Biology subjects

Islam, M. M.

Publications and source records attributed to Islam, M. M..

At least 19 recordsLinked to original sources

bioRxiv2026

Dosage-dependent disruption of corticofugal neuron development by Fezf2 loss

Background. Fezf2 (Forebrain Embryonic Zinc Finger 2) is a dosage-sensitive transcription factor required for the specification of corticofugal projection neurons, and rare predicted-deleterious Fezf2 variants are associated with human neurodevelopmental disorders. The cell-type-specific and temporal manner in which Fezf2 dosage shapes cortical development remains incompletely resolved at single-cell resolution. This study aims to quantify the temporal and cell-type-specific transcriptional response to progressive Fezf2 loss by re-analyzing a public single-cell atlas of mouse cortical development. Methods. We re-analyzed 20 single-cell RNA-seq samples spanning embryonic day (E) 10 to postnatal day (P) 4 across wild-type (WT), Fezf2-heterozygous (Het), and Fezf2-knockout (KO) genotypes, together with 3 wild-type single-cell ATAC-seq samples, from Di Bella et al. (2021) (GSE153164). After quality control, consensus doublet removal, and batch integration with two complementary methods (Harmony and scVI), we annotated cell types and quantified genotype effects. Because the source design includes a single library per genotype at most stages, we report effect sizes with bootstrap 95% confidence intervals and calibrate candidate dysregulation against a biological-noise null estimated from the two WT replicates at P1, rather than reporting formal inferential p-values. Results. We recovered 121,889 high-quality cells and 20,982 genes, resolving 17 transcriptionally distinct cell types. The most prominent genotype effect was a dosage-dependent depletion of subcerebral projection neurons (SCPNs) at P1 - the only timepoint at which all three genotypes were sampled: SCPNs comprised 2.21% of WT cells (mean of two replicates) but 0.18% of Het and 0.15% of KO cells, an approximately 13 -15-fold reduction. Using the WT-replicate null (99th percentile |log2 fold-change| = 0.34), we identified 947, 1,119, and 838 genes exceeding this threshold in Maturing, Corticothalamic, and Callosal projection neurons, respectively. Genes downregulated in KO projection neurons were convergently enriched for cytoplasmic translation and ribosome biogenesis (adjusted P {approx} 8.8*10^-30; in Corticothalamic Neurons). Regulon-activity analysis nominated Six4, Atf5, and Myt1 as increased and Hdac7, Rest, and Kdm5a as decreased in KO cells. Conclusions. Progressive Fezf2 loss is associated with a dosage-dependent, cell-type-specific disruption of cortical development, most notably near-elimination of the SCPN compartment by birth and a convergent downregulation of translational/maturation-associated genes across projection neurons. These associations nominate candidate downstream programs for experimental follow-up; they do not by themselves establish a mechanism.

neuroscience↗

bioRxiv2026

Lipoproteins modulate the uptake and biological functionof cationic cell-penetrating peptides across the animal lineage.

Cationic cell-penetrating peptides (+CPPs) are pervasive killer peptides produced by all animals and found in innate immune systems, venoms and neurodegenerative diseases. Despite their ubiquity, the mechanisms underlying their uptake remain opaque and intensely debated. Here, we interrogate +CPPs spanning 600 million years of animal evolution and identify endocytosis as a conserved/convergent uptake mechanism across cell types and organisms, at physiological concentrations. By combining multiplex imaging, genetic screening, cryo-electron tomography, and biophysical methods, we uncover that +CPPs seemingly universally enter eukaryote cells via 'hitchhiking' on lipoproteins. We further show that the interaction of +CPPs with lipoproteins modulates their antimicrobial function. Combined, our findings establish a unified molecular framework describing a pan-eukaryote cell penetrance mechanism. As hyperlipidemia is a common comorbidity, our findings have direct implications for human health. Lastly, the insights gleaned from this work highlight design principles that may inform the future engineering of peptide-based therapeutics and delivery vehicles.

