Biology subjects

Moustafa, A.

Publications and source records attributed to Moustafa, A..

13 recordsLinked to original sources

bioRxiv2026

Transcriptomic signature of Human Cardiac Fibroblast in Hypertrophic Obstructive Cardiomyopathy

BackgroundHypertrophic Cardiomyopathy (HCM) is a cardiac disorder characterized by an increased interstitial fibrosis and Extracellular Matrix (ECM) remodeling. Cardiac fibroblasts (CFs) have a crucial role in ECM remodeling as well as influencing contractility. There is accumulating evidence that the phenotype of CFs is disease-specific. Here, we investigate the transcriptome signature of CFs in Hypertrophic obstructive Cardiomyopathy (HOCM) patients and its functional significance. MethodsPrimary CFs were isolated from myectomy specimens of 12 clinically phenotyped HOCM patients and 3 controls. RNA libraries were prepared from cell isolates for whole transcriptome sequencing. Differential expression analysis was conducted using the Tuxedo pipeline. Pathway and Gene Ontology (GO) enrichment were performed using Protein-protein interaction network analysis and functional clustering were performed using Cytoscape StringApp. Expression of candidate genes and proteins was validated using quantitative PCR, Immunocytochemistry, immunohistochemistry, cytokines/chemokines profiling and western blotting, in patient-derived CFs extracts, CFs-conditioned media, and myocardial tissue sections. ResultsWhole transcriptome analysis identified 265 significant differentially expressed genes (DEGs) in HOCM fibroblasts compared to controls. The most significant GO terms identified were associated with ECM organization and inflammatory response. The most significant GO terms identified were associated with ECM organization and inflammatory response, with circos plot analysis further highlighting pathway-specific gene overlap within inflammatory and structural signaling clusters. The DEGs encompassed gene families such as collagens, proteases, fibulins, inflammatory cytokines, integrins and signaling receptors and kinases. MYC was upregulated alongside chemokine ligands and receptors, highlighting a MYC-linked chemokine signaling axis within the inflammatory HOCM-CFs phenotype. The protein expression of selected extracellular Matrix organization and inflammatory response genes confirmed the transcriptome results. ConclusionTranscriptomic profiling of patient-derived HOCM-CFs identified genes and pathways associated with inflammatory signaling, ECM remodeling, and altered cell-cell/matrix communication. These findings show that advanced HOCM-CFs acquire an inflammatory-remodeling phenotype, with ECM genes other than collagens. Novelty and SignificanceO_ST_ABSWhat Is Known?C_ST_ABSO_LIHypertrophic cardiomyopathy is characterized by myocardial hypertrophy, interstitial fibrosis, extracellular matrix remodelling, and inflammatory signalling. C_LIO_LIMost human HCM transcriptomic studies have been performed using whole myocardial tissue, which limits resolution of fibroblast-specific disease programmes. C_LI What New Information Does This Article Contribute?O_LIPatient-derived cardiac fibroblasts from patients with advanced hypertrophic obstructive cardiomyopathy exhibit a distinct transcriptomic signature enriched for inflammatory signalling, extracellular matrix organization, chemokine activity, and altered cell-matrix communication. C_LIO_LIThe HOCM-CF signature is not characterized by uniform collagen gene induction, but by selective ECM remodelling involving fibulins, proteases, integrins, matricellular genes, and downregulation of selected collagen-associated matrix components. C_LIO_LIComparison with bulk myocardial RNA-sequencing and publicly available human cardiomyopathy data supports the biological relevance of the fibroblast-derived signature while highlighting the limited sensitivity of bulk tissue transcriptomics for resolving fibroblast-specific programmes. C_LI What Is the Significance?This study identifies a disease-associated inflammatory and ECM-remodelling state in patient-derived HOCM cardiac fibroblasts. The findings extend the role of cardiac fibroblasts in HOCM beyond classical collagen deposition and suggest that fibroblast-mediated matrix remodelling, cytokine/chemokine signalling, and altered cell-matrix communication may contribute to myocardial remodelling in advanced obstructive disease. These data support the value of fibroblast-focused profiling for uncovering disease-relevant mechanisms that may be masked in whole myocardial tissue analyses and provide candidate pathways for future mechanistic and therapeutic studies.

