiv2020

SPaRTAN, a computational framework for linking cell-surface receptors to transcriptional regulators

The identity and functions of specialized cell types are dependent on the complex interplay between signaling and transcriptional networks. We present SPaRTAN (Single-cell Proteomic and RNA based Transcription factor Activity Network), a computational method to link cell-surface receptors to transcription factors (TFs) by exploiting cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) datasets with cis-regulatory information. SPaRTAN is applied to immune cell types in the blood to predict the coupling of signaling receptors with cell context-specific TFs. The predictions are validated by prior knowledge and flow cytometry analyses. SPaRTAN is then used to predict the signaling coupled TF states of tumor infiltrating CD8+ T cells in malignant peritoneal and pleural mesotheliomas. SPaRTAN greatly enhances the utility of CITE-seq datasets to uncover TF and cell-surface receptor relationships in diverse cellular states. SignificanceRecently single-cell technologies such as CITE-seq have been developed that enable simultaneous quantitative analysis of cell-surface receptor expression with transcriptional states. To date, these datasets have not been used to systematically develop cell-context-specific maps of the interface between signaling and transcriptional regulators orchestrating cellular identity and function. We developed a computational framework, SPaRTAN (Single-cell Proteomic and RNA based Transcription factor Activity Network) that integrates single-cell proteomic and transcriptomic data based on CITE-seq with cis-regulatory information. We applied our method to publicly available (PBMCs) and new CITE-seq datasets (mesothelioma). Our predicted TF activity and cell-surface receptor relationships are validated by prior knowledge as well as experimental testing. SPaRTAN reveals many previously unidentified surface-receptor associated TFs.

bioinformatics↗

bioRxiv2020

Measuring effects of trainee professional development on research productivity: A cross-institutional meta-analysis

PhD-trained scientists are essential contributors to the workforce in diverse employment sectors that include academia, industry, government, and non-profit organizations. Hence, best practices for training the future biomedical workforce are of national concern. Complementing coursework and laboratory research training, many institutions now offer professional training that enables career exploration and develops a broad set of skills critical to various career paths. The National Institutes of Health funded academic institutions to design innovative programming to enable this professional development through a mechanism known as Broadening Experiences in Scientific Training (BEST). Programming at the BEST awardee institutions included career panels, skill-building workshops, job-searching workshops, site visits, and internships. An initial concern was since doctoral training is lengthy and requires focused attention on dissertation research, having students participate in additional complementary training activities might lengthen time to degree and hamper student research productivity. To address this concern, using time to degree and publication records as measures of efficiency and productivity, metrics were analyzed from ten BEST awardee institutions. Comparing doctoral students who participated to those who did not, results revealed that across these diverse academic institutions, there were no differences in time to degree or manuscript output. Furthermore, a few institutions even demonstrated a positive correlation between participation in career and professional development activities and productivity. Our findings suggest that doctoral students should be encouraged to participate in career and professional development opportunities to ensure their preparedness for a variety of diverse and important careers in the workforce. Significance StatementOur study is unique in that it compiled doctoral degree durations at ten different universities, recorded individual participation in career and professional development activities in terms of dosage, and tracked individual engagement in real-time rather than relying on surveys sent to trainees after graduation. Participation in career and professional development activities, including internships, did not decrease efficiency or productivity. Our findings suggest that doctoral students should be encouraged to participate in career and professional development opportunities to ensure their preparedness for a variety of diverse and important careers in the workforce.

scientific communication and education↗

bioRxiv2020

Mutational signatures in countries affected by SARS-CoV-2: Implications in host-pathogen interactome