cell biology↗

bioRxiv2026

Quantitative and mutational analysis of soluble HIV-1 Vpu and calmodulin interactions

HIV-1 Vpu supports viral adaptation through host-protein interactions. Although mainly membrane-associated, we recently identified a soluble Vpu form that forms a stable complex with Ca2+-bound calmodulin (Ca2+-CaM), potentially influencing Vpu trafficking. Here, to determine the binding affinity and identify regions of soluble Vpu involved in CaM binding, we used ensemble Forster Resonance Energy Transfer (eFRET). We tested Cy3-labeled full-length (FL) Vpu, a C-terminal fragment (helices 2 and 3), and a Cy3-labeled FL Vpu V22A/W23Y/I33N mutant with substitutions of key residues in Vpu helix 1 and helices 1-to-2 loop having a role in the interaction with Ca2+-CaM. All Vpu variants were labeled at residue L42C. Ca2+-CaM was tagged with Cy5 at residue S39C. eFRET analysis of 100 nM Cy3-Vpu variants mixed with Cy5-Ca2+-CaM (in the range 100 nM-2.5 microM) revealed heterocomplexes formation with dissociation constants (Kd) and binding free energies (deltaG). FL Vpu-Ca2+-CaM showed highest stability (Kd ~74 nM, deltaG ~-9.7 kcal/mol), while the truncated C-terminal region and V22A/W23Y/I33N mutant formed weaker complexes with Ca2+-CaM (Kd ~182 nM and 800 nM, deltaG ~-9.2 kcal/mol and ~-8.3 kcal/mol). The reduced Vpu-Ca2+-CaM stability after disruption of binding sites in and near Vpu helix 1 may explain how this interaction is regulated, including through lipid competition that promotes Vpu membrane insertion. We propose that, at the membrane, hydrophobic helix 1 dissociates from Ca2+-CaM and inserts into the lipid bilayer, weakening the complex and releasing CaM. These findings clarify HIV-1 Vpu interactions with cellular components and may inform antiviral development.

biophysics↗

bioRxiv2026

DksA-Dependent Stringent Stress Response Drives Virulence and Gastrointestinal Persistence of Klebsiella pneumoniae

Successful gastrointestinal colonization (GI) by bacterial pathogens requires adaptation to nutrient competition and host-derived stresses in the gut, with adaptation via the bacterial stringent stress response playing a critical role. Epidemiological data suggest that the GI tract serves as a reservoir from where K. pneumoniae can spread and cause invasive disease or transmit to another host. DksA is a conserved stringent response transcriptional regulator that was identified in an in vivo transposon mutagenesis screen as an important K. pneumoniae gut determinant. However, its role in K. pneumoniae pathogenesis and gut colonization remains uncharacterized. Here, we demonstrate that DksA is required for survival against membrane-targeting antibiotics, consistent with a role in cell envelope stress tolerance. In addition, DksA positively regulates capsule biosynthesis gene expression and hypermucoviscosity and is essential for robust biofilm formation. Using a murine model, we show that DksA functions as a determinant of GI colonization independently of the resident gut microbiota. Furthermore, we demonstrate that DksA is important for environmental survival and transmission by regulating RpoS, thereby providing a mechanistic link between the stringent stress response, environmental survival, and subsequent transmission. Together, these findings establish DksA as a central integrator of the stringent response, coordinating membrane stress resistance, virulence traits, and gastrointestinal colonization in K. pneumoniae. ImportanceK. pneumoniae, a pathobiont, is responsible for multidrug-resistant infections and poses a major threat in hospital settings as well as community-acquired invasive infections. The bacterium tightly coordinates its virulence-associated traits to adapt to diverse environmental conditions and survive; however, the regulatory mechanisms remain poorly understood. In this study, we demonstrated that the conserved stringent response regulator DksA contributes to bacterial membrane stability, thereby affecting antibiotic resistance, inherent virulence, and persistence traits of K. pneumoniae. Additionally, DksA was identified as required for gut colonization, environmental survival through dysregulation of RpoS, and transmission to a naive host. These results enhance our overall understanding of the K. pneumoniae stringent response and will provide new avenues for controlling K. pneumoniae infections.

