Biology subjects

Rahman, M. S.

Publications and source records attributed to Rahman, M. S..

At least 55 records · Page 3Linked to original sources

bioRxiv2022

Identifying Genes with Location Dependent Noise Variance in Spatial Transcriptomics Data

Spatial transcriptomics (ST) holds the promise to identify the existence and extent of spatial variation of gene expression in complex tissues. Such analyses could help identify gene expression signatures that distinguish between healthy and disease samples. Existing tools to detect spatially variable genes assume a constant noise variance across location. This assumption might miss important biological signals when the variance could change across spatial locations, e.g., in the tumor microenvironment. In this paper, we propose NoVaTeST, a framework to identify genes with location-dependent noise variance in ST data. NoVaTeST can model gene expression as a function of spatial location with a spatially variable noise. We then compare the model to one with constant noise to detect genes that show significant spatial variation in noise. Our results show genes detected by NoVaTeST provide complimentary information to existing tools while providing important biological insights.

systems biology↗

bioRxiv2022

Epstein-Barr virus nuclear antigen proteins deploy diverse mechanisms to bind the human genome

Viral transcription factors (vTFs) are known to bind the human genome and impact critical biological processes. However, a vTF can infect multiple cell-types, and the extent to which they deploy the same mechanisms for DNA binding across different cell-types is poorly understood. We used the Epstein-Barr virus Nuclear Antigen Proteins 2 and 3 (EBNA2 and EBNA3) to address this gap, the two widely studied vTFs associated with cancers, as case studies. We analyze multiple ChIP-seq datasets of these vTFs from different human cell-lines using a state-of-the-art convolutional neural network model. We show that each vTF uses both cell-type specific and cell-type independent cofactors to bind the human genome. Interestingly, each vTF requires [~]10 cofactors consistently across all binding peaks. Such dependency on a large number of cofactors is in contrast to human TFs since human TFs typically co-bind with 2-3 cofactors. Our de novo motif finding approach also reveals novel sequence motifs at the ChIP peaks of these vTFs. These critical signals were missed by previous studies doing enrichment analysis using known human TFs. Finally, we find that although a vTF impacts similar biological processes across cell-types, it also impacts distinct biological processes in a cell-type specific manner. Our study provides a roadmap for integrative analysis of vTF binding and pinpointing their diverse mechanisms of targeting the human genome across different human cell-types.

genomics↗

bioRxiv2022

DeepBend: An Interpretable Model of DNA Bendability

The bendability of genomic DNA impacts chromatin packaging and protein-DNA binding. However, beyond a handful of known sequence motifs, such as certain dinucleotides and poly(A)/poly(T) sequences, we do not have a comprehensive understanding of the motifs influencing DNA bendability. Recent high-throughput technologies like Loop-Seq offer an opportunity to address this gap but the lack of accurate and interpretable machine learning models still poses a significant challenge. Here we introduce DeepBend, a convolutional neural network model built as a visible neural network where we designed the convolutions to directly capture the motifs underlying DNA bendability and how their periodic occurrences or relative arrangements modulate bendability. Through extensive benchmarking on Loop-Seq data, we show that DeepBend consistently performs on par with alternative machine learning and deep learning models while giving an extra edge through mechanistic interpretations. Besides confirming the known motifs of DNA bendability, DeepBend also revealed several novel motifs and showed how the spatial patterns of motif occurrences influence bendability. DeepBends genome-wide prediction of bendability further showed how bendability is linked to chromatin conformation and revealed the motifs controlling bendability of topologically associated domains and their boundaries.

bioinformatics↗

bioRxiv2022

Pathogenic Rickettsia species evade autophagosomal maturation and reduce anti-microbicidal pro-inflammatory IL-1 responses to support their intracellular survival.

