Biology subjects

Hassan, M.

Publications and source records attributed to Hassan, M..

At least 19 recordsLinked to original sources

bioRxiv2026

The Lifespan Architecture of Human EEG

Human brain maturation and aging are highly nonlinear, yet their organization at the level of large-scale electrophysiological activity remains poorly understood. We analyzed resting-state electroencephalography (EEG) recordings from 1,763 healthy individuals aged 5-85 years to map lifespan trajectories across spectral, complexity, and morphological features. Age-sensitive features clustered into distinct nonlinear trajectories, most commonly showing rapid change during childhood and adolescence followed by stabilization in adulthood, while a smaller subset exhibited turning points in midlife. These trajectories also differed in their spatial expression, ranging from highly conserved scalp-wide patterns to heterogeneous profiles in which the same feature followed distinct trajectories across scalp regions. Transition ages revealed recurring regional sequences, suggesting that diverse EEG features share common reorganization chronologies. Together, these results show that lifespan EEG variation is structured through complementary temporal trajectories, spatial architectures, and regional transition sequences. This normative framework provides a basis for investigating brain development and aging and for identifying atypical patterns associated with neurological and psychiatric disorders.

neuroscience↗

bioRxiv2026

Schwann Cell Mapping and Characterization in Bone of Different Embryonic Origins

Schwann cells (SCs) provide support for nerves throughout the body. Despite importance for nerve function and repair, the morphology and distribution of SCs in bone remains largely undefined. In this study we used a "Schwann Cell Mapper" mouse (Mpz-Cre+/-;TdT+/+;Ngfr-eGFP+/+) and a "p75 Lineage Tracer" mouse (Ngfr-CreERT2+/-;ZsGreen1+/+) to study SC localization and morphology within the adult mouse calvaria, limb, and vertebrae. We found that all nerves in bone were covered by mature myelinating or non-myelinating SCs, labeled by MPZ and p75-NGFR, respectively. Mature SCs populated the periosteum and entered the bone marrow through transcortical canals. Non-myelinating SCs outnumbered myelinating SCs in bone, with a ratio of [~]2:1 by length density. Non-myelinating SCs in periosteum had more branching and increased size relative to myelinating SCs. In addition, we identified two candidate populations of MPZ lineage+ and p75-NGFR+ immature SCs (iSCs) that were distributed throughout the calvarial periosteum. Similar to mature SCs, p75-NGFR+ candidate iSCs were more prevalent at a ratio of [~]3:1. Overall, neural crest-derived calvarial bone had evidence of increased SC maturity relative to mesoderm-derived sites, identifying niche-level differences in SC maturation. Lastly, lineage tracing revealed that mesenchymal lineages in bone were largely negative for both MPZ and p75-NGFR (<0.1% labeling). These findings provide a framework of SC organization in bone and highlight previously unrecognized diversity across skeletal compartments. By defining distribution and morphology, this work lays the foundation for future studies investigating how SCs contribute to bone biology, including roles in repair, pain, and homeostasis.

bioengineering↗

bioRxiv2026

Biological foundation models illuminate annotation blind spots in evolutionarily divergent genomes

Chromosome-scale assemblies are increasingly available for non-model organisms, but functional annotation remains limited when deep evolutionary divergence erodes primary amino-acid sequence identity even though protein structural similarity can remain conserved. We present a hybrid annotation framework that decouples gene-model discovery from cross-species similarity assignment by combining Evo2-based ab initio prediction of exon-intron structures with ESM-2 protein-embedding-based structural similarity mapping. Applied to the sea lamprey, the framework derives high- or medium-confidence cross-species similarity assignments for 73,485 Evo2-derived translated protein models, including 35,395 high-confidence calls, and expands the deduplicated structural catalog to 31,286 loci, including 20,871 additions absent from the Ensembl baseline. A joint alignment-structure classification identifies 21,391 structurally supported catalog loci that a fixed human DIAMOND protein search does not confidently assign on its own, including 21,184 loci with no detectable human protein-sequence match and 207 loci with only low-confidence matches in the classical 20-30% amino-acid-identity twilight zone. These rescue-space totals describe catalog loci rather than validated one-to-one human-absent genes. In a single-cell RNA sequencing application, a stricter UTR-aware Ensembl+Evo2 reference improves gene recovery and expands the interpretable feature space of the lamprey immune compartment relative to the Ensembl baseline. This enables more resolved annotation of four transcriptionally defined immune cell states, including VLRA+-associated T-like and VLRB+-associated B-like programs together with oxidative iron-handling and iron-associated VLR-linked states. Together, these results show that structural protein signal often persists beyond the limits of pairwise sequence alignment and that an embedding-based annotation layer can extend that signal to improve downstream comparative and single-cell analyses in evolutionarily divergent genomes.

