Biology subjects

Carvalho, A. M.

Publications and source records attributed to Carvalho, A. M..

4 recordsLinked to original sources

bioRxiv2026

Uncovering Latent Structure in Gliomas Using Multi-Omics Factor Analysis

BackgroundGliomas are the most common malignant brain tumors in adults, characterized by a poor prognosis. Although the current World Health Organization (WHO) classification provides clear guidelines for classifying oligodendroglioma, astrocytoma, and glioblastoma patients, significant heterogeneity persists within each class, limiting the effectiveness of current treatment strategies. With the increase of large-scale multi-omics datasets due to advancements in sequencing technologies, and online databases that provide them, such as The Cancer Genome Atlas (TCGA), it is now possible to investigate these tumors at multiple molecular levels. MethodsIn this work, we apply integrative multi-omics analysis to explore the interplay between genomic (mutations), epigenomic (DNA methylation), and transcriptomic (mRNA and miRNA) layers. Our approach relies on Multi-Omics Factor Analysis (MOFA), a Bayesian latent factor analysis model designed to capture sources of variation across different omics types. ResultsOur results highlight distinct molecular profiles across the three glioma types and identify potential relationships between methylation and genetic expression. In particular, we uncover novel candidate biomarkers with prognostic value, as well as a transcriptional profile associated with neural system development. ConclusionsThese findings may contribute to more personalized therapeutic strategies, potentially enhancing treatment effectiveness and improving survival outcomes for this disease.

bioinformatics↗

bioRxiv2025

Nose-to-Brain Healing: Hypoxia-Preconditioned Mesenchymal Stem Cells Prompt Recovery in Hypoxic-Ischemic Encephalopathy Rats

Neonatal HIE poses a significant risk factor for neurodevelopment impairment. Therapeutic hypothermia, the current standard of care for this condition, has several constraints and reduced effectivity, especially in more severe cases. Thus, it is necessary to explore novel therapeutics, like MSCs. Although previous studies report that administration of MSCs (from different sources) prompted the recovery of HIE-lesioned animals, high doses are currently used. First, this study compared the efficacy of IN versus IV administration of 50,000 UC-MSCs in a rat model of neonatal HI brain injury. For this cell dose, only IN-UC-MSC therapy reduced infarct volume, an effect accompanied by improvements of motor skills and recognition memory. Also, IN-UC-MSC administration restored myelination in the corpus callosum and mitigated glial reactivity more effectively than IV administration. In a second part of the study, to potentiate the effect of UC-MSCs administration, postnatal rats that underwent HI injury received 25,000 hypoxia-preconditioned UC-MSCs or its secretome two days later, via IN route. The administration of a low-dose of hypoxia-preconditioned UC-MSCs was sufficient to induce neurological recovery and modulation of glial response. Moreover, the administration of the secretome of these cells was enough to induce the same extent of recovery. These findings support the higher potential of IN-UC-MSC administration, compared to IV administration, while enhancing our understanding of hypoxia-preconditioning and the role of the MSCs secretome in driving a positive therapeutic response, contributing to the development of more effective and feasible treatments for neonatal HIE.

neuroscience↗

bioRxiv2023

The effect of estradiol during the early stages of osteoclast differentiation is associated with the accumulation of phosphorylated p53 in mitochondria and the inhibition of mitochondrial metabolism.

Estrogen deficiency increases bone resorption and is a major contributor to osteoporosis. However, the molecular mechanisms mediating the effects of estrogen on osteoclasts remain unclear. This study aimed at elucidating the early metabolic effects of RANKL - the essential cytokine for osteoclastogenesis - and 17-beta-estradiol (E2) on osteoclast progenitor cells, using RAW 264.7 macrophage cell line and bone marrow-derived macrophages as biological models. RANKL stimulated complex I activity, oxidative phosphorylation (OXPHOS), and mitochondria-derived ATP production, as early as 3 to 6 h. This up-regulation of mitochondrial bioenergetics was associated with an increased capacity to oxidize TCA cycle substrates, fatty acids, and amino-acids. E2 inhibited all effects of RANKL on mitochondria metabolism. In the presence of RANKL, E2 also decreased cell number and stimulated the mitochondrial-mediated apoptotic pathway, detected as early as 3h. Surprisingly, the pro-apoptotic effects of E2 were associated with an accumulation of p392S-p53 in mitochondria. These findings elucidate early effects of RANKL on osteoclast progenitor metabolism and suggest novel p53-mediated mechanisms that contribute to postmenopausal osteoporosis.

cell biology↗

bioRxiv2018

Scalable probabilistic matrix factorization for single-cell RNA-seq analysis

MotivationThe gene expression profile of a cell dictates its function in molecular processes, and can be used to probe its health status. This represents a step forward in the deep characterization of diseases such as cancer and may lead to breakthroughs in their treatment. The technology used to measure the gene expression of isolated cells, single-cell RNA-seq (scRNA-seq), has emerged in the last decade as a key enabler of this progress. However, the use of existing methods for dimensionality reduction, clustering and differential expression is limited by the specificities of the data obtained from scRNA-seq experiments, where technical factors may confound analyses of the true biological signal and contribute to spurious results. To overcome this issue, a possible approach is designing probabilistic generative models of the data with hidden variables encoding different underlying processes.\n\nResultsWe propose two novel probabilistic models for scRNA-seq data: modified probabilistic count matrix factorization (m-pCMF) and Bayesian zero-inflated negative binomial factorization (ZINBayes). These build upon previous models in the literature while leveraging scalable Bayesian inference via variational methods. We show that the proposed methods are competitive with the state-of-the-art models for robust dimensionality reduction in modern data sets, and improve upon the current best Bayesian model for small numbers of cells. The results show that building probabilistic models of latent variables which encode domain knowledge and using variational inference constitute a promising approach to analyse scRNA-seq data in a scalable way.\n\nAvailabilitym-pCMF and ZINBayes are publicly available as Python packages at https://github.com/pedrofale/, along with the code to reproduce all the results.\n\nContactsusanavinga@tecnico.ulisboa.pt

bioinformatics↗