Biology subjects

Khalid, M.

Publications and source records attributed to Khalid, M..

10 recordsLinked to original sources

bioRxiv2026

The single-PDZ domain protein TXBP-3 regulates contractility in the C. elegans spermatheca

In the C. elegans reproductive system, oocyte transit through the spermatheca relies on parallel calcium and Rho-dependent signaling pathways to regulate actomyosin contractility. Here, we identify the conserved single-PDZ domain protein TXBP-3/TAX1BP3 as a dosage-dependent regulator of spermathecal contractility. TXBP-3 is expressed in contractile tissues, including the spermatheca and spermathecal-uterine (sp-ut) valve. Animals lacking txbp-3 do not display overt phenotypes or defects in ovulation. However, in nematodes expressing the genetically encoded calcium sensor GCaMP, deletion of txbp-3 results in more rapid transit of embryos through the spermatheca. Conversely, tissue-specific overexpression of TXBP-3 results in slower transit times due to hypercontraction of the spermathecal neck and sp-ut valve, causing oocyte severing and failures of embryos to exit the spermatheca. We find TXBP-3 promotes the active phosphorylated state of the calcium/calmodulin-dependent protein kinase II (CaMKII) UNC-43. When TXBP-3 is overexpressed or unc-43 is depleted, ovulations in which the embryo fails to exit show disrupted calcium signaling. Additionally, TXBP-3 functions independently of the RhoGEF RHGF-1 and in parallel with the RhoGAP SPV-1 to maintain the alignment of actin fiber bundles and protect embryo structural integrity. Together, our results establish TXBP-3 as a regulator of contractility, kinase activation, and cytoskeletal architecture in contractile tissues.

cell biology↗

bioRxiv2026

Secreted ORF8 reprograms macrophages to enhance SARS-CoV-2 infection of lung epithelial cells

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primarily targets the respiratory epithelium, yet severe disease features diffuse lung injury and hyperinflammatory syndromes driven by dysregulated immune activation. Emerging evidence indicates that resident and infiltrating immune cells in the lung can encounter the virus early in infection and, under specific conditions, become infected (1, 2). This process amplifies local inflammation and facilitates viral propagation in the lower airways and distal lung regions (3, 4), where angiotensin converting enzyme 2 (ACE2) expression is limited (5, 6). However, how epithelial and immune cell compartments interact to produce the hallmark pulmonary pathology of SARS-CoV-2 infection remains unresolved. Here we show that secreted ORF8, a SARS-CoV-2 accessory protein, drives inflammatory lung pathology by increasing macrophage permissiveness to infection, triggering pyroptosis, and amplifying viral replication in alveolar epithelial cells. Co-culture of macrophages with human alveolar type II (AT2) cells overrides ORF8s previously reported inhibition of AT2 infection (7, 8), restoring productive viral replication. In vivo, IL-17RA blockade counteracts ORF8 activity, lowering viral burden and attenuating pulmonary inflammation and fibrosis. These findings reveal a paracrine role for ORF8 in reprogramming macrophages, thereby establishing a feedforward proviral circuit that accelerates lung pathology in COVID-19 and are clinically relevant given the recurrent emergence of SARS-CoV-2 variants with either intact or deleted ORF8 since the beginning of the pandemic.

microbiology↗

bioRxiv2024

XAI-MRI: An Ensemble Dual-Modality Approach for 3D Brain Tumor Segmentation Using Magnetic Resonance Imaging

