Biology subjects

Ahammad, I.

Publications and source records attributed to Ahammad, I..

At least 19 recordsLinked to original sources

bioRxiv2025

Linking Genomic Landscape to Disease Mechanism: Core Genetic Factors Underlying Pathogenesis and Antimicrobial Resistance in Diarrheal Pathogens

BackgroundDiarrheal diseases remain a major global health burden, as they severely affect children, particularly in Bangladesh. After decades of research, the molecular mechanisms of diarrheal pathogens for disease pathogenesis and antibiotic resistance are still unknown, notably in Gram-negative bacteria. This pilot study fills the gap by employing whole genome sequencing and pan-genome analysis to analyze Bangladeshi diarrheal pathogens to identify genetic variables that cause disease pathogenesis and antibiotic resistance. ResultsHence, we investigated the genetic diversity of bacterial isolates from 31 clinical stool samples by a combination of whole-genome sequencing (WGS) and pan-genomic analysis. A core group of 50 genes, conserved across a significant number of strains, was identified via pan-genomic analysis, with considerable variation in accessory genes. This signifies a significant degree of genetic flexibility. Gene ontology analysis yielded substantial insights into prospective therapeutic targets by emphasizing the critical function of these core genes in bacterial survival and pathogenicity. Furthermore, the findings of the antimicrobial susceptibility test (AST) revealed concerning resistance trends, particularly to fluoroquinolones and beta-lactams, underscoring the necessity for enhanced surveillance and alternative therapeutic approaches. ConclusionThis study provides a comprehensive genetic framework to improve understanding of the complexity of diarrheal infections and the mechanisms underlying their resistance, fostering opportunities for potential therapeutic advancements.

microbiology↗

bioRxiv2025

APDeeM: A machine Learning strategy towards Effective Peptide Vaccine Candidates Identification against Different Types of Viruses

Viral infections pose significant global health challenges, underscoring the urgent need for improved medications. Nevertheless, traditional medicinal approaches depend significantly on labor-intensive laboratory tests, which impede efficient identification and prolong vaccine development, particularly when screening a huge number of samples. To address these obstacles, we present a comprehensive Antiviral Peptide (AVP) Detection Dataset, comprising 14 unique features to improve the characterization of antiviral and non-antiviral peptides. Subsequently, we introduce the Antiviral Peptide detection enhanced by Ensemble Machine Learning (APDeeM) system. This advanced computational framework considerably reduces the time required for AVP detection by utilizing ensemble learning methodologies. The APDeeM system incorporates Gradient Boosting, Random Forest, K-Nearest Neighbors (KNN), and AdaBoost algorithms to facilitate the swift selection of AVP candidates without requiring urgent laboratory testing. Our proposed ensemble methodology showed superior performance, with an accuracy of 85.99%, F1 score of 87.60%, recall of 88.91%, and precision of 86.32%, exceeding the efficacy of all tested antiviral peptide prediction models in this research. The APDeeM approach signifies a substantial improvement over conventional detection techniques, expediting the identification of prospective vaccine candidates and facilitating the advancement of more effective antiviral peptides. The most promising AVP candidates may urge laboratory validation, optimize resources, and accelerate vaccine development.

bioinformatics↗

bioRxiv2024

Dissecting Breast Cancer Heterogeneity Through Transcriptomics Insights of Diverse Etiological Factors for Common Biomarker Discovery

Breast cancer has many different causes, and the key to finding effective treatments is understanding the diseases heterogeneity. The present study used three gene expression datasets from 110 female samples related to stress, drug and hormonal imbalance, diet and nutrition, and physical activity and light exposure at night to predict differential gene expression. Interestingly, all gene expression datasets shared 22 upregulated and 4 downregulated genes, regardless of etiology. This suggests these genes share the core molecular mechanism and the biological pathway that causes breast cancer. Notably, these genes were significantly enriched in some important pathways, including cycle regulation, endoplasmic reticulum stress, and transcriptional regulation, demonstrating their potential as therapeutic targets. Further, we found UBE2J2 from upregulated genes and ZCCHC7 from downregulated genes as the top hub and bottleneck genes, which may help network connectivity and functional gene interactions. Computational study further asserted the strong binding affinity of drug-target complexes. Later, molecular dynamics simulations confirmed the predicted drug-target complexes stability and dynamic behavior, demonstrating these two genes as potential therapeutic targets. The findings from this analysis provide the molecular basis into the complex interplay between diverse etiologic factors and breast cancer pathogenesis, paving the way for innovative biomarker-targeted therapies.

