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MetGEMs Toolbox: Metagenome-scale models as integrative toolbox for uncovering metabolic functions and routes of human gut microbiome.
INF, B5

Abstract (Expand)

Investigating metabolic functional capability of a human gut microbiome enables the quantification of microbiome changes, which can cause a phenotypic change of host physiology and disease. One possible … way to estimate the functional capability of a microbial community is through inferring metagenomic content from 16S rRNA gene sequences. Genome-scale models (GEMs) can be used as scaffold for functional estimation analysis at a systematic level, however up to date, there is no integrative toolbox based on GEMs for uncovering metabolic functions. Here, we developed the MetGEMs (metagenome-scale models) toolbox, an open-source application for inferring metabolic functions from 16S rRNA gene sequences to facilitate the study of the human gut microbiome by the wider scientific community. The developed toolbox was validated using shotgun metagenomic data and shown to be superior in predicting functional composition in human clinical samples compared to existing state-of-the-art tools. Therefore, the MetGEMs toolbox was subsequently applied for annotating putative enzyme functions and metabolic routes related in human disease using atopic dermatitis as a case study.

Authors: P. Patumcharoenpol, M. Nakphaichit, Gianni Panagiotou, A. Senavonge, N. Suratannon, W. Vongsangnak

Date Published: 6th Jan 2021

Journal: PLoS Comput Biol

Metabolic modeling predicts specific gut bacteria as key determinants for Candida albicans colonization levels.
INF, A5, C1, C2, B5

Abstract (Expand)

Candida albicans is a leading cause of life-threatening hospital-acquired infections and can lead to Candidemia with sepsis-like symptoms and high mortality rates. We reconstructed a genome-scale C. … albicans metabolic model to investigate bacterial-fungal metabolic interactions in the gut as determinants of fungal abundance. We optimized the predictive capacity of our model using wild type and mutant C. albicans growth data and used it for in silico metabolic interaction predictions. Our analysis of more than 900 paired fungal-bacterial metabolic models predicted key gut bacterial species modulating C. albicans colonization levels. Among the studied microbes, Alistipes putredinis was predicted to negatively affect C. albicans levels. We confirmed these findings by metagenomic sequencing of stool samples from 24 human subjects and by fungal growth experiments in bacterial spent media. Furthermore, our pairwise simulations guided us to specific metabolites with promoting or inhibitory effect to the fungus when exposed in defined media under carbon and nitrogen limitation. Our study demonstrates that in silico metabolic prediction can lead to the identification of gut microbiome features that can significantly affect potentially harmful levels of C. albicans.

Authors: Mohammad Mirhakkak, Sascha Schäuble, Tilman Klassert, S. Brunke, Philipp Brandt, D. Loos, R. V. Uribe, F. Senne de Oliveira Lino, Y. Ni, Slavena Vylkova, Hortense Slevogt, Bernhard Hube, Esther Weiß, M. O. A. Sommer, Gianni Panagiotou

Date Published: 15th Dec 2020

Journal: ISME J

Mining, analyzing, and integrating viral signals from metagenomic data.
INF, B5

Abstract (Expand)

BACKGROUND: Viruses are important components of microbial communities modulating community structure and function; however, only a couple of tools are currently available for phage identification and … analysis from metagenomic sequencing data. Here we employed the random forest algorithm to develop VirMiner, a web-based phage contig prediction tool especially sensitive for high-abundances phage contigs, trained and validated by paired metagenomic and phagenomic sequencing data from the human gut flora. RESULTS: VirMiner achieved 41.06% +/- 17.51% sensitivity and 81.91% +/- 4.04% specificity in the prediction of phage contigs. In particular, for the high-abundance phage contigs, VirMiner outperformed other tools (VirFinder and VirSorter) with much higher sensitivity (65.23% +/- 16.94%) than VirFinder (34.63% +/- 17.96%) and VirSorter (18.75% +/- 15.23%) at almost the same specificity. Moreover, VirMiner provides the most comprehensive phage analysis pipeline which is comprised of metagenomic raw reads processing, functional annotation, phage contig identification, and phage-host relationship prediction (CRISPR-spacer recognition) and supports two-group comparison when the input (metagenomic sequence data) includes different conditions (e.g., case and control). Application of VirMiner to an independent cohort of human gut metagenomes obtained from individuals treated with antibiotics revealed that 122 KEGG orthology and 118 Pfam groups had significantly differential abundance in the pre-treatment samples compared to samples at the end of antibiotic administration, including clustered regularly interspaced short palindromic repeats (CRISPR), multidrug resistance, and protein transport. The VirMiner webserver is available at http://sbb.hku.hk/VirMiner/ . CONCLUSIONS: We developed a comprehensive tool for phage prediction and analysis for metagenomic samples. Compared to VirSorter and VirFinder-the most widely used tools-VirMiner is able to capture more high-abundance phage contigs which could play key roles in infecting bacteria and modulating microbial community dynamics. TRIAL REGISTRATION: The European Union Clinical Trials Register, EudraCT Number: 2013-003378-28 . Registered on 9 April 2014.

Authors: T. Zheng, J. Li, Y. Ni, K. Kang, M. A. Misiakou, L. Imamovic, B. K. C. Chow, A. A. Rode, P. Bytzer, M. Sommer, Gianni Panagiotou

Date Published: 19th Mar 2019

Journal: Microbiome

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