bioinformatics↗

bioRxiv2026

A high-resolution atlas of cattle regulatory variants and their cross-species activity in matched human cells

Identifying causal noncoding variants underlying complex traits in cattle remains challenging because high-resolution functional maps of regulatory variation are lacking. Here we combine massively parallel reporter assays with graph genomics to measure autonomous transcriptional activity from >1.5 billion DNA fragments spanning both cattle subspecies. In primary bovine cells, we assay >15 million variants and identify >150,000 expression-modulating variants enriched at cattle eQTL and GWAS loci. This enables the refinement of broad association signals to small sets of candidate functional regulatory variants. Our haplotype-aware framework captures rare, multi-allelic and tightly linked variants poorly resolved by conventional eQTL studies, and quantifies the disproportionate impact of larger variants on transcription. Furthermore, we use these data to train a deep-learning model that successfully predicts bovine promoter activity directly from sequence. Profiling the same cattle DNA in matched primary human cells reveals widespread conservation of promoter and enhancer activity, allelic effects and regulatory grammar, supporting the transfer of annotations and models across species. However, species-dependent effects are enriched in evolutionarily young sequences and p53-family motifs, highlighting the limits to simple cross-species extrapolation. Together, these data provide a high-resolution atlas of cattle regulatory variation and a framework for prioritising causal noncoding variants for cattle trait improvement.

genetics↗

bioRxiv2026

Sex-specific remodeling of the human adipose tissue vascular niche in obesity

AO_SCPLOWBSTRACTC_SCPLOWObesity remodels the subcutaneous adipose tissue (SAT) vasculature and contributes to cardiometabolic risk, yet potential sex differences in this process remain poorly defined. Here, we integrated single-nucleus transcriptomics and histological analyses of human SAT to reveal pronounced sexual dimorphism within the vascular niche. Obese males exhibit mural cell loss, increased collagen deposition, and inflammatory endothelial activation, including enhanced antigen presentation programs. In contrast, females display preserved mural coverage and increased lipid-handling and redox-adaptive pathways. These coordinated structural and transcriptional differences position the adipose endothelium as a sex-divergent regulator of obesity-associated cardiometabolic vulnerability.

cell biology↗

bioRxiv2026

Hierarchical Deep Learning Framework Integrating Structural Interaction Potentials and Evolutionary Information for Protein-Protein Interaction Affinity Prediction

Protein-protein interactions (PPIs) are fundamental to cellular biology, making accurate prediction of protein-protein interaction affinity (PPIA) crucial for drug discovery and disease mechanism elucidation. Existing computational methods often lack explicit physical constraints or operate as "black boxes," hindering interpretability and generalization. To address these limitations, we introduce StructFuncNet, a novel hierarchical deep learning framework designed for highly accurate, generalizable, and interpretable PPIA prediction. Our approach uniquely integrates multi-level Structural Interaction Potentials (SIP) with rich evolutionary information and advanced graph neural networks (GNNs). This dual paradigm leverages SIP for physical and geometric realism, while GNNs and Transformer-like components learn synergistic dynamics and contextual corrections. StructFuncNet incorporates comprehensive input feature categories, including pre-trained protein residue features, SIP interface energies, evolutionary conservation scores, global molecular descriptors, and interface interaction counts. We extensively evaluated StructFuncNet on diverse and challenging benchmarks, including those for mutation affinity, multi-domain complexes, and intrinsically disordered proteins, as well as complexes derived from predicted structures. Our framework consistently achieves state-of-the-art performance, demonstrating high correlations across all tested scenarios. Furthermore, StructFuncNet proves robust on complexes derived from predicted structures. Ablation studies confirm the synergistic contributions of our multi-modal features and architectural components, underscoring StructFuncNets capacity to provide a robust, interpretable, and efficient solution for complex PPIA prediction challenges.

