Chromatin immunoprecipitation improvements for the processing of small frozen pieces of adipose tissue.

Chromatin immunoprecipitation (ChIP) has gained importance to identify links between the genome and the proteome. Adipose tissue has emerged as an active tissue, which secretes a wide range of molecules that have been related to metabolic and obesity-related disorders, such as diabetes, cardiovascular failure, metabolic syndrome, or cancer. In turn, epigenetics has raised the importance in discerning the possible relationship between metabolic disorders, lifestyle and environment. However, ChIP application in human adipose tissue is limited by several factors, such as sample size, frozen sample availability, high lipid content and cellular composition of the tissue. Here, we optimize the standard protocol of ChIP for small pieces of frozen human adipose tissue. In addition, we test ChIP for the histone mark H3K4m3, which is related to active promoters, and validate the performance of the ChIP by analyzing gene promoters for factors usually studied in adipose tissue using qPCR. Our improvements result in a higher performance in chromatin shearing and DNA recovery of adipocytes from the tissue, which may be useful for ChIP-qPCR or ChIP-seq analysis

Análisis de ácidos grasos en grasa subcutánea procedentes de animales de alimentación fiable y segura

Iberian pig fat characteristics depend on the type of feeding at the end of its finish-fattening period. The routine analysis to differentiate among the qualities of the feeding types given to the pigs in the fattening stage has been the use of fatty acid profiles by gas chromatography. Due to de doubts about the effectiveness of this analysis in the montanera period, the aim of this global study was to test the validity of various analytical methods to determine the feeding type of Iberian pigs, focusing on the fatty acid profile. Three montanera periods with a total of 749 samples from 38 batches have been studied; using a total of 144 dry-cured shoulder shanks, 99 of which are of known pig origin. Results showed that the determination of the fatty acid profile using gas chromatography is not a consistent method to classify the animals according to diet in the recebo category, although it provided good percentages of success for classifying the bellota and cebo categories.Las características de la grasa de cerdo Ibérico dependen del tipo de alimentación recibida en el último estadío de engorde. El análisis que se ha utilizado hasta ahora para diferenciar las diferentes calidades de alimentación de los cerdos en este período ha sido el análisis de los perfiles de ácidos grasos de la grasa por técnicas de cromatografía de gases. Debido a las dudas sobre la efectividad de esta técnica en la montanera, el objetivo del proyecto global (RTA2008-0026) fue probar la validez de varios métodos analíticos para determinar el tipo de alimentación del cerdo ibérico, centrándonos en este trabajo en el estudio de los perfiles de ácidos grasos. Para el desarrollo de este estudio se utilizaron tres campañas de montanera con un total de 749 muestras de 38 partidas, y con 144 paletas de las cuales 99 tenían una trazabilidad completa. Los resultados mostraron que la determinación de la alimentación de los cerdos ibéricos usando el análisis del perfil de ácidos grasos no es un método consistente para clasificar los animales de acuerdo a la categoría de recebo, mientras que para las categorías de bellota y cebo, los resultados encontrados mostraron unos buenos porcentajes de éxito

Role of Gut Microbiota on Cardio-Metabolic Parameters and Immunity in Coronary Artery Disease Patients with and without Type-2 Diabetes Mellitus

