AI-Based Comparative Transcriptomics and Gene Network Profiling of Staphylococcus aureus in Astronaut-Associated Missions
Tome Castro, X. M.
Show abstract
IntroductionSpaceflight-associated microgravity alters microbial physiology, raising concerns about the adaptability and pathogenicity of opportunistic bacteria such as Staphylococcus aureus. Understanding transcriptomic responses in this context is essential for astronaut health and mission safety. MethodsPublicly available transcriptomic datasets were retrieved from the NASA GeneLab repository and analyzed in two stages. First, three experimental scenarios were compared: (1) BRIC-23 mission (in vitro Petri dish cultures aboard the ISS; 9 space vs. 9 ground), (2) SpaceX Inspiration4 mission microbiota (40 samples from 10 body sites of 4 astronauts across flight phases), and (3) Dragon capsule surface cultures (30 samples across 10 capsule zones and 3 time points). Differential expression (DESeq2), functional enrichment, heatmaps, co-expression network analysis (WGCNA), and bootstrapping were applied to identify conserved transcriptomic signatures. In the second stage, five candidate genes were selected from 45 consistently altered genes across all conditions. These were validated through statistical significance (BRIC-23, p < 0.05, significant log2 fold change) and consistent presence within co-expression modules. Candidate genes were further integrated with literature-curated virulence and biofilm-associated genes to construct a functional metabolic network using pathway analysis and K-means clustering. ResultsAcross independent datasets, S. aureus displayed convergence in transcriptomic profiles, particularly involving genes linked to virulence, adhesion, biofilm formation, and metabolic adaptation. Network-level integration revealed metabolic reprogramming and transcriptional plasticity as conserved responses, suggesting tightly regulated adaptation rather than random changes. DiscussionThe consistent identification of virulence and biofilm-associated genes across heterogeneous datasets indicates that microgravity imposes selective pressure favoring traits that enhance persistence in extreme environments. These findings support the hypothesis that S. aureus utilizes adaptive regulatory circuits to balance growth, survival, and pathogenic potential in the spaceflight niche. ConclusionsOur integrative bioinformatics approach reveals conserved adaptive strategies in S. aureus under microgravity, characterized by transcriptomic convergence and metabolic reprogramming. These insights underscore the necessity of experimental validation and phenotypic assays to assess microbial risks for astronaut health and to design countermeasures for future long-duration missions.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- SARS-CoV-2 virus in Raw Wastewater from Student Residence Halls with concomitant 16S rRNA Bacterial Community Structure changes 95%
- Exploring Taxonomic and Functional Microbiome of Hawaiian Stream and Spring Irrigation Water Systems Using Illumina and Oxford Nanopore Sequencing Platforms 95%
- Predicting environmental stressor levels with machine learning: a comparison between amplicon sequencing, metagenomics, and total RNA sequencing based on taxonomically assigned data 95%
Similar papers in this journal
- Gene co-expression network analysis of the human gut commensal bacterium Faecalibacterium prausnitzii based on WGCNA in R-Shiny 95%
- From the Andes to the desert: First characterization of bacterial communities in the Rimac river, the main source of water for Lima, Peru 95%
- Resistome metagenomics from plate to farm: the resistome and microbial composition during food waste feeding and composting on a Vermont poultry farm 95%
Similar papers in this journal
- Addressing the dynamic nature of reference data: a new nt database for robust metagenomic classification 96%
- GSR-DB: a manually curated and optimised taxonomical database for 16S rRNA amplicon analysis 95%
- A simple, cost-effective and automation-friendly direct PCR approach for bacterial community analysis 94%
Similar papers in this journal
- Interactive Analysis of Biosurfactants in Fruit-Waste Fermentation Samples using BioSurfDB and MEGAN 95%
- Station and train surface microbiomes of Mexico City’s metro (subway/underground) 95%
- Manually weighted taxonomy classifiers improve species-specific rumen microbiome analysis compared to unweighted or average weighted taxonomy classifiers 95%
Similar papers in this journal
- Contamination detection and microbiome exploration with GRIMER 96%
- Global ocean resistome revealed: exploring Antibiotic Resistance Genes (ARGs) abundance and distribution on TARA oceans samples through machine learning tools 95%
- gNOMO2: a comprehensive and modular pipeline for integrated multi-omics analyses of microbiomes 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.