bioinformatics↗

bioRxiv2024

Mapping the PTEN Mutation Landscape: Structural and Functional Drivers of Lung Cancer

Lung cancer is the predominant form of cancer globally, arising from the dysfunction of genetic mutations. Although PTEN mutation is crucial in the aetiology of lung cancer, the mapping of these major drivers has to be determined. We leverage computational algorithms on 43,855 SNPs of PTEN to discover the mutational impact contributing to lung cancer. Fifteen variations were identified as detrimental, and no pertinent studies have previously addressed their structural and functional aspects. Notably, seven variations were identified as the most significant contributors to lethal effects in functional aberration, as demonstrated by the computational assessment. Subsequently, molecular simulation elucidated the structural instability associated with these alterations. Furthermore, drug binding experiments at the mutational site corroborated the destabilization experiments by demonstrating the conformational alteration of the structure, resulting in varied amino acid interactions. In summary, the present study elucidates the influence of mutations in PTEN structure on its functional architecture.

cancer biology↗

bioRxiv2023

Unraveling Coinfection Dynamics into 100 Whole Genome of Diarrheal Pathogens: A Genome-to-Systems Biology Approach with Plesiomonas shigelloides

Diarrhea is the second leading cause of mortality among infants under the age of five. One of the main causes of this disease is multipathogenic infections, which can make the conditions of patients even worse. Plesiomonas shigelloides (P. shigelloides) is one of the pathogenic bacteria that contributes to the pathophysiology of diarrhea and may be implicated in coinfection with other diarrheal pathogens. Therefore, the purpose of this study is to investigate the hypothetical proteins to explore the genetic insights of P. shigelloides and its relationships with common diarrheal diseases. For this reason, we used 16S rRNA sequencing together with several biochemical tests to identify the bacteria that we isolated from diarrheal patients (8 years). Afterwards, the whole genome of P. shigelloides was sequenced, assembled and annotated in order to obtain the genomic insights of P. shigelloides. In addition, the common virulence genes of ten (10) common diarrhea-causing bacteria were identified from 100 whole genome sequences. Finally, the system biology approach was applied to predict the coinfection pattern between P. shigelloides and the virulence genes of 10 bacteria. The complete genome sequencing analysis of this bacterium revealed 899 hypothetical proteins from which 33 hypothetical proteins shared the clusters with the 109 virulence genes of 10 distinct diarrheal pathogens, forming a strong interaction based on biological processes, molecular functions, subcellular localization, or pathways. All diarrhea causing bacteria were found to have P. shigelloides microbial interactions; however, V. cholerae exhibited the strongest relationships, while C. difficile showed the weakest. The results of this investigation clearly imply that P. shigelloides shares a coinfection pattern with other bacteria that cause diarrhea. Finally, the findings from the complete genome provide new avenues for researchers to pursue their investigation of the pathophysiology of diarrhea.

systems biology↗

bioRxiv2023

Genome-Wide Exploration of the Opportunistic Providencia stuartii Unveils the Novel Genetic Interactions with the Virulence Gene of Diarrheal Pathogens

