Biology subjects

Colombo, B. M.

Publications and source records attributed to Colombo, B. M..

4 recordsLinked to original sources

bioRxiv2026

Kruppel-like factors KLF5 & KLF8 emerge as master transcriptional regulators of Alzheimers disease, as revealed on cell fate regulomes in human brain organoids

In addition to the well described beta-amyloid plates accumulation and tau hyper-phosphorylation, Alzheimers disease (AD) is accompanied by major changes in gene expression. Herein, we aimed at revealing master transcription factors (TFs) responsible for gene expression changes during AD progression. For this, we have used human brain organoids (BORGs) harbouring AD-related genetic mutations (APP-Swedish, PSEN1-M146V), which were traced over multiple time-points by bulk and spatially-resolved transcriptomics. By reconstructing gene regulatory networks (GRNs) that recapitulate BORG development, we have identified a subset of 110 AD-specific master TFs, and for 75 of them we retrieved KLF5 and/or KLF8 binding motifs within their promoters. Furthermore, 64 of the AD-specific TFs found in BORGs are significantly over-expressed on AD human patients samples, confirming the relevance of these factors beyond the context of the familial genetic mutations. Finally, we have demonstrated that this AD-specific regulome is at least partially controlled by the aberrant CREB3L2-ATF4 heterodimer previously described as being induced by the beta-amyloid plates deposition. Overall, these findings reconstitute the regulome behind the progression of AD and highlights key TFs as potential druggable targets for the treatment of the disease.

systems biology↗

bioRxiv2023

PSEN1-M146V and PSEN1-A246E mutations associated with Alzheimers disease impair proper microglia differentiation

Genetic variants associated with the late onset of Alzheimers disease (AD), were correlated with genes known to be expressed in microglia, suggesting for an AD-genetic component directly influencing microglia behavior. Instead, the role of the familial AD (fAD) genetic mutations was systematically studied from the angle of the Amyloid-Beta pathway; leaving their participation in microglia homeostasis unexplored. Here we demonstrate that two previously described fAD-related PSEN1 mutations directly impair proper microglia differentiation. While human induced pluripotent stem cells harboring the PSEN1-M146V mutation did not give rise to hematopoietic precursor (HPC) intermediate during microglia differentiation, a PSEN1-A246E mutant line managed to produce HPCs, but died within the first days of microglia differentiation. Detailed transcriptomics/epigenomics and functional assays revealed the setup of a pro-apoptotic program in the PSEN1-A246E mutant, which was circumvented when HPCs were grafted in brain organoids (BORGs). Microglia obtained in BORGs presented preferentially pro-inflammatory signatures, further supported by their correlation with recent data providing a detailed stratification of the various microglia populations within AD-patient samples. Overall, this study contributes to reconsider the influence of the previously identified familial mutations in the homeostasis of this immune component of the central nervous system.

neuroscience↗

bioRxiv2022

A synergistic activation of RARb and RARg nuclear receptors restores cell-types specialization during stem cells differentiation by hijacking RARa-controlled program

How cells respond to different external cues to develop along defined cell lineages to form complex tissues is a major question in systems biology. Here, we investigated the potential of retinoic acid receptor (RARs)-selective synthetic agonists to activate the gene-regulatory programs driving cell specialization during nervous tissue formation from P19 stem cells. Specifically, we found that the synergistic activation of the RAR{beta} and RAR{gamma} by selective ligands (BMS641 or BMS961) induces cell maturation to specialized neuronal subtypes, as well as to astrocytes and oligodendrocyte precursors. Using RAR istoype knockout lines exposed to RAR-specific agonists, interrogated by global transcriptome landscaping and in silico modeling of transcription regulatory signal propagation, revealed major RAR-driven gene programs essential for optimal neuronal cell specialization, and hijacked by the synergistic activation of the RAR{beta} and RAR{gamma} receptors. Overall, this study provides a systems biology view of the gene programs accounting for the previously observed redundancy between RAR receptors, paving the way towards their potential use for directing cell specialization during nervous tissue formation.

molecular biology↗

bioRxiv2020

Inferring functionally relevant molecular tissue substructures by agglomerative clustering of digitized spatial transcriptomes

Developments on spatial transcriptomics (ST) are providing means to interrogate organ/tissue architecture from the angle of the gene programs defining their molecular complexity. However, computational methods to analyze ST data under-exploits the spatial signature retrieved within the maps. Inspired by contextual pixel classification strategies applied to image analysis, we have developed MULTILAYER, allowing to stratify ST maps into functionally-relevant molecular substructures. For it, MULTILAYER applies agglomerative clustering strategies within contiguous locally-defined transcriptomes (herein defined as gene expression elements or Gexels), combined with community detection methods for graph partitioning. MULTILAYER has been evaluated over multiple public ST data, including developmental tissues but also tumor biopsies. Its performance has been challenged for the processing of high-resolution ST maps and it has been used for an enhanced comparison of multiple public tissue biopsies issued from a cancerous prostate. MULTILAYER provides a digital perspective for the analysis of spatially-resolved transcriptomes and anticipates the application of contextual gexel classification strategies for developing self-supervised molecular diagnostics solutions. Overall, the development of MULTILAYER anticipates the application of contextual gexel classification strategies for developing self-supervised molecular diagnostics solutions.

bioinformatics↗