bioinformatics↗

bioRxiv2025

Sample size critically shapes the reliability of EEG case-control findings in psychiatry

Electroencephalography (EEG) studies in psychiatry have produced highly inconsistent findings, particularly in case-control designs. Small sample sizes are widely assumed to contribute to this variability, yet their impact on the reliability of EEG group comparisons has not been systematically quantified at scale. Here, we address this key gap using a large multisite resting-state EEG dataset comprising 2,874 participants aged 5-18 years, including individuals with attention-deficit/hyperactivity disorder, autism spectrum disorder, anxiety, learning disorders, and healthy controls. We extracted a comprehensive set of spectral, temporal, and complexity EEG features and performed repeated case-control comparisons across a wide range of sample sizes using extensive random subsampling. Results from small samples were unstable, with inflated and highly variable effect sizes across iterations. By contrast, larger samples produced consistent and reproducible findings, converging on uniformly small but robust effects. Statistical power rose steeply with sample size, whereas false positive rates remained relatively stable. These results demonstrate the central role of sample size in shaping EEG study outcomes and challenge the utility of conventional case-control approaches for biomarker discovery in psychiatry.

neuroscience↗

bioRxiv2025

Beta-burst dynamics in the motor cortex are reshaped through sensorimotor refinement

Beta-band activity (15-30 Hz) in motor cortex is closely linked to movement-related processing, yet its transient burst dynamics during long-term learning remain poorly understood. We used High-density electroencephalography (HD-EEG) to examine how beta bursts change over nine sessions of a bimanual coordination task, under adaptive and non-adaptive training conditions. Both training conditions led to motor skill learning, with the adaptive group improving more during training, but the non-adaptive group showing better retention. With training, contralateral primary motor cortex showed stronger beta desynchronization during movement and greater synchronization after movement. These changes reflected underlying burst dynamics: post-movement bursts became more temporally confined and consistent, with increased probability and reduced timing variability across sessions. Only the adaptive group showed a session-related increase in burst amplitude. These results demonstrate that beta burst features reorganize with practice, providing a temporally precise neural readout of training progression and revealing how different learning conditions shape cortical dynamics over time.

neuroscience↗

bioRxiv2025

Genome structural plasticity and copy number dynamics shape adaptive evolution in a barley fungal pathogen

Leaf rust, caused by Puccinia hordei, is one of the most important diseases of barley globally. Here, we report the first haplotype-resolved chromosome-level genome assembly for P. hordei. To gain deeper insight and understanding of the P. hordei genome, we generated such assemblies for two Australian isolates with contrasting virulence, one collected in the 1970s and one in 2001. The two haplotype-phased and chromosome-level assemblies were used to investigate genome architecture in P. hordei and to investigate variability and evolutionary relationships of 41 virulence-divergent isolates that were collected over a 54-year period (1966-2020) in Australia. We report, for the first time in a rust fungus, evidence of a conserved chromosome, chromosome 9, which is enriched for structural variation and accessory features that imply it functions as a compartmentalized core chromosome with regions subject to rapid diversification, potentially acting as a structural hotspot in adaptive evolution in P. hordei. Clear genetic stratification into clonal and recombining lineages was uncovered, with evidence of both long-term asexual propagation and recombination. Analyses of haplotype-resolved mating-type gene phylogenies and chromosome 9 k-mer profiles confirm divergence at key loci underpinning reproductive modes, distinguishing clonally derived from sexually recombined groups. We also identified pervasive copy number variation across the P. hordei genomes, with clonal lineage-specific duplications. Consistent with this finding, the study for the first time links copy number variation of the Cyp51 gene to fungicide insensitivity. This latter discovery is highly significant as it represents the first documented case of fungicide insensitivity in a rust pathogen in which an underlying genetic mechanism has been identified. Together, these findings illuminate how genome structure, recombination, and structural variation shape the evolution and adaptation of P. hordei, providing a guidance for future surveillance and management of this pathogen.

genomics↗

bioRxiv2025

Optimisation and validation of a rapid RP-HPLC method for budesonide quantification from polymeric nanoparticles with application to stability and drug release studies