We are in the midst of the third severe coronavirus outbreak caused by SARS-CoV-2 with unprecedented health and socio-economic consequences due to the COVID-19. Globally, the major thrust of scientific efforts has shifted to the design of potent vaccine and anti-viral candidates. Earlier genome analyses have shown global dominance of some mutations purportedly indicative of similar infectivity and transmissibility of SARS-CoV-2 worldwide. Using high-quality large dataset of 25k whole-genome sequences, we show emergence of new cluster of mutations as result of geographic evolution of SARS-CoV-2 in local population ([≥]10%) of different nations. Using statistical analysis, we observe that these mutations have either significantly co-occurred in globally dominant strains or have shown mutual exclusivity in other cases. These mutations potentially modulate structural stability of proteins, some of which forms part of SARS-CoV-2-human interactome. The high confidence druggable host proteins are also up-regulated during SARS-CoV-2 infection. Mutations occurring in potential hot-spot regions within likely T-cell and B-cell epitopes or in proteins as part of host-viral interactome, could hamper vaccine or drug efficacy in local population. Overall, our study provides comprehensive view of emerging geo-clonal mutations which would aid researchers to understand and develop effective countermeasures in the current crisis. SignificanceOur comparative analysis of globally dominant mutations and region-specific mutations in 25k SARS-CoV-2 genomes elucidates its geo-clonal evolution. We observe locally dominant mutations (co-occurring or mutually exclusive) in nations with contrasting COVID-19 mortalities per million of population) besides globally dominant ones namely, P314L (ORF1b) and D164G (S) type. We also see exclusive dominant mutations such as in Brazil (I33T in ORF6 and I292T in N protein), England (G251V in ORF3a), India (T2016K and L3606F in ORF1a) and in Spain (L84S in ORF8). The emergence of these local mutations in ORFs within SARS-CoV-2 genome could have interventional implications and also points towards their potential in modulating infectivity of SARS-CoV-2 in regional population.

bioinformatics↗

bioRxiv2020

New insights of zoogeographical distribution of Himalayan goral (Naemorhedus goral) from Indian Himalayan Region

In the present study, we explored the intraspecific genetic variation and phylogeographic relationship among all the reported species in the genus Naemorhedus distributed in a wide range of habitats. The Bayesian based phylogeny demonstrated that Himalayan goral, is a highly diverged species from the other reported species of gorals. We claim the presence of two valid sub-species of Himalayan goral, i.e. N. g. bedfordi and N. g. goral, distributed in the western and central Himalaya, respectively. The comparative analysis with the inclusion of data available from different ranges, suggests the presence of plausibly six species of gorals across the distribution with a few valid subspecies. Further, we report that N. griseus is a valid species and not the synonyms of N. goral considering the observed discrepancy in the available sequences. We recommend all the sub-species present at distant locations may be considered as Evolutionary Significant Units (ESUs) and, therefore, appeal to provide them special attention for long term conservation and management.

genetics↗

bioRxiv2020

Antibiofilm activity of Fmoc-phenylalanine against Gram-positive and Gram-negative bacterial biofilms

BackgroundBiofilm associated infections are the major contributor of mortality, morbidity and financial burden in patients with bacterial infection. Molecules with surfactant behaviour are known to show significant antibiofilm effect against these infections. Thus, newly discovered antibacterial Fmoc-phenylalanine (Fmoc-F) and other Fmoc-amino acids (Fmoc-AA) with surfactant properties, could have potential antibiofilm properties. ObjectivesTo evaluate and characterise the antibiofilm activity of Fmoc-F and some Fmoc-AA against various clinically relevant bacteria. MethodsBiofilm inhibition and eradication was evaluated by crystal violet staining procedure along with scanning electron microscopy (SEM). Attenuated Total Reflection - Fourier Transform Infrared Spectroscopy (ATR-FTIR), Biochemical assays and Congo red staining were employed to investigate mechanism of antibiofilm action. ResultsWe showed that Fmoc-F not only inhibits the biofilm formation in S. aureus and P. aeruginosa, but also eradicates the already formed biofilms over the surface. Further, Fmoc-F coated glass surface resists S. aureus and P. aeruginosa biofilm formation and attachment, when biofilm is grown over the surface. The mechanistic investigation suggests that Fmoc-F reduces the ECM components such as proteins carbohydrates and eDNA in the biofilm and affect its stability via direct interactions with ECM components and/ or indirectly through reducing bacterial cell population. Finally, we showed that Fmoc-F treatment in combination with other antibiotics such as vancomycin and ampicillin synergistically inhibit biofilm formation. ConclusionsOverall, the study demonstrates the potential application of Fmoc-F and other Fmoc-AA molecules individually as well as in combination as antibiofilm agents and antibiofilm coating material for treating biofilm associated infections.

