Biology subjects

Terezol, M.

Publications and source records attributed to Terezol, M..

4 recordsLinked to original sources

bioRxiv2024

MOTL: enhancing multi-omics matrix factorization with transfer learning

Joint matrix factorization is popular for extracting lower dimensional representations of multi-omics data but loses effectiveness with limited samples. Addressing this limitation, we introduce MOTL (Multi-Omics Transfer Learning), a framework that enhances MOFA (Multi-Omics Factor Analysis) by inferring latent factors for small multi-omics target datasets with respect to those inferred from a large heterogeneous learning dataset. We evaluated MOTL by designing simulated and real data protocols and demonstrated that MOTL improves the factorization of limited-sample multi-omics datasets when compared to factorization without transfer learning. When applied to actual glioblastoma samples, MOTL enhanced delineation of cancer status and subtype.

bioinformatics↗

bioRxiv2024

Molecular signatures and associated regulators of the pea leaf response to sulfur deficiency and water deficit as revealed by multi-omics analyses

Sulfur availability affects crop yield, seed quality, and tolerance to environmental constraints. To understand how pea (Pisum sativum) leaves respond to sulfur deficiency alone or combined with moderate water deficit during the early reproductive phase, we employed a multi-omics approach. Sulfur deficiency reduced plant height, biomass and leaf carbon, and increased the nitrogen-to-sulfur ratio. Under this condition, 38 genes were up-regulated at both transcript and protein levels, including genes involved in sulfur metabolism and antioxidant responses, suggesting coordinated molecular adjustments that may mitigate low leaf sulfur status. Moderate water deficit alone had limited effects, but markedly altered plant growth, gene regulation and metal accumulation when combined with sulfur deficiency. Among synergistically up-regulated genes, twenty were linked to reactive oxygen species responses and activated early, while seven genes with sustained activation encoded glutathione S-transferases. This was associated with higher GST activity and likely contributed to limiting H2O2 accumulation in double-stressed leaves. One-third of differentially accumulated proteins were encoded by genes showing no transcriptional change under stress, including temperature-induced lipocalins with potential protective roles under combined stress. These findings enhance our understanding of multilevel molecular responses to stress interactions, which is essential for improving crop resilience under multi-stress conditions. HighlightModerate water deficit amplifies molecular responses to sulfur deficiency in Pisum sativum, revealing synergistic responses at multiple layers of regulation under this stress combination.

plant biology↗

bioRxiv2023

Collaborative network analysis for the interpretation of transcriptomics data in rare diseases, an application to Huntington's disease

BackgroundRare diseases may affect the quality of life of patients and in some cases be life-threatening. Therapeutic opportunities are often limited, in part because of the lack of understanding of the molecular mechanisms that can cause disease. This can be ascribed to the low prevalence of rare diseases and therefore the lower sample sizes available for research. A way to overcome this is to integrate experimental rare disease data with prior knowledge using network-based methods. Taking this one step further, we hypothesized that combining and analyzing the results from multiple network-based methods could provide data-driven hypotheses of pathogenicity mechanisms from multiple perspectives. ResultsWe analyzed a Huntingtons disease (HD) transcriptomics dataset using six network-based methods in a collaborative way. These methods either inherently reported enriched annotation terms or their results were fed into enrichment analyses. The resulting significantly enriched Reactome pathways were then summarized using the ontological hierarchy which allowed the integration and interpretation of outputs from multiple methods. Among the resulting enriched pathways, there are pathways that have been shown previously to be involved in HD and pathways whose direct contribution to disease pathogenesis remains unclear and requires further investigation. ConclusionsIn summary, our study shows that collaborative network analysis approaches are well-suited to study rare diseases, as they provide hypotheses for pathogenic mechanisms from multiple perspectives. Applying different methods to the same case study can uncover different disease mechanisms that would not be apparent with the application of a single method.

bioinformatics↗

bioRxiv2023

ODAMNet: a Python package to identify molecular relationships between chemicals and rare diseases using overlap, active module and random walk approaches

Environmental factors are external conditions that can affect the health of living organisms. For a number of rare genetic diseases, an interplay between genetic and environmental factors is known or suspected. However, the studies are limited by the scarcity of patients and the difficulties in gathering reliable exposure information. In order to aid in fostering research between environmental factors and rare diseases, we propose ODAMNet, a Python package to investigate the possible relationships between chemicals, which are a subset of environmental factors, and rare diseases. ODAMNet offers three different and complementary bioinformatics approaches for the exploration of relationships: overlap analysis, active module identification and random walk with restart. ODAMNet allows systematic analysis of chemical - rare disease relationships and generation of hypotheses for further investigation of effect mechanisms. Metadata O_TBL View this table: org.highwire.dtl.DTLVardef@19841cdorg.highwire.dtl.DTLVardef@1081a59org.highwire.dtl.DTLVardef@f9dc0dorg.highwire.dtl.DTLVardef@1ddf5adorg.highwire.dtl.DTLVardef@12c58cb_HPS_FORMAT_FIGEXP M_TBL C_TBL

bioinformatics↗