A critical review of wastewater-based epidemiology as a tool to evaluate the unintentional human exposure to potentially harmful chemicals.

Wastewater-based epidemiology (WBE) is a powerful tool to gather epidemiological insights at the community level, providing objective data on population exposure to harmful substances. A considerable portion of the human exposure to these potentially harmful chemicals occurs unintentionally, unlike substances such as pharmaceuticals, illicit drugs, or alcohol. In this context, this comprehensive review analyzes WBE studies focused on classes of organic chemicals to which humans are unintentionally exposed, namely organophosphorus flame retardants, per- and polyfluoroalkyl substances (PFAS), benzotriazoles and benzothiazoles, phthalates and terephthalates, benzophenones, pesticides, bisphenols, and parabens.

Organ-specific metabolic pathways distinguish prediabetes, type 2 diabetes, and normal tissues.

Environmental and genetic factors cause defects in pancreatic islets driving type 2 diabetes (T2D) together with the progression of multi-tissue insulin resistance. Mass spectrometry proteomics on samples from five key metabolic tissues of a cross-sectional cohort of 43 multi-organ donors provides deep coverage of their proteomes. Enrichment analysis of Gene Ontology terms provides a tissue-specific map of altered biological processes across healthy, prediabetes (PD), and T2D subjects.

Interpretable machine learning identifies paediatric Systemic Lupus Erythematosus subtypes based on gene expression data.

Transcriptomic analyses are commonly used to identify differentially expressed genes between patients and controls, or within individuals across disease courses. These methods, whilst effective, cannot encompass the combinatorial effects of genes driving disease. We applied rule-based machine learning (RBML) models and rule networks (RN) to an existing paediatric Systemic Lupus Erythematosus (SLE) blood expression dataset, with the goal of developing gene networks to separate low and high disease activity (DA1 and DA3).

Machine Learning-Based Analysis of Glioma Grades Reveals Co-Enrichment.

Gliomas develop and grow in the brain and central nervous system. Examining glioma grading processes is valuable for improving therapeutic challenges. One of the most extensive repositories storing transcriptomics data for gliomas is The Cancer Genome Atlas (TCGA).

MetaFetcheR: An R Package for Complete Mapping of Small-Compound Data.

Small-compound databases contain a large amount of information for metabolites and metabolic pathways. However, the plethora of such databases and the redundancy of their information lead to major issues with analysis and standardization. A lack of preventive establishment of means of data access at the infant stages of a project might lead to mislabelled compounds, reduced statistical power, and large delays in delivery of results.

The Thioesterase as a Regulator of Lipid Metabolism in Type 2 Diabetes Detected in a Multi-Omics Study of Human Liver.

Type 2 diabetes (T2D) is characterized by pathophysiological alterations in lipid metabolism. One strategy to understand the molecular mechanisms behind these abnormalities is to identify -regulatory elements (CREs) located in chromatin-accessible regions of the genome that regulate key genes. In this study we integrated assay for transposase-accessible chromatin followed by sequencing (ATAC-seq) data, widely used to decode chromatin accessibility, with multi-omics data and publicly available CRE databases to identify candidate CREs associated with T2D for further experimental validations.

Mapping chromatin accessibility and active regulatory elements reveals pathological mechanisms in human gliomas.

Chromatin structure and accessibility, and combinatorial binding of transcription factors to regulatory elements in genomic DNA control transcription. Genetic variations in genes encoding histones, epigenetics-related enzymes or modifiers affect chromatin structure/dynamics and result in alterations in gene expression contributing to cancer development or progression. Gliomas are brain tumors frequently associated with epigenetics-related gene deregulation.

Multifaceted regulation of hepatic lipid metabolism by YY1.

Recent studies suggested that dysregulated plays a pivotal role in many liver diseases. To obtain a detailed view of genes and pathways regulated by YY1 in the liver, we carried out RNA sequencing in HepG2 cells after YY1 knockdown. A rigid set of 2,081 differentially expressed genes was identified by comparing the YY1-knockdown samples (n = 8) with the control samples (n = 14).

A non-coding cancer mutation disrupting an HNF4α binding motif affects an enhancer regulating genes associated to the progression of liver cancer.

Background: Somatic mutations in coding regions of the genome may result in non-functional proteins that can lead to cancer or other diseases, however cancer mutations in the non-coding regions have rarely been studied and the interpretation of their effects is difficult. Non-coding mutations might act by breaking or creating transcription factor binding motifs in promoters, enhancers or silencers resulting in altered expression of target gene(s). A high number of mutations have been reported in coding and non-coding regions in cells of liver cancer.

Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is a heterogeneous neuropsychiatric disorder with a complex genetic background. Analysis of altered molecular processes in ASD patients requires linear and nonlinear methods that provide interpretable solutions. Interpretable machine learning provides legible models that allow explaining biological mechanisms and support analysis of clinical subgroups.

R.ROSETTA: an interpretable machine learning framework.

Background: Machine learning involves strategies and algorithms that may assist bioinformatics analyses in terms of data mining and knowledge discovery. In several applications, viz. in Life Sciences, it is often more important to understand how a prediction was obtained rather than knowing what prediction was made.

Nucleolar rDNA folds into condensed foci with a specific combination of epigenetic marks.

Arabidopsis thaliana 45S ribosomal genes (rDNA) are located in tandem arrays called nucleolus organizing regions on the termini of chromosomes 2 and 4 (NOR2 and NOR4) and encode rRNA, a crucial structural element of the ribosome. The current model of rDNA organization suggests that inactive rRNA genes accumulate in the condensed chromocenters in the nucleus and at the nucleolar periphery, while the nucleolus delineates active genes. We challenge the perspective that all intranucleolar rDNA is active by showing that a subset of nucleolar rDNA assembles into condensed foci marked by H3.

Single nucleus transcriptomics data integration recapitulates the major cell types in human liver.

Aim: The aim of this study was to explore the benefits of data integration from different platforms for single nucleus transcriptomics profiling to characterize cell populations in human liver.

Methods: We generated single-nucleus RNA sequencing data from Chromium 10X Genomics and Drop-seq for a human liver sample. We utilized state of the art bioinformatics tools to undertake a rigorous quality control and to integrate the data into a common space summarizing the gene expression variation from the respective platforms, while accounting for known and unknown confounding factors.

Integration of whole-body [F]FDG PET/MRI with non-targeted metabolomics can provide new insights on tissue-specific insulin resistance in type 2 diabetes.

Alteration of various metabolites has been linked to type 2 diabetes (T2D) and insulin resistance. However, identifying significant associations between metabolites and tissue-specific phenotypes requires a multi-omics approach. In a cohort of 42 subjects with different levels of glucose tolerance (normal, prediabetes and T2D) matched for age and body mass index, we calculated associations between parameters of whole-body positron emission tomography (PET)/magnetic resonance imaging (MRI) during hyperinsulinemic euglycemic clamp and non-targeted metabolomics profiling for subcutaneous adipose tissue (SAT) and plasma.

Assessment of the chemical pollution status of the Dniester River Basin by wide-scope target and suspect screening using mass spectrometric techniques.

The quality of the Dniester River Basin has been seriously impacted by the chemicals released by agriculture, industry, and wastewater discharges. To assess its current chemical pollution status, a transboundary monitoring campaign was conducted in May 2019. Thirteen surface water, 13 sediment, and three biota samples were collected and analyzed using generic sample preparation methods for the determination of organic substances by liquid chromatography high-resolution mass spectrometry (LC-HRMS) and metals by inductively coupled plasma mass spectrometry (ICP-MS).

A Multi-Omics Approach to Liver Diseases: Integration of Single Nuclei Transcriptomics with Proteomics and HiCap Bulk Data in Human Liver.

The liver is the largest solid organ and a primary metabolic hub. In recent years, intact cell nuclei were used to perform single-nuclei RNA-seq (snRNA-seq) for tissues difficult to dissociate and for flash-frozen archived tissue samples to discover unknown and rare cell subpopulations. In this study, we performed snRNA-seq of a liver sample to identify subpopulations of cells based on nuclear transcriptomics.

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.

The discovery of drivers of cancer has traditionally focused on protein-coding genes. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods.

Author Correction: Intra- and inter-individual metabolic profiling highlights carnitine and lysophosphatidylcholine pathways as key molecular defects in type 2 diabetes.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Wide-scope target and suspect screening methodologies to investigate the occurrence of new psychoactive substances in influent wastewater from Athens.

Almost all licit and illicit drugs consumed by the society end up either unchanged or as a mixture of metabolites in the sewage systems. The analysis of influent wastewater samples and the estimation of drug consumption is the field of wastewater-based epidemiology (WBE). A new trend of WBE is the estimation of the consumption of New Psychoactive Substances (NPS), which are legal replacements of established narcotic and psychotropic drugs with slightly modified chemical structures and similar or new effects.