Diarrhea typically indicates an intestinal disorder, which can occur from viruses, parasites or bacterial infection. Along with the common diarrhea-causing pathogens, opportunistic bacteria may also play a role in the etiology of diarrheal disease. One of the opportunists bacteria that can cause diarrhea in both children and adults is Providencia stuartii. Therefore, the goal of this study is to explore the genetic mechanism of the opportunistic P. stuartii in microbial interactions with common diarrheal pathogens. Hence, P. stuartii was identified by utilizing the morphological observation and molecular techniques. Afterwards, the entire genome of P. stuartii was sequenced, assembled and annotated to explore the genomic insights. In addition, the virulence genes of 100 whole genome sequences from ten prevalent diarrhea-causing bacteria were identified and prioritized. Finally, the system biology approach was used to predict the protein-protein interaction network between P. stuartii and the virulence genes. The results of the present study suggests that complete genome sequencing of this bacteria contains 4011 proteins, which are crucial for this bacterium to survive. Additionally, 16 gene clusters provide 207 interacting genes that could interact with biological and molecular function, subcellular localization and pathway. The microbial interaction accompanying the virulence gene was found in all 10 diarrhea-causing bacteria except Clostridium difficile. These findings of this study could aid in the exploration of Providencia stuartii as the major causative agent of diarrhea. Additionally, the pathophysiology of diarrhea can be investigated using the microbial interactions between P. stuartii and the typical diarrheal bacteria. The results of this study may therefore be used to determine the most effective therapeutic targets for the development of medications to treat diarrhea.

genomics↗

bioRxiv2023

Mice Models to Bioinformatics Methods Led to the Discovery of Antidiabetic Compounds in the F. racemosa Plant Extracts

One of the primary health issues caused by inadequate blood sugar regulation is Diabetes Mellitus (DM). Diabetes and its consequences remain clinically significant even with the development of oral hypoglycemic medications. Therefore, Ficus racemosa (F. racemosa) plant has been studied for assessing of its antidiabetic potential coupling with animal model and in silico experiments. Drug Alloxan (150 mg/kg) was injected to induce the experimental diabetes in Swiss Albino mice, and two doses methanol extract of the F. racemosa fruit (300 and 500 mg/kg) along with glibenclamide (5 mg/kg) were given orally. Oral Glucose Tolerance Test (OGTT) and acute toxicity were performed as well in both diabetic and non-diabetic mice. Later, in silico experiments including ADMET profiling, molecular docking and simulations were performed. The administration of a dosage less than 3000 mg/kg has been observed to be well-tolerated by mice, with no reported instances of mortality or adverse effects. Following oral administration for 7 days, the blood glucose level (BGL) was significantly decreased in mice model in both doses of extracts, indicating the effect of F. racemosa. Subsequent to this, molecular docking and simulations have indicated that the SIRT1 receptor exhibits a higher binding affinity towards four specific compounds, namely Friedelin, Lupeol Acetate, Gluanol, and Ferulic Acid, as indicated by the dynamics parameters and interacting residues. The current investigation provided evidence that the fruit extract of F. racemosa significantly mitigated the hyperglycemic impact. Moreover, a total of four substances have been found that play a crucial role in the mechanisms behind the reduction of diabetic effects. Hence, the current investigation could potentially serve as a viable therapeutic approach in the treatment of diabetes.

microbiology↗

bioRxiv2023

In vivo and in silico experiment on Mitragyna speciosa offers new insight of antidiabetic potentials

Diabetes Mellitus (DM) is a serious metabolic disease with several treatments available for managing it, however, they can be expensive and have side effects. Medicinal plants have been used for many years to treat numerous diseases. Mitragyna speciosa (M. speciosa) plant has been shown to have anti-diabetic properties in preclinical studies. Hence, this study aimed to investigate the phytochemical compositions and anti-diabetic effects of M. speciose, using both in vivo and in silico approaches. In the in vivo study, experimental diabetes was induced in Swiss albino mice using the drug alloxan (150 mg/kg). Four groups of diabetic mice were taken. Two of the groups were given extracts at doses of 200 mg/kg and 400 mg/kg respectively. Diabetic mice treated with the reference drug, glibenclamide (5mg/kg) were chosen as a positive control, and mice with only vehicles were considered as a negative control. The Oral Glucose Tolerance Test (OGTT) and the acute toxicity test were performed. In the in silico study, molecular docking and dynamics were performed for the identification of the plant compounds that could effectively bind with the DPP4 receptor. Analysis of the study suggested that the lethal dose (LD50) values were greater than 2000 mg/kg, indicating that a dose below this level can be selected. The OGTT results showed that both doses of M. speciose extracts significantly reduced blood glucose levels (P<0.0001). However, neither dose exhibited a significantly higher blood glucose reduction compared to glibenclamide (5 mg/kg) (P > 0.05). Phytochemical screening and the ADMET profile analysis suggested four key compounds: Mitragynine, Corynantheidine, Corynoxine, and Speciociliatine in M. speciosa. The molecular docking analysis revealed these four compounds as potential antidiabetic agents considering their high binding affinity to the DPP4 receptor. These compounds were also found to be stable in the DPP4 binding pocket, as evidenced by the molecular dynamics simulation. Lastly, it can be demonstrated that the in silico experiments confirmed M. speciosa extracts to be capable of reducing blood glucose levels because of the presence of compounds.