Species of genus Rickettsia are obligate intracellular bacterial parasites of a wide range of arthropod and vertebrate hosts. Some Rickettsia species are responsible for several serious human diseases. One fascinating feature of these stealthy group of pathogens is their ability to exploit host cytosolic defense responses to their benefits. However, the precise mechanism by which pathogenic Rickettsia elude host immune defense responses remains to be determined. Here, we observed that pathogenic R. typhi and R. rickettsii, but not non-pathogenic R. montanensis become ubiquitinated and induce autophagy upon entry into bone-marrow-derived macrophages. Moreover, unlike R. montanensis, R. typhi, and R. rickettsii colocalized with LC3B but not with Lamp2 upon host cell entry. Finally, we observed that pathogenic but not non-pathogenic Rickettsia reduce pro-inflammatory IL-1 responses. In sum, we identified a previously unappreciated pathway by which pathogenic, but not non-pathogenic, Rickettsia become ubiquitinated and induced autophagy, but avoided autophagolysosomal destruction as well as inflammasome-mediated anti-microbicidal IL-1 cytokine responses to establish an intracytosolic niche in macrophages.

microbiology↗

bioRxiv2022

Succinylated lysine residue prediction revisited

MotivationLysine succinylation is a kind of post-translational modification (PTM) which plays a crucial role in regulating the cellular processes. Aberrant succinylation may cause inflammation, cancers, metabolism diseases and nervous system diseases. The experimental methods to detect succinylation sites are time-consuming and costly. This thus calls for computational models with high efficacy and attention has been given in the literature for developing such models, albeit with only moderate success in the context of different evaluation metrics. One important aspect in this context is the biochemical and physicochemical properties of amino acids, which appear to be useful as features for such computational predictors. However, some of the existing computational models did not use the biochemical and physicochemical properties of amino acids, while some others used them without considering the inter-dependency among the properties. ResultsThe combinations of biochemical and physicochemical properties derived through our optimization process achieve better results than the results achieved by the combination of all the properties. We propose three deep learning architectures, CNN+Bi-LSTM (CBL), Bi-LSTM+CNN (BLC) and their combination (CBL_BLC). We find that CBL_BLC is outperforming the other two. Ensembling of different models successfully improves the results. Notably, tuning the threshold of the ensemble classifiers further improves the results. Upon comparing our work with other existing works on two datasets, we find that we successfully achieve better sensitivity and specificity through varying the threshold value. Availabilityhttps://github.com/Dariwala/Succinylation-with-biophysico-and-deep-learning Contactmsrahman@cse.buet.ac.bd

bioinformatics↗

bioRxiv2022

Expression Analysis, Molecular Characterization and Prognostic Evaluation on TMED4 and TMED9 Gene Expression in Glioma

Here, we utilized a database mining approach to unfold the prognostic and therapeutic potentials of Transmembrane EmP24 Trafficking Protein 4 (TMED4) and 9 (TMED) coding gene expressions in glioma. Both the genes were found to be overexpressed at the mRNA and protein level in low grade glioma (LGG) and glioblastoma multiforme (GBM) tissues including different glioma cell lines. Significant increase in the expression level of these genes with advancing glioma patients age, glioma grades and histological subtypes was observed. Differential and distinct promoter and coding sequence methylation pattern of TMED4 and TMED9 was observed in LGG and GBM tissues that may aid in methylation-sensitive diagnosis of glioma patients. The presence of multiple heterozygous genetic alterations (frequency: 0.4-1.1%) in those genes unveiled their potentials in high-throughput screening of glioma patients. The overexpression of TMED4 and TMED9 genes was associated with poor overall survival (OS) of LGG and GBM patients (HR:>1.6). Association of the expression levels of these genes with different immune cell infiltration levels i.e., B cell and T cell and modulators like CD274 and IL10RB was observed providing assurance in TMED-based diagnostic measure and therapeutic intervention discovery. Furthermore, functional enrichment analysis of the neighbor genes of TMED4 and TMED9 revealed that they are involved in metal ion binding, focal adhesion of cells and protein processing, and the deregulation of these activities are associated with gliomagenesis. Altogether, this study suggests that TMED4 and TMED9 are potential prognostic and therapeutic targets for glioma. However, further laboratory research is warranted.