microbiology↗

bioRxiv2020

Inferring cellular trajectories from scRNA-seq using Pseudocell Tracer

Single cell RNA sequencing (scRNA-seq) can be used to infer a temporal ordering of dynamic cellular states. Current methods for the inference of cellular trajectories rely on unbiased dimensionality reduction techniques. However, such biologically agnostic ordering can prove difficult for modeling complex developmental or differentiation processes. The cellular heterogeneity of dynamic biological compartments can result in sparse sampling of key intermediate cell states. This scenario is especially pronounced in dynamic immune responses of innate and adaptive immune cells. To overcome these limitations, we develop a supervised machine learning framework, called Pseudocell Tracer, which infers trajectories in pseudospace rather than in pseudotime. The method uses a supervised encoder, trained with adjacent biological information, to project scRNA-seq data into a low-dimensional cellular state space. Then a generative adversarial network (GAN) is used to simulate pesudocells at regular intervals along a virtual cell-state axis. We demonstrate the utility of Pseudocell Tracer by modeling B cells undergoing immunoglobulin class switch recombination (CSR) during a prototypic antigen-induced antibody response. Our results reveal an ordering of key transcription factors regulating CSR, including the concomitant induction of Nfkb1 and Stat6 prior to the upregulation of Bach2 expression. Furthermore, the expression dynamics of genes encoding cytokine receptors point to the existence of a regulatory mechanism that reinforces IL-4 signaling to direct CSR to the IgG1 isotype.

bioinformatics↗

bioRxiv2020

Membrane-associated α-tubulin is less acetylated in postmortem prefrontal cortex from depressed subjects relative to controls: cytoskeletal dynamics, HDAC6 and depression

Cytoskeletal proteins and post-translational modifications play a role in mood disorders. Post-translational modifications of tubulin also alter microtubule dynamics. Furthermore, tubulin interacts closely with Gs, the G-protein responsible for activation of adenylyl cyclase. Postmortem tissue derived from depressed suicide brain showed increased Gs in lipid-raft domains compared to normal subjects. Gs, when ensconced in lipid-rafts, couples less effectively with adenylyl cyclase to produce cAMP and this is reversed by antidepressant treatment. A recent in-vitro study demonstrated that tubulin anchors Gs to lipid-rafts and that increased tubulin acetylation (due to HDAC-6 inhibition) and antidepressant treatment decreased the proportion of Gs complexed with tubulin. This suggested that deacetylated-tubulin might be more prevalent in depression. This study, examined tubulin acetylation in whole tissue homogenate, plasma-membrane and lipid-raft membrane domains in tissue from normal control (NC) subjects, depressed suicides and depressed non-suicides. While tissue homogenate showed no changes in [<]-tubulin/tubulin acetylation between control, depressed suicides and depressed non-suicides, plasma-membrane associated tubulin showed significant decreases in acetylation in depressed suicides and depressed non-suicides compared to controls. No change was seen in expression of the enzymes responsible for tubulin acetylation or deacetylation. These data suggest that during depression, membrane localized tubulin maintains a lower acetylation state, permitting increased sequestration of Gs in lipid-raft domains, where it is less likely to couple to adenylyl cyclase for cAMP production. Thus, membrane tubulin may play a role in mood disorders which could be exploited for diagnosis and treatment. Significance StatementThere is little understanding about the molecular mechanisms involved in the development of depression and in severe cases, suicide. Evidence for the role of microtubule modifications in progression of depressive disorders is emerging. These postmortem data provide strong evidence for membrane tubulin modification leading to reduced efficacy of the G protein, Gs, in depression. This study reveals a direct link between decreased tubulin acetylation in human depression and the increased localization of Gs in lipid-raft domains responsible for attenuated cAMP signaling. The evidence presented here suggest a novel diagnostic and therapeutic locus for depression.

neuroscience↗

bioRxiv2019

Single cell transcriptomic analysis identifies Langerhans cells immunocompetency is critical for IDO1- dependent ability to induce tolerogenic T cells.

