Biology subjects

Sousa, L.

Publications and source records attributed to Sousa, L..

5 recordsLinked to original sources

bioRxiv2025

Gene-based analysis identifies novel microRNA candidates for Autism Spectrum Disorder

BackgroundAutism Spectrum Disorder (ASD) is a complex neurodevelopmental condition with unclear physiopathology. Biomarker-based diagnostic tools for early detection and targeted treatments have yet to be developed. MicroRNAs (miRNAs), which are critical regulators of genes involved in brain development and function, are emerging as promising candidates as molecular mechanisms, biomarkers, and therapeutic targets for ASD. This study aimed to identify miRNAs involved in ASD and evaluate their potential functions through in silico analysis. MethodsA comprehensive analysis was performed to find genomic variants with a predicted functional impact on miRNA activity. Using large datasets of Single Nucleotide Variants (SNVs, N = 4300) and Copy Number Variants (CNVs, N = 3570) from ASD cases, we identified miRNAs targeted by CNVs or containing SNVs predicted to disrupt their function in ASD subjects, and compared their frequencies with controls. Selected regulatory miRNA-mRNA networks and their associated biological functions were characterized. ResultsSeventy mature miRNAs were identified, including both previously reported and novel ASD candidates, predicted to regulate 2742 brain-expressed genes. Enrichment analysis revealed their involvement in cellular signaling, transcription regulation, protein metabolism, and chromatin organization - biological processes strongly related to ASD. Notably, 63% of these miRNAs are predicted to target 71 known ASD risk genes and the KCNB1, MECP2, NCKAP1 and ZBTB20 genes are each regulated by at least four miRNAs. ConclusionsThis gene-based analysis identified miRNAs regulating gene networks and biological pathways implicated in brain function and plasticity, frequently disrupted in ASD. These findings strengthen the evidence for miRNA involvement in ASD, paving the way for novel diagnostic and therapeutic strategies.

genetics↗

bioRxiv2021

Revealing the secrets beneath grapevine and Plasmopara viticola early communication: a picture of host and pathogen proteomes

Plant apoplast is the first hub of plant-pathogen communication where pathogen effectors are recognized by plant defensive proteins and cell receptors and several signal transduction pathways are activated. As a result of this first contact, the host triggers a defence response that involves the modulation of several extra and intracellular proteins. In grapevine-pathogen interactions, little is known about the communication between cells and apoplast. Also, the role of apoplastic proteins in response to pathogens still remains a blackbox. In this study we focused on the first 6 hours after Plasmopara viticola inoculation to evaluate grapevine proteome modulation in the apoplastic fluid (APF) and whole leaf tissue. Plasmopara viticola proteome was also assessed enabling a deeper understanding of plant and pathogen communication. Our results showed that oomycete recognition, plant cell wall modifications, ROS signalling and disruption of oomycete structures are triggered in Regent after P. viticola inoculation. Our results highlight a strict relation between the apoplastic pathways modulated and the proteins identified in the whole leaf proteome. On the other hand, P. viticola proteins related to growth/morphogenesis and virulence mechanisms were the most predominant. This pioneer study highlights the early dynamics of extra and intracellular communication in grapevine defence activation that leads to the successful establishment of an incompatible interaction.

plant biology↗

bioRxiv2021

Time and spatial trends in landing per unit of effort as support to fisheries management in a multi-gear coastal fishery

Landings by the multi-gear coastal fleet operating off the Portuguese continental coast include near 300 species, from which only a few are the object of management plans. In this study, daily landings (kg trip_1) are used, along with an effort indicator, vessel length overall (LoA), to obtain landings per unit of effort (LPUE) as a proxy for the species relative abundance for a total of 48 species. LPUE indices were then used as a response variable in linear models where year (2012-2016), season, region (north and south) and NAO index were included as explanatory variables. Season and region effects were found to significantly affect species abundance for a total of 41 and 40 species respectively, while interannual effects were found to be significant for 19 species, and finally, the NAO index for 3 species. Global LPUE density maps are presented for a number of selected species and a subsample of trips where VMS records were available. For the species analysed, it is proposed that geographic and seasonal changes in LPUE indexes can be used to understand trends in abundance and obtain information that can be used in support of the definition of regional management plans.