cancer biology↗

bioRxiv2022

Genomic characteristics, virulence and antimicrobial resistance in avian pathogenic Escherichia coli MTR_BAU02 strain isolated from layer farms in Bangladesh

BackgroundColibacillosis, caused by avian pathogenic Escherichia coli (APEC), is one of the most significant infectious diseases affecting poultry worldwide. APEC is one of the leading causes of mortality and morbidity associated with significant economic losses in the poultry industry. ObjectiveThis study was aimed to determine the genomic diversity, virulence factor genes (VFGs) and antimicrobial resistance (AMR) genes in the APEC isolated from layer chickens using whole-genome sequencing (WGS). MethodAPEC MTR_BAU02 strain was isolated from the sick and dead birds. Genomic DNA from APEC MTR_BAU02 strain was extracted using commercial DNA extraction kit, WGS libraries were prepared using the Nextera DNA Flex Library Prep Kit, and finally, paired-end (2 x 250) WGS performed using Illumina MiSeq sequencer. ResultsThe genome size of strain APEC MTR_BAU02 is 4,924,680 bp with a GC content of 51.1% and 4,681 protein-coding sequences. Among the annotated WGS reads, 99.71% reads mapped to Enterobacteriaceae genomes. Based on the phylogenetic analysis of the APEC MTR_BAU02 genome and 99 reference genomes of E. coli, the APEC MTR_BAU02 genome showed sequence similarities with enterotoxigenic E. coli strains isolated from infections of different clinical severity. Metabolic functional annotations detected 380 SEED subsystems including genes coding for carbohydrate metabolism (17.34%), amino acid and derivatives (14.20%), protein metabolism (10.64%), cofactors, vitamins, prosthetic groups and pigments (7.49%), respiration (4.72%), membrane transport (4.49%), stress response (4.47%), motility and chemotaxis (4.46%), and virulence, disease and defense (2.22%). We also detected 92 VFGs and 122 AMR genes in the APEC MTR_BAU02 strain. ConclusionAssessment of these genomic features with functional genomic validation in commonly occurring serogroups of APEC will increase our understanding of the molecular pathogenesis, pave the way to early diagnosis and more effective control of colibacillosis, and improved animal welfare.

genomics↗

bioRxiv2022

Rickettsia typhi peptidoglycan mapping with data-dependent tandem mass spectrometry

Rickettsia species are diverse Gram-negative obligate intracellular bacteria often pervasive in numerous invertebrates, as well as fungal, nematode and microeukaryotic hosts. Certain species are etiological agents for well-known arthropod-borne illnesses; e.g., R. rickettsii (Rocky Mountain Spotted Fever), R. prowazekii (Epidemic Typhus), and R. typhi, (Endemic Typhus). Living freely in eukaryotic cytosol presumably exposes rickettsiae to host cell immune receptors, particularly those recognizing bacterial cell envelope glycoconjugates. However, the mechanics of host recognition of rickettsiae remain poorly defined. As rickettsiae synthesize a canonical Gram-negative cell envelope that includes peptidoglycan (PGN) and lipopolysaccharide (LPS), structural insight on these macromolecules is important for deciphering host responses to these pathogens. In this work, PGN from R. typhi was digested and the resultant subunits were analyzed by two different, albeit complementary, sample preparation methods. Both approaches were subsequently subjected to liquid chromatography/mass spectrometry analysis to infer PGN structure. R. typhi PGN was determined to be similar to most other Gram-negative bacteria, with mDAP-type muropeptide subunits. However, additional alanine residues were observed elongating the muropeptide stems, rather than the glycine residues usually observed in Gram-negative bacterial PGN. Despite this deviation, R. typhi contains a murein layer that is predicted to agonize host cellular PGN receptors and be susceptible to PGN-targeting antimicrobials. This same structure is likely synthesized by all Rickettsia species, as bioinformatics and comparative genomics analyses indicate the biosynthesis of PGN is highly conserved. Determining how host cells process this canonical glycoconjugate during infection is crucial for identifying factors behind rickettsial pathogenesis, including immunoavoidance or proinflammatory mechanisms possibly employed by rickettsiae with varying pathogenic potential.

