Biology subjects

Khamis, A.

Publications and source records attributed to Khamis, A..

6 recordsLinked to original sources

bioRxiv2026

Socotra Cormorants in the Arabian Gulf represent a large, but isolated population with low genetic diversity

The Socotra Cormorant (Phalacrocorax nigrogularis) is a threatened seabird endemic to the coastal areas of the Arabian Gulf and the Arabian Sea, two regions separated by the Strait of Hormuz. Conserving threatened species requires clear delineation of population boundaries and the evaluation of genetic diversity. However, information on population structure and genetic variation, necessary for such an assessment, is lacking for the Socotra Cormorants. In this study, we assessed population structure and genetic diversity of Socotra Cormorants using two contrasting genetic markers: (1) maternally inherited mtDNA cytochrome oxidase 1 (COI) and (2) a nuclear non-coding region, {beta}-fibrinogen intron 7 (FIB7). A total of 279 individuals were sampled from four colonies in the Arabian Gulf and one colony on Hasikiyah Island in the Arabian Sea. Findings based on COI-variation suggest that the Arabian Gulf colonies represent one large population with extensive gene flow between Gulf colonies--except for the most distant pair of colonies--but isolated from Hasikiyah in the Arabian Sea. COI-variation indicated significant differentiation between the colonies inside the Gulf and the Hasikiyah colony. This is consistent with the reported distribution patterns, and may reflect phylogeographic processes of the region. The Gulf population showed substantially lower COI-diversity, with significantly lower nucleotide and haplotype diversity compared to Hasikiyah. In contrast, FIB7 results indicated extensive connectivity among colonies, with no detectable population structure or significant differences between the Gulf population and Hasikiyah. This study presents the first characterization of population structure and genetic diversity of Socotra Cormorants. The low genetic diversity coupled with relative isolation of the Gulf Socotra Cormorants raises conservation concerns regarding their long-term viability by potentially reducing fitness and eroding their evolutionary capacity to adapt to environmental change. LAY SUMMARYO_LIThe Socotra Cormorant is a threatened seabird found in the Arabian Gulf and Arabian Sea, but little was previously known about its population structure and genetic diversity. C_LIO_LIWe analyzed 279 birds from five nesting colonies (4 in the Gulf and 1 in the Arabian Sea), using two genetic markers to assess population connectivity and variation. C_LIO_LIWe found that the Socotra cormorants inside the Gulf appear to form a large, genetically isolated population with relatively low genetic diversity. C_LIO_LIThis is the first study that evaluates population structure and genetic diversity of this endangered seabird. C_LIO_LIThis is important information for the conservation of the Gulf Socotra cormorants because low genetic diversity, coupled with relative isolation, is associated with reduced fitness, and suggests that they may have a lower chance to adapt to environmental changes. C_LI

ecology↗

bioRxiv2026

Counting strands in outer membrane beta-barrels

Beta-barrel structures are critical components of bacterial outer membranes, where they facilitate transport, cell signaling, antibiotic resistance, and structural integrity. A key feature of beta-barrels is their strand count, which influences pore diameter, binding site locations, and functional properties. However, because of breaks in strands and the presence of strands in periplasmic domains and plug domains, manual counting is inefficient and current algorithms do not accurately determine barrel strand count. To address this, we refined our previous beta-barrel structural assessment tool, PolarBearal, to improve strand number identification in large-scale datasets. To enhance the accuracy of barrel strand number labeling, our updated algorithm integrates three structural criteria, namely inter-residue vector angles, hydrogen-bonding distances, and strand connectivity. Using this algorithm, we labeled strand numbers for 571,760 predicted outer membrane beta-barrel structures obtained from the AlphaFold2 database. Our algorithm has 97% accuracy in strand number assignments, and the resulting dataset facilitates assessment of the homogeneity of strand counts for different types of outer membrane proteins. The strand labeling also provides insights on beta-barrel strand distribution and evolutionary patterns, supporting further research in protein structure prediction and design. SignificanceThis work contributes an accurate, automated method for counting beta-barrel strands in bacterial outer membrane proteins. Methodological ImpactThe algorithm achieves 97% accuracy in strand counting, solving a technical problem that has hindered large-scale structural analysis. Previous manual methods were inefficient, and existing algorithms failed to handle the structural complexities of real beta-barrel proteins, including strand breaks and additional domains. Scale of ContributionBy annotating over 571,000 predicted structures from the AlphaFold2 database, this work represents the largest systematic characterization of beta-barrel strand distributions to date. This labeled dataset provides a resource for the structural biology community. Broader ApplicationsThis tool enables researchers understand structure-function relationships in outer membrane proteins, supporting advances in protein design, drug development targeting bacterial membranes.

bioinformatics↗

bioRxiv2025

Deep learning models reading clinical data and liver omics strongly distinguish NASH from steatosis and suggest new genes involved in liver disease severity