cell biology↗

bioRxiv2026

EGP1K: Whole-Genome Sequencing of 1,024 Egyptians Characterizes Population Structure and Genetic Diversity

Middle Eastern and North African populations remain underrepresented in genomic databases, comprising less than 1% of genome-wide association study participants despite representing approximately 6% of the global population. Here we present the Egypt Genome Project (EGP1K), in which we performed whole-genome sequencing on 1,024 unrelated Egyptian individuals originating from 21 of Egypts 27 governorates, recruited through eight clinical and research centers across Upper and Lower Egypt. We identified over 51.3 million variants, of which 17.1 million (33.4%) were absent from dbSNP. Allele frequency comparisons across 6.5 million shared variants showed the strongest concordance with Middle Eastern populations ({tau} = 0.977). Principal component analysis and ADMIXTURE modeling at K = 7 revealed that Egyptians share a dominant ancestry component (71.8%) with Middle Eastern populations and carry a smaller Egyptian-enriched component (18.5%) that distinguishes them from neighboring groups. Runs of homozygosity varied substantially across subregions, with Upper Egypt showing the highest burden, paralleling elevated consanguinity rates. Carrier frequency analysis identified MEFV (Familial Mediterranean Fever) at 9.1% as the most prevalent pathogenic carrier state; when adjusted for the national consanguinity rate, MEFV carrier status alone projects approximately 6,600 affected births per year. HLA class I typing identified allele frequencies placing Egyptians within the Levantine-Eastern Mediterranean cluster, providing baseline immunogenetic data currently absent from international databases. Analysis of polygenic risk score distributions revealed substantial differences in threshold-based risk stratification between Egyptians and European reference populations. When the Europeanderived 90th percentile threshold was applied, 83.3% of Egyptians were assigned to high-risk strata for stroke, 76.4% for chronic kidney disease, and 72.8% for gout, compared to the intended 10% high-risk proportion. These distributional shifts were observed across several cardiometabolic traits (Cohens d = 1.55-1.61), while other traits showed closer cross-population concordance, indicating that the degree of threshold miscalibration varies by trait. Together, these findings establish EGP1K as a genomic reference for Egypt and indicate that European-derived risk stratification thresholds may not be directly transferable to the Egyptian population, supporting the need for population-specific calibration of polygenic risk scores.

genomics↗

bioRxiv2025

A Neurodevelopment-Inspired Deep Spiking Neural Network for Auditory Spatial Attention Detection using Single Trial EEG

This study presents a deep probabilistic spiking neural network designed to extract discriminative spatiotemporal features from EEG signals associated with rightward and leftward auditory attention. The network self-organizes its synaptic weights and inter-layer connectivity through a biologically inspired learning rule shaped by probabilistic feedback inhibition and spike-based synchronization dynamics. To characterize the models behavior and identify optimal operating conditions, we systematically examined the effects of key parameters including inhibition strength, synchronization parameters, and preprocessing thresholds on network stability and classification performance. Simulation results show that feedback inhibition is essential for preventing uncontrolled synaptic growth, maintaining sparse connectivity, and enabling stable learning. Strong inhibition suppresses connectivity and weight development, whereas weak inhibition leads to excessive synaptic expansion. An intermediate inhibition regime achieves a balance between adaptability and stability, resulting in the most effective feature extraction. Output-layer neurons exhibited heterogeneous activation dynamics, reflecting diverse encoding of EEG input patterns. The excitatory firing probability was tightly modulated by the inhibition parameter, confirming its central role in shaping network responses. Synchronization parameters further influenced synaptic dynamics, producing nonlinear effects on spike coordination and weight evolution. Classification accuracy peaked at intermediate synchronization levels, revealing an optimal regime where inhibition and synchronization jointly support efficient classification of rightward and leftward EEG data. Additional analyses demonstrated that synaptic pruning substantially improves accuracy across all inhibition levels and that the model performs best when input spikes are generated using a preprocessing threshold of 0.3. Moreover, experimental results demonstrate that the developed neural network model achieves an average accuracy of 90% while utilizing only 10% of the available EEG data. Overall, the findings show that the proposed spiking neural network achieves its highest classification performance under moderate inhibition, intermediate synchronization, sparse connectivity, and appropriate pruning. These results highlight the promise of biologically inspired spiking architectures for decoding EEG-based attentional states, particularly in settings with limited training data and strong temporal structure.