microbiology↗

bioRxiv2022

Metagenomic analysis reveals the abundance and diversity of opportunistic fungal pathogens in the nasopharyngeal tract of COVID-19 patients

The nasopharyngeal tract (NT) of human is a habitat of a diverse microbial community that work together with other gut microbes to maintain the host immunity. In our previous study, we reported that SARS-CoV-2 infection reduces human nasopharyngeal commensal microbiome (bacteria, archaea and commensal respiratory viruses) but increases the abundance of pathobionts. This study aimed to assess the possible changes in the resident fungal diversity by the inclusion of opportunistic fungi due to the infection of SARS-CoV-2 in the NT of humans. Twenty-two (n = 22) nasopharyngeal swab samples (including COVID-19 = 8, Recovered = 7, and Healthy = 7) were collected for RNAseq-based metagenomics analyses. Our results indicate that SARS-CoV-2 infection significantly increased (p < 0.05, Wilcoxon test) the population and diversity of NT fungi with a high inclusion of opportunistic pathogens. We detected 863 fungal species including 533, 445, and 188 species in COVID-19, Recovered, and Healthy individuals, respectively that indicate a distinct microbiome dysbiosis due to the SARS-CoV-2 infection. Remarkably, 37% of the fungal species were exclusively associated with SARS-CoV-2 infection, where S. cerevisiae (88.62%) and Phaffia rhodozyma (10.30%) were two top abundant species in the NT of COVID-19 patients. Importantly, 16% commensal fungal species found in the Healthy control were not detected in either COVID-19 patients or when they were recovered from the COVID-19. Pairwise Spearmans correlation test showed that several altered metabolic pathways had significant positive correlations (r > 0.5, p < 0.01) with dominant fungal species detected in three metagenomes. Taken together, our results indicate that SARS-CoV-2 infection causes significant dysbiosis of fungal microbiome and alters some metabolic pathways and expression of genes in the NT of human. Findings of our study might be helpful for developing microbiome-based diagnostics, and also devising appropriate therapeutic regimens including antifungal drugs for prevention and control of concurrent fungal coinfections in COVID-19 patients. Author summaryThe SARS-CoV-2 is a highly transmissible and pathogenic betacoronavirus that primarily enters into the human body through NT to cause fearsome COVID-19 disease. Recent high throughput sequencing and downstream bioinformatic analyses revealed that microbiome dysbiosis associated with SARS-CoV-2 infection are not limited to bacteria, and fungi are also implicated in COVID-19 development in susceptible individuals. This study demonstrates that SARS-CoV-2 infection results in remarkable depletion of NT commensal fungal microbiomes with inclusion of various opportunistic fungal pathogens. We discussed the role of these altered fungal microbiomes in the pathophysiology of the SARS-CoV-2 infection. Our results suggest that dysbiosis in fungal microbiomes and associated altered metabolic functional pathways (or genes) possibly play a determining role in the progression of SARS-CoV-2 pathogenesis. Thus, the identifiable changes in the diversity and composition of the NT fungal population and their related genomic features demonstrated in this study might lay a foundation for better understanding of the underlying mechanism of co-pathogenesis, and the ongoing development of therapeutic agents including antifungal drugs for the resolution of COVID-19 pandemic.

microbiology↗

bioRxiv2021

QT-GILD: Quartet Based Gene Tree Imputation Using Deep Learning Improves Phylogenomic Analyses Despite Missing Data

