Biology subjects

Malod-Dognin, N.

Publications and source records attributed to Malod-Dognin, N..

7 recordsLinked to original sources

bioRxiv2025

iTissue

Tumors are highly heterogeneous tissues, and this heterogeneity impacts metastasis and its progression. Spatial data are essential for studying these hetrogeneity. However, its limited resolution makes crucial the integration of spatial data with molecular networks to identify relevant genes for the progression of metastasis. We develop a new data integration method, named iTissue, based on Graph Non-negative Matrix Tri-Factorization that integrates spatial and single-cell transcriptomics with molecular interaction networks, producing low-dimensional space. For three clear cell renal cell carcinoma (ccRCC) patients, we applied iTissue to their tumour-core and tumour-normal interface samples. We identified the top 100 with the highest euclidean distance between these two samples, since we show that the genes with the highest euclidean distance are relevant for ccRCC. By taking the intersection of the top 100 genes for the three patients, we identify 50 genes enriched in GO-BP terms relevant for metastasis. Moreover, we find that we can cluster the three regions of the tumour-normal interface (healthy tissue, tumour tissue and interface) using the identified genes. Our framework is general and can enable insight into other diseases.

systems biology↗

bioRxiv2024

Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data

MotivationCombining omics and images, can lead to a more comprehensive clustering of individuals than classic single-view approaches. Among the various approaches for multi-view clustering, nonnegative matrix tri-factorization (NMTF) and nonnegative Tucker decomposition (NTD) are advantageous in learning low-rank embeddings with promising interpretability. Besides, there is a need to handle unwanted drivers of clusterings (i.e. confounders). ResultsIn this work, we introduce a novel multi-view clustering method based on NMTF and NTD, named INMTD, that integrates omics and 3D imaging data to derive unconfounded subgroups of individuals. In the application to real-life facial-genomic data, INMTD generated biologically relevant embeddings for individuals, genetics and facial morphology. By removing confounded embedding vectors, we derived an unconfounded clustering with better internal and external quality; the genetic and facial annotations of each derived subgroup highlighted distinctive characteristics. In conclusion, INMTD can effectively integrate omics data and 3D images for unconfounded clustering with biologically meaningful interpretation. Availability and implementationhttps://github.com/ZuqiLi/INMTD

bioinformatics↗

bioRxiv2024

MONFIT: Multi-omics factorization-based integration of time-series data sheds light on Parkinson's disease

Parkinsons disease (PD) is a severe and complex multifactorial neurodegenerative disease with still elusive pathophysiology preventing the development of curative treatments. Molecular deep phenotyping by longitudinal multi-omics is a promising approach to identify mechanisms of PD aetiology and its progression. However, the heterogeneous data require new analysis frameworks to understand disease progression across biological entities and processes. Here, we present MONFIT, a holistic analysis pipeline that integrates and mines time-series single-cell RNA-sequencing data with bulk proteomics and metabolomics data by non-negative matrix tri-factorization, enabling prior knowledge incorporation from molecular networks. First, MONIFT integrates time-point-specific data and then holistically mines the integrated data across time points. By applying MONFIT to longitudinal multi-omics data of differentiation of PD and control patient-derived induced pluripotent stem cells into dopaminergic neurons, we identify novel PD-associated genes, emphasize molecular pathways that play important roles in PD pathology, and suggest new intervention opportunities using drug-repurposing. MONFIT is fully adaptable to other multi-omics data sets.

bioinformatics↗

bioRxiv2023

Multi-omics integration of scRNA-seq time series data predicts new intervention points for Parkinson's disease

Parkinsons disease (PD) is a complex neurodegenerative disorder without a cure. The onset of PD symptoms corresponds to 50% loss of midbrain dopaminergic (mDA) neurons, limiting early-stage understanding of PD. To shed light on early PD development, we study time series scRNA-seq datasets of mDA neurons obtained from patient-derived induced pluripotent stem cell differentiation. We develop a new data integration method based on Non-negative Matrix Tri-Factorization that integrates these datasets with molecular interaction networks, producing condition-specific "gene embeddings". By mining these embeddings, we predict 193 PD-related genes that are largely supported (49.7%) in the literature and are specific to the investigated PINK1 mutation. Enrichment analysis in Kyoto Encyclopedia of Genes and Genomes pathways highlights 10 PD-related molecular mechanisms perturbed during early PD development. Finally, investigating the top 20 prioritized genes reveals 12 previously unrecognized genes associated with PD that represent interesting drug targets.

bioinformatics↗

bioRxiv2023

The axes of biology: a novel axes-based network embedding paradigm to decipher the functional mechanisms of the cell.

