Biology subjects

Ibrahim, M. M.

Publications and source records attributed to Ibrahim, M. M..

6 recordsLinked to original sources

bioRxiv2026

MONICA: A Web Application for Automated Whole Optic Nerve Contour Extraction and Morphometric Analysis Validated Across Taxonomic Orders and Image Quality Levels

Quantitative assessment of optic nerve health requires metrics beyond axon counts alone. Axon density and glial coverage fraction correlate with clinical measures of visual function, yet no existing automated tool extracts optic nerve cross-sectional boundaries to enable their calculation. We developed MONICA (Morphometrics from Optic Nerve Imaging Contour Analysis), a web application that integrates AxonDeepSeg deep learning segmentation with a novel morphology-based contour extraction algorithm to automatically derive whole nerve boundaries alongside axon and myelin masks. The contour extraction algorithm was validated against manual ground truth annotations using 15 optic nerve cross-sections spanning two taxonomic orders (mouse, rabbit), two mouse strains (BXD29, BXD51), and varying preparation quality levels (modern and archival samples). Automated contour extraction demonstrated excellent agreement with manual annotations, achieving an overall Dice similarity coefficient (a measure of segmentation overlap) of 0.987 {+/-} 0.009. Balanced precision (0.985) and recall (0.989) values indicated that the algorithm neither systematically over-segments nor under-segments nerve boundaries. MONICA requires no local software installation and runs entirely in-browser, providing batch processing for high-throughput phenotyping alongside a full suite of per-axon morphometrics. MONICA provides researchers with an accessible tool for complete nerve cross-section morphometry.

neuroscience↗

bioRxiv2026

Evidence of Anopheles stephensi involvement in the transmission of Plasmodium vivax in Djibouti, 2024

PurposeAnopheles stephensi is a malaria mosquito vector that has been raising international concern due to its invasive nature in Africa, including the nation of Djibouti. Since its initial detection in Djibouti in 2012, malaria morbidity and mortality have increased exponentially in the county. While there is an observed association increase in human malaria cases since the arrival of An. stephensi, high-quality evidence of An. stephensi carrying infective sporozoites is essential to determine the role of the invasive vector in malaria dynamics in Djibouti. This study seeks to confirm the link between An. stephensi and malaria transmission in Djibouti and examine genetic relatedness between Djiboutian An. stephensi populations and populations across the Horn of Africa. Such information regarding the An. stephensi populations and the Plasmodium species they transmit is necessary to devise appropriate control strategies and limit malaria transmission within and beyond the country. MethodsOne hundred and ninety-six adult An. stephensi mosquitoes from Djibouti were collected, molecularly confirmed, analyzed for a portion of the cytochrome c oxidase subunit 1 (COI), and tested for infective sporozoites using a highly sensitive and specific multiplex circumsporozoite enzyme linked immunosorbent assay (csELISA) bead assay. The COI sequences of one hundred and fourteen samples were further used to characterize the population genetic structure of the sampled An. stephensi and its genetic relatedness to other An. stephensi populations across the Horn of Africa. ResultsAll 196 samples were morphologically and molecularly confirmed to be An. stephensi. Plasmodium vivax210 sporozoites were detected with a positivity rate of 1.02%. An analysis of the COI region showed that the infected An. stephensi have the most prevalent COI haplotypes of invasive An. stephensi circulating in the Horn of Africa. ConclusionsThe findings from this study confirm the involvement of An. stephensi in P. vivax transmission in Djibouti and describe the genetic relatedness of Djiboutian An. stephensi populations to other populations across the Horn of Africa. This highlights the threat of An. stephensi invasion and supports a rapid and comprehensive response to mitigate the harm that An. stephensi populations cause, particularly through surveillance and control of adult populations. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=119 SRC="FIGDIR/small/707780v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@19f762corg.highwire.dtl.DTLVardef@7624forg.highwire.dtl.DTLVardef@c7492borg.highwire.dtl.DTLVardef@194c11f_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2019

genesorteR: Feature Ranking in Clustered Single Cell Data

Marker genes identified in single cell experiments are expected to be highly specific to a certain cell type and highly expressed in that cell type. Detecting a gene by differential expression analysis does not necessarily satisfy those two conditions and is typically computationally expensive for large cell numbers.\n\nHere we present genesorteR, an R package that ranks features in single cell data in a manner consistent with the expected definition of marker genes in experimental biology research. We benchmark genesorteR using various data sets and show that it is distinctly more accurate in large single cell data sets compared to other methods. genesorteR is orders of magnitude faster than current implementations of differential expression analysis methods, can operate on data containing millions of cells and is applicable to both single cell RNA-Seq and single cell ATAC-Seq data.\n\ngenesorteR is available at https://github.com/mahmoudibrahim/genesorteR.