Brain tumor segmentation from Magnetic Resonance Images (MRI) presents significant challenges due to the complex nature of brain tumor tissues. This complexity makes distinguishing tumor tissues from healthy tissues difficult, mainly when radiologists perform manual segmentation. Reliable and accurate segmentation is crucial for effective tumor grading and treatment planning. In this paper, we proposed a novel ensemble dual-modality approach for 3D brain tumor segmentation using MRI. Initially, individual U-Net models are trained and evaluated on single MRI modalities (T1, T2, T1ce, and FLAIR) to establish each modalitys performance. Subsequently, we trained U-net models using combinations of the best-performing modalities to exploit the complementary information and improve segmentation accuracy. Finally, we suggested the ensemble dual-modality by combining the best-performing two pre-trained dual-modalities models to enhance segmentation performance. Experimental results show that the proposed model enhanced the segmentation result and achieved a Dice Coefficient of 97.73% and a Mean IoU of 60.08% on the BraTS2020 dataset. The results illustrate that the ensemble dual-modality approach outperforms single-modality and dual-modality models. This study shows that ensemble dual-modality models can help improve the accuracy and dependability of brain tumor segmentation based on MRI. Our code publicly available at: https://github.com/Ahmeed-Suliman-Farhan/Ensemble-Dual-Modality-Approach

bioinformatics↗

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

PRCnet: An Efficient Model for Automatic Detection of Brain Tumor in MRI Images

Brain tumors are the most prevalent and life-threatening cancer; an early and accurate diagnosis of brain tumors increases the chances of patient survival and treatment planning. However, manual tumor detection is a complex, cumbersome and time-consuming task and is prone to errors, which relies on the radiologists experience. As a result, the development of accurate and automatic system for tumor detection is critical. In this paper, we proposed a new model called Parallel Residual Convolutional Network (PRCnet) model to classify brain tumors from Magnetic Resonance Imaging. The PCRnet model uses several techniques (such as filters of different sizes with parallel layers, connections between layers, batch normalization layer, and ReLU) and dropout layer to overcome the over-fitting problem, for achieving accurate and automatic classification of brain tumors. The PRCnet model is trained and tested on two different datasets and obtained an accuracy of 94.77% and 97.1% for dataset A and dataset B, respectively which is way better as compared to the state-of-the-art models.

bioinformatics↗

bioRxiv2022

Host independent deletion hotspots in the SARS-CoV-2 genome

SARS-CoV-2 infects a wide range of hosts in varying degrees. The RNA genome of SARS-CoV-2 makes it prone to mutations. Advantageous mutations help the virus to evolve and the virus maintains such mutations across species. Here in this study, all non-human hosts-derived SARS-CoV-2 genomic sequences from the GISAID database were analyzed, and identified several deletion hotspots, which are maintained by the virus, across various host species, indicating their important role in the virus evolution. Several of these deletion hotspots are also found in human-derived SARS-CoV-2 genomic sequences. These deletion hotspots have the potential to affect the pathogenicity and virulence of the virus and have a role in molecular and serological diagnostics. Potentially, they can lead to immune escape, resulting in vaccine failure and drug-resistant variants.

microbiology↗

bioRxiv2022

Genome similarities between human-derived and mink-derived SARS-CoV-2 make mink a potential reservoir of the virus

The SARS-CoV-2 has RNA as the genome, which makes the virus more prone to mutations. Occasionally, mutations help a virus to cross the species barrier. The SARS-CoV-2 infection to humans and minks (Neovison vison) are examples of zoonotic spillover. Many studies have been published on the analysis of human-derived SARS-CoV-2, here we performed mutation analysis on the minks-derived SARS-CoV-2 genome sequences. We analyzed all available full-length mink derived SARS-CoV-2 genome sequences on GISAID (214 from Netherlands and 133 from Denmark). We found that the mutation pattern in the Netherlands and Denmark derived samples were different. Out of a total of 201 mutations, we found in this study, only 13 mutations were common in the Netherlands and Denmark derived samples. We found 4 mutations prevailed in the Netherlands and Denmark mink derived samples and these 4 mutations are also reported to prevail in human-derived SARS-CoV-2.

genomics↗

bioRxiv2020

Host Specific SARS-CoV-2 Mutations: Insertion of the Phenylalanine in the NSP6 Linked to the United Kingdome and Premature Termination of the ORF-8 Associated with the European and the United States of America Derived Samples.