Species tree estimation is frequently based on phylogenomic approaches that use multiple genes from throughout the genome. However, for a combination of reasons (ranging from sampling biases to more biological causes, as in gene birth and loss), gene trees are often incomplete, meaning that not all species of interest have a common set of genes. Incomplete gene trees can potentially impact the accuracy of phylogenomic inference. We, for the first time, introduce the problem of imputing the quartet distribution induced by a set of incomplete gene trees, which involves adding the missing quartets back to the quartet distribution. We present QT-GILD, an automated and specially tailored unsupervised deep learning technique, accompanied by cues from natural language processing (NLP), which learns the quartet distribution in a given set of incomplete gene trees and generates a complete set of quartets accordingly. QT-GILD is a general-purpose technique needing no explicit modeling of the subject system or reasons for missing data or gene tree heterogeneity. Experimental studies on a collection of simulated and empirical data sets suggest that QT-GILD can effectively impute the quartet distribution, which results in a dramatic improvement in the species tree accuracy. Remarkably, QT-GILD not only imputes the missing quartets but it can also account for gene tree estimation error. Therefore, QT-GILD advances the state-of-the-art in species tree estimation from gene trees in the face of missing data. QT-GILD is freely available in open source form at https://github.com/pythonLoader/QT-GILD.

bioinformatics↗

bioRxiv2021

Pathogenic, but not non-pathogenic, Rickettsia manipulate inflammasome-dependent IL-1 responses to facilitate their replication and host dissemination.

Rickettsia species (spp.) are strict obligate intracellular bacteria, with some being pathogenic in their mammalian host, including humans. One critical feature of these stealthy group of pathogens is their ability to manipulate hostile cytosolic environments to their benefits. Although our understanding of Rickettsia cell biology and pathogenesis are evolving, the mechanisms by which pathogenic Rickettsia spp. evade host innate immune detection remains elusive. Here, we showed that disease severity in wild-type (WT) C57BL/6J mice infected with R. typhi (etiologic agent of murine typhus) and R. rickettsii (etiologic agent of Rocky Mountain Spotted Fever), but not with non-pathogenic R. montanensis, correlated with levels of bacterial burden as detected in the spleens, as well as the serum concentrations of pro-inflammatory cytokine IL-1 and to a lesser extent IL- 1{beta}. Antibody-mediated neutralization of IL-1 confirmed a key role in controlling mortality rates and bacterial burdens of rickettsiae-infected WT mice. As macrophages are a primary source of both IL-1 and IL-1{beta} cytokines, we determined the mechanism of the anti-rickettsial activities using bone-marrow-derived macrophages. We found that pathogenic R. typhi and R. rickettsii, but not non-pathogenic R. montanensis, eluded pro- IL-1 induction and benefited pre-dominantly from the reduced IL-1 secretion, via a Caspase-11-Gsdmd-dependent pathway, to facilitate intracytosolic replication. Adoptative transfer experiments identified that IL-1 secretion by macrophages was critical for controlling rickettsiosis in WT mice. In sum, we identified a previously unappreciated pathway by which pathogenic, unlike non-pathogenic, rickettsiae preferentially target the Caspase-11-Gsdmd-IL-1 signaling axis in macrophages thus supporting their replication within the host. IMPORTANCECurrently, no vaccines are available to prevent rickettsioses, while vector-borne rickettsial infections in humans are on the rise globally. In fact, the insufficient understanding of how pathogenic Rickettsia species circumvent host immune defense mechanisms has significantly hindered the development of more effective therapeutics. Here, we identified a previously unappreciated role for the Caspase-11-Gsdmd-IL-1 signaling axis, to limiting the replication of pathogenic R. rickettsia and R. typhi species in murine macrophages and wild-type (WT) C57BL/6J mice. Adoptative transfer studies further identified IL-1-secreting macrophages as critical mediators in controlling rickettsial infection in WT mice. Collectively, these findings provide insight into the potential mechanism of how pathogenic, but not non-pathogenic Rickettsia spp., benefit from a reduction in the Caspase-11-Gsdmd-mediated release of IL-1 to support host colonization.

microbiology↗

bioRxiv2021

Microbiome dysbiosis regulates the level of energy production under anaerobic condition

