Biology subjects

Omar, M.

Publications and source records attributed to Omar, M..

11 recordsLinked to original sources

bioRxiv2026

CCT2 expression marks a translation-activated state in sporadic ALS motor neurons and is associated with functional decline

Sporadic disease accounts for about 90% of amyotrophic lateral sclerosis (ALS) cases, and its biological heterogeneity is a major contributor to the failure of trials that enroll patients under a single diagnosis. Because sporadic ALS lacks a shared genetic anchor, identifying biological signals is critical for effective patient stratification. We analyzed induced pluripotent stem cell derived motor neurons from 619 AnswerALS participants using transcriptomics, proteomics, and whole-genome sequencing and identified CCT2 as the first single-gene marker of a molecular state within sporadic ALS, providing a measurable basis for trial enrollment according to biology. We first anchored the analysis in known ALS genotypes. C9orf72 and SOD1 motor neurons did not converge on a common signature but retained genotype-specific differences that bulk postmortem tissue compresses. Scoring 391 sporadic patients along three transcriptomic axes (bioenergetic stress, cell cycle, and inflammation) resolved three states. One state was marked by CCT2, a TRiC chaperonin subunit, which distinguished it from the quiescent state with an area under the curve of 0.892. CCT2 was also elevated at the protein level and associated with the rate of ALSFRS-R decline, although this association weakened after adjustment for culture composition. Upstream regulator analysis placed this state downstream of MYC and mTORC1 activation. No germline test across 360 whole genomes explained these states. Validation using Target ALS data showed that the relationship between CCT2 and the cell cycle program was also present in postmortem cortex enriched for layer 5b projection neurons.

bioinformatics↗

bioRxiv2024

Leveraging the Red List of Ecosystems for national action on coral reefs through the Kunming-Montreal Global Biodiversity Framework

Countries have committed to conserving and restoring ecosystems after signing the Kunming-Montreal Global Biodiversity Framework (GBF). The IUCN Red List of Ecosystems (RLE) will serve as a headline indicator to track progress of countries towards achieving this goal, and to guide action across the GBFs targets. Using Kenyan coral reefs, we demonstrate how nations implementing the GBF, can use standardised estimates of ecosystem degradation from RLE assessments to support site-specific management decisions. We undertook a reef-by-reef analysis to evaluate the relative severity of decline of four key ecosystem components over the past 50-years: hard corals, macroalgae, parrotfish and groupers. Using the two benthic indicators, we also calculated standardised estimates of state to identify reef sites which maintain a better condition through time relative to adjacent sites. Over the past 50 years, Kenyas coral reefs have degraded across all four ecosystem components. At more than half the monitored sites both parrotfish and grouper abundance declined by more than 50%, while coral cover and macroalgae-coral ratio declined by at least 30%. This resulted in a Vulnerable threat status for coral reefs in Kenya based on degradation (under criterion D of the RLE). The temporal trends in coral cover revealed four sites which maintained an above average condition over their monitoring history (15-25 years). The results can guide management actions to contribute to at least nine of the 23 GBF targets. For example, we identified several sites with relatively healthy benthic and fish communities as candidate areas for protection measures under Target 3. We also found that Marine Protected Areas and Locally Managed Marine Areas, which restrict fishing and control gears, had lower declines in groupers compared to unmanaged areas, providing further evidence for their expansion. The RLE has a key role to play in monitoring and meeting the goals and targets of the Global Biodiversity Framework, and our work demonstrates how using the wealth of data within these assessments can inform local-scale ecosystem management and amplify the GBFs impact.