Common approaches for deciphering biological networks involve network embedding algorithms. These approaches strictly focus on clustering the genes embedding vectors and interpreting such clusters to reveal the hidden information of the networks. However, the difficulty in interpreting the genes clusters and the limitations of the functional annotations resources hinder the identification of the currently unknown cells functioning mechanisms. Thus, we propose a new approach that shifts this functional exploration from the embedding vectors of genes in space to the axes of the space itself. Our methodology better disentangles biological information from the embedding space than the classic gene-centric approach. Moreover, it uncovers new data-driven functional interactions that are unregistered in the functional ontologies, but biologically coherent. Furthermore, we exploit these interactions to define new higher-level annotations that we term Axes-Specific Functional Annotations and validate them through literature curation. Finally, we leverage our methodology to discover evolutionary connections between cellular functions and the evolution of species.

systems biology↗

bioRxiv2023

A multi-omics integrative approach unravels novel genes and pathways associated with senescence escape after targeted therapy in NRAS mutant melanoma

Therapy Induced Senescence (TIS) leads to sustained growth arrest of cancer cells. The associated cytostasis has been shown to be reversible and cells escaping senescence further enhance the aggressiveness of cancers. Together with targeted therapeutics, senolytics, specifically targeting senescent cancer cells, constitute a promising avenue for improved cancer treatments. Understanding how cancer cells evade senescence is needed to optimise the clinical benefits of this therapeutic approach. Here we characterised the response of three different NRAS mutant melanoma cell lines to a combination of CDK4/6 and MEK inhibitors over 33 days. Transcriptomic data show that all cell lines trigger a senescence programme coupled with strong induction of interferons. Kinome profiling revealed the activation of Receptor Tyrosine Kinases (RTKs) and enriched downstream signaling of neurotrophin, ErbB and insulin pathways. Characterisation of the miRNA interactome associates miR-211-5p with resistant phenotypes. Finally, iCELL-based integration of bulk and single-cell RNA-seq data identified biological processes perturbed during senescence, and predicts new genes involved in its escape. Overall, our data associate insulin signaling with persistence of a senescent phenotype and suggest a new role for interferon gamma in senescence escape through the induction of EMT and the activation of ERK5 signaling.

cancer biology↗

bioRxiv2018

Graphlet Laplacians: graphlet-based neighbourhoods highlight topology-function and topology-disease relationships

MotivationLaplacian matrices capture the global structure of networks and are widely used to study biological networks. However, the local structure of the network around a node can also capture biological information. Local wiring patterns are typically quantified by counting how often a node touches different graphlets (small, connected, induced sub-graphs). Currently available graphlet-based methods do not consider whether nodes are in the same network neighbourhood.\n\nContributionTo combine graphlet-based topological information and membership of nodes to the same network neighbourhood, we generalize the Laplacian to the Graphlet Laplacian, by considering a pair of nodes to be adjacent if they simultaneously touch a given graphlet.\n\nResultsWe utilize Graphlet Laplacians to generalize spectral embedding, spectral clustering and network diffusion. Applying our generalization of spectral clustering to model networks and biological networks shows that Graphlet Laplacians capture different local topology corresponding to the underlying graphlet. In biological networks, clusters obtained by using different Graphlet Laplacians capture complementary sets of biological functions. By diffusing pan-cancer gene mutation scores based on different Graphlet Laplacians, we find complementary sets of cancer driver genes. Hence, we demonstrate that Graphlet Laplacians capture topology-function and topology-disease relationships in biological networks

bioinformatics↗