Consistency across multi-omics layers in a drug-perturbed gut microbial community

Multi-omics analyses are used in microbiome studies to understand molecular changes in microbial communities exposed to different conditions. However, it is not always clear how much each omics data type contributes to our understanding and whether they are concordant with each other. Here, we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers (16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics and metabolomics). We find that all the omics methods with species resolution are highly consistent in estimating relative species abundances. Furthermore, different omics methods complement each other for capturing functional changes. For example, while nearly all the omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control. Metabolomics revealed a decrease in oligosaccharide uptake, likely caused by Bacteroidota depletion. Our study highlights how multi-omics datasets can be utilized to reveal complex molecular responses to external perturbations in microbial communities

Predicting human immunodeficiency virus (HIV) drug resistance using recurrent neural networks

Predicting HIV resistance to drugs is one of many problems for which bioinformaticians have implemented and trained machine learning methods, such as neural networks. Predicting HIV resistance would be much easier if we could directly use the three-dimensional (3D) structure of the targeted protein sequences, but unfortunately we rarely have enough structural information available to train a neural network. Fur-thermore, prediction of the 3D structure of a protein is not straightforward. However, characteristics related to the 3D structure can be used to train a machine learning algorithm as an alternative to take into account the information of the protein folding in the 3D space. Here, starting from this philosophy, we select the amino acid energies as features to predict HIV drug resistance, using a specific topology of a neural network. In this paper, we demonstrate that the amino acid ener-gies are good features to represent the HIV genotype. In addi-tion, it was shown that Bidirectional Recurrent Neural Networks can be used as an efficient classification method for this prob-lem. The prediction performance that was obtained was greater than or at least comparable to results obtained previously. The accuracies vary between 81.3% and 94.7%

New insights into the mechanisms underlying 5-fluorouracil-induced intestinal toxicity based on transcriptomic and metabolomic responses in human intestinal organoids

5-Fluorouracil (5-FU) is a widely used chemotherapeutical that induces acute toxicity in the small and large intestine of patients. Symptoms can be severe and lead to the interruption of cancer treatments. However, there is limited understanding of the molecular mechanisms underlying 5-FU-induced intestinal toxicity. In this study, well-established 3D organoid models of human colon and small intestine (SI) were used to characterize 5-FU transcriptomic and metabolomic responses. Clinically relevant 5-FU concentrations for in vitro testing in organoids were established using physiologically based pharmacokinetic simulation of dosing regimens recommended for cancer patients, resulting in exposures to 10, 100 and 1000 mu M. After treatment, different measurements were performed: cell viability and apoptosis; image analysis of cell morphological changes; RNA sequencing; and metabolome analysis of supernatant from organoids cultures. Based on analysis of the differentially expressed genes, the most prominent molecular pathways affected by 5-FU included cell cycle, p53 signalling, mitochondrial ATP synthesis and apoptosis. Short time-series expression miner demonstrated tissue-specific mechanisms affected by 5-FU, namely biosynthesis and transport of small molecules, and mRNA translation for colon; cell signalling mediated by Rho GTPases and fork-head box transcription factors for SI. Metabolomic analysis showed that in addition to the effects on TCA cycle and oxidative stress in both organoids, tissue-specific metabolic alterations were also induced by 5-FU. Multi-omics integration identified transcription factor E2F1, a regulator of cell cycle and apoptosis, as the best key node across all samples. These results provide new insights into 5-FU toxicity mechanisms and underline the relevance of human organoid models in the safety assessment in drug development