The microbiome of the anaerobic digester (AD) regulates the level of energy production. To assess the microbiome dysbiosis in different stages of anaerobic digestion, we analyzed 16 samples dividing into four groups (Group-I = 2; Group-II = 5; Group-III = 5 and Group-IV = 4) through whole metagenome sequencing (WMS). The physicochemical analysis revealed that highest CH4 production (74.1%, on Day 35 of digestion) was associated with decreased amount of non-metal (phosphorus and sulfur) and heavy metals (chromium, lead and nickel). The WMS generated 380.04 million reads mapped to ~ 2800 distinct bacterial, archaeal and viral genomes through PathoScope (PS) and MG-RAST (MR) analyses. The PS analysis detected 768, 1421, 1819 and 1774 bacterial strains in Group-I, Group-II, Group-III and Group-IV, respectively which were represented by Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, Spirochaetes and Fibrobacteres (> 93.0% of the total abundances). The archaeal fraction of the AD microbiomes was represented by 343 strains, of which 95.90% strains shared across these metagenomes. The indicator species analysis showed that Methanosarcina vacuolate, Dehalococcoides mccartyi, Methanosarcina sp. Kolksee and Methanosarcina barkeri were the highly specific for energy production in Group-III and Group-IV. However, most of the indicator phylotypes displayed reduced abundance in the initial stage of biogas production (Group-I and Group-II) compared to their increased relative abundances in Group-IV (Day 35). The correlation network analysis showed that different strains of Euryarcheota and Firmicutes phyla were associated with highest level (74.1%) of energy production (Group-IV). In addition to taxonomic dysbiosis, top CH4 producing microbiomes showed increased genomic functional activities related to one carbon and biotin metabolism, oxidative stress, proteolytic pathways, MT1-MMP pericellular network, acetyl-CoA production, motility and chemotaxis. This study reveals distinct changes in composition and diversity of the AD microbiomes including different indicator species, and their genomic features that are highly specific for energy production.

microbiology↗

bioRxiv2021

Angiotensin II disrupts the cytoskeletal architecture of human urine-derived podocytes and results in activation of the renin-angiotensin system

High blood pressure is one of the major public health problems which causes severe disorders in several tissues including the human kidney. One of the most important signaling pathways associated with the regulation of blood pressure is the renin-angiotensin system (RAS), with its main mediator angiotensin II (ANGII). Elevated levels of circulating and intracellular ANGII and aldosterone lead to pro-fibrotic, -inflammatory and -hypertrophic milieu that causes remodelling and dysfunction in cardiovascular and renal tissues. Furthermore, ANGII has been recognized as major risk factor for the induction of apoptosis in podocytes, ultimately leading to chronic kidney disease (CDK). In the past, disease modeling of kidney-associated malignancies was extremely difficult, as the derivation of kidney originated cells is very challenging. Here we describe a differentiation protocol for reproducible differentiation of SIX2-positive urine derived renal progenitor cells (UdRPCs) into mature podocytes bearing typical foot processes. The UdRPCs-derived podocytes show the ability to execute Albumin endocytosis and the activation of the renin-angiotensin system by being responsive to ANGII stimulation. Our data reveals the ANGII dependent downregulation of NPHS1 and SYNPO, resulting in the disruption of the complex podocyte cytoskeletal architecture, as shown by immunofluorescence-based detection of -ACTININ. In the present manuscript we confirm and propose UdRPCs as a unique cell type useful for studying nephrogenesis and associated diseases. Furthermore, the responsiveness of UdRPCs-derived podocytes to ANGII implies potential applications in nephrotoxicity studies and drug screening.

cell biology↗

bioRxiv2021

Metagenomic diagnosis and pathogenic network profile of SARS-CoV-2 in patients co-morbidly affected by type 2 diabetes

