Kévin Contrepois

Optimized Analytical Procedures for the Untargeted Metabolomic Profiling of Human Urine and Plasma by Combining Hydrophilic Interaction (HILIC) and Reverse-Phase Liquid Chromatography (RPLC)–Mass Spectrometry

Profiling of body fluids is crucial for monitoring and discovering metabolic markers of health and disease and for providing insights into human physiology. Since human urine and plasma each contain an extreme diversity of metabolites, a single liquid chromatographic system when coupled to mass spectrometry (MS) is not sufficient to achieve reasonable metabolome coverage. Hydrophilic interaction liquid chromatography (HILIC) offers complementary information to reverse-phase liquid chromatography (RPLC) by retaining polar metabolites. With the objective of finding the optimal combined chromatographic solution to profile urine and plasma, we systematically investigated the performance of five HILIC columns with different chemistries operated at three different pH (acidic, neutral, basic) and five C18-silica RPLC columns. The zwitterionic column ZIC-HILIC operated at neutral pH provided optimal performance on a large set of hydrophilic metabolites. The RPLC columns Hypersil GOLD and Zorbax SB aq were proven to be best suited for the metabolic profiling of urine and plasma, respectively. Importantly, the optimized HILIC-MS method showed excellent intrabatch peak area reproducibility (CV < 12%) and good long-term interbatch (40 days) peak area reproducibility (CV < 22%) that were similar to those of RPLC-MS procedures. Finally, combining the optimal HILIC- and RPLC-MS approaches greatly expanded metabolome coverage with 44% and 108% new metabolic features detected compared with RPLC-MS alone for urine and plasma, respectively. The proposed combined LC-MS approaches improve the comprehensiveness of global metabolic profiling of body fluids and thus are valuable for monitoring and discovering metabolic changes associated with health and disease in clinical research studies.

Ultra-High Performance Liquid Chromatography−Mass Spectrometry for the Fast Profiling of Histone Post-Translational Modifications

Histones are subjected to extensive post-translational modifications (PTMs) that are known to play key roles in many biological processes. In this study, we report a fast, efficient, highly reproducible, and easily automated method involving ultra-high performance liquid chromatography (UHPLC) coupled to a high resolution/high mass accuracy LTQ-Orbitrap mass spectrometer to profile core histone modifications/variants from WI-38 primary human fibroblasts. The whole analysis was performed on intact unfractionated histones within 19 min, which is ∼3-fold faster than previously published procedures. High mass accuracy measurements combined with top-down tandem mass spectrometry (MS) experiments enable accurate histone identification. Experimental and biological variations were thoroughly assessed and were 8% and 16% on average, respectively. With a sample preparation reduced to the minimum, characterization of the most abundant histones can be achieved in a single experiment. Semi-quantitative information can be obtained with respect to the relative abundances of the detected isoforms through a label-free approach. Isoform identities and relative distributions were further confirmed by the LC-MS/MS analysis of tryptic digests. Overall, our UHPLC-MS approach for histone profiling offers a sensitive and reproducible tool that will be of great value for exploring PTMs and variants and can readily be applied to clinical or pharmaceutical studies.

Deacetylation of H4-K16Ac and heterochromatin assembly in senescence

BackgroundCellular senescence is a stress response of mammalian cells leading to a durable arrest of cell proliferation that has been implicated in tumor suppression, wound healing, and aging. The proliferative arrest is mediated by transcriptional repression of genes essential for cell division by the retinoblastoma protein family. This repression is accompanied by varying degrees of heterochromatin assembly, but little is known regarding the molecular mechanisms involved.ResultsWe found that both deacetylation of H4-K16Ac and expression of HMGA1/2 can contribute to DNA compaction during senescence. SIRT2, an NAD-dependent class III histone deacetylase, contributes to H4-K16Ac deacetylation and DNA compaction in human fibroblast cell lines that assemble striking senescence-associated heterochromatin foci (SAHFs). Decreased H4-K16Ac was observed in both replicative and oncogene-induced senescence of these cells. In contrast, this mechanism was inoperative in a fibroblast cell line that did not assemble extensive heterochromatin during senescence. Treatment of senescent cells with trichostatin A, a class I/II histone deacetylase inhibitor, also induced rapid and reversible decondensation of SAHFs. Inhibition of DNA compaction did not significantly affect the stability of the senescent state.ConclusionsVariable DNA compaction observed during senescence is explained in part by cell-type specific regulation of H4 deacetylation and HMGA1/2 expression. Deacetylation of H4-K16Ac during senescence may explain reported decreases in this mark during mammalian aging and in cancer cells.