pharmacology and toxicology↗

bioRxiv2023

A Non-invasive Detection of Parkinson's Disease using PitArray: An Integrative Meta-Analysis and Machine Learning Approach

Parkinsons disease (PD) is a progressive neurodegenerative disorder affecting the central nervous system, often diagnosed in its advanced stages due to the absence of sensitive biomarkers. With this objective in mind, our study conducted a comprehensive analysis of differentially expressed genes (DEGs) sourced from blood-based microarray datasets to uncover potential biomarkers and developed a machine learning based classifier to conduct two step validations. By analyzing gene expression of three projects, we identified 678 DEGs, consisting of 337 genes showing upregulation and 341 genes presenting downregulation. Additionally, insights from functional enrichment and the protein-protein network analysis indicate that HLA-F, IRF-1, and RPS28 have the potential to serve as biomarkers for diagnosing PD. Simultaneously, we employed feature selection techniques such as Least Absolute Shrinkage and Selection Operator with Cross Validation (LassoCV) followed by Recursive Feature Elimination with Cross Validation (REFCV) to filter our initial dataset of 13,249 genes down to 43 genes, which were subsequently used to train the machine learning-based classifier models. These 43 genes formed the basis for training and testing various machine learning models, including logistic regression, random forest, naive Bayes, k-nearest neighbors, support vector machine, and deep learning based artificial neural networks. Our models demonstrated robust performance, with Support Vector Machine outperforming others by 0.65 accuracy (95%CI: 0.58-0.66), 0.70 AUC-ROC (95%CI: 0.70-0.71) and 0.35 MCC (95%CI: 0.34-0.39). The model was implemented to develop the PitArray tool for non-invasive detection of PD from blood. PitArray is available at: https://github.com/Arittra95/PitArray. Key PointsO_LIHLA-F, IRF-1, and RPS28 were identified as potential biomarkers for Parkinsons disease diagnosis. C_LIO_LISeveral sophisticated feature selection methods recognized 43 genes which were then used to build a machine learning model. C_LIO_LIA Support Vector Machine based tool named PitArray was developed which could distinguish Parkinsons disease patients from healthy people based on blood transcriptome data. C_LI

bioinformatics↗

bioRxiv2023

An Integrated Comparative Genomics, Subtractive Proteomics and Immunoinformatics Framework for the Rational Design of a Pan-Salmonella Multi-Epitope Vaccine

Salmonella infections are a global public health issue due to the high cost of illness surveillance, prevention, and treatment. In this study, we explored the core proteome in Salmonella to design a multi-epitope vaccine through Subtractive Proteomics and immunoinformatics approaches. A total of 2395 core proteins presents in 30 different strains of Salmonella (reference strain-NZ CP014051) were curated. Utilizing the subtractive proteomics approach on the Salmonella core proteome, Curlin major subunit A (CsgA) was selected as the vaccine candidate. csgA is a conserved gene that is related with biofilm formation. Immunodominant B and T cell epitopes from CsgA were predicted using numerous immunoinformatics tools. T lymphocyte epitopes had adequate population coverage and their corresponding MHC alleles showed significant binding scores after peptide-protein based molecular docking. Afterward, a multiepitope vaccine was constructed with peptide linkers and Human Beta Defensin-2 (as an adjuvant). The vaccine was found to be highly antigenic, non-toxic, non-allergic, and had physicochemical properties. Additionally, Molecular Dynamics Simulation and Immune Simulation demonstrated that the vaccine can bind with Toll Like Receptor 4 and elicit robust immune response. Using in vitro, in vivo, and clinical trials, our results would yield a Pan-Salmonella vaccine that will provide protection against various Salmonella species.