neuroscience↗

bioRxiv2025

Synaptic Synchronization-Based Learning of Pattern Separation in Self-Organizing Probabilistic Spiking Neural Networks

Neuroscience-inspired neural networks bridge biology and technology, offering powerful tools to model brain function while enabling adaptive, efficient control in robotics. In this work, we present a neuroscience-inspired synaptic learning rule based on the synchronization of synaptic inputs to single excitatory neurons within a feedforward spiking neural network. The model consists of three excitatory layers and two feedback inhibitory layers, with initially low connection probabilities and weak synaptic weights assigned to the excitatory neurons. Under an unsupervised learning paradigm, stimulus patterns were presented to the network, allowing synaptic weights and connectivity to evolve dynamically across training trials. We investigated how these dynamics depended on feedback inhibition intensity and identified conditions under which the network achieved stable activity. Furthermore, we evaluated the models pattern separation efficacy and its relationship to network dynamics. The results highlight the critical role of feedback inhibition in both stabilizing the network and enhancing pattern separation. In particular, results show balanced synchronization between excitatory and inhibitory populations maximizes separation efficacy. Beyond providing a novel computational framework for understanding information processing in neural systems, this model also offers insights into cognitive disorders associated with impaired inhibition and pattern separation, such as autism and schizophrenia. Finally, we embedded the trained network within a simulated agent navigating a two-dimensional environment, where it was tasked with identifying a trained stimulus as an obstacle and avoiding it. The model offers a framework for advancing cognitive robotics by enabling novel approaches that mimic natural intelligence and support the learning of complex environmental patterns.

neuroscience↗

bioRxiv2024

Red blood cell-tumour cell interactions promote tumour cell progression

One critical step in the metastatic cascade is the survival of circulating tumour cells (CTCs) within the bloodstream. While numerous interactions between CTCs and various hematopoietic cells have been described, the role of red blood cells (RBCs) in this process remains underexplored. This study investigates the interactions between tumour cells and RBCs from breast and lung cancer patients, revealing significant phenotypic and functional changes in the tumour cells, unlike when the contact is with RBCs from healthy donors. In vitro co-culture of cancer cell lines with RBCs from metastatic cancer patients resulted in increased tumour cell attachment accompanied by morphological changes. Additionally, RBCs-primed tumour cells showed increased adhesion and disruption of the endothelial barrier in vitro and increased invasiveness both in vitro and in vivo. Transcriptomic analysis showed that RBCs from metastatic breast cancer patients induce significant gene expression changes, notably upregulating PAK4, which enhances migration and epithelial-mesenchymal transition. PAK4 inhibition reduced these effects. Proteomic studies revealed substantial remodelling, including actin-related changes and the accumulation of VASP at cell edges, promoting directional migration. Clinically, higher RBC distribution width (RDW) in metastatic breast cancer patients is associated with increased CTC counts and worse outcome. This study highlights the previously unrecognized role of RBCs in promoting metastatic behaviours in cancer cells and suggests potential therapeutic targets, such as PAK4, to counteract these effects. Further exploration of RBCs-tumour cell interactions could provide new insights into metastatic mechanisms and improve cancer prognosis and treatment strategies. Key PointsO_LIThis study reveals the previously unknown role of RBCs in enhancing tumour cell invasiveness and metastatic potential. C_LIO_LITumour cells undergo significant phenotypic and functional changes after contact with RBCs from cancer patients. C_LI

cancer biology↗

bioRxiv2023

Test-retest reliability of spectral parameterization by 1/f characterization using SpecParam