Gut microbiota composition has been reported as a factor linking host metabolism with the development of cardiovascular diseases (CVD) and intestinal immunity. Such gut microbiota has been shown to aggravate CVD by contributing to the production of trimethylamine N-oxide (TMAO), which is a pro-atherogenic compound. Treg cells expressing the transcription factor Forkhead box protein P3 (FoxP3) play an essential role in the regulation of immune responses to commensal microbiota and have an atheroprotective role. However, the aim of this study was to analyze the role of gut microbiota on cardio-metabolic parameters and immunity in coronary artery disease (CAD) patients with and without type-2 diabetes mellitus (DM2). The study included 16 coronary CAD-DM2 patients, and 16 age, sex, and BMI matched CAD patients without DM2 (CAD-NDM2). Fecal bacterial DNA was extracted and analyzed by sequencing in a GS Junior 454 platform followed by a bioinformatic analysis (QIIME and PICRUSt). The present study indicated that the diversity and composition of gut microbiota were different between the CAD-DM2 and CAD-NDM2 patients. The abundance of phylum Bacteroidetes was lower, whereas the phyla Firmicutes and Proteobacteria were higher in CAD-DM2 patients than those in the CAD-NDM2 group. CAD-DM2 patients had significantly less beneficial or commensal bacteria (such as Faecalibacterium prausnitzii and Bacteroides fragilis) and more opportunistic pathogens (such as Enterobacteriaceae, Streptococcus, and Desulfovibrio). Additionally, CAD-DM2 patients had significantly higher levels of plasma zonulin, TMAO, and IL-1B and significantly lower levels of IL-10 and FOXP3 mRNA expression than CAD-NDM2. Moreover, in the CAD-MD2 group, the increase in Enterobacteriaceae and the decrease in Faecalibacterium prausnitzii were significantly associated with the increase in serum TMAO levels, while the decrease in the abundance of Bacteroides fragilis was associated with the reduction in the FOXP3 mRNA expression, implicated in the development and function of Treg cells. These results suggest that the presence of DM2 is related to an impaired regulation of the immune system in CAD patients, mediated in part by the gut microbiota composition and functionality and the production and effects of their gut microbiota derived molecules

Human adipose tissue H3K4me3 histone mark in adipogenic, lipid metabolism and inflammatory genes is positively associated with BMI and HOMA-IR.

Adipose tissue is considered an important metabolic tissue, in charge of energy storage as well as being able to act in systemic homeostasis and inflammation. Epigenetics involves a series of factors that are important for gene regulation or for chromatin structure, mostly DNA methylation and histone-tail modifications, which can be modified by environmental conditions (nutrition, lifestyle, smoking…). Since metabolic diseases like obesity and diabetes are closely related to lifestyle and nutrition, epigenetic deregulation could play an important role in the onset of these diseases and vice versa. However, little is known about histone marks in human adipose tissue. In a previous work, we developed a protocol for chromatin immunoprecipitation (ChIP) of frozen human adipose tissue. By using this method, this study investigates, for the first time, the H3K4 trimethylation (H3K4me3) mark (open chromatin) on the promoter of several factors involved in adipogenesis, lipid metabolism and inflammation in visceral adipose tissue (VAT) from human subjects with different degrees of body mass index (BMI) and metabolic disease. VAT was collected and frozen at -80°C. 100 mg VAT samples were fixed in 0.5% formaldehyde and homogenized. After sonication, the sheared chromatin was immune-precipitated with an anti-H3K4me3 antibody linked to magnetic beads and purified. H3K4me3 enrichment was analyzed by qPCR for LEP, LPL, SREBF2, SCD1, PPARG, IL6, TNF and E2F1 promoters. mRNA extraction on the same samples was performed to quantify gene expression of these genes. H3K4me3 was enriched at the promoter of E2F1, LPL, SREBF2, SCD1, PPARG and IL6 in lean normoglycemic compared to morbid obese subjects with prediabetes. Accordingly H3K4me3 mark enrichment at E2F1, LPL, SREBF2, SCD1, PPARG and IL6 promoters was positively correlated with the BMI and the HOMA-IR. Regression analysis showed a strong relationship between the BMI with H3K4me3 at the promoter of E2F1 and LPL, and with mRNA levels of LEP and SCD. In the case of HOMA-IR, the regression analysis showed associations with H3K4me3 enrichment at the promoter of SCD1 and IL6, and with the mRNA of LEP and SCD1. Moreover H3K4me3 at the E2F1 promoter was positively associated to E2F1 mRNA levels. H3K4me3 enrichment in the promoter of LEP, LPL, SREBF2, SCD1, PPARG, IL6, TNF and E2F1 is directly associated with increasing BMI and metabolic deterioration. The H3k4me3 mark could be regulating E3F1 mRNA levels in adipose tissue, while no associations between the promoter enrichment of this mark and mRNA levels existed for the other genes studied