The coronavirus belongs to the order Nidovirales, which is known for the longest RNA genome virus. The polymerase enzyme of SARS-CoV-2 has proofreading functions, but still, the RNA viruses have a higher mutation rate than DNA viruses. The mutations in the viral genome provide a replication advantage in any population/geographical location and that may have profound consequences in the outcome and pathogenesis, diagnosis and patient management of the viral infection. In the present study, we have analysed full-length SARS-CoV-2 genome sequences, derived from symptomatic/asymptomatic COVID-19 patients from all six continents to investigate the common mutations globally. Our results revealed that SARS-CoV-2 is mutating independently, we identified total 313 mutations and some (21 mutations) of them are prevailing over time irrespective of geographical location. Another important finding, we are reporting here is, the mutation rate of the virus varies in different geographical locations suggesting the virus is adapting different strategies in the infected populations, having different genetic backgrounds across the globe. We have identified 11085TTT insertion (insertion of the Phenylalanine in NSP6 at position 38) mutation, which is mainly linked to the UK derived SARS-CoV-2 samples, we have also discovered non-sense mutation in ORF-8 after 17 amino acid is linked to the European and the USA derived SARS-CoV-2 samples.

genomics↗

bioRxiv2020

Revisiting the Association between Human Leukocyte Antigen and End-Stage Renal Disease

Multiple works have studied possible associations between human leukocyte antigen (HLA) alleles and end stage renal disease (ESRD) showing, however, contradictory and inconsistent results. Here, we revisit the association between ESRD and HLA antigens, comparing HLA polymorphism (at HLA-A, -B, -C, -DRB1, -DQB1 and DQA1 loci) in ESRD patients (n=497) and controls (n=672). Our data identified several HLA alleles that displayed a significant positive or negative association with ESRD. We also determined whether heterozygosity or homozygosity of the ESRD-associated HLA alleles at different loci could modify the prevalence of the disease. Few HLA allele combinations displayed significant associations with ESRD among which HLA-A*3-HLA-A*26 combination showed the highest strength of association (OR= 4.488, P[≤] 0.05) with ESRD. Interestingly, the age of ESRD onset was not affected by HLA allele combinations at different loci. We also performed an extensive literature analysis to determine whether the association of HLA to ESRD can be similar across different ethnic groups. Our analysis showed at least for certain alleles, HLA-A*11, HLA-DRB1*11, and HLA-DRB1*4, a significant association of HLA to ESRD in different ethnic groups. The findings of our study will help in determining possible protective or susceptible roles of various HLA alleles in ESRD.

molecular biology↗

bioRxiv2018

Stimulus-responsive self-assembly of enzymatic fractal structures by computational design

Fractal topologies, which are statistically self-similar over multiple length scales, are pervasive in nature. The recurrence of patterns at increasing length scales in fractal-shaped branched objects, e.g., trees, lungs, and sponges, results in high effective surface areas, and provides key functional advantages, e.g., for molecular trapping and exchange. Mimicking these topologies in designed protein-based assemblies will provide access to novel classes of functional biomaterials for wide ranging applications. Here, we describe a modular, multi-scale computational design method for the reversible self-assembly of proteins into tunable supramolecular fractal-like topologies in response to phosphorylation. Computationally-guided atomic-resolution modeling of fusions of symmetric, oligomeric proteins with Src homology 2 (SH2) binding domain and its phosphorylatable ligand peptide was used to design iterative branching leading to fractal-like assembly formation by enzymes of the atrazine degradation pathway. Structural characterization using various microscopy techniques and Cryo-electron tomography revealed a variety of dendritic, hyperbranched, and sponge-like topologies which are self-similar over three decades ([~]10nm-10m) of length scale, in agreement with models from multi-scale computational simulations. We demonstrate control over mesoscale topology (by linker design), formation dynamics, and functional enhancements due to dynamic multi-component assemblies constructed with three atrazine degradation pathway enzymes. The described design method should enable the construction of a variety of novel, spatiotemporally responsive catalytic biomaterials featuring fractal topologies.

biophysics↗