Biology subjects

Hasan, N. A.

Publications and source records attributed to Hasan, N. A..

10 recordsLinked to original sources

bioRxiv2026

Diet-Associated Differences in the Rumen Microbiome and Resistome of Bangladeshi Cattle

Diet influences the composition, diversity, and functional capacity of the cattle gut microbiome. However, the extent to which feeding practices affect the microbial community and resistome under real-world conditions remains poorly understood, particularly in low- and middle-income settings. Here, we applied metagenomics to fecal samples from Bangladeshi cattle fed either a natural or a mixed diet to examine differences in microbial composition, functional potential, and resistome associated with feed type. Natural-fed cattle harbored higher microbial diversity and distinct bacterial phyla, including Bacteroidota, Campylobacteriota, and Mycoplasmatota. Acinetobacter, Aliarcobacter, Comamonas, Dysosmobacter, and Sharpea were enriched in natural-fed cattle, whereas Anaerotignum, Aristaeella, Oscillibacter, and Clostridium were more abundant in the mixed-fed group. Notably, the emerging zoonotic genus Aliarcobacter was detected in the natural-fed cohort. Alpha diversity analysis showed higher richness and evenness in natural-fed cattle, and a clear separation between dietary groups in beta diversity analysis (PERMANOVA, p = 0.01). Differential analysis identified Oscillibacter ruminantium as a biomarker of natural feeding, while Succinivibrio faecicola and Anaerovibrio slackiae for mixed feeding. Resistome profiles demonstrated clear differences. Mixed-fed cattle showed a consistent enrichment of tetracycline resistance genes, whereas the natural-fed group displayed a more variable resistome. Functional analysis suggested diet-associated differences in metabolic potential, with glutathione metabolism enriched in natural-fed cattle (p<0.05) and bile secretion and fatty acid metabolism moderately enriched in the mixed-fed group. These findings indicate that feeding practices are associated with differences in rumen microbial communities and resistome profiles in Bangladeshi cattle, providing baseline insights into microbiome-resistome relationships under field conditions.

microbiology↗

bioRxiv2025

In Silico Evaluation and Therapeutic Targeting of LVDD9B Protein for WSSV Inhibition: Molecular and Ecological Insights for Aquaculture Solutions

BackgroundThis study aimed to investigate structural dynamics, binding interactions, stability, pharmacokinetics, ecological risks, and bioactivity of shrimp receptor protein LVDD9B to identify potential therapeutic candidates against White Spot Syndrome Virus (WSSV). MethodsLVDD9B proteins 3D structure was predicted using SWISS-MODEL and validated with ProSA and Ramachandran plots. Protein-protein docking between LVDD9B and VP26 (WSSV protein) was performed using HADDOCK 2.4 server. Molecular docking, dynamics simulations, binding-free energy calculations, principal component analysis (PCA), electrostatic, and vibrational frequency analyses evaluated binding affinity, stability and polarity of complexes. Results128-amino-acids of LVDD9B protein was predicted as predominantly cytoplasmic with stable, and hydrophilic, with structural analysis identified key secondary structures and conserved chitin-binding site. Docking studies revealed strong interactions between LVDD9B and VP26, supported by hydrogen-bonds and salt bridges. Molecular dynamics simulations demonstrated stable complexes with fluctuating RMSD values, and MM/GBSA calculations indicated favorable binding free energies. Pharmacokinetic analysis highlighted promising bioavailability and drug-like properties for Luteolin and Quercetin from Cuscuta reflexa, while ecological assessment identified Cosmosiin as least hazardous, with Quercetin and Luteolin showing higher toxicity. PCA revealed stable protein-ligand complexes with flexibility in Apo form. Isorhoifolin exhibited the lowest internal energy (-2099.4722 Hartree) and highest dipole moment (8.1833 Debye). Frontier orbital analysis showed HOMO-LUMO gaps (4.05-4.34 eV) influencing reactivity, while MEP and vibrational frequency analyses supported compound stability and bioactivity. ConclusionsThis study explores LVDD9Bs structural and interaction dynamics for developing antiviral therapy against WSSV, highlighting therapeutic potential of Cosmosiin, Isorhoifolin, Quercetin and Luteolin based on their pharmacokinetic and ecological profiles.

bioinformatics↗

bioRxiv2024

Yanomami skin microbiome complexity challenges prevailing concepts of healthy skin.