bioinformatics↗

bioRxiv2023

A meta-analysis of bulk RNA-seq datasets identifies potential biomarkers and repurposable therapeutics against Alzheimers disease

Alzheimers disease (AD) poses a major challenge due to its impact on the elderly population and the lack of effective early diagnosis and treatment options. In an effort to address this issue, a study focused on identifying potential biomarkers and therapeutic agents for AD was carried out. Using RNA-Seq data from AD patients and healthy individuals, 12 differentially expressed genes (DEGs) were identified, with 9 expressing upregulation (ISG15, HRNR, MTATP8P1, MTCO3P12, DTHD1, DCX, ST8SIA2, NNAT, and PCDH11Y) and 3 expressing downregulation (LTF, XIST, and TTR). Among them, TTR exhibited the lowest gene expression profile. Interestingly, functional analysis tied TTR to amyloid fiber formation and neutrophil degranulation through enrichment analysis. These findings suggested the potential of TTR as a diagnostic biomarker for AD. Additionally, druggability analysis revealed that the FDA-approved drug Levothyroxine might be effective against the Transthyretin protein encoded by the TTR gene. Molecular docking and dynamics simulation studies of Levothyroxine and Transthyretin suggested that this drug could be repurposed to treat AD. However, additional studies using in vitro and in vivo models are necessary before these findings can be applied in clinical applications.

bioinformatics↗

bioRxiv2023

AITeQ: A machine learning framework for Alzheimer's prediction using a distinctive 5-gene signature

Neurodegenerative diseases, such as Alzheimers disease, pose a significant global health challenge with their complex etiology and elusive biomarkers. In this study, we developed the Alzheimers Identification Tool using RNA-Seq (AITeQ), a machine learning model based on an optimized random forest algorithm for identification of Alzheimers from RNA-Seq data. Analysis of RNA-Seq data from 433 individuals, including 293 Alzheimers patients and 140 controls led to the discovery of 47,929 differentially expressed genes. This was followed by a machine learning protocol involving feature selection, model training, performance evaluation, and hyperparameter tuning. The feature selection process undertaken in this study, employing a combination of 4 different methodologies, culminated in the identification of a compact yet impactful set of 5 genes. Ten diverse machine learning models were trained and tested using these 5 genes (ITGA10, CXCR4, ADCYAP1, SLC6A12, VGF). Performance metrics, including precision, recall, F1-score, accuracy, receiver operating characteristic area under the curve, and confusion matrices, were assessed before and after hyperparameter tuning. Overall, the random forest model with optimized hyperparameters was identified as the best and was used to develop AITeQ. AITeQ is available at: https://github.com/ishtiaque-ahammad/AITeQ Key PointsO_LIA set of 5 genes (ITGA10, CXCR4, ADCYAP1, SLC6A12, VGF) were identified following differential gene expression and feature importance analysis. C_LIO_LITen diverse machine learning algorithms were trained and tested using the gene expression patterns of the identified 5 genes. The random forest algorithm with customized hyperparameters was found to be the best-performing model for differentiating Alzheimers disease samples from control. C_LIO_LIAITeQ, a user-friendly, reliable, and accurate machine learning framework for Alzheimers disease prediction was developed based on the 5 gene signature. C_LI

bioinformatics↗

bioRxiv2023

Shotgun metagenomics unravels higher antibiotic resistome profile in Bangladeshi gut microbiome