microbiology↗

bioRxiv2026

MGIDI selection and machine learning reveal harvest index driving traits in sodium azide-induced rice mutants with SSR-based genetic diversity

Sodium azide mutagenesis offers a powerful approach to generate genetic diversity for rice improvement, yet comprehensive characterization of mutant populations using integrated modern breeding tools remains limited. M mutants of BRRI dhan28 induced with sodium azide, were evaluated for 17 agronomic traits and genetic diversity was characterized using 30 SSR markers. The MGIDI was used to characterize elite genotypes and machine learning approaches were used to dissect trait architecture underlying harvest index. The phenotypic variation captured by principal component analysis was 52.12%, and yield was the trait with the highest genotypic variance (278.22) and genotypic coefficient of variation (29.07%). MGIDI analysis detected 10 elite mutants that significantly outperformed within the same environment in combined yield and harvest index. The main predictors of harvest index variability were examined using a Random Forest analysis, and this showed that grain and straw yield were the main predictors of harvest index variability. The SSR markers showed high level of genetic diversity (PIC = 0.264), population structure analysis revealed two subgroups (Fst = 0.0437) and the pairwise genetic distance ranged from 0.000 to 0.733. Procrustean alignment showed a high correlation between molecular and phenotypic variation. An integrated approach of MGIDI selection and prediction of diversity using machine learning underpinned the identification of elite mutants that can be quickly forwarded to breeding programs. This study provides valuable genetic resources and demonstrates that sodium azide mutagenesis combined with modern analytical tools accelerates genetic gains in rice improvement.

plant biology↗

bioRxiv2026

Activity-based anorexia enhances glutamatergic synaptic transmission and neuronal excitability within the nucleus accumbens of female mice

Anorexia nervosa is a severe psychiatric disorder characterized by persistent food restriction and often excessive physical activity, implicating dysfunction in neural circuits governing motivation, reward, and behavioral persistence. The nucleus accumbens (NAc) is a central component of these circuits, yet synaptic and cellular adaptations within this region during anorexia-like states remain poorly defined. Using the activity-based anorexia (ABA) paradigm in adult female mice, we examined glutamatergic signaling and intrinsic neuronal properties in the NAc shell. ABA exposure produced rapid weight loss, reduced food intake, and progressively increased running-wheel activity. Biochemical analyses of NAc shell tissue revealed elevated membrane-associated GluA2 AMPA receptor protein. Consistent with this finding, whole-cell patch-clamp recordings from medium spiny neurons showed increased amplitude of spontaneous excitatory postsynaptic currents. ABA also enhanced intrinsic neuronal excitability, reflected by greater firing in response to depolarizing current injections. Together, these convergent biochemical and electrophysiological results demonstrate that ABA induces coordinated postsynaptic strengthening and increased intrinsic excitability in NAc shell medium spiny neurons. These adaptations suggest a sustained increase in accumbal output that may bias motivational circuit function and contribute to excessive activity and suppressed feeding during anorexia-like conditions, paralleling glutamatergic plasticity observed in other compulsive disorders, including substance use disorder.

neuroscience↗

bioRxiv2025

Plasmodium falciparum aquaglyceroporin (PfAQP) orchestrates glycerol-dependent lipid metabolism, membrane biogenesis, metabolomic flux, parasite growth and stress adaptation