The adult skin microbiome contributes to skin homeostasis and generally comprises relatively low microbial complexity, especially sebaceous sites, where lipophilic Cutibacterium and Malassezia spp. predominate. Current understanding of the healthy skin microbiome derives predominantly from studies of western, industrialized populations, with limited representation of diverse cultures and lifestyles. In this study, the skin microbiome of a remote indigenous Yanomami community was investigated, revealing a complex microbial community comprised of 115 novel bacterial genomes. The bacterial community composition included genera common to western skin microbiota and additional diverse taxa, which formed multiplex interactions with a dominant eukaryote, Malassezia globosa. Metatranscriptome-derived functional attributes of the microbial communities contributed to skin homeostasis, fortifying barrier integrity via lipid metabolism and acid production and protecting against oxidative stress. The Yanomami skin microbiome comprised a suite of microbial taxa co-detected within their various surroundings. Longitudinal monitoring of Western expeditioner microbiome revealed acquisition of the Yanomami skin microbiome following immersion into the Amazon and subsequent loss upon return to an industrialized setting. These findings challenge the prevailing Western-centric view of what comprises a healthy adult skin microbiome, suggesting a diverse community that includes bacteria of environmental origin confers benefits not recognized in the current model of healthy skin. Importantly, we highlight the malleability of adult skin microbiome composition, which includes changes sustained by lifestyle modification.

microbiology↗

bioRxiv2023

Exploring Microbial Diversity and Functional Potential along the Bay of Bengal Coastline in Bangladesh: Insights from Amplicon Sequencing and Shotgun Metagenomics

Although the Bay of Bengal (BoB) is the worlds largest bay, possessing distinct physiochemical properties, it has garnered little research focus concerning its microbial diversity and ecological importance. Here, we present amplicon (16S and 18S) profiling and shotgun metagenomics data regarding microbial communities from BoBs eastern coast, viz., Saint Martin and Coxs Bazar, Bangladesh. From the 16S sequencing data, Proteobacteria appeared to be the dominant phylum in both locations, with Alteromonas, Methylophaga, Anaerospora, Marivita, and Vibrio dominating in Coxs Bazar and Pseudoalteromonas, Nautella, Marinomonas, Vibrio, and Alteromonas dominating the Saint Martin site. From the 18S sequencing data, Ochrophyta, Chlorophyta, and Protalveolata appeared among the most abundant eukaryotic divisions in both locations, with significantly higher abundance of Choanoflagellida, Florideophycidae, and Dinoflagellata in Coxs Bazar. Functional annotations revealed that the microbial communities in these samples harbor genes for biofilm formation, quorum sensing, xenobiotics degradation, antimicrobial resistance, and a variety of other processes. Together, these results provide the first molecular insight into the functional and phylogenetic diversity of microbes along the BoB coast of Bangladesh and lay the foundation for further in-depth assessment of microbial community dynamics and functional potential in the context of global change in this region.

microbiology↗

bioRxiv2022

The Diversity and Ubiquity of Antibiotic Resistant Genes in Finfish Culture Ponds in Bangladesh

In Bangladesh, fish provide over 60% of animal-source food with 56.2% of this coming from aquaculture produced predominantly in rural freshwater ponds. Increasing demand for fish products is driving intensification and resulting in higher disease prevalence, posing a risk to food security. Biosecurity is often absent in rural aquaculture practices in Bangladesh and antibiotics are commonly used to treat and prevent disease outbreaks. Antibiotics are often administered incorrectly - a key factor associated with the development of antimicrobial resistance (AMR). AMR can be disseminated rapidly within microbial ecosystems via mobile genetic elements, posing a risk for humans and animals infected with AMR pathogens as treatments with antibiotics become ineffective. Early AMR detection and understanding of the spread of antimicrobial resistant genes (ARGs) in rural aquaculture practices is critical for both food security and human health protection. Here, we apply a metagenomic approach to assess the ARG composition in pond water from six finfish (tilapia and pangasius) farms in the Mymensingh division of North-central Bangladesh. We found microbial communities within the ponds had similar alpha and beta diversities, with multiple ARGs predicted to confer resistance to eighteen different classes of antimicrobials. The most common ARGs conferred resistance to aminoglycosides and sulphonamides and were present in taxa associated with both fish and human pathogens. This ARG diversity potentially confers resistance to a wide variety of antibiotic classes and questions the effectiveness of current and future treatment of diseases with antibiotics in earthen aquaculture ponds. The microbial and ARG compositions between fish ponds within each farm were similar, which may relate to parallels in farming practices creating similar microbial selection pressures and thus comparable microbial populations. Without a more controlled approach towards antibiotic usage, will inevitably further exacerbate the challenges in treating and preventing disease outbreaks as aquaculture production intensifies in Bangladesh. HighlightsO_LIARGs in Bangladesh rural fishponds indicate resistance to 18 different antibiotics C_LIO_LIThe most common AMR were to aminoglycosides and sulphonamides C_LIO_LIARGs were present in plasmids and taxa-associated pathogens C_LIO_LIFarming practices strongly influence microbial and ARG compositions C_LIO_LIIdentified ARGs question antibiotic treatment of disease in rural aquaculture C_LI

microbiology↗

bioRxiv2021

Large scale genomic and evolutionary study reveals SARS-CoV-2 virus isolates from Bangladesh strongly correlate with European origin and not with China.