Antibiotic resistance management is a challenging task in Low and Middle-Income Countries (LMICs) such as Bangladesh. Improper regulation and uncontrolled spreading of Antibiotic Resistant Genes (ARGs) from LIMCs pose a great threat to global public health. The human gut microbiome is a massive reservoir of Antibiotic Resistant Genes (ARGs). In this study, we unraveled the ARGs in the gut microbiome of the Bangladeshi population and compared them with several other countries around the world. Here, 31 fecal samples from different ethnic groups living in Bangladesh namely Bengali (n=9), Chakma (n=6), Khyang (n=5), Marma (n=6), and Tripura (n=5) were collected. Shotgun metagenomic sequencing method was implemented for revealing the ARGs. The resistome profiling was executed on three levels-the total microbiome, the plasmidome, and the virome. In all three levels, samples from Bangladeshi cohorts showed higher ARG profiles compared to foreign samples. On average, the number of ARGs in the Bangladeshi samples ranged between 75.11 and 88. Among them, class C beta-lactamases, quinolone resistance genes, and tetracycline efflux pumps were relatively more abundant. Additionally, the MexPQ-OpmE drug resistance pathway was found to be more prevalent. Findings from our study suggest that the spread of antibiotic resistance within the Bangladeshi population is being facilitated by the gut microbiome especially via the mobilome. Therefore, strict regulation on antibiotic usage is necessary to halt the spread of ARGs.

genomics↗

bioRxiv2023

Gut Microbiome Composition Reveals the Distinctiveness between the Bengali people and the Indigenous Ethnicities in Bangladesh

Human gut microbiome is influenced by ethnicity and other factors. In this study, we have explored the gut microbiome of Bengali population (n=13) and four Tibeto-Burman indigenous communities-Chakma (n=15), Marma (n=6), Khyang (n=10), and Tripura (n=11) using 16S rRNA amplicon sequencing. A total of 19 characterized phyla were identified in 5 cohorts, with Firmicutes and Bacteroidetes being the most prevalent. At the genus level, the abundance of Prevotella was relatively similar across all ethnicities. However, the Chakma population demonstrated higher Bacteroides abundance. Chakma people were more distinct than other ethnicities and exhibited a higher quantity of differentially abundant microbial features. The Bengali population had relatively low bacterial richness and Firmicutes to Bacteroidetes ratio than others with lower qualitative microbial diversity. A phylosymbiotic link between Bangladeshi indigenous people and certain ethnic groups in India have also been discovered. A comparative analysis between all Bangladeshi samples (n=55) and several tropical and subtropical countries (n=132) such as Australia, Egypt, Indonesia, Malaysia, Mexico, Thailand, and Vietnam revealed that the gut microbiota profile of Bangladeshi people is remarkably distinct from others. The insights from this study will aid further epidemiological and translational research.

genomics↗

bioRxiv2021

A comprehensive in silico investigation into the nsSNPs of Drd2 gene predicts significant functional consequences in dopamine signaling and pharmacotherapy

DRD2 is a neuronal cell surface protein involved in brain development and function. Variations in the Drd2 gene have clinical significance since DRD2 is a pharmacotherapeutic target for treating psychiatric disorders like ADHD and schizophrenia. Despite numerous studies on the disease association of single nucleotide polymorphisms (SNPs) in the intronic regions, investigation into the coding regions is surprisingly limited. In this study, we aimed at identifying potential functionally and pharmaco-therapeutically deleterious non-synonymous SNPs of Drd2. A wide array of bioinformatics tools was used to evaluate the impact of nsSNPs on protein structure and functionality. Out of 260 nsSNPs retrieved from the dbSNP database, initially 9 were predicted as deleterious by 15 tools. Upon further assessment of their domain association, conservation profile, homology models and inter-atomic interaction, the mutant F389V was considered as the most impactful. In-depth analysis of F389V through Molecular Docking and Dynamics Simulation revealed a decline in affinity for its native agonist dopamine and an increase in affinity for the antipsychotic drug risperidone. Remarkable alterations in binding interactions and stability of the protein-ligand complex in simulated physiological conditions were also noted. These findings will improve our understanding of the consequence of nsSNPs in disease-susceptibility and therapeutic efficacy.