Aquaglyceroporins (AQPs) are integral membrane channel proteins that facilitate transport of water and other solutes such as glycerol, thereby playing vital roles in cellular homeostasis. Here, we functionally characterized Plasmodium falciparum aquaglyceroporin (PfAQP) through localization, inducible knockdown, and comprehensive metabolomic analyses revealing its key role in lipid homeostasis and metabolomic flux. PfAQP localizes to the parasite plasma membrane and food vacuole membrane, at critical interfaces for nutrient and metabolite exchange. A glmS-ribozyme mediated inducible knock-down of PfAQP severely impairs parasite development from trophozoite to schizont, leading to marked suppression in parasite growth. Ultra-Expansion Microscopy (UExM) analyses show profound defects in membrane biogenesis, along with disruption in cytokinesis under PfAQP knock-down conditions. Detailed lipidomic analyses suggests that PfAQP disruption cause altered lipid homeostasis, characterized by reduced membrane phospholipid biosynthesis, impaired lipid scavenging, and accumulation of storage lipids. Metabolomic analyses further indicate metabolic dysregulation characterized by reduction in amino acid levels and dysregulated mitochondrial metabolism, including impaired tricarboxylic acid (TCA) cycle. Strikingly, PfAQP expression gets transiently upregulated under nutrient starvation condition, and its ablation drastically compromise parasite recovery, highlighting its role in adaptive responses. Overall, these results establish PfAQP as a critical regulator of glycerol-dependent metabolic pathways, orchestrating membrane biogenesis, energy metabolism, and stress resilience--functions indispensable for parasite growth and survival.

microbiology↗

bioRxiv2025

omicML: An Integrative Bioinformatics and Machine Learning Framework for Transcriptomic Biomarker Identification

IntroductionTranscriptomic biomarker discovery has been a challenge due to variation in datasets and platforms, complexity in statistical and computational methods, integration of multiple programming languages, and intricacy of ML workflow to evaluate biomarkers. Standard workflows necessitate several stages (quality control, normalization, differential expression), typically executed in R or Python, resulting in bottlenecks for non-experts. Existing platforms have alleviated certain challenges by offering graphical interfaces for data loading, normalization, differential gene expression analysis, and functional analysis; nevertheless, they typically do not incorporate integrated machine learning procedures for biomarker selection. MethodIn this regard, we present omicML, an intuitive graphical user interface (GUI) that combines transcriptomic data analysis with machine learning (ML)-based classification via integrating R and Python packages/libraries. It supports both RNA-Seq and microarray data, automating preprocessing (data import, quality control, and normalization) and differential expression analysis. The tool annotates differentially expressed genes (DEGs) with descriptions, gene ontology, and pathway information and incorporates comparative analysis. Our extensive ML pipeline enables both supervised and unsupervised learning, integrates various datasets based on candidate gene signatures, standardizes and eliminates less significant features, benchmarks multiple ML classifiers with robust performance metrics (e.g., AUROC, AUPRC), assesses feature importance, develops single-gene and multi-gene predictive models, and systematically finalizes the biomarker algorithm. All functionalities are available in omicML, hence reducing the barrier for biologists without computational proficiency. ResultIn a case study, omicML identified a six-gene diagnostic model that distinguishes Mpox (monkeypox virus) infections from those caused by other viruses, including SARS-CoV-2, HIV, Ebola, and varicella-zoster. These results illustrate omicMLs capacity to discern clinically relevant biomarkers from complex transcriptome data. ConclusionThrough the unified system, omicML (https://omicml.org), integrating data preprocessing, differential gene expression analysis, annotation, heatmap analysis, dataset integration, batch effect correction, machine learning approach, and functional analysis can diminish technical barriers and accelerates the conversion of expression data into diagnostic insights for clinicians and bench scientists.

bioinformatics↗

bioRxiv2025

Biophysical and Colloidal Properties Govern Anti-Drug Antibody Generation: A Case Study Using the Tumoricidal mAb 4713

