Biology subjects

Batista, S.

Publications and source records attributed to Batista, S..

5 recordsLinked to original sources

bioRxiv2025

Freshwater fish community assessment using eDNA metabarcoding vs. capture-based methods: differences in efficiency and resolution coupled to habitat and ecology

Environmental DNA (eDNA) metabarcoding has revolutionized ecological and environmental research by describing communities without relying on direct observations, making it a powerful, non-invasive, and cost-effective tool in biodiversity monitoring. However, implementation of eDNA as a standard protocol in long-term monitoring programs, that have traditionally relied on capture-based methods, poses challenges in terms of data comparability. Here, we compared freshwater fish communities assessed through eDNA metabarcoding and electrofishing, across 35 sampling sites in the lower Tagus River basin, Portugal. For most species or species-groups analyzed individually (13 out of 17), there was a significant correspondence between electrofishing and eDNA metabarcoding detections. The correspondence was weaker when comparing the number of specimens captured by electrofishing with the number of eDNA metabarcoding reads, with seven out of 13 taxa showing significant relationships. Species richness estimates based on the two methods were very similar at the basin level. The methods yielded significantly different species compositions, although these differences were driven by samples collected in the Tagus main channel, which is wider and has higher flow rates than tributaries. Benthic and shoreline fish communities showed similar species composition in the two methods, but this was not the case for pelagic communities, probably due to the higher water turnover of the pelagic zone and electrofishing inefficiency. Our results highlight the high potential of eDNA metabarcoding as a complementary method to electrofishing for freshwater fish monitoring, though further validation is needed to assess biases related to site-specific hydrological conditions and the ecology of the target species.

ecology↗

bioRxiv2023

Computational Framework for Statistical Epistasis Supports XOR Penetrance Function in a Living System

Statistical epistasis has been studied extensively because of its potential to provide evidence for genetic interactions for phenotypes, but there have been methodological limitations to its exhaustive, widespread application. We present new algorithms for the interaction coefficients for standard regression models for epistasis that permit many varied encodings for the interaction terms for loci and efficient memory usage. The algorithms are given for two-way and three-way epistasis and may be generalized to higher order epistasis. Statistical tests for the interaction coefficients are also provided. We also present an efficient matrix based algorithm for permutation testing for two-way epistasis. We offer a proof and experimental evidence that methods that look for epistasis only at loci that have main effects may not be justified. Given the computational efficiency of the algorithm, we applied the method to a rat data set and mouse data set, with at least 10000 loci and 1000 samples each, using the standard Cartesian encoding and the XOR penetrance function for the interactions, to test for evidence of statistical epistasis for the phenotype of body mass index. This study revealed that the XOR penetrance function found greater evidence for statistical epistasis in many more pairs of loci in both data sets and in the rat data set, those pairs of loci found using the XOR penetrance function are enriched for biologically relevant pathways. Author summaryEpistasis, the interaction between two or more genes, is likely integral to the study of genetics and present throughout nature. Yet, it is seldom fully explored as most approaches primarily focus on single-locus effects (such as GWAS), partly because analyzing all pairwise and higher-order interactions requires significant computational resources. Many current methods for epistasis detection only consider a Cartesian encoding for interaction terms. This is likely limiting as epistatic interactions can evolve to produce varied relationships between genes, some non-linear. In this work we describe computationally efficient algorithms for the detection of statistical epistasis that allow for varied interaction encodings for modeling epistasis. Our methodology efficiently detects pairwise and three-way epistatic interactions in two closely related species (rat and mouse) under both Cartesian and XOR interaction encodings. Our results in both species show that many biologically relevant epistatic relationships would have been undetected if only one interaction encoding was applied providing evidence that more varied models for interaction may need to be applied to describe epistasis that occurs in living systems.

genetics↗

bioRxiv2022

Native size exclusion chromatography-based mass spectrometry (SEC-MS) identifies novel components of the Heat Shock Protein 90-dependent proteome

The molecular chaperone heat shock protein 90 (HSP90) works in concert with co-chaperones to stabilize its client proteins, which include multiple drivers of oncogenesis and malignant progression. Pharmacologic inhibitors of HSP90 have been observed to exert a wide range of effects on the proteome, including depletion of client proteins, induction of heat shock proteins, dissociation of co-chaperones from HSP90, disruption of client protein signaling networks, and recruitment of the protein ubiquitylation and degradation machinery--suggesting widespread remodeling of cellular protein complexes. However, proteomics studies to date have focused on inhibitor-induced changes in total protein levels, often overlooking protein complex alterations. Here, we use size-exclusion chromatography in combination with mass spectrometry (SEC-MS) to characterize the changes in native protein complexes following treatment with the HSP90 inhibitor tanespimycin (17-AAG) in the HT29 colon adenocarcinoma cell line. After confirming the signature cellular response to HSP90 inhibition (e.g., induction of heat shock proteins, decreased total levels of client proteins), we were surprised to find only modest perturbations to the global distribution of protein elution profiles in inhibitor-treated cells. Similarly, co-chaperones that co-eluted with HSP90 displayed no clear difference between control and treated conditions. However, two distinct analysis strategies identified multiple inhibitor-induced changes, including several known components of the HSP90 proteome, as well as numerous proteins and protein complexes with no previous links to HSP90. We present this dataset as a resource for the HSP90, proteostasis, and cancer communities (https://www.bioinformatics.babraham.ac.uk/shiny/HSP90/SEC-MS/), laying the groundwork for future mechanistic and therapeutic studies related to HSP90 pharmacology. Data are available via ProteomeXchange with identifier PXD033459.

biochemistry↗

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↗

bioRxiv2020

Population Analysis of Extracellular Vesicles in Microvolumes of Biofluids

Extracellular Vesicles (EVs), membrane vesicles released by all cells, are emerging mediators of cell-cell communication. By carrying biomolecules from tissues to biofluids, EVs have attracted attention as non-invasive sources of clinical biomarkers in liquid biopsies. Although frequently employed for content characterization of EVs, the study of bulk preparations lacks information on sub-populations and the intrinsic heterogeneity of vesicles. Importantly, these strategies also difficult the characterization of EVs from small quantities of samples. We here present a Flow Cytometry strategy that enables detailed population analysis of EVs, at the same time decreasing sample volume requirements and accelerating the overall processing time. We show its unique application for quality control of isolates of EVs by comparing the proportion of vesicular and non-vesicular particles in samples prepared by different protocols. In addition, we demonstrate its suitability for the study of populations of EVs from samples characterized by challenging small volumes. To illustrate that, we perform longitudinal non-lethal analysis of EVs in mouse plasma and in single-animal collections of murine vitreous humor. By allowing for the analysis of EVs from minimal amounts of sample, our Flow Cytometry strategy has an unexplored potential in the study of EVs in clinical samples with intrinsically limited volumes. When compared to conventional methods, it also multiplies by several times the number of different analytes that can be studied from a single collection of biofluid.

cell biology↗