bioinformatics↗

bioRxiv2018

Benchmark and integration of resources for the estimation of human transcription factor activities

Prediction of transcription factor (TF) activities from the gene expression of their targets (i.e. TF regulon) is becoming a widely-used approach to characterize the functional status of transcriptional regulatory circuits. Several strategies and datasets have been proposed to link the target genes likely regulated by a TF, each one providing a different level of evidence. The most established ones are: (i) manually curated repositories, (ii) interactions derived from ChIP-seq binding data, (iii) in silico prediction of TF binding on gene promoters, and (iv) reverse-engineered regulons from large gene expression datasets. However, it is not known how these different sources of regulons affect the TF activity estimations, and thereby downstream analysis and interpretation. Here we compared the accuracy and biases of these strategies to define human TF regulons by means of their ability to predict changes in TF activities in three reference benchmark datasets. We assembled a collection of TF-target interactions among 1,541 TFs, and evaluated how the different molecular and regulatory properties of the TFs, such as the DNA-binding domain, specificities or mode of interaction with the chromatin, affect the predictions of TF activity changes. We assessed their coverage and found little overlap on the regulons derived from each strategy and better performance by literature-curated information followed by ChIP-seq data. We provide an integrated resource of all TF-target interactions derived through these strategies with a confidence score, as a resource for enhanced prediction of TF activities.

bioinformatics↗

bioRxiv2018

A functional landscape of chronic kidney disease entities from public transcriptomic data

To develop efficient therapies and identify novel early biomarkers for chronic kidney disease an understanding of the molecular mechanisms orchestrating it is essential. We here set out to understand how differences in CKD origin are reflected in gene expression. To this end, we integrated publicly available human glomerular microarray gene expression data for nine kidney disease entities that account for a majority of CKD worldwide. We included data from five distinct studies and compared glomerular gene expression profiles to that of non-tumor parts of kidney cancer nephrectomy tissues. A major challenge was the integration of the data from different sources, platforms and conditions, that we mitigated with a bespoke stringent procedure. This allowed us to perform a global transcriptome-based delineation of different kidney disease entities, obtaining a landscape of their similarities and differences based on the genes that acquire a consistent differential expression between each kidney disease entity and nephrectomy tissue. Furthermore, we derived functional insights by inferring activity of signaling pathways and transcription factors from the collected gene expression data, and identified potential drug candidates based on expression signature matching. We validated representative findings by immunostaining in human kidney biopsies indicating e.g. that the transcription factor FOXM1 is significantly and specifically expressed in parietal epithelial cells in RPGN whereas not expressed in control kidney tissue. These results provide a foundation to comprehend the specific molecular mechanisms underlying different kidney disease entities, that can pave the way to identify biomarkers and potential therapeutic targets. To facilitate this, we provide our results as a free interactive web application: https://saezlab.shinyapps.io/ckd_landscape/. Translational StatementChronic kidney disease is a combination of entities with different etiologies. We integrate and analyse transcriptomics analysis of glomerular from different entities to dissect their different pathophysiology, what might help to identify novel entity-specific therapeutic targets.

pathology↗

bioRxiv2017

Determinants of Transcription Initiation Directionality in Metazoans

Divergent transcription from promoters and enhancers is pervasive in many species, but it remains unclear if it is a general and passive feature of all eukaryotic cis regulatory elements. To address this, we define promoters and enhancers in C. elegans, D. melanogaster and H. sapiens using ATAC-Seq and investigate the determinants of their transcription initiation directionalities by analyzing genome-wide nascent, cap-selected, polymerase run-on assays. All three species initiate divergent transcription from separate core promoter sequences. Sequence asymmetry downstream of forward and reverse initiation sites, known to be important for termination and stability in H. sapiens, is unique in each species. Chromatin states of divergent promoters are not entirely conserved, but in all three species, the levels of histone modifications on the +1 nucleosome are independent from those on the -1 nucleosome, arguing for independent initiation events. This is supported by an integrative model of H3K4me3 levels and core promoter sequence that is highly predictive of promoter directionality and of two types of promoters: those with balanced initiation directionality and those with skewed directionality. Lastly, D. melanogaster enhancers display variation in chromatin architecture depending on enhancer location, and D. melanogaster promoter regions with dual enhancer/promoter potential are enriched for divergent transcription. Our results point to a high degree of variation in regulatory element transcription initiation directionality within and between metazoans, and to non-passive regulatory mechanisms of transcription initiation directionality in those species.

molecular biology↗