ecology↗

bioRxiv2021

Optimizing EEG source reconstruction with concurrent fMRI-derived spatial priors

Reconstructing EEG sources involves a complex pipeline, with the inverse problem being the most challenging. Multiple inversion algorithms are being continuously developed, aiming to tackle the non-uniqueness of this problem, which has been shown to be partially circumvented by also including fMRI-derived spatial priors in the inverse models. However, only task activation maps and resting-state networks (RSNs) have been explored so far, overlooking the recent, but already accepted, notion that brain networks exhibit dynamic functional connectivity (dFC) fluctuations. Moreover, there is no consensus regarding the inversion algorithm of choice, nor a systematic comparison between different sets of spatial priors. Using simultaneous EEG-fMRI data, here we compared four different inversion algorithms (MN, LORETA, EBB and MSP) under a Bayesian framework, each with three different sets of priors consisting of: 1) those specific to the algorithm (S1); 2) S1 plus fMRI task activation maps and RSNs (S2); and 3) S2 plus network modules of task-related dFC states estimated from the dFC fluctuations (S3). The quality of the reconstructed EEG sources was quantified in terms of model-based metrics, namely the free-energy and variance explained of the inversion models, and the overlap/proportion of brain regions known to be involved in the visual perception tasks that the participants were submitted to, and RSN templates, with/within EEG source components. Model-based metrics suggested that model parsimony is preferred, with the combination MSP+S1 exhibiting the best performance. However, optimal overlap/proportion values were found using EBB+S2 or MSP+S3, respectively, indicating that fMRI spatial priors, including dFC state modules, are crucial for the EEG source components to reflect neuronal activity of interest. Our results pave the way towards a more informative selection of the optimal EEG source reconstruction approach, which may be crucial in future studies.

neuroscience↗

bioRxiv2019

Deciphering the many maps of the Xingu, an assessment of deforestation from land cover classifications at multiple scales

Remote sensing is an invaluable tool to objectively illustrate the rapid decline in habitat extents worldwide. The many operational Earth Observation platforms provide options for the generation of land cover maps, each with unique characteristics, as well as considerable semantic differences in the definition of classes. As a result, differences in baseline estimates are inevitable. Here we compare forest cover and surface water estimates over four time periods spanning three decades (1989-2018) for [~]1.3 million km2 encompassing the Xingu river basin, Brazil, from published, freely accessible remotely sensed classifications. While all datasets showed a decrease in forest extent over time, we found a large range in the total area reported by each product for all time periods. The greatest differences ranged from 9% (year 2000) to 17% of the total area (2014-2018 period). We also show the high sensitivity of forest fragmentation metrics (entropy and foreground area density) to data quality and spatial resolution, with cloud cover and sensor artefacts resulting in errors. We further show the importance of choosing surface water datasets carefully because they differ greatly in location and amount of surface water mapped between sources. In several of the datasets illustrating the land cover following operationalization of the Belo Monte dam, the large reservoirs are notably absent. Freshwater ecosystem health is influenced by the land cover surrounding water bodies (e.g. Riparian zones). Understanding differences between the many remotely sensed baselines is fundamentally important to avoid information misuse, and to objectively choose the most appropriate dataset for conservation, taxonomy or policy-making. The differences in forest cover between the datasets examined here are not a failure of the technology, but due to different interpretations of forest and characteristics of the input data (e.g. spatial resolution). Our findings demonstrate the importance of transparency in the generation of remotely sensed datasets and the need for users to familiarize themselves with the characteristics and limitations of each chosen data set.

ecology↗