RationaleThe global public health is in serious crisis due to emergence of SARS-CoV-2 virus. Studies are ongoing to reveal the genomic variants of the virus circulating in various parts of the world. However, data generated from low- and middle-income countries are scarce due to resource limitation. This study was focused to perform whole genome sequencing of 151 SARS-CoV-2 isolates from COVID-19 positive Bangladeshi patients. The goal of this study was to identify the genomic variants among the SARS-CoV-2 virus isolates in Bangladesh, to determine the molecular epidemiology and to develop a relationship between host clinical trait with the virus genomic variants. MethodSuspected patients were tested for COVID-19 using one step commercial qPCR kit for SARS-CoV-2 Virus. Viral RNA was extracted from positive patients, converted to cDNA which was amplified using Ion AmpliSeq SARS-CoV-2 Research Panel. Massive parallel sequencing was carried out using Ion AmpliSeq Library Kit Plus. Assembly of raw data is done by aligning the reads to a pre-defined reference genome (NC_045512.2) while retaining the unique variations of the input raw data by creating a consensus genome. A random forest-based association analysis was carried out to correlate the viral genomic variants with the clinical traits present in the host. ResultAmong the 151 viral isolates, we observed the 413 unique variants. Among these 8 variants occurred in more than 80 % of cases which include 241C to T, 1163A to T, 3037C to T,14408C to T, 23403A to G, 28881G to A, 28882 G to A, and finally the 28883G to C. Phylogenetic analysis revealed a predominance of variants belonging to GR clade, which have a strong geographical presence in Europe, indicating possible introduction of the SARS-CoV-2 virus into Bangladesh through a European channel. However, other possibilities like a route of entry from China cannot be ruled out as viral isolate belonging to L clade with a close relationship to Wuhan reference genome was also detected. We observed a total of 37 genomic variants to be strongly associated with clinical symptoms such as fever, sore throat, overall symptomatic status, etc. (Fishers Exact Test p-value<0.05). The most mention-worthy among those were the 3916CtoT (associated with causing sore throat, p-value 0.0005), the 14408C to T (associated with protection from developing cough, p-value= 0.027), and the 28881G to A, 28882G to A, and 28883G to C variant (associated with causing chest pain, p-value 0.025). ConclusionTo our knowledge, this study is the first large scale phylogenomic studies of SARS-CoV-2 virus circulating in Bangladesh. The observed epidemiological and genomic features may inform future research platform for disease management, vaccine development and epidemiological study.

genetics↗

bioRxiv2020

A composite method to infer drug resistance with mixed genomic data

BackgroundThe increasing incidence of drug resistance in tuberculosis and other infectious diseases poses an escalating cause for concern, emphasizing the urgent need to devise robust computational and molecular methods identify drug resistant strains. Although machine learning-based approaches using whole-genome sequence data can facilitate the inference of drug resistance, current implementations do not optimally take advantage of information in public databases and are not robust for small sample sizes and mixed attribute types. ResultsIn this paper we introduce the Composite MetaDistance method, an approach for feature selection and classification of high-dimensional, unbalanced datasets with mixed attribute features from various data sources. We introduce a mixed-attribute, multi-view distance function to calculate distances between samples, with optimal handling of nominal features and different feature views. We also introduce a novel feature set for drug resistance prediction in Mycobacterium tuberculosis, using data from diverse sources. We compare the performance of Composite MetaDistance to multiple machine learning algorithms for Mycobacterium tuberculosis drug resistance prediction for three drugs. Composite MetaDistance consistently outperforms existing algorithms for small sample training sets, and performs as well as other algorithms for training sets with larger sample sizes. ConclusionThe feature set formulation introduced in this paper is utilizes mutational and publicly available information for each gene, and is much richer than ever devised previously. The prediction algorithm, Composite MetaDistance, is sample size agnostic and robust especially given small sample sizes. Proper handling of nominal features improves performance even with a very small number of nominal features. We expect Composite MetaDistance to be even more robust for datasets with a higher percentage of nominal features. The algorithm is application independent and can be used for any mixed attribute dataset.

bioinformatics↗

bioRxiv2019

Population Genomics of Nontuberculous Mycobacteria Recovered from United States Cystic Fibrosis Patients

