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Parra, V.

Publications and source records attributed to Parra, V..

2 recordsLinked to original sources

Profiling extremophile bacterial communities recovered from a mining tailing against soil ecosystems through comparative genome-resolved metagenomics and evolutionary analysis

Microbial communities inhabiting mining environments harbor a diverse array of bacteria with specialized metabolic capacities adapted to extreme conditions. Here, we utilized comparative genome-resolved metagenomics of a high-quality Illumina-sequenced sample from the Cauquenes copper tailing in central Chile. We investigate the metabolic roles and evolutionary behaviors of the resident microorganisms, focusing on capacities related to copper, iron, and sulfur metabolism. We recovered 44 medium and high-quality metagenome-assembled genomes (MAGs), primarily classified belonging to phylum Actinobacteriota (21), Proteobacteria (10), and Acidobacteriota (6). These MAGs were compared to the Global Soil MAGs project (SMAG catalog), which includes bacteria from conventional or natural ecosystems, to uncover specialized properties of mining bacteria. Notably, we discovered a new phylum, Nitrospirota_A, and provided insights into the unexplored taxonomic classifications at the lowest ranks such as genus and species. Functional potential analysis revealed that the mining community has enhanced molecular capabilities associated with sulfur and copper metabolism. Evolutionary analysis revealed that mining genes involved in targeted metabolism are under strong negative selection, indicating conservative evolutionary pressure within the mining environment. In particular, it was possible to identify a MAG from the genus Acidithrix with a global dN/dS ratio greater than 1, suggesting positive selection. Additionally, core proteins essential for bacterial survival, such as flagellar motors, cell cycle regulators, and biogenesis proteins, were also under positive selection. The latter points to the need for enhanced mobility in these microorganisms to locate resources efficiently. We demonstrate that copper mining communities are diverse and possess a significant metabolic repertoire under extreme conditions in sulfur and copper proteins. Those specialized genes appear to be in a conservative state rather than undergoing adaptive evolution. This study enhances our understanding of extremophile mining microbiomes, highlighting their high variability in classification, metabolic functions, evolution, and adaptation, which can be leveraged for further biotechnological applications.

genomics↗

Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell and pathomics data to find sample level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics and pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell and pathomics data. Moreover, PILOT provides a statistical approach to delineate non-linear changes in cell populations, gene expression, and tissue structures related to the disease trajectories supporting interpretation of predictions.

bioinformatics↗