Antibiotic resistance and metabolic profiles as functional biomarkers that accurately predict the geographic origin of city metagenomics samples. - Casimiro-Soriguer, Carlos S; Loucera, Carlos; Perez Florido, Javier; López-López, Daniel; Dopazo, Joaquin (Biol Direct, 2019)
BACKGROUND: The availability of hundreds of city microbiome profiles allows the development of increasingly accurate predictors of the origin of a sample based on its microbiota composition. Typical microbiome studies involve the analysis of bacterial abundance profiles. RESULTS: Here we use a transformation of the conventional bacterial strain or gene abundance profiles to functional profiles that account for bacterial metabolism and other cell functionalities. These profiles are used as features for city classification in a machine learning algorithm that allows the extraction of the most relevant features for the classification. CONCLUSIONS: We demonstrate here that the use of functional profiles not only predict accurately the most likely origin of a sample but also to provide an interesting functional point of view of the biogeography of the microbiota. Interestingly, we show how cities can be classified based on the observed profile of antibiotic resistances. REVIEWERS: Open peer review: Reviewed by Jin Zhuang Dou, Jing Zhou, Torsten Semmler and Eran Elhaik.
Keywords: biomarkers; Cities; Drug Resistance, Microbial; Machine Learning; Metabolome; Metagenome; metagenomics; Microbiota

Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape. - Cubuk, Cankut; Hidalgo, Marta R; Amadoz, Alicia; Pujana, Miguel A; Mateo, Francesca; Herranz, Carmen; Carbonell-Caballero, José; Dopazo, Joaquin (Cancer Res, 2018)
Metabolic reprogramming plays an important role in cancer development and progression and is a well-established hallmark of cancer. Despite its inherent complexity, cellular metabolism can be decomposed into functional modules that represent fundamental metabolic processes. Here, we performed a pan-cancer study involving 9,428 samples from 25 cancer types to reveal metabolic modules whose individual or coordinated activity predict cancer type and outcome, in turn highlighting novel therapeutic opportunities. Integration of gene expression levels into metabolic modules suggests that the activity of specific modules differs between cancers and the corresponding tissues of origin. Some modules may cooperate, as indicated by the positive correlation of their activity across a range of tumors. The activity of many metabolic modules was significantly associated with prognosis at a stronger magnitude than any of their constituent genes. Thus, modules may be classified as tumor suppressors and oncomodules according to their potential impact on cancer progression. Using this modeling framework, we also propose novel potential therapeutic targets that constitute alternative ways of treating cancer by inhibiting their reprogrammed metabolism. Collectively, this study provides an extensive resource of predicted cancer metabolic profiles and dependencies. Combining gene expression with metabolic modules identifies molecular mechanisms of cancer undetected on an individual gene level and allows discovery of new potential therapeutic targets. .
Keywords: Cell Line, Tumor; Cluster Analysis; Disease Progression; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Gene Regulatory Networks; Humans; Kaplan-Meier Estimate; Metabolome; mutation; Neoplasms; Oncogenes; Phenotype; Prognosis; RNA, Small Interfering; Sequence Analysis, RNA; Transcriptome; Treatment Outcome