bioinformatics↗

bioRxiv2020

Whole Genome Sequencing for Revealing the Point Mutations of SARS-CoV-2 Genome in Bangladeshi Isolates and their Structural Effects on Viral Proteins

Coronavirus disease-19 (COVID-19) is the recent global pandemic caused by the virus Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). The virus has already killed more than one million people worldwide and billions are at risk of getting infected. As of now, there is neither any drug nor any vaccine in sight with conclusive scientific evidence that it can cure or provide protection against the illness. Since novel coronavirus is a new virus, mining its genome sequence is of crucial importance for drug/vaccine(s) development. Whole genome sequencing is a helpful tool in identifying genetic changes that occur in a virus when it spreads through the population. In this study, we performed complete genome sequencing of SARS-CoV-2 to unveil the genomic variation and indel, if present. We discovered thirteen (13) mutations in Orf1ab, S and N gene where seven (7) of them turned out to be novel mutations from our sequenced isolate. Besides, we found one (1) insertion and seven (7) deletions from the indel analysis among the 323 Bangladeshi isolates. However, the indel did not show any effect on proteins. Our energy minimization analysis showed both stabilizing and destabilizing impact on viral proteins depending on the mutation. Interestingly, all the variants were located in the binding site of the proteins. Furthermore, drug binding analysis revealed marked difference in interacting residues in mutants when compared to the wild type. Our analysis also suggested that eleven (11) mutations could exert damaging effects on their corresponding protein structures. The analysis of SARS-CoV-2 genetic variation and their impacts presented in this study might be helpful in gaining a better understanding of the pathogenesis of this deadly virus.

bioinformatics↗

bioRxiv2018

Identification of Key Proteins Associated with Myocardial Infarction using Bioinformatics and Systems Biology

Myocardial infarction, more commonly known as heart attack, is a huge health problem around the world. It is a result of inadequate blood supply to certain parts of the heart and death of heart muscle cells in that region. Although it has been around for a long time, newer and newer ways of probing myocardial infarction is being followed. Bioinformatics and Systems Biology are relatively recent fields to try to give new insights into myocardial infarction. Following the footsteps of others, this in silico study has tried to chime in on the investigation into myocardial infarction. The study began with the gene expression omnibus dataset uploaded to the NCBI from a whole-genome gene expression analysis carried out at Mayo Clinic in Rochester, Minnesota. From the dataset, differentially expressed genes following first-time myocardial infarction were identified and classified into up-regulated and down-regulated ones. Gene Set Enrichment Analysis (GSEA) was carried out on the up-regulated genes which were statistically significant and corresponded with the NCBI generated annotations. Protein-Protein Interaction Network for these genes was constructed. GSEA revealed 5 transcription factors, 5 microRNAs and 3 pathways significantly associated with them. From the Protein-Protein Interaction Network, 6 key proteins (hub nodes) have been identified. These 6 proteins may open a new window of opportunity for the discovery/design of new drugs for mitigating the damage caused by myocardial infarction.

bioinformatics↗

bioRxiv2018

Isolation, Detection and Characterization of Aerobic Bacteria from Honey Samples of Bangladesh

Honey is a sweet substance made from the nectar of flowers and other chemical secretions from the bees bodies who collect nectar from the flowers and bring it to their hives to transform it to the thick, golden and sweet liquid that we call honey. The benefits of honey are not just limited to its basic use as a natural sweetener, but also its medicinal properties. The purpose of this study was to identify the bacteria that are present in honey commonly found in Bangladesh, which can tolerate the antimicrobial conditions of honey and survive in it. Fortunately, such bacteria could be detected, isolated and characterized by morphological and biochemical tests. The predominant type of bacteria commonly found in both raw and commercial honeys of Bangladesh are gram positive cocci such as streptococci, staphylococci, micrococci, bacilli and lactobacilli. Few gram negative bacteria were also isolated like Escherichia coli (8%) and Klebsiella pneumonia (8%) and some gram negative/gram variable Micrococcus luteus (75%). Hopefully, such knowledge would benefit people in the future as they will know more about the microorganisms present in honey and about the safety and quality of the honey they are about to buy or consume.

microbiology↗