Gnimpieba, E. Z. | bioRxiv Science

bioRxiv2025

The genome assembly and detection of biosynthetic gene clusters among four novel microbial isolates from deep subsurface rock biofilms

Tunnels in deep underground mines provide a unique interface between the surface and deep subsurface habitats. There, microbes from the surface may enter the deep tunnels as surface air is drawn into the deep mine tunnels to provide ventilation. This extreme hosting environment provides a condition for microbes to develop novel capabilities, such as the production of natural products of biotechnology or medicinal importance. This study characterized the genomes of four novel isolates from deep subsurface biofilms of the previously abandoned gold mine, which was used as a model for the underground study. Here the microbiome samples were obtained from thin, whitish, glistening biofilm samples naturally formed on the rock walls (1478 meters deep at SURF). These samples are herein referred to as "cave silver" biofilms. The samples provide 10 GB of high-quality whole genome sequences that were assembled into contigs/scaffolds and structurally and functionally annotated against various databases. Subsequently, the genomes were analyzed for biosynthetic gene clusters (BGCs) encoding secondary metabolites of biotechnology and medical importance using the antiSMASH and the NaPDoS web servers, respectively. In brief, the assemblies produced four drafted genomes of different lengths and annotated features for each strains genome, including gene clusters involved in the quorum sensing (QS) pathway. Several BGC-encoded secondary metabolites of natural products or compounds such as polyketides (PKS, PKS III, ketosynthase domain-KS), non-ribosomal peptides (condensation domain, NRPS), and terpenoids (terpenes I, polyenes type II), were identified. Furthermore, many overexpressed enzymes, most of which are shared among the strains and some unique to individual bacteria strains, were identified, revealing possible shared and individualized strain activities in the biofilms during the sample collection. CRISPR peptides were also detected in three of the four bacteria strains. In this study, we examine the genomes of microbes colonizing extreme subsurface environments and how these conditions promote the development of microbial systems that are potentially capable of producing medicinally natural products and secondary metabolites that may be used to enhance advancements in biotechnology products. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/675994v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@17a82f0org.highwire.dtl.DTLVardef@2a73f0org.highwire.dtl.DTLVardef@121c3e9org.highwire.dtl.DTLVardef@1c51f9b_HPS_FORMAT_FIGEXP M_FIG C_FIG ImportanceDeep subsurface environments host unique microbial communities with specialized adaptations. By characterizing genomes from "cave silver" biofilms, this study reveals biosynthetic gene clusters and pathways for secondary metabolite production, including polyketides, peptides, and terpenoids. These findings highlight the potential of extreme-environment microbes as a novel source of biotechnologically and medically valuable natural products.

genomics↗

bioRxiv2025

Forecasting Urban Wastewater Microbiome Dynamics Using a Digital Twin Framework

Urban wastewater microbiomes are complex and temporally dynamic, offering valuable insight into community-scale microbial ecology and potential public health trends. However, existing wastewater-based studies often remain descriptive, lacking tools for predictive modeling. In this study, we introduce a digital twin framework that forecasts microbial abundance trajectories in urban wastewater using an interpretable generative model, Q-net. Trained on a 30-week longitudinal metagenomic dataset from seven wastewater treatment plants, the model captures temporal microbial dynamics with high fidelity (R2 > 0.97 for key taxa; R2 = 0.998 at the final timepoint). Beyond accurate forecasting, Q-net provides transparent model structure through conditional inference trees and enables simulation of realistic microbial trends under hypothetical scenarios. This work demonstrates the potential of digital twins to move wastewater microbiome studies from static snapshots to dynamic, predictive systems, with broad implications for environmental monitoring and microbial ecosystem modeling.

bioinformatics↗

bioRxiv2023

Classification of bacterial nanowire proteins using Machine Learning and Feature Engineering model

Nanowires (NW) have been extensively studied for Shewanella spp. and Geobacter spp. and are mostly produced by Type IV pili or multiheme c-type cytochrome. Electron transfer via NW is the most studied mechanism in microbially induced corrosion, with recent interest in application in bioelectronics and biosensor. In this study, a machine learning (ML) based tool was developed to classify NW proteins. A manually curated 999 protein collection was developed as an NW protein dataset. Gene ontology analysis of the dataset revealed microbial NW is part of membranal proteins with metal ion binding motifs and plays a central role in electron transfer activity. Random Forest (RF), support vector machine (SVM), and extreme gradient boost (XGBoost) models were implemented in the prediction model and were observed to identify target proteins based on functional, structural, and physicochemical properties with 89.33%, 95.6%, and 99.99% accuracy. Dipetide amino acid composition, transition, and distribution protein features of NW are key important features aiding in the models high performance.

systems biology↗