Biology subjects

Khademi, A.

Publications and source records attributed to Khademi, A..

6 recordsLinked to original sources

bioRxiv2026

Switch-TRIBE: Lineage-specific high-resolution profiling of endogenous RNA-protein interactions

RNA-binding proteins (RBPs) play vital roles in regulation of cell fate and function. Here, we introduce Switch-TRIBE, which conditionally tags endogenous RBPs with the adenosine deaminase catalytic domain to detect physiological RBP-RNA interactions. We apply Switch-TRIBE to the TRIM-NHL RBPs, BRAT and MEI-P26, essential regulators of Drosophila neurogenesis. Single-cell Switch-TRIBE in neural stem cell lineages defines cell-type specific targets and identifies novel neurogenesis regulators. Beyond target identification, Switch-TRIBE highlights the complex interplay between co-regulating RBPs, uncovering distinct binding site topographies. Our analysis uncovers a potential strategy used by BRAT and MEI-P26 to regulate lineage growth through direct binding of transcripts that encode the core metabolic and translation machineries. Switch-TRIBE provides a robust tool for characterizing RBP-RNA interactions in complex systems without compromising biological fidelity.

genomics↗

bioRxiv2026

Multiomic screening platform uncovers the impact of histone mutations on chromatin and cell fate

Somatic missense mutations in histone genes, often referred to as oncohistones, have been identified in diverse types of human cancers. The functional and mechanistic impact of most oncohistones remains unknown. To address this gap, we developed CHANCLA, a modular platform for high-throughput functional screening of oncohistones using multiomic phenotypic readouts. We used CHANCLA to systematically measure the impact of 303 human oncohistones on cellular proliferation, differentiation, histone-specific post-translational modifications, and chromatin accessibility. Integrative multiomic analyses revealed discrete oncohistone molecular classes that promote proliferation, block lineage-specific differentiation, and physically remodel the chromatin landscape by altering specific histone modifications and reducing nucleosome stability. Structural mapping and computational modeling studies uncovered that functionally convergent mutations are clustered at key nucleosome interfaces, particularly H2B-H4, and that chromatin accessibility-promoting mutations are linked to mono-nucleosome destabilization. Leveraging this multiomic resource, we discovered that the H3.3-Q5H mutant histone is a bona fide human oncohistone that accelerates lung adenocarcinoma growth in vivo. Mechanistically, we found that H3.3-Q5H expression leads to suppression of promoter-associated H3K4me3 and expansion of repressive H3K27me3 domains, resulting in increased KRAS signaling and gene expression programs associated with epithelial-to-mesenchymal transition. Together, this work provides a multiomic functional atlas of cancer-associated histone mutations, identifies structural and mechanistic principles governing chromatin reprogramming by oncohistones, and establishes CHANCLA as a modular platform for systematic discovery of mechanisms and vulnerabilities associated with these genetic lesions.

cancer biology↗

bioRxiv2026

White Matter Lesion Asymmetry and Carotid Intraplaque Hemorrhage: A Novel Imaging Phenotype for Stroke Risk in Older Men

BackgroundWhile identifying high-risk carotid disease remains a significant clinical challenge, the specific role of carotid intraplaque hemorrhage (IPH) is poorly understood. Although IPH has been linked to white matter lesion (WML) burden, current assessments overlook the directional impact of plaque instability on the brain. This study sought to determine whether IPH is an independent driver of asymmetric WML pathology and evaluate if this phenotype can identify a high-risk demographic for TIA and stroke. MethodsThis multi-center retrospective study analyzed 264 participants (mean age 71.8 years) from the Canadian Atherosclerosis Imaging Network (2010-2015). Participants underwent 3T MRI to assess carotid IPH and WMLs. We quantified WMLs using a deep-learning pipeline to extract three biomarkers: volume (WML-ICV), intensity (WML-Intensity), and intensity ratio (WML-IR). The Asymmetry Index Measure (AIM) defined the inter-hemispheric log-ratio, while the association between IPH and AIM was examined using multivariable linear regression adjusted for age, sex, stenosis, and scanner manufacturer. A secondary composite outcome of TIA/stroke was analyzed via logistic regression to evaluate the interaction between IPH, age, and sex. ResultsWhile whole-brain WML burden did not significantly differ by IPH status (p > 0.60), IPH status was a robust independent predictor of hemispheric asymmetry (WML-ICV: p = 0.01; WML-Intensity, p = 0.01). Post-hoc analysis confirmed WML burden was significantly higher in IPH+ older males ([≥] 70 years) compared to younger cohorts (p < 0.04). This older male subgroup also demonstrated 4.57-fold higher adjusted odds of TIA/stroke (p = 0.02) compared to other demographic subgroups (all p > 0.87). ConclusionsCarotid IPH is independently associated with a rightward asymmetric WML phenotype not captured by global metrics. This imaging marker identifies a high-risk demographic of older males with a nearly five-fold increase in clinical events, suggesting that hemispheric-level analysis provides critical prognostic value for stroke risk stratification.

neuroscience↗

bioRxiv2020

piNET: An Automated Proliferation Index Calculator Framework for Ki67 Breast Cancer Images

In this work, a novel proliferation index (PI) calculator for Ki67 images called piNET is proposed. It is successfully tested on four datasets, from three scanners comprised of patches, tissue microarrays (TMAs) and wholeslide images (WSI), representing a diverse multicentre dataset for evaluating Ki67 quantification. Compared to state-of-the-art methods, piNET consistently performs the best over all datasets with an average PI difference of 5.603%, PI accuracy rate of 86% and correlation coefficient R = 0.927. The success of the system can be attributed to a number of innovations. Firstly, this tool is built based on deep learning, which can adapt to wide variability of medical images - and it was posed as a detection problem to mimic pathologists workflow which improves accuracy and efficiency. Secondly, the system is trained purely on tumour cells, which reduces false positives from non-tumour cells without needing the usual pre-requisite tumour segmentation step for Ki67 quantification. Thirdly, the concept of learning background regions through weak supervision is introduced, by providing the system with ideal and non-ideal (artifact) patches that further reduces false positives. Lastly, a novel hotspot analysis is proposed to allow automated methods to score patches from WSI that contain "significant" activity.

bioinformatics↗

bioRxiv2020

The RNA-binding protein, Rasputin/G3BP, enhances the stability and translation of its target mRNAs

G3BP RNA-binding proteins are important components of stress granules (SGs). Here we analyze the role of Drosophila G3BP, Rasputin (RIN), in unstressed cells, where RIN is not SG associated. Immunoprecipitation followed by microarray analysis identified over 550 mRNAs that copurify with RIN. The mRNAs found in SGs are long and translationally silent. In contrast, we find that RIN-bound mRNAs, which encode core components of the transcription, splicing and translation machinery, are short, stable and highly translated. We show that RIN is associated with polysomes and provide evidence for a direct role for RIN and its human homologs in stabilizing and upregulating the translation of their target mRNAs. We propose that when cells are stressed the resulting incorporation of RIN/G3BPs into SGs sequesters them away from their short target mRNAs. This would downregulate the expression of these transcripts, even though they are not incorporated into stress granules.

genomics↗

bioRxiv2018

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-the-art algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, ...), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.

neuroscience↗