Anti-drug antibodies (ADA) are a well-recognized clinical problem, but their sporadic occurrence has limited detailed biophysical characterization of causative factors. We investigated how different stress conditions influence aggregation of a murine monoclonal antibody, mAb 4713, and the resulting potential to induce ADA in mice. Dynamic light scattering (DLS) revealed that stirring, freeze-thaw, and heat stress each generated subvisible aggregates (30-700 nm), whereas non-stressed mAb 4713 remained predominantly monomeric. Stirring- and freeze-thaw-induced aggregates retained native-like secondary and tertiary structures, thermal and biochemical stability, and anti-tumor cytotoxicity, as shown by circular dichroism, tryptophan fluorescence, thermal shift assay, limited proteolysis, and cell death assay. In contrast, heat-induced aggregates displayed complete structural denaturation and loss of cytotoxic activity. Immunization of Jcl: ICR mice demonstrated that heat-induced aggregates were only mildly immunogenic, while freeze-thawed and non-stressed mAb 4713 elicited no detectable immune response, and ADA generated under these conditions did not affect anti-tumor activity. Strikingly, stirred aggregates triggered a strong anti-mAb antibody response, as measured by ELISA, and the resulting antisera completely abolished mAb 4713 cytotoxicity against Raji cells, a Burkitt lymphoma cell line, indicating induction of neutralizing ADA. These results show that aggregate immunogenicity depends not simply on aggregate presence but critically on their structural and colloidal properties and formation pathways, highlighting the need to assess both structural and colloidal properties of aggregates in biopharmaceutical development to mitigate ADA risks.

biophysics↗

bioRxiv2025

LFA-1 Interaction with GBP-130 on Plasmodium falciparum-infected Red Blood Cells mediates NK Cell Activation and Parasite Control

Natural Killer (NK) cells contribute to early immunity against Plasmodium falciparum by recognizing and eliminating infected red blood cells (iRBCs), a process mediated in part by the integrin LFA-1. However, the cognate parasite ligand for LFA-1 has remained unknown. Here, we identify Glycophorin Binding Protein-130 (PfGBP-130) as a surface-expressed ligand on iRBCs that binds the I-domain of LFA-1 (LFA-1 I). Using an LFA-1 I-Fc fusion protein, we demonstrate stage-specific binding to iRBCs, and LC-MS/MS analysis of immunopreciptates of I-Fc bound to iRBC revealed PfGBP-130 as a high-confidence interactor. Recombinant PfGBP-130 binds NK and THP-1 cells in an LFA-1-dependent manner. Co-culture assays show that PfGBP-130 promotes NK cell activation, degranulation, and facilitates contact-dependent killing of iRBCs. Neutralizing antibodies against PfGBP-130 significantly impair these responses. Our findings establish PfGBP-130 as the LFA-1 ligand on iRBCs, providing new insight into NK cell-mediated immunity in malaria and identifying a potential target for host-directed interventions.

immunology↗

bioRxiv2025

Elucidating the perception, awareness and biosecurity practices among the live poultry/poultry-meat sellers to mitigate the risk of zoonotic salmonellosis

Zoonotic salmonellosis remains a major public health concern, especially in low-resource settings where poultry handling practices are often informal. Biosecurity measure of live poultry/poultry-meat vending shop is necessary to address this zoonoses. This study investigated the knowledge, attitudes, and biosecurity practices of poultry/poultry-meat sellers toward the risk of zoonotic salmonellosis in Bangladesh. A quantitative, descriptive, cross-sectional study was conducted among the 432 poultry/poultry-meat sellers through a structured questionnaire in multistage sampling approach. Majority of the poultry/poultry-meat sellers (n=432) were male (75.5%), age between 26-34 years (31.9%), had primary education (28.9%), experience of [≤]5 years (40.3%), and monthly income was <20000 BDT (43.3%). Additionally, minimal had training on live poultry marketing (33.1%), and poultry-meat processing and marketing (28.5%). The 33.34% and 62.03% of poultry/poultry-meat sellers had the good knowledge and positive attitudes respectively. While 41.43% were involved in correct biosecurity practice. Moreover, the mean KAP score was 53.12{+/-}0.89, 68.59{+/-}0.79 and 54.58{+/-}0.73 respectively. From the multivariate analyses, higher education was a strong predictor of good knowledge (Bachelors degree: OR=6.64, 95%CI:2.59-17.04, p<0.01), positive attitude (Bachelors degree: OR=13.48, 95%CI:3.72-48.87, p<0.01), and correct biosecurity practices (HS: OR=5.52, 95%CI:2.62-11.63, p<0.01; Bachelors degree: OR=4.81, 95%CI:2.03-11.39, p<0.01). The sellers with good knowledge, positive attitudes, and correct biosecurity practices perceived higher zoonotic risk (median=7, p<0.05), with perception co-relation that significantly influenced by education (r=0.131, p<0.01) and training (r=0.084, p<0.05). Despite moderate awareness, sellers showed notable gaps in safe practices, highlighting the need for practical training that enhances risk perception, promotes behaviour change, and builds hands-on biosecurity skills.