BackgroundThe mortality of COVID-19 disease is very high among males or elderly or individuals having comorbidities with obesity, cardiovascular diseases, lung infections, hypertension, and/or diabetes. Our study characterizes SARS-CoV-2 infected patients metagenomic features with or without type 2 diabetes to identify the microbial interactions associated with its fatal consequences. MethodThis study compared the baseline nasopharyngeal microbiome of SARS-CoV-2 infected diabetic and non-diabetic patients with controls adjusted with age and gender. The mNGS were performed using Ion GeneStudio S5 Series and the data were analyzed by the Vegan-package in R. ResultsAll three groups possessed significant bacterial diversity and dissimilarity indexes (p<0.05). Spearmans correlation coefficient network analysis illustrated 183 significant positive correlations and 13 negative correlations of pathogenic bacteria (r=0.6-1.0, p<0.05), and 109 positive correlations among normal-flora and probiotic bacteria (r>0.6, p<0.05). The SARS-CoV-2 diabetic group exhibited a significant increase of pathogens (p<0.05) and opportunistic pathogens (p<0.05) with a simultaneous decrease of normal-flora (p<0.05). The molecular docking analysis of Salivaricin, KLD4 (alpha), and enterocin produced by several enriched probiotic strains presented strong binding affinity with Shiga toxin, outer membrane proteins (ompA, omp33) or hemolysin. ConclusionThe dysbiosis of the bacterial community might be linked with severe consequences of COVID-19 infected diabetic patients, although few probiotic strains inhibited numerous pathogens in the same pathological niches. This study suggested that the promotion of normal-flora and probiotics through dietary changes and reduction of excessive pro-inflammatory states by preventing pathogenic environment might lead to a better outcome for those co-morbid patients.

genomics↗

bioRxiv2020

wQFM: Statistically Consistent Genome-scale Species Tree Estimation from Weighted Quartets

MotivationSpecies tree estimation from genes sampled from throughout the whole genome is complicated due to the gene tree-species tree discordance. Incomplete lineage sorting (ILS) is one of the most frequent causes for this discordance, where alleles can coexist in populations for periods that may span several speciation events. Quartet-based summary methods for estimating species trees from a collection of gene trees are becoming popular due to their high accuracy and statistical guarantee under ILS. Generating quartets with appropriate weights, where weights correspond to the relative importance of quartets, and subsequently amalgamating the weighted quartets to infer a single coherent species tree allows for a statistically consistent way of estimating species trees. However, handling weighted quartets is challenging. ResultsWe propose wQFM, a highly accurate method for species tree estimation from multi-locus data, by extending the quartet FM (QFM) algorithm to a weighted setting. wQFM was assessed on a collection of simulated and real biological datasets, including the avian phylogenomic dataset which is one of the largest phylogenomic datasets to date. We compared wQFM with wQMC, which is the best alternate method for weighted quartet amalgamation, and with ASTRAL, which is one of the most accurate and widely used coalescent-based species tree estimation methods. Our results suggest that wQFM matches or improves upon the accuracy of wQMC and ASTRAL. AvailabilitywQFM is available in open source form at https://github.com/Mahim1997/wQFM-2020.

bioinformatics↗

bioRxiv2020

Diversity and genomic determinants of the microbiomes associated with COVID-19 and non-COVID respiratory diseases

The novel coronavirus disease 2019 (COVID-19) is a rapidly emerging and highly transmissible disease caused by the Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2). Understanding the microbiomes associated with the upper respiratory tract infection (URTI), chronic obstructive pulmonary disease (COPD) and COVID-19 diseases has clinical interest. We hypothesized that the diversity of microbiome compositions and their genomic features are associated with different pathological conditions of these human respiratory tract diseases (COVID-19 and non-COVID; URTI and COPD). To test this hypothesis, we analyzed 21 whole metagenome sequences (WMS) including eleven COVID-19 (BD = 6 and China = 5), six COPD (UK = 6) and four URTI (USA = 4) samples to unravel the diversity of microbiomes, their genomic features and relevant metabolic functions. The WMS data mapped to 534 bacterial, 60 archaeal and 61 viral genomes with distinct variation in the microbiome composition across the samples (COVID-19>COPD>URTI). Notably, 94.57%, 80.0% and 24.59% bacterial, archaeal and viral genera shared between the COVID-19 and non-COVID samples, respectively, however, the COVID-19 related samples had sole association with 16 viral genera other than SARS-CoV-2. Strain-level virome profiling revealed 660 and 729 strains in COVID-19 and non-COVID sequence data, respectively and of them 34.50% strains shared between the conditions. Functional annotation of metagenomics sequences of thevCOVID-19 and non-COVID groups identified the association of several biochemical pathways related to basic metabolism (amino acid and energy), ABC transporters, membrane transport, replication and repair, clustering-based subsystems, virulence, disease and defense, adhesion, regulation of virulence, programmed cell death, and primary immunodeficiency. We also detected 30 functional gene groups/classes associated with resistance to antibiotics and toxic compounds (RATC) in both COVID-19 and non-COVID microbiomes. Furthermore, a predominant higher abundance of cobalt-zinc-cadmium resistance (CZCR) and multidrug resistance to efflux pumps (MREP) genes were detected in COVID-19 metagenome. The profiles of microbiome diversity and associated microbial genomic features found in both COVID-19 and non-COVID (COPD and URTI) samples might be helpful for developing the microbiome-based diagnostics and therapeutics for COVID-19 and non-COVID respiratory diseases. However, future studies might be carried out to explore the microbiome dynamics and the cross-talk between host and microbiomes employing larger volume of samples from different ethnic groups and geoclimatic conditions.