ecology↗

bioRxiv2023

Distinct mesenchymal cell states mediate prostate cancer progression

Alterations in tumor stroma influence prostate cancer progression and metastatic potential. However, the molecular underpinnings of this stromal-epithelial crosstalk are largely unknown. Here, we compare mesenchymal cells from four genetically engineered mouse models (GEMMs) of prostate cancer representing different stages of the disease to their wild-type (WT) counterparts by single-cell RNA sequencing (scRNA-seq) and, ultimately, to human tumors with comparable genotypes. We identified 8 transcriptionally and functionally distinct stromal populations responsible for common and GEMM-specific transcriptional programs. We show that stromal responses are conserved in mouse models and human prostate cancers with the same genomic alterations. We noted striking similarities between the transcriptional profiles of the stroma of murine models of advanced disease and those of of human prostate cancer bone metastases. These profiles were then used to build a robust gene signature that can predict metastatic progression in prostate cancer patients with localized disease and is also associated with progression-free survival independent of Gleason score. Taken together, this offers new evidence that stromal microenvironment mediates prostate cancer progression, further identifying tissue-based biomarkers and potential therapeutic targets of aggressive and metastatic disease.

cancer biology↗

bioRxiv2022

Using Attention-based Deep Learning to Predict ERG:TMPRSS2 Fusion Status in Prostate Cancer from Whole Slide Images

Prostate cancer (PCa) is associated with several genetic alterations which play an important role in the disease heterogeneity and clinical outcome including gene fusion between TMPRSS2 and members of the ETS family of transcription factors specially ERG. The expanding wealth of pathology whole slide images (WSIs) and the increasing adoption of deep learning (DL) approaches offer a unique opportunity for pathologists to streamline the detection of ERG:TMPRSS2 fusion status. Here, we used two large cohorts of digitized H&E-stained slides from radical prostatectomy specimens to train and evaluate a DL system capable of detecting the ERG fusion status and also detecting tissue regions of high diagnostic and prognostic relevance. Slides from the PCa TCGA dataset were split into training (n=318), validation (n=59), and testing sets (n=59) with the training and validation sets being used for training the model and optimizing its hyperparameters, respectively while the testing set was used for evaluating the performance. Additionally, we used an internal testing cohort consisting of 314 WSIs for independent assessment of the models performance. The ERG prediction model achieved an Area Under the Receiver Operating Characteristic curve (AUC) of 0.72 and 0.73 in the TCGA testing set and the internal testing cohort, respectively. In addition to slide-level classification, we also identified highly attended patches for the cases predicted as either ERG-positive or negative which had distinct morphological features associated with ERG status. We subsequently characterized the cellular composition of these patches using HoVer-Net model trained on the PanNuke dataset to segment and classify the nuclei into five main categories. Notably, a high ratio of neoplastic cells in the highly-attended regions was significantly associated with shorter overall and progression-free survival while high ratios of immune, stromal and stromal to neoplastic cells were all associated with longer overall and metastases-free survival. Our work highlights the utility of deploying deep learning systems on digitized histopathology slides to predict key molecular alteration in cancer together with their associated morphological features which would streamline the diagnostic process.

pathology↗

bioRxiv2022

Type 1 interferon remodels normal and neoplastic hematopoiesis in human

Inflammation perturbs evolutionary dynamics of hematopoietic stem cell (HSC) clones in clonal hematopoiesis and myeloid neoplasms. We studied HSCs, progenitors and immune cells from patients with myeloproliferative neoplasm (MPN) at baseline and following interferon- (IFN) treatment, the only MPN therapy to deplete clonal stem cells. We focused on essential thrombocythemia, an informative model of early-phase neoplastic hematopoiesis. We integrated somatic genotyping, transcriptomes, immunophenotyping, and chromatin accessibility across single cells. IFN simultaneously activated HSCs into two polarized states, a lymphoid progenitor expansion associated with an anti-inflammatory state and an IFN-specific inflammatory granulocytic progenitor (IGP) state derived directly from HSCs. The augmented lymphoid differentiation balanced the typical MPN-induced myeloid bias, associated with normalized blood counts. Clonal fitness upon IFN exposure was due to resistance of clonal stem cells to differentiate into IGPs. These results support a paradigm wherein inflammation perturbs clonal dynamics by HSC induction into the precipitous IGP differentiation program. One-Sentence SummaryInflammation accelerates clonal evolution by driving stem cell differentiation into an alternate interferon--induced progenitor state.