scientific communication and education↗

bioRxiv2025

The soluble HIV-1 Vpu protein interacts with calmodulin in a Ca2+-dependent manner

The HIV-1 Vpu membrane protein is crucial to the virus lifecycle. Our recent studies revealed soluble Vpu oligomers, prompting further investigation into their interactions with cellular proteins. Notably, Vpu may form a complex with calmodulin (CaM) due to its putative CaM-binding motif; however, experimental proof of this association remained unavailable. Here, we present definitive experimental evidence that the soluble Vpu complex interacts in vitro with calcium-bound CaM (Ca2+-CaM) its active form. Using double electron electron-resonance (DEER) spectroscopy and protein spin labeling, we detected the formation of a soluble Vpu-Ca2+-CaM complex. Both the full-length (FL) and truncated C-terminal regions of Vpu bind Ca2+-CaM. DEER experiments on a spin-labeled CaM cysteine mutant S39C/A103C revealed that, upon association with Vpu, Ca2+-CaM undergoes a transition from an open to a more closed conformation, consistent with previous reports of Ca2+-CaM interactions with other proteins. Furthermore, we observed that the binding of Vpu to Ca2+-CaM leads to dissociation of soluble Vpu oligomers, as evidenced by a reduction in DEER modulation depth for FL Vpu spin-labeled at residue L42C. FRET analysis with a fluorescently labeled C-terminal cysteine mutant of Vpu confirmed this result. Like FL Vpu, the Vpu C-terminal region forms soluble homooligomers that dissociate upon binding to Ca2+-CaM. Collectively, our results suggest that soluble Vpu and Ca2+-CaM form an equimolar complex. DEER analysis of Vpu C-terminal region spin-labeled at residues Q36C/I61C demonstrated that Vpu undergoes significant conformational changes to facilitate Ca2+-CaM binding. These findings could be relevant to Vpu-CaM interactions under physiological conditions.

biophysics↗

bioRxiv2025

PathX-CNN: An Enhanced Explainable Convolutional Neural Network for Survival Prediction and Pathway Analysis in Glioblastoma

MotivationConvolutional neural networks (CNNs) offer potential for analyzing non-grid structured data, such as biological array data, by converting it into image-like formats using principal component analysis (PCA) of pathway genes. However, PCA-derived principal components (PCs) from the entire dataset capture global variance but fail to extract sub-cohort (class-specific) variances. Consequently, CNNs trained on global PCs perform poorly in survival prediction of glioblastoma multiforme (GBM), and the corresponding explanation of CNN outcomes may not align with disease-relevant pathways. ResultsWe present PathX-CNN, an explainable CNN framework that addresses these limitations by integrating multi-omics data through pathway-based images derived from sub-cohort-specific PCs. PathX-CNN outperformed existing pathway-based methods in predicting long-term survival (LTS) versus non-LTS in GBM. By leveraging SHAP (SHapley Additive exPlanations), a cooperative game theory-based explainable AI method, PathX-CNN identified biologically plausible pathways associated with GBM survival. Additionally, experiments on other cancer types demonstrated superior performance compared to traditional approaches. PathX-CNN demonstrates the potential of CNNs for multiomics integration, offering both improved prediction accuracy and pathway-specific insights into disease mechanisms. Code Availabilityhttps://github.com/codebysobhan/PathX-CNN Contactamondal@fiu.edu Supplementary informationSupplementary data are available at Bioinformatics online.