Nontuberculous mycobacteria (NTM) pose a threat to individuals with cystic fibrosis (CF) due to an increased prevalence of pulmonary infections, innate drug resistance of the bacteria, and potential transmission between CF patients. To explore the genetic diversity of NTM isolated from CF patients within the United States (US) and to identify potential transmission events, we sequenced and analyzed the genomes of 341 NTM isolates from 191 CF patients as part of a nationwide surveillance study. The most abundant species in the isolate cohort were Mycobacterium abscessus (59.5%), followed by species in the Mycobacterium avium complex (37.5%). Phylogenomic analyses of the three M. abscessus subspecies revealed that more than half of CF patients had isolates in one of four dominant clones, including two dominant clones of M. abscessus subspecies abscessus and two dominant clones of M. abscessus subspecies massiliense. M. avium isolates from US CF patients, however, do not have dominant clones and are phylogenetically diverse. Longitudinal NTM isolates were compared to determine genome-wide single nucleotide polymorphisms (SNPs) that occur within patients over time. This information was used to compare between and within-patient SNP distributions, to quantitatively define SNP thresholds suggestive of transmission, and calculate a posterior probability of recent transmission given the SNP distance between two isolates from different patients. Out of 114 patients with M. abscessus subspecies, ten clusters of highly similar isolates from 26 patients were identified. Among the 26 patients in the M. abscessus clusters, 12 attended the same CF care centers. No highly similar isolate clusters were observed in M. avium. Our study reveals the contrasting genomic diversity and epidemiology of two major NTM taxa and the potential for between-patient exposure and cross-transmission of these emerging pathogens.

microbiology↗

bioRxiv2017

Metagenomic sequencing to replace semi-quantitative urine culture for detection of urinary tract infections: a proof of concept

ABSTRACTSemi-quantitative bacterial culture is the standard method to diagnose urinary tract infections (UTI), but bacterial growth rate limits diagnostic speed and it is unreliable when patients have been pre-treated with antibiotics. Metagenomics could increase diagnostic speed and accuracy by sequencing the microbiome and resistome directly from urine samples, bypassing culture. However, a semi-quantitative approach - as needed for diagnosing UTIs - has not been established.\n\nMetagenomics was deployed to identify and semi-quantify bacterial presence indicative of UTI, predict antimicrobial susceptibility (AMR), and results were compared to semi-quantitative culture. Whole genome sequencing of the corresponding uropathogens was done for comparison. Analysis time and cost were tracked.\n\nForty-one consecutive urine samples underwent metagenomic analysis. All culture positive samples contained >200ng of DNA, suggestive of a threshold below which UTI could be ruled out solely based on DNA quantity. A semi-quantitative Diagnostic Index (DI) was created by multiplying the total DNA quantity by the relative abundance of uropathogens per urine sample. The DI allowed discrimination of UTI from non-UTI samples in all but 1 case. Metagenomic detection of AMR determinants correctly predicted the phenotype of uropathogens in 20 of 32 cases. The metagenomic work-flow was 31h and cost {euro}116 per sample, but could be reduced to 4.5h and {euro}5 for low-DNA-yield non-UTI samples.\n\nThe genomic determinants of AMR and their distribution across uropathogens need to be better understood for prediction of AMR phenotypes by metagenomics. The introduction of the DI demonstrates the potential of semi-quantitative metagenomics to replace culture as rapid diagnostic method for UTI.

microbiology↗

bioRxiv2017

Comprehensive Benchmarking and Ensemble Approaches for Metagenomic Classifiers

BackgroundOne of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole genome shotgun sequencing data, comprehensive comparisons of these methods are limited. In this study, we use the largest (n=35) to date set of laboratory-generated and simulated controls across 846 species to evaluate the performance of eleven metagenomics classifiers. We also assess the effects of filtering and combining tools to reduce the number of false positives.\n\nResultsTools were characterized on the basis of their ability to (1) identify taxa at the genus, species, and strain levels, (2) quantify relative abundance measures of taxa, and (3) classify individual reads to the species level. Strikingly, the number of species identified by the eleven tools can differ by over three orders of magnitude on the same datasets. However, various strategies can ameliorate taxonomic misclassification, including abundance filtering, ensemble approaches, and tool intersection. Indeed, leveraging tools with different heuristics is beneficial for improved precision. Nevertheless, these strategies were often insufficient to completely eliminate false positives from environmental samples, which are especially important where they concern medically relevant species and where customized tools may be required.\n\nConclusionsThe results of this study provide positive controls, titrated standards, and a guide for selecting tools for metagenomic analyses by comparing ranges of precision and recall. We show that proper experimental design and analysis parameters, including depth of sequencing, choice of classifier or classifiers, database size, and filtering, can reduce false positives, provide greater resolution of species in complex metagenomic samples, and improve the interpretation of results.

genomics↗