Biology subjects

Baptista, A.

Publications and source records attributed to Baptista, A..

5 recordsLinked to original sources

bioRxiv2025

Associations between reproduction-focused life strategy, sex and Borderline Personality Disorder symptom expression: Evidence from the NESARC national study.

IntroductionBorderline personality disorder (BPD) features span internalizing and externalizing dimensions of psychopathology, and their expression varies across biological sexes. Despite substantial research efforts, major gaps remain in our understanding of this heterogeneity, particularly regarding its developmental underpinnings. Life history theory, a leading framework in evolutionary developmental biology, can help make sense of this heterogeneity. MethodsIn a large nationally representative prospective survey (n = 34 653), the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), we used Multi-group MIMIC models to investigate whether and how the degree to which individuals trade off somatic maintenance against short-term reproductive goals relates to severity of BPD at a general and a single symptom level, and whether these associations differ between men and women in the general adult population. ResultsMen and women prioritizing short-term reproductive goals over somatic maintenance were more likely to endorse each of the 9 DSM-IV BPD symptoms through higher BPD severity, and were more likely to express impulsivity (b=0.11; SE, 0.018; p<.001) and suicidal/self-mutilation behavior (b =0.24; SE, 0.026; p< .001) and less likely to endorse stress-related paranoid ideation (b=-0.066; SE, 0.023; p=.004). Finally, females with a reproduction-oriented life history strategy were more likely to endorse affective instability than males (Females: b=0.12; SE, 0.025; p<.001; Males: b=-0.015; SE, 0.044; p=0.73). ConclusionsAn evolutionary-developmental framework, rooted in human life history theory can help make sense of the heterogeneous manifestations of BPD. Our work also opens the door for future research on the interplay of the reproduction/maintenance trade-off with other genetic and environmental factors and developmental processes in explaining the occurrence of BPD symptoms.

evolutionary biology↗

bioRxiv2025

SMART: A Spatio-Molecular Atlas of Response Trajectories in Triple-Negative Breast Cancer

A major challenge in treating Triple-Negative Breast Cancer (TNBC) lies in its molecular, morphological and clinical heterogeneity, which hampers accurate prediction of responses to neoadjuvant treatment. To address this, we introduce SMART: Spatio-Molecular Atlas of Response Trajectories, a comprehensive, multimodal resource compiled from 129 TNBC samples across 89 patients, obtained before, during, and after neoadjuvant chemotherapy (NACT). SMART comprises of 5,096 high quality manually selected spatial transcriptomic profiles enriched for epithelial, immune, or stromal compartments; paralleled with histological annotations, imagebased network analysis and protein expression. Seven novel spatial epithelial archetypes (EAs), seven tumour-immune microenvironments (TIMEs) and their co-localisation patterns were defined, revealing an opposing prevalence of functionally divergent EAs between response groups and the prognostic significance of B-cell enriched TIMEs, in particular those surrounding histologically normal epithelium adjacent to the tumour. The SMART dataset and analytical tools are publicly available via the PharosAI platform, providing the research community with the most comprehensive, manually annotated spatio-molecular transcriptomics atlas of NACT-treated TNBC to date.

cancer biology↗

bioRxiv2025

Unraveling Cross-Cellular Communication in Cannabis sativa Sex Determination: A Network Ontology Transcript Annotation (Nota) Analysis.

Cannabis sativa L. (marijuana, hemp, cannabis) is an angiosperm species currently evolving sex chromosomes. Genetic mechanisms, primarily an XY chromosome system, dictate cannabis sex expression in dioecious populations. However, sexual expression is also governed by the interplay of hormone regulatory gene networks, influenced by both genetic and environmental factors. Within the species, some populations exhibit dioecy, monoecy, or a gradient of both. Dioecious individuals produce exclusively male or female flowers, while monoecious plants bear both male and female flowers. Remarkably, through interruption of phytohormone signal transduction via abiotic stressors, genetically male or female cannabis are able to produce flowers of the opposite sex. Previous transcriptomic analysis have identified genes associated with masculinization through the application of phytohormone signal disruption using silver thiosulfate treatment. We analyzed transcriptomic data from cannabis treated with colloidal silver to similarly induce masculinization. Using Nota (Network Ontology Transcript Annotation), a multilayer network analysis (random walk with restart) tool, we identified candidate genes involved in sex-determination. Nota and a companion program Jack facilitate multi-layer network analyses, enabling discovery and annotation of gene-trait associations. Our findings highlight Notas robust application to enrich the genetic architecture of complex traits, particularly in non-model systems like cannabis, and complex traits such as sex determination. Our analyses identified genes associated with cell wall morphogenesis and embryogenic tissue homeostasis, indicating that silver ion treatment perturbs phytohormone signal transduction through metal ion imbalance. In this reproductive strategy, cannabis is able to make use of its widely investigated sex-determining genetic architecture to navigate transient changes in co-expression and cross-cellular signaling driving embryogenic cell wall re-patterning.

systems biology↗

bioRxiv2023

Random Walk with Restart on multilayer networks: from node prioritisation to supervised link prediction and beyond

BackgroundBiological networks have proven invaluable ability for representing biological knowledge. Multilayer networks, which gather different types of nodes and edges in multiplex, heterogeneous and bipartite networks, provide a natural way to integrate diverse and multi-scale data sources into a common framework. Recently, we developed MultiXrank, a Random Walk with Restart algorithm able to explore such multilayer networks. MultiXrank outputs scores reflecting the proximity between an initial set of seed node(s) and all the other nodes in the multilayer network. We illustrate here the versatility of bioinformatics tasks that can be performed using MultiXrank. ResultsWe first show that MultiXrank can be used to prioritise genes and drugs of interest by exploring multilayer networks containing interactions between genes, drugs, and diseases. In a second study, we illustrate how MultiXrank scores can also be used in a supervised strategy to train a binary classifier to predict gene-disease associations. The classifier performance are validated using outdated and novel gene-disease association for training and evaluation, respectively. Finally, we show that MultiXrank scores can be used to compute diffusion profiles and use them as disease signatures. We computed the diffusion profiles of more than 100 immune diseases using a multilayer network that includes cell-type specific genomic information. The clustering of the immune disease diffusion profiles reveals shared shared phenotypic characteristics. ConclusionOverall, we illustrate here diverse applications of MultiXrank to showcase its versatility. We expect that this can lead to further and broader bioinformatics applications.

bioinformatics↗

bioRxiv2023

Charting cellular differentiation trajectories with Ricci flow

Complex biological processes, such as cellular differentiation, require an intricate rewiring of intra-cellular signalling networks. Previous characterisations of these networks revealed that promiscuity in signalling, quantified by a raised network entropy, underlies a less differentiated and malignant cell state. A theoretical connection between entropy and Ricci curvature has led to applications of discrete curvatures to characterise biological signalling networks at distinct time points during differentiation and malignancy. However, understanding and predicting the dynamics of biological network rewiring remains an open problem. Here we construct a framework to apply discrete Ricci curvature and Ricci flow to the problem of biological network rewiring. By investigating the relationship between network entropy and Forman-Ricci curvature, both theoretically and empirically on single-cell RNA-sequencing data, we demonstrate that the two measures do not always positively correlate, as has been previously suggested, and provide complementary rather than interchangeable information. We next employ discrete normalised Ricci flow, to derive network rewiring trajectories from transcriptomes of stem cells to differentiated cells, which accurately predict true intermediate time points of gene expression time courses. In summary, we present a differential geometry toolkit for investigation of dynamic network rewiring during cellular differentiation and cancer.

bioinformatics↗