Biology subjects

Abramowicz, M.

Publications and source records attributed to Abramowicz, M..

3 recordsLinked to original sources

bioRxiv2023

The microcephaly gene ASPM is required for the timely generation of human outer-radial glia progenitors by controlling mitotic spindle orientation

Abnormal spindle-like, microcephaly-associated (ASPM) is the most commonly mutated gene in primary microcephaly (MCPH), characterized by reduced brain size and intellectual deficiency. The mechanisms underlying MCPH have remained unclear, as ASPM disruption leads to distinct phenotypes depending on the species studied. Here, we studied the impact of ASPM pathogenic mutations on human corticogenesis in organoid models. We found that at earliest stages of neurogenesis, ASPM mutant cortical progenitors, located at the apical surface of the neuroepithelium, display transient mitotic spindle randomization. The mutant progenitors then delaminate basally to adopt precociously the characteristics of outer radial glia cells (oRGC), a population of progenitors selectively amplified in human corticogenesis. Subsequently, cortical progenitors are depleted through decreased amplification and increased apoptosis. Thus, ASPM regulates the timely generation of oRGC by controlling mitotic spindle orientation, shedding light on how species-specific features of neurogenesis may confer vulnerability to neurodevelopmental diseases.

neuroscience↗

bioRxiv2023

A novel human iPSC model of COL4A1/A2 small vessel disease unveils a key pathogenic role of matrix metalloproteinases in extracellular matrix abnormalities

Abstract/SummaryCerebral small vessel disease (SVD) affects the small vessels in the brain and is a leading cause of stroke and dementia. Emerging evidence supports a role of the extracellular matrix (ECM), at the interface between blood and brain, in the progression of SVD pathology but this remains poorly characterized. To address ECM role in SVD, we developed a co-culture model of mural and endothelial cells using human induced pluripotent stem cells from patients with COL4A1/A2 SVD-related mutations. This model revealed that these mutations induce apoptosis, migration defects, ECM remodelling and transcriptome changes in mural cells. Importantly, these mural cell defects exert a detrimental effect on endothelial cells tight junctions through paracrine actions. COL4A1/A2 models also express high levels of matrix metalloproteinases (MMP) and inhibiting MMP activity partially rescues the ECM abnormalities and mural cell phenotypic changes. These data provide a basis for targeting MMP as a therapeutic opportunity in SVD. HighlightsO_LIA novel human iPSC-derived model of genetic SVD due to collagen IV (COL4A1/A2) mutations is described C_LIO_LIMural cells expressing COL4A1/A2 mutations have prominent ECM abnormalities as seen in patients and mouse models and contribute to endothelial cells defects C_LIO_LIECM and endothelial cells abnormalities can be rescued by MMP inhibition in the COL4A1/A2 model C_LI

cell biology↗

bioRxiv2017

DiGeST: Distributed Computing for Scalable Gene and Variant Ranking with Hadoop/Spark

BackgroundThe advent of next-generation sequencing technologies has opened new avenues for clinical genomics research. In particular, as sequencing costs continue to decrease, an ever-growing number of clinical genomics institutes now rely on DNA sequencing studies at varying scales - genome, exome, mendeliome - for uncovering disease-associated variants or genes, in both rare and non-rare diseases.\n\nA common methodology for identifying such variants or genes is to rely on genetic association studies (GAS), that test whether allele or genotype frequencies differ between two groups of individuals, usually diseased subjects and healthy controls. Current bioinformatics tools for performing GAS are designed to run on standalone machines, and do not scale well with the increasing size of study designs and the search for multi-locus genetic associations. More efficient distributed and scalable data analysis solutions are needed to address this challenge.\n\nResultsWe developed a Big Data solution stack for distributing computations in genetic association studies, that address both single and multi-locus associations. The proposed stack, called DiGeST (Distributed Gene/variant Scoring Tool) is divided in two main components: a Hadoop/Spark high-performance computing back-end for efficient data storage and distributed computing, and a Web front-end providing users with a rich set of options to filter, compare and explore exome data from different sample populations. Using exome data from the 1000 Genomes Project, we show that our distributed implementation smoothly scales with computing resources. We make the resulting software stack Open-Source, and provide virtualisation scripts to run the complete environment both on standalone machine or Hadoop-based cluster.\n\nConclusionsHadoop/Spark provides a powerful and well-suited distributed computing framework for genetic association studies. Our work illustrates the flexibility, ease of use and scalability of the framework, and more generally advocates for its wider adoption in bioinformatics pipelines.

bioinformatics↗