cancer biology↗

bioRxiv2025

Interpreting Lung Cancer Health Disparity at Transcriptome Level

Lung cancer is a leading cause of cancer-related mortality, with disparities in incidence and outcomes observed across different racial and sex groups. Understanding the genetic factors of these disparities is critical for developing targeted treatment therapies. This study aims to identify both patient-specific and cohort-specific biomarker genes that contribute to lung cancer health disparities among African American males (AAMs), European American males (EAMs), African American females (AAFs), and European American females (EAFs). The real-world data is highly imbalanced with respect to race, and the lung cancer dataset is no exception. So, classification with race labels will generate highly biased results toward the larger cohort. We developed a computational framework by designing the classification problems with disease conditions instead of races and leveraging the local interpretability of explainable AI, SHAP (SHapley Additive exPlanations). This study used three disease conditions of lung cancer, including Lung Adenocarcinoma (LUAD), Lung Squamous Cell Carcinoma (LUSC), and Healthy samples (HEALTHY) to design four classification tasks: one 3-class problem (LUAD-LUSC-HEALTHY) and three 2-class problems (LUAD-LUSC, LUAD-HEALTHY, and LUSC-HEALTHY). This multiple-classification approach allows a LUAD patient to be interrogated via three classification problems, namely LUAD-LUSC-HEALTHY, LUAD-LUSC, and LUAD-HEALTHY, thus providing a robust approach of retrieving disparity information for individual patients through the local interpretation of SHAP. The proposed method successfully discovered the sets of genes and pathways related to health disparities in lung cancer between two cohorts, including AAMs vs. EAMs, AAFs vs. EAFs, AAMs vs. AAFs, and EAMs vs. EAFs. The discovered list of genes and pathways provide a short list for biological scientists to conduct wet lab experiment.

cancer biology↗

bioRxiv2025

Virus and Cell Specific HMGB1 Secretion and Subepithelial Infiltrate Formation in Adenovirus Keratitis

A highly contagious infection caused by human adenovirus species D (HAdV-D), epidemic keratoconjunctivitis (EKC) results in corneal subepithelial infiltration (SEI) by leukocytes, the hallmark of the infection. To date, the pathogenesis of corneal SEI formation in EKC is unresolved. HMGB1 (high-mobility group box 1 protein) is an alarmin expressed in response to infection and a marker of sepsis. Earlier studies using a different adenovirus species, HAdV-C, showed retention of HMGB1 in the infected cell nucleus by adenovirus protein VII, enabling immune evasion. Here, using HAdV-D we show cell-specific HMGB1 secretion by infected cells, and provide an HAdV-D specific mechanism for SEI formation in EKC. HMGB1 was secreted only upon infection of human corneal epithelial cells, not from other cell types, and only upon infection by HAdV-D types associated with EKC. Acetylated HMGB1 translocation from the nucleus to the cytoplasm, then to the extracellular milieu, was tightly controlled by CRM1 and LAMP1, respectively. Primary stromal cells when stimulated by rHMGB1 expressed proinflammatory chemokines. In a novel 3D culture system in tune with the architecture of the cornea, HMGB1 released by infected corneal epithelial cells induced leukocytic infiltrates either directly and/or indirectly via stimulated stromal cells, which together explains SEI formation in EKC.

microbiology↗

bioRxiv2024

Highly versatile small virus-encoded proteins in cellular membranes: A structural perspective on how proteins' inherent conformational plasticity couples with host membranes properties to control cellular processes