cancer biology↗

bioRxiv2022

Multi-omics biomarkers aid prostate cancer prognostication

Effective biomarkers and diagnostic tools are urgently needed in clinical settings for improved management of prostate cancer patients, especially to reduce over-treatment of indolent tumors and for early identification of aggressive disease. Gene expression signatures are currently the "gold standard" to provide guide clinical decision, however their clinical utility and interpretability is questionable. Multi-modal molecular profiling provides an holistic approach to systematically unravel the biological complexity underlying cancer pathogenesis, hence biomarkers developed using such an integrated approach hold the potential to more accurately capture cancer-driving alterations than signatures based on a single omics modality. Currently, however, robust and reproducible multi-omics biomarkers are still lacking for prostate cancer. In this study, we analyzed transcriptomics and metabolomics profiles jointly in a prostate cancer cohort and identified two prognostic signatures with high statistical powers (signature 1: EGLN3, succinate, trans-4-hydroxyprolin; and signature 2: IL6, SLC22A2, histamine). Our approach leveraged a priori biological knowledge of the cellular metabolism and gene circuitry, enabling the identification of dysregulated network modules. Functional bioinformatics analyses suggest that these signatures can capture relevant molecular alterations in prostate cancer tissues, including dysregulations of cellular signaling, cell cycle progression, and immune system modulation, stratifying patients in distinct risk groups. Next, we trained two gene expression signatures as a proxy for the multi-omics ones, extending our investigation to publicly available data, further confirming their prognostic values in independent patient cohorts. In summary, the analysis of multi-modal molecular grounded in cellular network biology represents a promising approach for the development of robust prognostic biomarkers of detecting and discriminating high grade disease.

systems biology↗

bioRxiv2021

Building tools for machine learning and artificial intelligence in cancer research: best practices and a case study with the PathML toolkit for computational pathology

Imaging datasets in cancer research are growing exponentially in both quantity and information density. These massive datasets may enable derivation of insights for cancer research and clinical care, but only if researchers are equipped with the tools to leverage advanced computational analysis approaches such as machine learning and artificial intelligence. In this work, we highlight three themes to guide development of such computational tools: scalability, standardization, and ease of use. We then apply these principles to develop PathML, a general-purpose research toolkit for computational pathology. We describe the design of the PathML framework and demonstrate applications in diverse use-cases. PathML is publicly available at www.pathml.com.

cancer biology↗

bioRxiv2021

Biological Constraints Can Improve Prediction inPrecision Oncology

Many gene signatures have been developed by applying machine learning (ML) on omics profiles, however, their clinical utility is often hindered by limited interpretability and unstable performance in different datasets. Here, we show the importance of embedding prior biological knowledge in the decision rules yielded by ML approaches to build robust classifiers. We tested this by applying different ML algorithms on gene expression data to predict three difficult cancer phenotypes: bladder cancer progression to muscle invasive disease; response to neoadjuvant chemotherapy in triple-negative breast cancer, and prostate cancer metastatic progression. We developed two sets of classifiers: mechanistic, by restricting the training process to features capturing a specific biological mechanism; and agnostic, in which the training didnt use any a priori biological information. Mechanistic models had a similar or better performance to their agnostic counterparts in the testing data, with enhanced stability, robustness, and interpretability. Our findings support the use of biological constraints to develop robust and interpretable gene signatures with high translational potential. MotivationOmics-based gene signatures often suffer from overfitting and reduced performance when tested on independent data. This usually results from the discrepancy between the high number of features compared to the much smaller number of samples used in the training process, which results in the machine learning algorithm perfectly fitting the training data with a subsequent deterioration in performance in independent cohorts. We introduce a mechanistic framework to mitigate overfitting and improve interpretability by constraining the training process to simple rank-based decision rules recapitulating relevant, cancer-related, biological mechanisms. Our approach aims at reducing the number of training variables to a pre-defined set of biologically important features in the form of gene pairs. The classification mechanism depends entirely on the relative ordering of these pairs, making it robust to data preprocessing techniques, improving the overall interpretability of the resulting models with significant translational implications. Most importantly, these pairs are configured in such a way that the decision rules resulting from the genes relative order embed and recapitulate specific biological mechanism, inherently enhancing the classifiers interpretability.

bioinformatics↗

bioRxiv2021

Targeting an anchored phosphatase-deacetylase unit restores renal ciliary homeostasis.