Robust and sustainable analytical tools are essential for the design, manufacture, and evaluation of advanced drug delivery systems. This study reports the first validated reversed-phase high-performance liquid chromatography (RP-HPLC) method for quantifying budesonide encapsulated within poly(lactic-co-glycolic acid) (PLGA)-based, layer-by-layer (LbL) coated nanoparticles intended for colonic delivery in inflammatory bowel disease (IBD). Using response surface methodology with a central composite design, optimal conditions were identified as acetonitrile:water (80:20, v/v) under isocratic mode, achieving complete separation within 5 min at a flow rate of 0.34 mL/min and detection at 244 nm. The method is buffer-free, rapid, and solvent-efficient, resulting in a favorable greenness profile, further confirmed by GAPI and NEMI assessments. Validation in line with ICH Q2(R2) demonstrated excellent linearity (R{superscript 2} > 0.999), precision (%RSD < 2 %), accuracy, specificity, and sensitivity (LOD: 0.04 {micro}g/mL; LOQ: 1.2 {micro}g/mL). Forced degradation studies under acidic, alkaline, oxidative, thermal, photolytic, and photostatic conditions showed that the LbL coating markedly enhanced drug stability, particularly against thermal and photostatic stress, while >75% degradation occurred in alkaline and oxidative environments. In vitro release profiling under simulated gastrointestinal conditions demonstrated sustained, pH-responsive release (20.6% over 48 h), consistent with colonic targeting. This validated, green, and stability-indicating method integrates controlled release assessment with comprehensive performance evaluation, providing a versatile platform for quality control of nanoparticulate drug delivery systems and supporting their progression from formulation development to clinical translation

pharmacology and toxicology↗

bioRxiv2025

Metalog: curated and harmonised contextual data for global metagenomics samples

Metagenomic sequencing enables the in-depth study of microbes and their functions in humans, animals and the environment. While sequencing data is deposited in public databases, the associated contextual data is often not complete and needs to be retrieved from primary publications. This lack of access to sample-level metadata like clinical data or in situ observations impedes cross-study comparisons and meta-analyses. We therefore created the Metalog database, a repository of manually curated metadata for metagenomics samples across the globe. It contains 73,082 samples from humans (including 58,506 of the gut microbiome), 10,703 animal samples, 5,146 ocean water samples, and 21,802 samples from other environmental habitats such as soil, sediment, or fresh water. Samples have been consistently annotated for a set of habitat-specific core features, such as demographics, disease status and medication for humans, host species and captivity status for animals, and filter sizes and salinity for marine samples. Additionally, all original metadata is provided in tabular form, simplifying focused studies e.g. into nutrient concentrations. Pre-computed taxonomic profiles facilitate rapid data exploration, while links to the SPIRE database enable genome-based analyses. The database is freely available for browsing and download at https://metalog.embl.de/.

microbiology↗

bioRxiv2025

Maternal high-fat/high-sugar diet has short-term dental effects and long-term sex-specific skeletal effects on adult offspring mice

BackgroundMaternal nutrition is increasingly recognized as a modulator of offspring skeletal development. While genetics has long been considered the primary determinant of craniofacial morphology, emerging evidence suggests that prenatal and early postnatal dietary exposures also influence facial morphology. However, how maternal diet differentially affects male and female craniofacial structures remains unclear. This study aimed to examine the effects of a maternal high-fat, high-sugar (HFHS) diet on craniofacial and dental morphology in first-(F1) and second-(F2) generation adult mice. Materials and MethodsFemale mice were fed a HFHS diet for six weeks before mating and throughout pregnancy and lactation. F1 offspring were weaned to a standard chow diet, and a subset of female F1 offspring were bred to produce F2 offspring, also maintained on chow. Craniofacial skeletal and dental structures of adult F1 and F2 mice at 1-year of age were assessed using micro-computed tomography for linear and geometric morphometrics. ResultsHFHS diet exposure significantly reduced midfacial and mandibular length in F1 females, and these effects persisted in F2 females. Mandibular shape differences were also observed in both generations of females. In males, skull size remained unchanged, though subtle mandibular shape changes were noted in F1 only. Tooth size was reduced in both sexes of F1 offspring but not in F2. ConclusionMaternal HFHS diet induces sex- and jaw-specific alterations in craniofacial morphology, with skeletal changes persisting in females across generations, while dental effects did not persist beyond one generation. These findings highlight the potential for maternal dietary habits to exert lasting, intergenerational influences on offspring facial form.