SpecParam (formally known as FOOOF) allows for the refined measurements of electroencephalography periodic and aperiodic activity, and potentially provides a non-invasive measurement of excitation:inhibition balance. However, little is known about the psychometric properties of this technique. This is integral for understanding the usefulness of SpecParam as a tool to determine differences in measurements of cognitive function, and electroencephalography activity. We used intraclass correlation coefficients (ICC) to examine the test-retest reliability of parameterized activity across three sessions (90 minutes apart and 30 days later) in 49 healthy young adults at rest with eyes open (EO), eyes closed (EC), and during three EC cognitive tasks including subtraction (Math), music recall (Music), and episodic memory (Memory). ICCs were good for the aperiodic exponent and offset (ICCs > .70) and parameterized periodic activity (ICCs > .66 for alpha and beta power, central frequency, and bandwidth) across conditions. Across all three sessions, SpecParam performed poorly in EO (40% of participants had poor fits over non-central sites) and had poor test-retest reliability for parameterized periodic activity. SpecParam mostly provides reliable metrics of individual differences in parameterized neural activity. More work is needed to understand the suitability of EO resting data for parameterization using SpecParam.

neuroscience↗

bioRxiv2022

Pre-epidemic evolution of the USA300 clade and a molecular key for classification

USA300 has remained the dominant community and healthcare associated methicillin-resistant Staphylococcus aureus (MRSA) clone in the United States and in northern South America for at least the past 20 years. In this time, it has experienced epidemic spread in both of these locations. However, its pre-epidemic evolutionary history and origins are incompletely understood. Large sequencing databases, such as NCBI, PATRIC, and Staphopia, contain clues to the early evolution of USA300 in the form of sequenced genomes of USA300 isolates that are representative of lineages that diverged prior to the establishment of the South American (SAE) and North American (NAE) epidemics. In addition, historical isolates collected prior to the emergence of epidemics can help reconstruct early events in the history of this lineage. Here, we take advantage of the accrued, publicly available data, as well as two newly sequenced pre-epidemic historical isolates from 1996, and a very early diverging ACME-negative NAE genome to understand the pre-epidemic evolution of USA300. We use database mining techniques to emphasize genomes similar to pre-epidemic isolates, with the goal of reconstructing the early molecular evolution of the USA300 lineage. Phylogenetic analysis with these genomes confirms that the North American Epidemic and South American Epidemic USA300 lineages diverged from a most recent common ancestor around 1970 with high confidence, and it also pinpoints the independent acquisition events of the of the ACME and COMER loci with greater precision than in previous studies. We solidify evidence for a North American origin of the USA300 lineage and identify multiple introductions of USA300 into South America from North America. Notably, we describe a third major USA300 clade (the pre-epidemic branching clade; PEB1) consisting of both MSSA and MRSA isolates circulating around the world that diverged from the USA300 lineage prior to the establishment of the South American and North American epidemics. We present a detailed analysis of specific sequence characteristics of each of the major clades, and present diagnostic positions that can be used to classify new genomes.

genomics↗

bioRxiv2021

A Neuroscience-Inspired Spiking Neural Network for Auditory Spatial Attention Detection Using Single-Trial EEG