Background & AimsMetabolic dysfunction-associated steatotic liver disease (MASLD, previously NAFLD) is a frequent co-morbidity of obesity and diabetes, with prevalence increasing worldwide in all age groups and both sexes. Only early stages of the disease are fully reversible. Recognising liver disease stages and elucidating the molecular underpinning of their progression are thus medically important. We developed a deep learning model to recognise simple steatosis from steatohepatitis combining liver transcriptomics, epigenetics, and clinical data. MethodsWe used clinical data, liver gene expression and liver DNA methylation gathered from 300 patients with obesity of the ABOS cohort (80 without NAFLD, 137 with simple steatosis, 83 with steatohepatitis). We selected non-redundant clinical variables, gene expressions and CpGs methylation levels most associated with severity using unsupervised approaches. We designed a multi-module, multi-layer perceptron to predict patients liver status. We trained five model instances on independent training/test sets and combined the predictions. ResultsWe used a score based on gene expression/DNA methylation and relevant principal component analysis (PCA) loadings to select 200 genes and 260 CpG methylations. Models trained on the three modalities reached an AUC of 0.945 overall on a validation set with accuracies above 81% for simple steatosis and 88% for NASH, outperforming any other machine learning model so far. We retrieved patient clusters previously found using clinical variables in the latent space of our clinical data module, but not in the gene expression and DNA methylation modules. While all three modules are needed to reach the best prediction accuracy in all classes, the gene expression module had the most impact on the decision. Independent models weighted gene expression inputs similarly, shining light on their importance. The most impactful genes were linked to immune responses and extracellular matrix. However, many of those genes were previously unassociated with steatotic liver disease onset or progression. ConclusionsA multi-omics deep-learning model can recognise steatohepatitis from simple liver steatosis with an AUC of 0.945 and identify new genes potentially involved in NAFLD progression. Gene expressions profiles predicting disease severity are largely different from those specific of clinical variable clusters. Impact and implicationsThis study suggests that clinical variables are not sufficient to recognise the severity of steatotic liver disease with high accuracy, but model efficiency increases when used together with liver epigenetics and transcriptomics.

molecular biology↗

bioRxiv2022

Epigenetic changes associated with hyperglycaemia exposure in the longitudinal D.E.S.I.R. cohort.

AimUnderstanding DNA methylation dynamics associated with progressive hyperglycaemia exposure could provide early diagnostic biomarkers and an avenue for delaying type 2 diabetes (T2D) disease. We aimed to identify DNA methylation changes during a 6-year period associated with early hyperglycaemia exposure using the longitudinal D.E.S.I.R. cohort. MethodsWe selected individuals with progressive hyperglycaemia exposure based on T2D diagnostic criteria: 27 with long-term exposure, 34 with short-term exposure and 34 normoglycaemic controls. DNA from blood at inclusion and at the 6-years visit was subjected to methylation analysis using 850K methylation-EPIC arrays. A linear mixed model was used to perform an epigenome-wide association study (EWAS) and identify methylated changes associated with hyperglycaemia exposure during 6-year time-period. ResultsWe did not identify differentially methylated sites that reached FDR-significance in our cohort. Based on EWAS, we focused our analysis on methylation sites that had a constant effect during the 6-years across the hyperglycaemia groups compared to controls and found the most statistically significant site was the reported cg19693031 probe (TXNIP). We also performed an EWAS with HbA1c, using the inclusion and the 6-years methylation data and did not identify any FDR-significant CpGs. ConclusionsOur study reveals that DNA methylation changes are not robustly associated with hyperglycaemia exposure or HbA1c during a short-term period, however, our top loci indicate potential interest and should be replicated in larger cohorts.

genomics↗

bioRxiv2020

Chromatin 3D interaction analysis of the STARD10 locus unveils FCHSD2 as a new regulator of insulin secretion

Genome-wide association studies have identified thousands of genetic variants associated with type 2 diabetes (T2D) risk. Using chromatin conformation capture we show that an enhancer cluster in the STARD10 T2D locus forms a defined 3D chromatin domain. A 4.1 Kb region within this region, carrying five disease-associated variants, physically interacts with CTCF-binding regions and with an enhancer possessing strong transcriptional activity. Analysis of human islet 3D chromatin interaction maps identifies FCHSD2 gene as an additional target of the enhancer cluster. CRISPR-Cas9-mediated deletion of the variant region, or of an associated enhancer, in human pancreatic beta cells impaired glucose-stimulated insulin secretion. Expression of both STARD10 and FCHSD2, but not ARAP1, was reduced in cells harboring CRISPR deletions, and expression of STARD10 and FCHSD2 was associated with the possession of variant alleles in human islets. Finally, CRISPR-Cas9-mediated loss of STARD10 or FCHSD2 impaired regulated insulin secretion. Thus, multiple genes at the STARD10 locus influence {beta} cell function.

cell biology↗

bioRxiv2019

Histone deacetylase 9 promoter hypomethylation associated with adipocyte dysfunction is a statin-related metabolic effect

BackgroundAdipogenesis, the process whereby preadipocytes differentiate into mature adipocytes, is crucial for maintaining metabolic homeostasis. Cholesterol lowering statins increase type 2 diabetes (T2D) risk possibly by affecting adipogenesis and insulin resistance but the (epi)genetic mechanisms involved are unknown. Here, we characterised the effects of statin treatment on adipocyte differentiation using in vitro human preadipocytes cell model to identify putative effective genes. ResultsStatin treatment during adipocyte differentiation caused a reduction in key genes involved in adipogenesis, such as ADIPOQ, GLUT4 and ABCG1. Using Illuminas Infinium 850K Methylation EPIC array, we found a significant hypomethylation of cg14566882, located in the promoter of the histone deacetylase 9 (HDAC9) gene, in response to two types of statins (atorvastatin and mevastatin), which correlates with an increased HDAC9 mRNA expression. HDAC9 is a transcriptional repressor of the cholesterol efflux ABCG1 gene expression, which is epigenetically modified in obesity and prediabetic states. Thus, we assessed the putative impact of ABCG1 knockdown in mimicking the effect of statin in adipogenesis. ABCG1 KD reduced the expression of key genes involved in adipocyte differentiation and decreased insulin signalling and glucose uptake. In human blood cells from two cohorts, ABCG1 expression was impaired in response to statins, confirming that ABCG1 is in vivo targeted by these drugs. ConclusionsWe identified an epigenetic link between adipogenesis and adipose tissue insulin resistance in the context of T2D risk associated with statin use, which has important implications as HDAC9 and ABCG1 are considered potential therapeutic targets for obesity and metabolic diseases.

cell biology↗