developmental biology↗

bioRxiv2025

The Role of N-acetylcysteine Amide in Acute Graft-versus-host Disease Mouse Model

Graft-versus-host disease (GvHD) remains one of the major complications following allogeneic hematopoietic cell transplantation (allo-HCT), resulting in reduced quality of life, morbidity, and mortality in transplanted patients. Clinical strategies to prevent GvHD are frequently associated with off-target effects and dose-related toxicity. Given that oxidative stress is elevated in allo-HCT recipients and contributes to the pathogenesis of GvHD, the current study aims to characterize the role of N-acetylcysteine amide (NACA), a novel antioxidant, as the prophylactic treatment for acute GvHD. Using a murine GvHD model, we found that oral administration of NACA significantly reduced GvHD severity, prolonged survival, and improved the clinical manifestations and integrity of target organs compared to saline or N-acetylcysteine (NAC) treatment. NACA modulated splenic T cells differentiation with an increase in the regulatory (Treg) subset and a decrease in the cytotoxic (CD8+) subset. Moreover, inflammatory mediators, such as ROS and pro-inflammatory cytokines were downregulated by NACA treatment. In addition, NACA hindered donor T-cell proliferation in the recipients, and restrained Th1 and Th17, but not Th2 polarization. Importantly, NACA did not influence full donor engraftment in bone marrow and spleen. Taken together, our findings provide a new candidate for GvHD prophylactic treatment by targeting oxidative stress that can be easily translated to clinical use. Key PointsO_LINACA provides superior prophylactic effect against aGvHD compared to NAC in an allogeneic transplantation mouse model. C_LIO_LINACA treatment neither showed systemic toxicity nor altered the engraftment of the donor cells. C_LI

pharmacology and toxicology↗

bioRxiv2025

A novel antioxidant N-acetylcysteine Amide Alleviates Cyclophosphamide-induced Endothelial Damage

The alkylating agent cyclophosphamide (Cy) is one of the important corner stones in cancer treatment. Cy is used also as a part of conditioning regimens prior to hematopoietic cell transplantation and as a prophylactic treatment post transplantation in graft-versus-host disease. Existing evidence showed that high doses of Cy are associated with a number of side effects, including damage on arterial endothelium, which might contribute to late cardiovascular disorders. Oxidative stress has been characterized in such pathogenesis and is an exploitable target for treatment. Herein, the study aimed to investigate the protective role of the novel antioxidant N-acetylcysteine amide (NACA) in Cy-induced endothelial injury and explore the underlying mechanism. Our in vivo results showed that NACA partially reduced the endothelial injury and recovered the integrity of arterial endothelium in the mice treated with Cy. In addition, we found that NACA decreased the cytotoxicity of Cy on endothelial cells through alleviating caspase-dependent apoptosis, DNA damage and oxidative stress. Meanwhile, NACA pre-treatment rebalanced endothelial nitric oxide synthase (eNOS) and arginase I and preserved the angiogenic capability of endothelial cells which was compromised by Cy through blockage of Notch signaling pathway. Interestingly, in comparison to N-acetylcysteine (NAC), its amide derivative NACA showed superior ability to alleviate Cy-induced endothelial damage. In conclusion, the current study proved the robust endothelial protective potential of NACA, facilitating clinical use of the novel antioxidant.

pharmacology and toxicology↗

bioRxiv2024

Construction of a drug sensitised Candida auris strain

Candida auris is an emerging pathogenic fungus causing outbreaks of invasive disease in healthcare facilities worldwide with the majority of clinical isolates demonstrating intrinsic resistance to multiple drug classes. Therefore, there is a pressing need for new antifungals, but screening in wild-type C. auris strains may miss scaffolds which would be good subjects for med-chem projects as antifungals. Work presented here indicates that deletion of CDR1 and MDR1 in C. auris, respectively encoding an ATP-binding Cassette and a Major Facilitator Superfamily efflux pump, generates a strain that is hypersensitised to drugs and can act as a platform for antifungal drug screening. The cdr1{Delta}mdr1{Delta} mutant was 64- and 8- times more sensitive to fluconazole and voriconazole, respectively, compared with the wild-type strain. In a pilot study using a 2,000 compound library, use of the cdr1{Delta}mdr1{Delta} mutant identified over twice as many compounds compared to wild-type. When assessing the capacity to form biofilms, there was a modest difference in biomass between the wild-type and cdr1{Delta}mdr1{Delta} mutant, but there was little to no statistically significant difference observed when using a colorimetric dye that measured metabolic activity. Little to no statistically significant difference was observed between wild-type and the efflux pump mutant in virulence, as judged by the larval Galleria mellonella infectivity assay. Accordingly, the cdr1{Delta}mdr1{Delta} mutant will be useful when screening for drugs that limit formation of biofilms or limit fungal virulence, as these two properties are unaffected when drug efflux is compromised.