Recently, studies have shown that the alpha band (8-13 Hz) EEG signals enable the decoding of auditory spatial attention. However, deep learning methods typically requires a large amount of training data. Inspired by "sparse coding" in cortical neurons, we propose a spiking neural network model for auditory spatial attention detection. The model is composed of three neural layers, two of them are spiking neurons. We formulate a new learning rule that is based on firing rate of pre-synaptic and post-synaptic neurons in the first layer and the second layer of spiking neurons. The third layer consists of 10 spiking neurons that the pattern of their firing rate after training is used in test phase of the method. The proposed method extracts the patterns of recorded EEG of leftward and rightward attention, independently, and uses them to train network to detect the auditory spatial attention. In addition, a computational approach is presented to find the best single-trial EEG data as training samples of leftward and rightward attention EEG. In this model, the role of using low connectivity rate of the layers and specific range of learning parameters in sparse coding is studied. Importantly, unlike most prior model, our method requires 10% of EEG data as training data and has shown 90% accuracy in average. This study suggests new insights into the role of sparse coding in both biological networks and brain-inspired machine learning.

neuroscience↗

bioRxiv2021

Comparative Transcriptomics Identifies Potential Stemness-Related Markers for Mesenchymal Stromal/Stem Cells

Mesenchymal stromal/stem cells (MSCs) are multipotent cells residing in multiple tissues with the capacity for self-renewal and differentiation into various cell types. These properties make them promising candidates for regenerative therapies. MSC identification is critical in yielding pure populations for successful therapeutic applications; however, the criteria for MSC identification proposed by the International Society for Cellular Therapy (ISCT) is inconsistent across different tissue sources. In this study, we aimed to identify potential markers to be used together with the ISCTs criteria to provide a more accurate means of MSC identification. Thus, we carried out a comparative analysis of the expression of human and mouse MSCs derived from multiple tissues to identify the common differentially expressed genes. We show that six members of the proteasome degradation system are similarly expressed across MSCs derived from bone marrow, adipose tissue, amnion, and umbilical cord. Also, with the help of predictive models, we found that these genes successfully identified MSCs across all the tissue sources tested. Moreover, using genetic interaction networks, we showed a possible link between these genes and antioxidant enzymes in the MSC antioxidant defense system, thereby pointing to their potential role in prolonging the life span of MSCs. Our results suggest that these genes can be used as stemness-related markers.

genomics↗

bioRxiv2019

Toward One-Shot Learning in Neuroscience-Inspired Deep Spiking Neural Networks

Conventional deep neural networks capture essential information processing stages in perception. Deep neural networks often require very large volume of training examples, whereas children can learn concepts such as hand-written digits with few examples. The goal of this project is to develop a deep spiking neural network that can learn from few training trials. Using known neuronal mechanisms, a spiking neural network model is developed and trained to recognize hand-written digits with presenting one to four training examples for each digit taken from the MNIST database. The model detects and learns geometric features of the images from MNIST database. In this work, a novel biological back-propagation based learning rule is developed and used to a train the network to detect basic features of different digits. For this purpose, randomly initialized synaptic weights between the layers are being updated. By using a neuroscience inspired mechanism named synaptic pruning and a predefined threshold, some of the synapses through the training are deleted. Hence, information channels are constructed that are highly specific for each digit as matrix of synaptic connections between two layers of spiking neural networks. These connection matrixes named information channels are used in the test phase to assign a digit class to each test image. As similar to humans abilities to learn from small training trials, the developed spiking neural network needs a very small dataset for training, compared to conventional deep learning methods checked on MNIST dataset.

neuroscience↗

bioRxiv2018

Mobile genetic element-encoded hypertolerance to copper protects Staphylococcus aureus from killing by host phagocytes