Human epidermal Langerhans cells (LCs) can coordinate both immunogenic and tolerogenic immune responses, creating an attractive opportunity for immunomodulation strategies. To investigate transcriptional determinants of human primary LC tolerance we applied single cells RNA-sequencing combined with transcriptional network modelling and functional analysis. Unsupervised clustering of single cell transcriptomes revealed that steady-state LCs exist in immature and immunocompetent states, and become fully immunocompetent on migration. Interestingly, LC migration, which has been shown to result in upregulation of the transcription factor IRF4, led in parallel to increased expression of a tolerogenic gene module including IDO1, LGALS1, LAMTOR1 and IL10RA, which translated to efficient induction of regulatory T cells in co-culture assays by immunocompetent LCs. Using protein expression analysis and perturbation with inhibitors, we confirmed the role of IDO1 as a mediator of LC tolerogenic responses induced during LC migration. Computational analysis of regulons and Partial Information Decomposition analyses identified IRF4 as a key driver for LC tolerogenic programmes. The predicted IRF4-regulated genes were confirmed by analysis of CRISPR-Cas9 edited LCs. These findings suggest that efficient priming of tolerogenic responses by LCs requires upregulation of a migration-coupled maturation program which is superimposed with a tolerance-inducing genomic module.

systems biology↗

bioRxiv2019

Nutritional Immunity and Antibiotic Drug Treatments Influence Microbial Composition but Fail to Eliminate Urethral Catheter Biofilms in Recurrently Catheterized Patients

Polymicrobial biofilms that form on indwelling urethral catheters used by neurogenic bladder patients are known to recur following catheter replacements. Uropathogens dominate in catheter biofilms (CBs), grow and disperse as multi-cellular aggregates. Their microbial complexity, the characteristics of host immune responses and the molecular crosstalk in this ecosystem are incompletely understood. By surveying eight patients over up to six months with meta-omics analysis methods, we shed new light on the longitudinal microbial dynamics in CBs and the microbial-host crosstalk. There was evidence of chronic innate immune responses in all patients. Pathogens dominated the microbial contents. Proteus mirabilis often out-competed other species in cases of salt encrustation of catheters. The examination of proteomes in CBs and associated urinary pellets revealed many abundant bacterial systems for transition metal ion (TMI) acquisition. TMIs are sequestered by effector proteins released by activated neutrophils and urothelial cells, such as lactotransferrin and calgranulins, which were abundant in the host proteomes. We identified positive quantitative correlations among systems responsible for siderophore biosynthesis, TMI/siderophore uptake and TMI cellular import in bacterial species, suggesting competition for TMIs to support their metabolism and growth in CBs. Enterococcus faecalis was prevalent as a cohabitant of CBs and expressed three lipoproteins with apparent TMI acquisition functions. Fastidious anaerobic bacteria such as Veillonella, Actinobaculum, and Bifidobacterium grew in CB communities that appeared to be oxygen starved. Finally, antibiotic drug treatments were shown to influence microbial composition of CBs but failed to prevent re-colonization of urethral catheters with persisting and/or drug-resistant newly emerging pathogens.

microbiology↗

bioRxiv2019

Peptide Secondary Structure Prediction using Evolutionary Information

BACKGROUNDIn the past, large numbers of methods have been developed for predicting secondary structure of proteins. Best of authors knowledge no method has been specifically developed for predicting secondary structure of peptides. We analyzed secondary structure of peptides and proteins; it was observed that same peptide in protein adopt different secondary structures. Considering the wide application of peptides in therapeutic market, we made attempt to develop a method called PEP2D for predicting secondary structure of peptides. RESULTSIn this study, 3107 unique peptides have been used to train, test and evaluate peptide secondary structure prediction models. It was observed that regular secondary structure content (e.g., helix, beta-sheet) increased with length of peptides. Firstly, models based on various machine-learning techniques have been developed using binary profile of peptides and achieved maximum overall accuracy (Q3) 79.5%. The performance of models further improved from 79.5% to 83.5% using evolutionary information in the form of PSSM profile. We also evaluate performance of protein secondary structure prediction method PSIPRED on our dataset and achieved maximum accuracy 76.9%; particularly poor (Q3 71.4%) for small peptides having length less than 10 residues. Overall, PEP2D has better prediction of beta-sheets (Q3 74%) and coil region (Q3 87%) of peptides as compare to PSIPRED (Q3 54.4% for beta-sheet and Q3 77.9% for coil). We also measure performance of PSIPRED and PEP2D in terms of segment overlap (SOV); achieved 69.3 and 76.7 respectively. CONCLUSIONOur observations indicate that there is a need of developing separate method for predicting secondary structure of peptides. It was also observed that models based on PSSM profile perform poor on small peptides in comparison to long peptides. Based on our study, we developed method for predicting secondary structure of peptides. In order to provide service to user, a webserver/standalone has been developed (https://webs.iiitd.edu.in/raghava/pep2d/).

bioinformatics↗