Biology subjects

Habib, P.

Publications and source records attributed to Habib, P..

7 recordsLinked to original sources

bioRxiv2026

Focal neuronal loss and cell replacement in a connectome model of fly steering

Focal neuronal loss after stroke, traumatic brain injury or spinal cord injury can disrupt circuits and result in persistent motor deficits. Which cells and connections must be restored to recover a specific behavior is difficult to establish experimentally. Here we use a connectome-constrained spiking model of the fruit-fly nervous system and decode motor-neuron output to simulate visually guided steering. After loss of 4,000 neurons, path straightness ranged from 0.04 to 0.95 depending on lesion location. At a fixed sensorimotor site, a 2,000-neuron lesion reduced simulated displacement to 6.5% of intact. Across 100 stochastic simulation realizations of the connectome, replacing 100 cells with their original connections increased displacement to 62.5% of intact when motor-control pathways were prioritized, compared with 10.1% after random selection of 100 cells and 48.5% after random selection of 1,000 cells. When connections were instead inferred from cell type, the same 100 intended replacements reached 60.0% of intact with matched identities and 6.7% with non-target identities, indicating that cell identity and type-dependent integration are required for recovery. Our model generates testable predictions for genetic ablation experiments in fruit flies and identifies candidate cellular and circuit requirements for motor repair that can inform cell replacement therapies and future models of mammalian locomotion.

neuroscience↗

bioRxiv2026

Targeting integrin αvβ3 by chimeric antigen receptor neural stem cell (CAR-NSC) therapy for stroke

Stroke remains a leading cause of adult disability due to the brains limited regenerative capacity. Although stem cell therapies show favorable safety and feasibility profiles in early clinical trials, poor spatial retention and limited engagement with peri-infarct salvageable tissue constrain efficacy. Here, we engineered human induced pluripotent stem cell-derived neural stem cells (NSC) with a chimeric antigen receptor (CAR)-like architecture to enable targeted recognition of injury-associated cues. Specifically, cells were modified to express a membrane-anchored single chain variable fragment (scFv) targeting integrin v{beta}3, a receptor selectively upregulated in peri-infarct vasculature after stroke. Engineered CAR-NSC retained progenitor identity and selectively bound recombinant integrin v{beta}3 in vitro. Following focal transplantation into a photothrombotic stroke mouse model, CAR-NSC displayed broader dispersion within peri-infarct tissue and covered a greater proportion of the ischemic lesion compared to non-binding control-CAR-NSC. CAR-NSC grafts extended longer neurites that aligned more closely with the lesion border. In addition, CAR-NSC transplantation reduced microglial activation and was associated with increased vascular density and blood-brain barrier integrity in the peri-infarct zone. Together, these findings establish a CAR-like NSC strategy for stroke to direct the spatial distribution and tissue engagement of transplanted cells. Molecular targeting of injury-associated cues may improve the precision and regenerative efficacy of cell-based therapies for stroke and related neurological disorders.

neuroscience↗

bioRxiv2023

Temporal dynamics and genomic programming of plasma cell fates

Affinity-matured plasma cells (PCs) of varying lifespans are generated through a germinal center (GC) response. The developmental dynamics and genomic programs of antigen-specific PC precursors remain to be elucidated. Using a model antigen, we demonstrate biphasic generation of PC precursors, with those generating long-lived bone marrow PCs preferentially produced in the late phase of GC response. Clonal tracing using scRNA-seq+BCR-seq in spleen and bone marrow compartments, coupled with adoptive transfer experiments, reveal a novel PC transition state that gives rise to functionally competent PC precursors. The latter undergo clonal expansion, dependent on inducible expression of TIGIT. We propose a model for the proliferation and programming of precursors of long-lived PCs, based on extended antigen encounters followed by reduced antigen availability.

immunology↗

bioRxiv2020

COVATOR: A Software for Chimeric Coronavirus Identification

The term chimeric virus was not popular in the last decades. Recently, according to current sequencing efforts in discovering COVID-19 Secrets, the generated information assumed the presence of 6 Coronavirus main strains, but coronavirus diverges into hundreds of sub-strains. the bottleneck is the mutation rate. With two mutation/month, humanity will meet a new sub-strain every month. Tracking new sequenced viruses is urgently needed because of the pathogenic effect of the new substrains. here we introduce COVATOR, A user-friendly and python-based software that identifies viral chimerism. COVATOR aligns input genome and protein that has no known source, against genomes and protein with known source, then gives the user a graphical summary.

bioinformatics↗

bioRxiv2020

COVIDier: A Deep-learning Tool For Coronaviruses Genome And Virulence Proteins Classification

COVID-19, caused by SARS-CoV-2 infection, has already reached pandemic proportions in a matter of a few weeks. At the time of writing this manuscript, the unprecedented public health crisis caused more than 2.5 million cases with a mortality range of 5-7%. The SARS-CoV-2, also called novel Coronavirus, is related to both SARS-CoV and bat SARS. Great efforts have been spent to control the pandemic that has become a significant burden on the health systems in a short time. Since the emergence of the crisis, a great number of researchers started to use the AI tools to identify drugs, diagnosing using CT scan images, scanning body temperature, and classifying the severity of the disease. The emergence of variants of the SARS-CoV-2 genome is a challenging problem with expected serious consequences on the management of the disease. Here, we introduce COVIDier, a deep learning-based software that is enabled to classify the different genomes of Alpha coronavirus, Beta coronavirus, MERS, SARS-CoV-1, SARS-CoV-2, and bronchitis-CoV. COVIDier was trained on 1925 genomes, belonging to the three families of SARS retrieved from NCBI Database to propose a new method to train deep learning model trained on genome data using Multi-layer Perceptron Classifier (MLPClassifier), a deep learning algorithm, that could blindly predict the virus family name from the genome of by predicting the statistically similar genome from training data to the given genome. COVIDier able to predict how close the emerging novel genomes of SARS to the known genomes with accuracy 99%. COVIDier can replace tools like BLAST that consume higher CPU and time.

bioinformatics↗

bioRxiv2020

Pathdict: Machine learning software predict Drug-Pathway interaction in human Based on drug simplified molecular-input line-entry system (SMILES)

Predicting the target of unknown or/and drugs under investigation from data of already identified drugs is very important not only for the understanding of various drug and molecular interaction processes but also for the development of novel drugs. Here we introduce TarDict, a RandomForestClassifier based-software predict the target pathway or protein based on SMILES of chemical. TarDict receives SMILES and returns a list of the possible similar drug, then export list to the user the target that drug contribute in. Training data set of 20442 entry and testing reveal %95 accuracy.

bioinformatics↗

bioRxiv2019

SNPector: SNP inspecting tool to detect disease existence and drug response in direct naked sequencing depends on mutation index

Due to the ability to diagnose diseases early and evaluate the effectiveness of medicinal drugs, single nucleotide polymorphism (SNP) identification receives significant interest. Detection and diagnosis of genetic variation through skill-less computational tools would help researchers reducing the severity of such health complications and improving the well-tailored therapies using discovered and previously known information. We introduce SNPector, which is a standalone SNP inspection software could be used to diagnose gene pathogenicity and drug reaction in naked genomic sequences. It identifies and extracts gene-related SNPs, and reports their genomic position, associated phenotype disorder, associated diseases, linkage disequilibrium, in addition to various drug reaction information. SNPector detects and verifies the existence of an SNP in a given DNA sequence based on different clinically relevant SNP databases such as NCBI Clinvar database, Awesome database, and PharmGKB and generates highly informative visualizations of the recovered information.

bioinformatics↗