We investigated several small viral proteins that reside and function in cellular membranes, which belong to the viroporin family because they assemble into ion-conducting oligomers. However, despite forming similar oligomeric structures with analogous functions, these proteins have diverse amino acid sequences. In particular, the amino acid compositions of the proposed channel-forming transmembrane (TM) helices are vastly different--some contain residues (e.g., His, Trp, Asp, Ser) that could facilitate cation transport. Still, other voroporins TM helices encompass exclusively hydrophobic residues; therefore, it is difficult to explain their channels activity, unless other mechanisms (e.g., involving a negative lipid headgroup) take place. For this study, we selected the M2, Vpu, E, p13II, p7, and 2B proteins from the influenza A, HIV-1, human T-cell leukemia, hepatitis C, and picorna viruses, respectively. We discuss the current knowledge of these proteins structures as well as remaining questions about a more comprehensive understanding of their structures, conformational dynamics, and function. Finally, we outline strategies to utilize a multi-prong structural approach to overcome current deficiencies in the knowledge about these proteins. HighlightsO_LISmall viral proteins encoded homo-oligomerize and function in cellular membranes as ion channels C_LIO_LIThese proteins were combined in the family of viroporins C_LIO_LIDespite the similarity in their oligomeric structures and functions, these proteins have vastly different primary structures C_LIO_LIIt is imperative to understand how proteins with no homology in their primary structures fulfill similar functions for diverse viruses C_LIO_LIThere is a need for a multi-prong structural approach to explain the structure, conformational dynamics, and function of these proteins C_LI

biophysics↗

bioRxiv2024

Towards Reliable Diabetes Prediction: Innovations in Data Engineering and Machine Learning Applications

ObjectiveDiabetes is a metabolic disorder that causes the risk of stroke, heart disease, kidney failure, and other long-term complications because diabetes generates excess sugar in the blood. Machine learning (ML) models can aid in diagnosing diabetes at the primary stage. So, we need an efficient machine learning model to diagnose diabetes accurately. MethodsIn this paper, an effective data preprocessing pipeline has been implemented to process the data and random oversampling to balance the data, handling the imbalance distributions of the observational data more sophisticatedly. We used four different diabetes datasets to conduct our experiments. Several ML algorithms were used to determine the best models to predict diabetes faultlessly. ResultsThe performance analysis demonstrates that among all ML algorithms, RF surpasses the current works with an accuracy rate of 86% and 98.48% for dataset-1 and dataset-2; XGB and DT surpass with an accuracy rate of 99.27% and 100% for dataset-3 and dataset-4 respectively. Our proposal can increase accuracy by 12.15% compared to the model without preprocessing. ConclusionsThis excellent research finding indicates that the proposed models might be employed to produce more accurate diabetes predictions to supplement current preventative interventions to reduce the incidence of diabetes and its associated costs.

bioinformatics↗

bioRxiv2023

Alignment Free Phylogeny Construction Using Maximum Likelihood Using k-mer Counts

Estimating phylogenetic trees from molecular data often involves first performing a multiple sequence alignment of the sequences and then identifying the tree that maximizes likelihood computed under a model of nucleotide substitution. However, sequence alignment is computationally challenging for long sequences, especially in the presence of genomic rearrangements. To address this, methods for constructing phylogenetic trees without aligning the sequences i.e. alignment-free methods have been proposed. They are generally fast and can be used to construct phylogenetic trees of a large number of species but they primarily estimate phylogenies by computing pairwise distances and are not based on statistical models of molecular evolution. In this paper, we introduce a model for k-mer frequency change based on a birth-death-migration process which can be used to estimate maximum likelihood phylogenies from frequencies of k-mers in genomic sequences of species in an alignment-free approach. Experiments on real and simulated data demonstrate the efficacy of the model for likelihood based alignment-free phylogeny construction.

evolutionary biology↗