microbiology↗

bioRxiv2024

EEG connectome-based predictive modeling of nonverbal intelligence level in healthy subjects

Intelligence is increasingly recognized as a critical factor in successful behavioral and emotional regulation. Neuroimaging techniques coupled with machine learning algorithms have proven to be valuable tools for uncovering the neural foundations of individual cognitive abilities. Nevertheless, current electroencephalograph (EEG) studies primarily focus on classification tasks to predict the intelligence category of subjects (e.g., high, medium, or low intelligence), rather than providing quantitative intelligence level forecasts. Furthermore, the outcomes obtained are significantly impacted by the specific data processing pipeline chosen, which could potentially compromise result generalizability. In this study, we implemented a connectome-based predictive modeling approach on high-density resting state EEG data from healthy participants to predict their nonverbal intelligence level. This method was applied to three independently collected datasets (N = 255) with different functional connectivity methods, parcellation atlases, threshold p-values and curve fitting orders used to ensure the reliability of the findings. We found that the prediction accuracy expressed in terms of R{superscript 2} varied significantly depending on the processing pipeline configuration, ranging from negative R2 values up to 0.27. The most consistent results across datasets were found in the alpha frequency band. Furthermore, we employed a computational lesioning approach to identify the valuable edges that made the most significant contribution to predicting intelligence. This analysis highlighted the crucial role of frontal and parietal regions in complex cognitive computations. Overall, these findings support and expand upon previous research, underscoring the close relationship between alpha rhythm characteristics and cognitive functions and emphasizing the critical consideration of method selection in result evaluation.

neuroscience↗

bioRxiv2024

Equi-Under-Dispersed Possion Distribution (EUPoisson)

In this study, we propose an Equi-Under-dispersed Poisson distribution derived from a finite mixture of weighted Poisson distributions. This probability distribution is a simple, parsimonious, and flexible option suitable for modeling under-dispersed count data. It aims to overcome some of the weaknesses of existing methods in modeling Equi-Under-dispersed count data. Explicit expressions for the moment-generating function, mean, variance, and index of dispersion are derived. Real count data are used to compare its performance with that of the zero-inflated Poisson distribution and the finite mixture of Poisson distributions. Maximum likelihood estimation is implemented to estimate the parameters of the distribution, and goodness-of-fit statistical techniques are used to compare the fit of the competing distributions.

bioinformatics↗

bioRxiv2024

Learning-Induced Effects of Practice Schedule Variability on Stimuli Discrimination Efficiency: High-Density EEG Multi-scale Analyses of Contextual Interference Effect

Contextual interference (CI) enhances motor learning by practicing skill variations in a random rather than blocked order. It has been demonstrated that performing aiming distances in a random order increased electrophysiological (EEG) markers of perceptual, attentional, and working memory processes. However, only the effect of CI on these markers before training was assessed, without evaluating whether they would decrease with learning in participants trained under the random compared to the blocked condition, indicating enhanced neural efficiency. To address this, 35 participants practiced an aiming task involving three distances over nine sessions across three weeks. They were divided into two groups: one trained with distances in a random order (HCI group) and the other in a blocked order (LCI group). Electrophysiological activity was recorded for all participants in the random condition before and after the training program using a high-density EEG multiscale approach, including topographical, source estimation, and source connectivity analyses. EEG analyses revealed post-training neural dynamic differences between groups. The HCI group showed reduced and shorter P3a-like activity compared to the LCI group, while the LCI group exhibited greater occipito-temporal-frontal gamma-band synchronization. These findings suggest that random practice enhances the efficiency of perceptual and attentional processes, particularly of stimuli discrimination, compared to blocked practice.

neuroscience↗