Pathogens are exposed to toxic levels of copper during infection and copper tolerance may be a general virulence mechanism used by bacteria to resist host defences. In support of this, inactivation of copper-exporter genes has been found to reduce the virulence of bacterial pathogens in vivo. Here we investigate the role of copper-hypertolerance in methicillin resistant Staphylococcus aureus. We show that a copper-hypertolerance locus (copB-mco), carried on a mobile genetic element, is prevalent in a collection of invasive S. aureus strains and more widely among clonal complex 22, 30 and 398 strains. The copB and mco genes encode a copper efflux pump and a multicopper oxidase, respectively. Isogenic mutants lacking copB or mco had impaired growth in subinhibitory concentrations of copper. Transfer of a copB-mco encoding plasmid to a naive clinical isolate resulted in a gain of copper hypertolerance and enhanced bacterial survival inside primed macrophages. The copB and mco genes were upregulated within infected macrophages and their expression was dependent on the copper sensitive operon repressor CsoR. Isogenic copB and mco mutants were impaired in their ability to persist intracellularly in macrophages and were less resistant to phagocytic killing in human blood than the parent strain. The importance of copper-regulated genes in resistance to phagocytic killing was further elaborated using mutants expressing a copper-insensitive variant of CsoR. Our findings suggest that the gain of mobile genetic elements carrying copper-hypertolerance genes contributes to the evolution of virulent strains of S. aureus, better equipped to resist killing by host immune cells.

microbiology↗

bioRxiv2017

Microbial Metagenome Of Urinary Tract Infection

Urine culture and microscopy techniques are used to profile the bacterial species present in urinary tract infections. To gain insight into the urinary flora in infection and health, we analyzed clinical laboratory features and the microbial metagenome of 121 clean-catch urine samples. 16S rDNA gene signatures were successfully obtained for 116 participants, while whole genome shotgun sequencing data was successfully generated for samples from 49 participants. Analysis of these datasets supports the definition of the patterns of infection and colonization/contamination. Although 16S rDNA sequencing was more sensitive, whole genome shotgun sequencing allowed for a more comprehensive and unbiased representation of the microbial flora, including eukarya and viral pathogens, and of bacterial virulence factors. Urine samples positive by whole genome shotgun sequencing contained a plethora of bacterial (median 41 genera/sample), eukarya (median 2 species/sample) and viral sequences (median 3 viruses/sample). Genomic analyses revealed cases of infection with potential pathogens (e.g., Alloscardovia sp, Actinotignum sp, Ureaplasma sp) that are often missed during routine urine culture due to species specific growth requirements. We also observed gender differences in the microbial metagenome. While conventional microbiological methods are inadequate to identify a large diversity of microbial species that are present in urine, genomic approaches appear to comprehensively and quantitatively describe the urinary microbiome.

microbiology↗

bioRxiv2017

Accumulation of sensory evidence is impaired in Parkinson’s disease with visual hallucinations

Models of hallucinations across disorders emphasise an imbalance between sensory input and top-down influences over perception. However, the psychological and mechanistic correlates of this imbalance remain underspecified. Visual hallucinations in Parkinsons disease (PD) are associated with impairments in lower level visual processes and attention, accompanied by over activity and connectivity in higher-order association brain networks. PD therefore provides an attractive framework to explore the relative contributions of bottom-up versus top-down disturbances in hallucinations. Here, we characterised sensory processing in PD patients with and without visual hallucinations, and in healthy controls, by fitting a hierarchical drift diffusion model (hDDM) to an attentional task. The hDDM uses Bayesian estimates to decompose reaction time and response output into parameters reflecting drift rates of evidence accumulation, decision thresholds and non-decision time. We observed slower drift rates in PD patients with hallucinations, which were insensitive to changes in task demand. In contrast, wider decision boundaries and shorter non-decision times relative to controls were found in PD regardless of hallucinator status. Inefficient and less flexible sensory evidence accumulation emerge as unique features of PD hallucinators. We integrate these results with current models of hallucinations, suggesting that slow and inefficient sensory input in PD is less informative, and may therefore be down-weighted leading to an over reliance on top-down influences. Our findings provide a novel computational framework to better specify the impairments in dynamic sensory processing that are a risk factor for visual hallucinations.

neuroscience↗