Biology subjects

Pappas, G. J.

Publications and source records attributed to Pappas, G. J..

4 recordsLinked to original sources

bioRxiv2026

Viral communities in Metania sp. sponge microbiomes with possible effects on CO2 fixation

BackgroundBrazilian sponges of the genus Metania (phylum Porifera) are filter-feeding organisms from freshwater ecosystems. Here, we explored viral communities of Metania sp., their functional role in the sponge and how they differ from those in surrounding water. ResultsWe identified 1163 viral operational taxonomic units (vOTUs) from sponge tissue and adjacent water, with 555 vOTUs shared across habitats. Viral diversity was higher in sponges than in water, and community composition differed significantly (PERMANOVA, p = 0.037). Sponge-associated vOTUs exhibited broad phylogenetic diversity, including deep-branching and unclassified clades, and several exclusively sponge-associated Caudoviricetes. Virus-host predictions revealed 173 interactions, largely with sponge-associated bacteria, supported by CRISPR spacer matches, variant formation in multiple vOTUs across sponge individuals, and a high prevalence of microbial defence systems, particularly restriction-modification, abortive infection, and CRISPR-Cas pathways. Functionally, viral communities carried diverse auxiliary viral genes, including those involved in amino acid and central carbon metabolism, carbohydrate degradation, fatty acid biosynthesis, stress responses (e.g., metacaspase-1), and sulphur cycling. Nine sponge-associated vOTUs encoded carbonic anhydrase (CA), and phylogenomic as well as structural analyses showed strong conservation of CA active sites between sponge viruses, bacterial symbionts, and the sponge host. Protein-level homology searches revealed broad biogeographic distribution of viral CA homologs across global ocean microbiomes, despite limited nucleotide similarity, highlighting deep functional conservation. ConclusionsThese findings reveal a phylogenetically diverse and functionally rich viral community associated with freshwater Metania sp., characterized by extensive host interactions, diverse defence mechanisms, and auxiliary metabolic capacities. The structural conservation and widespread distribution of viral carbonic anhydrase genes further suggest ecologically significant roles in carbon transformation within freshwater sponges and potentially across aquatic ecosystems.

microbiology↗

bioRxiv2021

Asc-Seurat - Analytical single-cell Seurat-based web application

SummarySingle-cell RNA sequencing (scRNA-seq) has become a popular approach for studying the transcriptome, providing a powerful tool for discovering and characterizing cell types and their developmental trajectories. However, scRNA-seq analysis is complex, requiring a continuous, iterative process to refine the data processing and uncover relevant biological information. We present Asc-Seurat, a feature rich workbench, providing a user-friendly and easy-to-install web application encapsulating the necessary tools for an all-encompassing and fluid scRNA-seq data analysis. Availability and implementationAsc-Seurat is available at https://github.com/KirstLab/asc_seurat/ and released under GNU 3 license. Contactmkirst@ufl.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Is the brain macroscopically linear? A system identification of resting state dynamics

A central challenge in the computational modeling of neural dynamics is the trade-off between accuracy and simplicity. At the level of individual neurons, nonlinear dynamics are both experimentally established and essential for neuronal functioning. One may therefore expect the collective dynamics of massive networks of such neurons to exhibit an even larger repertoire of nonlinear behaviors. An implicit assumption has thus formed that an "accurate" computational model of whole-brain dynamics must inevitably be non-linear whereas linear models may provide a first-order approximation. To what extent this assumption holds, however, has remained an open question. Here, we provide new evidence that challenges this assumption at the level of whole-brain blood-oxygen-level-dependent (BOLD) and macroscopic field potential dynamics by leveraging the theory of system identification. Using functional magnetic resonance imaging (fMRI) and intracranial electroencephalography (iEEG), we model the spontaneous, resting state activity of 700 subjects in the Human Connectome Project (HCP) and 122 subjects from the Restoring Active Memory (RAM) project using state-of-the-art linear and nonlinear model families. We assess relative model fit using predictive power, computational complexity, and the extent of residual dynamics unexplained by the model. Contrary to our expectations, linear auto-regressive models achieve the best measures across all three metrics. To understand and explain this linearity, we highlight four properties of macroscopic neurodynamics which can counteract or mask microscopic nonlinear dynamics: averaging over space, averaging over time, observation noise, and limited data samples. Whereas the latter two are technological limitations and can improve in the future, the former two are inherent to aggregated macroscopic brain activity. Our results demonstrate the discounted potential of linear models in accurately capturing macroscopic brain dynamics. This, together with the unparalleled interpretability of linear models, can greatly facilitate our understanding of macroscopic neural dynamics, which in turn may facilitate the principled design of model-based interventions for the treatment of neuropsychiatric disorders.

neuroscience↗

bioRxiv2018

PaintOmics 3: a web resource for the pathway analysis and visualization of multi-omics data

The increasing availability of multi-omic platforms poses new challenges to data analysis. Joint visualization of multi-omics data is instrumental to understand interconnections across molecular layers and to fully leverage the biology discovery power offered by the multi-omics approach.\n\nWe present here PaintOmics 3, a web-based resource for the integrated visualization of multiple omic data types onto KEGG pathway diagrams. PaintOmics 3 combines server-end capabilities for data analysis with the potential of modern web resources for data visualization, providing researchers with a powerful framework for interactive exploration of their multi-omics information.\n\nUnlike other visualization tools, PaintOmics 3 covers a complete pathway analysis workflow, including automatic feature name/identifier conversion, multi-layered feature matching, pathway enrichment, network analysis, interactive heatmaps, trend charts, etc. It accepts a wide variety of omic types, including transcriptomics, proteomics and metabolomics, as well as region-based approaches such as ATAC-seq or ChIP-seq data. The tool is freely available at http://bioinfo.cipf.es/paintomics/.

bioinformatics↗