Biology subjects

Alachram, H.

Publications and source records attributed to Alachram, H..

3 recordsLinked to original sources

bioRxiv2026

Distinct mitochondrial DNA single-nucleotide variant signatures in TOP3A-deficient cardiomyocytes

The human heart has a continuous and exceptionally high demand for energy, which is met primarily through mitochondrial oxidative phosphorylation. This dependence places the maintenance and integrity of mitochondrial DNA (mtDNA) for proper mitochondrial function at the center of cardiac health, as mtDNA instability has been shown to cause mitochondrial dysfunction, which can ultimately impair cardiac function leading to cardiomyopathy (CM) and heart failure. However, the contribution of mtDNA instability to cardiac disease remains poorly understood. A growing number of nuclear-encoded proteins have emerged as essential regulators of mtDNA maintenance, organization and segregation. DNA Topoisomerase 3 (TOP3A) is expressed as two isoforms: one is a nuclear-related isoform involved in nuclear genome maintenance while the other localizes to the mitochondria to preserve mtDNA integrity. Recently, individuals bearing biallelic loss-of-function variants in TOP3A manifested phenotypic traits, including CM, typical for mitochondrial dysfunction, supporting a potential mechanistic link between mitochondrial genome instability and TOP3A-related cardiac disease. Here, we investigated the effects of TOP3A deficiency on mtDNA maintenance and stability in the context of TOP3A-associated CM. We employed isogenic wild-type, TOP3A-knockout and BLM-knockout induced pluripotent stem cells (iPSCs) to generate cardiomyocytes (iPSC-CMs) and established a high-throughput, ultra-deep mtDNA sequencing strategy achieving approximately 500,000X coverage to characterize low-frequency mtDNA mutational patterns. Loss of TOP3A triggered an early burst of low-frequency de novo mtDNA single-nucleotide variants during cardiac differentiation, with a striking enrichment within the mitochondrial ribosomal RNA genes, accompanied by a progressive increase in the heteroplasmy of low-frequency mtDNA variants inherited from the common isogenic background. These unique mtDNA signatures were associated with defective mtDNA copy-number expansion and impaired mitochondrial respiration in mature iPSC-CMs. Together, our approach uncovered a previously uncharacterized consequence of TOP3A deficiency and established a link between impaired mtDNA maintenance and mitochondrial dysfunction in TOP3A-associated CM.

genetics↗

bioRxiv2020

Text mining-based word representations for biomedical data analysis and machine learning tasks

Biomedical and life science literature is an essential way to publish experimental results. With the rapid growth of the number of new publications, the amount of scientific knowledge represented in free text is increasing remarkably. There has been much interest in developing techniques that can extract this knowledge and make it accessible to aid scientists in discovering new relationships between biological entities and answering biological questions. Making use of the word2vec approach, we generated word vector representations based on a corpus consisting of over 16 million PubMed abstracts. We developed a text mining pipeline to produce word2vec embeddings with different properties and performed validation experiments to assess their utility for biomedical analysis. An important pre-processing step consisted in the substitution of synonymous terms by their preferred terms in biomedical databases. Furthermore, we extracted gene-gene networks from two embedding versions and used them as prior knowledge to train Graph-Convolutional Neural Networks (CNNs) on breast cancer gene expression data to predict the occurrence of metastatic events. Performances of resulting models were compared to Graph-CNNs trained with protein-protein interaction (PPI) networks or with networks derived using other word embedding algorithms. We also assessed the effect of corpus size on the variability of word representations. Finally, we created a web service with a graphical and a RESTful interface to extract and explore relations between biomedical terms using annotated embeddings. Comparisons to biological databases showed that relations between entities such as known PPIs, signaling pathways and cellular functions, or narrower disease ontology groups correlated with higher cosine similarity. Graph-CNNs trained with word2vec-embedding-derived networks performed best for the metastatic event prediction task compared to other networks. Word representations as produced by text mining algorithms like word2vec, therefore capture biologically meaningful relations between entities.

bioinformatics↗

bioRxiv2020

NERO: A Biomedical Named-entity (Recognition) Ontology with a Large, Annotated Corpus Reveals Meaningful Associations Through Text Embedding

Machine reading is essential for unlocking valuable knowledge contained in the millions of existing biomedical documents. Over the last two decades 1,2, the most dramatic advances in machine-reading have followed in the wake of critical corpus development3. Large, well-annotated corpora have been associated with punctuated advances in machine reading methodology and automated knowledge extraction systems in the same way that ImageNet 4 was fundamental for developing machine vision techniques. This study contributes six components to an advanced, named-entity analysis tool for biomedicine: (a) a new, Named-Entity Recognition Ontology (NERO) developed specifically for describing entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientific sublanguages of molecular biology, genetics, biochemistry, and medicine; (b) detailed guidelines for human experts annotating hundreds of named-entity classes; (c) pictographs for all named entities, to simplify the burden of annotation for curators; (d) an original, annotated corpus comprising 35,865 sentences, which encapsulate 190,679 named entities and 43,438 events connecting two or more entities; (e) validated, off-the-shelf, named-entity recognition automated extraction, and; (f) embedding models that demonstrate the promise of biomedical associations embedded within this corpus.

systems biology↗