Application of an Exploratory Knowledge-Discovery Pipeline Based on Machine Learning to Multi-Scale OMICS Data to Characterise Myocardial Injury in a Cohort of Patients with Septic Shock: An Observational Study.

Currently, there is no therapy targeting septic cardiomyopathy (SC), a key contributor to organ dysfunction in sepsis. In this study, we used a machine learning (ML) pipeline to explore transcriptomic, proteomic, and metabolomic data from patients with septic shock, and prospectively collected measurements of high-sensitive cardiac troponin and echocardiography. The purposes of the study were to suggest an exploratory methodology to identify and characterise the multiOMICs profile of (i) myocardial injury in patients with septic shock, and of (ii) cardiac dysfunction in patients with myocardial injury. The study included 27 adult patients admitted for septic shock. Peripheral blood samples for OMICS analysis and measurements of high-sensitive cardiac troponin T (hscTnT) were collected at two time points during the ICU stay. A ML-based study was designed and implemented to untangle the relations among the OMICS domains and the aforesaid biomarkers. The resulting ML pipeline consisted of two main experimental phases: recursive feature selection (FS) assessing the stability of biomarkers, and classification to characterise the multiOMICS profile of the target biomarkers. The application of a ML pipeline to circulate OMICS data in patients with septic shock has the potential to predict the risk of myocardial injury and the risk of cardiac dysfunction.

Multi-omics analysis reveals that co-exposure to phthalates and metals disturbs urea cycle and choline metabolism.

This study aimed to evaluate the response of HepaRG cells after co-exposure to phthalates and heavy metals, using a high-dimensional biology paradigm (HDB). Liver is the main metabolism site for the majority of xenobiotics. For this reason, the HepaRG cell line was used as an in vitro model, and cells were exposed to two characteristic mixtures of phthalates and heavy metals containing phthalates (DEHP, DiNP, BBzP) and metals (lead, methylmercury, total mercury) in a concentration-dependent manner. The applied chemical mixtures were selected as the most abundant pollutants in the REPRO_PL and PHIME cohorts, which were studied using the exposome-wide approach in the frame of the EU project HEALS. These studies investigated the environmental causation of neurodevelopmental disorders in neonates and across Europe. The INTEGRA computational platform was used for the calculation of the effective concentrations of the chemicals in the liver through extrapolation from human biomonitoring data and this dose (and a ten-times higher one) was applied to the hepatocyte model. Multi-omics analysis was performed to reveal the genes, proteins, and metabolites affected by the exposure to these chemical mixtures. By extension, we could detect the perturbed metabolic pathways. The generated data were analyzed using advanced bioinformatic tools following the HEALS connectivity paradigm for multi-omics pathway analysis. Co-mapped transcriptomics and proteomics data showed that co-exposure to phthalates and heavy metals leads to perturbations of the urea cycle due to differential expression levels of arginase-1 and -2, argininosuccinate synthase, carbamoyl-phosphate synthase, ornithine carbamoyltransferase, and argininosuccinate lyase. Joint pathway analysis of proteomics and metabolomics data revealed that the detected proteins and metabolites, choline phosphate cytidylyltransferase A, phospholipase D3, group XIIA secretory phospholipase A2, α-phosphatidylcholine, and the a 1,2-diacyl-sn-glycero-3-phosphocholine, are responsible for the homeostasis of the metabolic pathways phosphatidylcholine biosynthesis I, and phospholipases metabolism. The urea, phosphatidylcholine biosynthesis I and phospholipase metabolic pathways are of particular interest since they have been identified also in human samples from the REPRO_PL and PHIME cohorts using untargeted metabolomics analysis and have been associated with impaired psychomotor development in children at the age of two. In conclusion, this study provides the mechanistic evidence that co-exposure to phthalates and metals disturb biochemical processes related to mitochondrial respiration during critical developmental stages, which are clinically linked to neurodevelopmental perturbations.

Differential protein expression of hippocampal cells associated with heavy metals (Pb, As, and MeHg) neurotoxicity: Deepening into the molecular mechanism of neurodegenerative diseases.

Chronic exposure to heavy metals such as Pb, As, and MeHg can be associated with an increased risk of developing neurodegenerative diseases. Our in vitro bioassays results showed the potency of heavy metals in the order of Pb < As < MeHg on hippocampal cells. The main objective of this study was combining in vitro label free proteomics and systems biology approach for elucidating patterns of biological response, discovering underlying mechanisms of Pb, As, and MeHg toxicity in hippocampal cells. The omics data was refined by using different filters and normalization and multilevel analysis tools were employed to explore the data visualization. The functional and pathway visualization was performed by using Gene ontology and PathVisio tools. Using these all integrated approaches, we identified significant proteins across treatments within the mitochondrial dysfunction, oxidative stress, ubiquitin proteome dysfunction, and mRNA splicing related to neurodegenerative diseases. The systems biology analysis revealed significant alterations in proteins implicated in Parkinson's disease (PD) and Alzheimer's disease (AD). The current proteomics analysis of three metals support the insight into the proteins involved in neurodegeneration and the altered proteins can be useful for metal-specific biomarkers of exposure and its adverse effects. SIGNIFICANCE: The proteomics techniques have been claimed to be more sensitive than the conventional toxicological assays, facilitating the measurement of responses to heavy metals (Pb, As, and MeHg) exposure before obvious harm has occurred demonstrating their predictive value. Also, proteomics allows for the comparison of responses between Pb, As, and MeHg metals, permitting the evaluation of potency differences hippocampal cells of the brain. Hereby, the molecular information provided by pathway and gene functional analysis can be used to develop a more thorough understanding of each metal mechanism at the protein level for different neurological adverse outcomes (e.g. Parkinson's disease, Alzheimer's diseases). Efforts are put into developing proteomics based toxicity testing methods using in vitro models for improving human risk assessment. Some of the key proteins identified can also potentially be used as biomarkers in epidemiologic studies. These heavy metal response patterns shed new light on the mechanisms of mRNA splicing, ubiquitin pathway role in neurodegeneration, and can be useful for the development of molecular biomarkers of heavy metals exposure.