Histone variant H2A.J accumulates in senescent cells and promotes inflammatory gene expression

The senescence of mammalian cells is characterized by a proliferative arrest in response to stress and the expression of an inflammatory phenotype. Here we show that histone H2A.J, a poorly studied H2A variant found only in mammals, accumulates in human fibroblasts in senescence with persistent DNA damage. H2A.J also accumulates in mice with aging in a tissue-specific manner and in human skin. Knock-down of H2A.J inhibits the expression of inflammatory genes that contribute to the senescent-associated secretory phenotype (SASP), and over expression of H2A.J increases the expression of some of these genes in proliferating cells. H2A.J accumulation may thus promote the signalling of senescent cells to the immune system, and it may contribute to chronic inflammation and the development of aging-associated diseases.

Integrative Personal Omics Profiles during Periods of Weight Gain and Loss

Advances in omics technologies now allow an unprecedented level of phenotyping for human diseases, including obesity, in which individual responses to excess weight are heterogeneous and unpredictable. To aid the development of better understanding of these phenotypes, we performed a controlled longitudinal weight perturbation study combining multiple omics strategies (genomics, transcriptomics, multiple proteomics assays, metabolomics, and microbiomics) during periods of weight gain and loss in humans. Results demonstrated that: (1) weight gain is associated with the activation of strong inflammatory and hypertrophic cardiomyopathy signatures in blood; (2) although weight loss reverses some changes, a number of signatures persist, indicative of long-term physiologic changes; (3) we observed omics signatures associated with insulin resistance that may serve as novel diagnostics; (4) specific biomolecules were highly individualized and stable in response to perturbations, potentially representing stable personalized markers. Most data are available open access and serve as a valuable resource for the community.

Can Metabolic Profiles Be Used as a Phenotypic Readout of the Genome to Enhance Precision Medicine

The past decade has witnessed considerable advancements in sequencing technologies, which have allowed comprehensive investigation of genetic variation of individuals at a moderate cost and within a reasonable time frame. Personalized disease risk and drug response predictions based on genomic sequences are a cornerstone of preventive precision medicine, and have also been successful at informing therapeutic decisions. However, genomics is limited in predicting the onset of most common diseases (i.e., cancer, diabetes, and cardiovascular disorders) because genetic information is mostly static and does not account for dynamic environmental (i.e., diet and lifestyle) or gut microbiota influences. Metabolomics, the study of a large collection of metabolites, offers the advantage to measure the functional readout of activity and phenotype encoded in the genome. Hence, combining genetic and metabolic information provides a unique opportunity to gain further insights on how the genetic program is translated into biological function through metabolites, and how alterations in the program associate with the onset of diseases. This approach has already proven very useful at diagnosing and understanding the pathogenesis of rare inherited metabolic disorders. Furthermore, metabolic profiles are influenced by the environment and the gut microbes; thus metabolomics has the potential to unravel the impact of nongenetic factors on disease onset as well as reveal early biomarkers that may improve risk assessment and diagnosis of complex diseases. Thanks to rapid improvements in technology, mass spectrometry–based metabolomics can now robustly profile a broad spectrum of metabolites at a relatively low cost (1). The concept that genetic variations can be captured at the metabolite level in a population was first demonstrated in 2008 by Gieger et al. (2). That study gave a glimpse of the usefulness of combining genetic information with metabolic traits to understand the pathogenesis of common diseases and the influence of environment. In 2015, Guo …

Value of Circulating Cytokine Profiling During Submaximal Exercise Testing in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome

Myalgic Encephalomyelitis or Chronic Fatigue Syndrome (ME/CFS) is a heterogeneous syndrome in which patients often experience severe fatigue and malaise following exertion. Immune and cardiovascular dysfunction have been postulated to play a role in the pathophysiology. We therefore, examined whether cytokine profiling or cardiovascular testing following exercise would differentiate patients with ME/CFS. Twenty-four ME/CFS patients were matched to 24 sedentary controls and underwent cardiovascular and circulating immune profiling. Cardiovascular analysis included echocardiography, cardiopulmonary exercise and endothelial function testing. Cytokine and growth factor profiles were analyzed using a 51-plex Luminex bead kit at baseline and 18 hours following exercise. Cardiac structure and exercise capacity were similar between groups. Sparse partial least square discriminant analyses of cytokine profiles 18 hours post exercise offered the most reliable discrimination between ME/CFS and controls (κ = 0.62(0.34,0.84)). The most discriminatory cytokines post exercise were CD40L, platelet activator inhibitor, interleukin 1-β, interferon-α and CXCL1. In conclusion, cytokine profiling following exercise may help differentiate patients with ME/CFS from sedentary controls.

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy

Motivation: Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full‐term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy‐related pathologies including preterm birth and preeclampsia. Results: We performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm‐enriched populations and in vivo analysis of immune‐modulating interventions based on the mechanisms identified. Availability and implementation: Datasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics‐pregnancy/. Supplementary information: Supplementary data are available at Bioinformatics online.