genomics↗

bioRxiv2020

Genome-wide diversity and differentiation of two novel multidrug-resistant populations of Pasteurella multocida type B:2 from fowl cholera

Pasteurella multocida is the etiologic agent of fowl cholera (FC), a highly contagious and severe disease in poultry with higher mortality and morbidity. Twenty-two P. multocida strains isolated from the FC outbreaks were subjected to phenotypic and genotypic characterization. The isolates were grouped into two distinct RAPD biotypes harboring a range of pathogenic genes; exbB, ompH, ptfA, nanB, sodC, and hgbA. Among these strains, 90.90% and 36.37% were multidrug-resistant and strong biofilm formers, respectively. Whole genome sequencing of the two representative RAPD isolates confirmed as P. multocida type B:L2:ST122 harboring a number of virulence factors, and antimicrobial resistance genes. Pan-genome analysis revealed 90 unique genes in these genomes associated with versatile metabolic functions, pathogenicity, virulence, and antimicrobial resistance. This study for the first time reports the association of P. multocida genotype B:L2:ST122 in the pathogenesis of FC, and provides a genetic context for future researches on P. multocida strains.

genomics↗

bioRxiv2020

Evolutionary dynamics of SARS-CoV-2 nucleocapsid protein (N protein) and its consequences

The emerging novel coronavirus SARS-CoV-2 has created a global confusing pandemic health crisis that warrants an accurate and detailed characterization of the rapidly evolving viral genome for understanding its epidemiology, pathogenesis and containment. We explored 61,485 sequences of the Nucleocapsid (N) protein, a potent diagnostic and prophylactic target, for identifying the mutations to review their roles in RT-PCR based diagnosis and observe consequent impacts. Compared to the Wuhan reference strain, a total of 1034 unique nucleotide mutations were identified in the mutant strains (49.15%, n=30,221) globally. Of these mutations, 367 occupy primer binding sites including 3-end mismatch to primer-pair of 11 well characterized primer sets. Noteworthy, CDC (USA) recommended N2 primer set contained lower mismatch than the other primer sets. Moreover, 684 amino acid (aa) substitutions located across 317 (75.66% of total aa) unique positions including 82, 21, and 83 of those in RNA binding N-terminal domain (NTD), SR-rich region, and C-terminal dimerization domain (CTD), respectively. Moreover, 11 in-frame deletions were revealed, mostly (n =10) within the highly flexible linker region, and the rest within the NTD region. Furthermore, we predicted the possible consequences of high-frequency mutations ([≥] 20) and deletions on the tertiary structure of the N protein. Remarkably, we observed that high frequency (67.94% of mutated sequences) coevolving mutations (R203K and G204R) destabilized and decreased overall structural flexibility. Despite being proposed as the alternate target to spike protein for vaccine and therapeutics, ongoing nonsynonymous evolution of the N protein may challenge the endeavors, thus need further immunoinformatics analyses. Therefore, continuous monitoring is required for tracing the ongoing evolution of the SARS-CoV-2 N protein in prophylactic and diagnostic interventions.

evolutionary biology↗