Pathophysiological defects in water homeostasis can lead to renal failure. Autosomal dominant polycystic kidney disease (ADPKD) is a common genetic disorder associated with abnormal cytoskeletal dynamics in the kidney collecting ducts and perturbed calcium and cAMP signaling in the ciliary compartment. We show that collecting ducts in mice lacking the A-Kinase anchoring protein AKAP220 exhibit enhanced development of primary cilia. Mechanistic studies reveal that AKAP220-associated protein phosphatase 1 (PP1) mediates this phenotype by promoting changes in the stability of histone deacetylase 6 (HDAC6) with concomitant defects in actin dynamics. This proceeds through a previously unrecognized adaptor function for PP1 as all ciliogenesis and cytoskeletal phenotypes are recapitulated in mIMCD3 knock-in cells expressing a phosphatase-targeting defective AKAP220-{Delta}PP1 mutant. Pharmacological blocking of local HDAC6 activity alters cilia development and reduces cystogenesis in kidney-on-chip and organoid models of ADPKD. These findings identify the AKAP220-PPI-HDAC6 pathway as a key effector in primary cilia development.

cell biology↗

bioRxiv2019

Beyond early development: observing zebrafish over 6 weeks with hybrid optical and optoacoustic imaging

Zebrafish animal models have traditionally been used in developmental biology studies but have recently become promising models of cancer, tissue regeneration and metabolic disorders, as well as efficient platforms for functional genomics and phenotype-based drug discovery. Most studies of zebrafish have examined only the embryonic or larval stages of development, yet many questions in developmental biology and biomedicine require analysis of adults, when zebrafish are large and opaque. Conventional microscopy methods are highly sensitive to light scattering and therefore cannot be applied to zebrafish older than a few weeks. We describe a novel multi-modality system that can observe zebrafish from the larval stage to adulthood. Using a hybrid platform for concurrent selective plane illumination microscopy (SPIM) and optoacoustic mesoscopy we show continuous imaging of fish growth over 47 days of development at a similar object size-to-resolution ratio. Using multiple wavelength illumination over the visible and short-wavelength infrared regions, we reveal that the optoacoustic method can follow GFP-based contrast used in SPIM, enabling molecular imaging interrogation in adult fish. Moreover we optoacoustically reveal many other features of zebrafish based on optical contrast not present in SPIM, including contrast from endogenous blood, water and lipids. The hybrid method presented can extend optical imaging to adult zebrafish employed as model systems for studying long-term processes in development, cancer, diabetes and other disorders.

biophysics↗

bioRxiv2018

Photoacoustic pigment relocalization sensor

Photoacoustic (optoacoustic) imaging can extract molecular information with deeper tissue penetration than possible by fluorescence microscopy techniques. However, there is currently still a lack of robust genetically controlled contrast agents and molecular sensors that can dynamically detect biological analytes of interest with photoacoustics. In this biomimetic approach, we took inspiration from cuttlefish who can change their color by relocalizing pigment-filled organelles in so-called chromatophore cells under neurohumoral control. Analogously, we tested the use of melanophore cells from Xenopus laevis, containing compartments (melanosomes) filled with strongly absorbing melanin, as whole-cell sensors for optoacoustic imaging. Our results show that pigment relocalization in these cells, which is dependent on binding of a ligand of interest to a specific G protein-coupled receptor (GPCR), can be monitored in vitro and in vivo using photoacoustic mesoscopy. In addition to changes in the photoacoustic signal amplitudes, we could furthermore detect the melanosome aggregation process by a change in the frequency content of the photoacoustic signals. Using bioinspired engineering, we thus introduce a photoacoustic pigment relocalization sensor (PaPiReS) for molecular photoacoustic imaging of GPCR-mediated signaling molecules.

bioengineering↗