An Innovative Approach for The Integration of Proteomics and Metabolomics Data In Severe Septic Shock Patients Stratified for Mortality.

In this work, we examined plasma metabolome, proteome and clinical features in patients with severe septic shock enrolled in the multicenter ALBIOS study. The objective was to identify changes in the levels of metabolites involved in septic shock progression and to integrate this information with the variation occurring in proteins and clinical data. Mass spectrometry-based targeted metabolomics and untargeted proteomics allowed us to quantify absolute metabolites concentration and relative proteins abundance. We computed the ratio D7/D1 to take into account their variation from day 1 (D1) to day 7 (D7) after shock diagnosis. Patients were divided into two groups according to 28-day mortality. Three different elastic net logistic regression models were built: one on metabolites only, one on metabolites and proteins and one to integrate metabolomics and proteomics data with clinical parameters. Linear discriminant analysis and Partial least squares Discriminant Analysis were also implemented. All the obtained models correctly classified the observations in the testing set. By looking at the variable importance (VIP) and the selected features, the integration of metabolomics with proteomics data showed the importance of circulating lipids and coagulation cascade in septic shock progression, thus capturing a further layer of biological information complementary to metabolomics information.

Multicenter experiment for quality control of peptide-centric LC-MS/MS analysis - A longitudinal performance assessment with nLC coupled to orbitrap MS analyzers.

Proteomic technologies based on mass spectrometry (MS) have greatly evolved in the past years, and nowadays it is possible to routinely identify thousands of peptides from complex biological samples in a single LC-MS/MS experiment. Despite the advancements in proteomic technologies, the scientific community still faces important challenges in terms of depth and reproducibility of proteomics analyses. Here, we present a multicenter study designed to evaluate long-term performance of LC-MS/MS platforms within the Spanish Proteomics Facilities Network (ProteoRed-ISCIII). The study was performed under well-established standard operating procedures, and demonstrated that it is possible to attain qualitative and quantitative reproducibility over time. Our study highlights the importance of deploying quality assessment metrics routinely in individual laboratories and in multi-laboratory studies. The mass spectrometry data have been deposited to the ProteomeXchange Consortium with the data set identifier PXD000205.This article is part of a Special Issue entitled: HUPO 2014.

Proteomic analysis of outer membrane vesicles from the probiotic strain Escherichia coli Nissle 1917.

Escherichia coli Nissle 1917 (EcN) is a probiotic used for the treatment of intestinal disorders. EcN improves gastrointestinal homeostasis and microbiota balance; however, little is known about how this probiotic delivers effector molecules to the host. Outer membrane vesicles (OMVs) are constitutively produced by Gram-negative bacteria and have a relevant role in bacteria-host interactions. Using 1D SDS-PAGE and highly sensitive LC-MS/MS analysis we identified in this study 192 EcN vesicular proteins with high confidence in three independent biological replicates. Of these proteins, 18 were encoded by strain-linked genes and 57 were common to pathogen-derived OMVs. These proteins may contribute to the ability of this probiotic to colonize the human gut as they fulfil functions related to adhesion, immune modulation or bacterial survival in host niches. This study describes the first global OMV proteome of a probiotic strain and provides evidence that probiotic-derived OMVs contain proteins that can target these vesicles to the host and mediate their beneficial effects on intestinal function. All MS data have been deposited in the ProteomeXchange with identifier PXD000367 (http://proteomecentral.proteomexchange.org/dataset/PXD000367).

Insights into Maize LEA proteins: from proteomics to functional approaches.

LEA (late embryogenesis abundant) proteins participate in plant stress tolerance responses, but the mechanisms by which protection occurs are not fully understood. In the present work the unfolded proteins from maize dry embryos were analyzed by mass spectrometry. Twenty embryo proteins were identified, and among them 13 corresponded to LEA-type proteins. We selected three major LEA proteins, Emb564, Rab17 and Mlg3, belonging to groups 1, 2 and 3, respectively, and we undertook a comparative study in order to highlight differences among them. The post-translational modifications of native proteins were analyzed and the anti-aggregation properties of recombinant Emb564, Rab17 and Mgl3 proteins were evaluated in vitro. In addition, the protective effects of the LEA proteins were assessed in living cells under stress in Escherichia coli cells and in Nicotiana bentamiana leaves agroinfiltrated with fluorescent LEA-green fluorescent protein (GFP) fusions. Protein visualization by confocal microscopy indicated that cells expressing Mg3-GFP showed reduced cell shrinkage effects during dehydration and that Rab17-GFP co-localized to leaf oil bodies after heat shock. Overall, the results highlight differences and suggest functional diversity among maize LEA groups.