The effect of colostrum source (goat vs. sheep) and timing of the first colostrum feeding (2h vs. 14h after birth) on body weight and immune status of artificially reared newborn lambs

Several factors can affect lamb body weight (BW) and immune status during the first days of life, including colostrum source and timing of the first colostrum feeding. The aim of this study was to evaluate the effects of colostrum source (goat or sheep) and timing of the first colostrum feeding (2 or 14h after birth) on lamb BW and immune status. In this study, 40 lambs were removed from their dams at birth and randomly assigned into 4 groups of 10 lambs each. Lambs were subsequently fed at 2 or 14h after birth with goat or sheep colostrum. Blood samples and BW recording were performed before feeding. Blood plasma was used to measure the immunoglobulin concentration (IgG and IgM), chitotriosidase activity, and complement system activity (total and alternative pathways). In general, no differences in any of the measured variables were observed among the 4 groups, indicating that neither colostrum source nor timing of the first colostrum feeding had an effect on these variables. These findings may improve management on lamb farms that raise animals under artificial conditions, because our results indicate that it is not necessary to feed colostrum to lambs immediately after birth and that goat colostrum may be used to feed newborn lambs

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.This study was supported by COST Action CA18131 “Statistical and machine learning techniques in human microbiome studies”. Estonian Research Council grant PRG548 (JT). Spanish State Research Agency Juan de la Cierva Grant IJC2019-042188-I (LM-Z). EO was founded and OA was supported by Estonian Research Council grant PUT 1371 and EMBO Installation grant 3573. AG was supported by Statutory Research project of the Department of Computer Networks and Systems

Pulmonary Arterial Hypertension Affects the Rat Gut Microbiome

We have analysed whether pulmonary arterial hypertension (PAH) alters the rat faecal microbiota. Wistar rats were injected with the VEGF receptor antagonist SU5416 (20 mg/kg s.c.) and followed for 2 weeks kept in hypoxia (10% O2, PAH) or injected with vehicle and kept in normoxia (controls). Faecal samples were obtained and microbiome composition was determined by 16S rRNA gene sequencing and bioinformatic analysis. No effect of PAH on the global microbiome was found (α- or β-diversity). However, PAH-exposed rats showed gut dysbiosis as indicated by a taxonomy-based analysis. Specifically, PAH rats had a three-fold increase in Firmicutes-to-Bacteroidetes ratio. Within the Firmicutes phylum, there were no large changes in the relative abundance of the bacterial families in PAH. Among Bacteroidetes, all families were less abundant in PAH. A clear separation was observed between the control and PAH clusters based on short chain fatty acid producing bacterial genera. Moreover, acetate was reduced in the serum of PAH rats. In conclusion, faecal microbiota composition is altered as a result of PAH. This misbalanced bacterial ecosystem might in turn play a pathophysiological role in PAH by altering the immunologic, hormonal and metabolic homeostasis.This study is supported by grants from Mineco (SAF2014-55399-R, SAF2014-55523-R, SAF2016-77222 and SAF2017-84494-C2-1R), Instituto de Salud Carlos III (PI15/01100), with funds from the European Union (Fondo Europeo de Desarrollo Regional FEDER). M.C., G.M-P. and S.E-R. are funded by Universidad Complutense, Fondo de Garantía Juvenil (Comunidad de Madrid) and Ciberes grant with funds from Fundación Contra la Hipertensión Pulmonar, a FPU grant from Ministerio de Educación, respectively. J.L.I.G is a CNIC IPP COFUND Fellow and has received funding from the People Programme (Marie Curie Actions) of the FP7/2007-2013 under REA grant agreement n° 600396. The CNIC is supported by MEIC-AEI and the Pro CNIC Foundation, and is a Severo Ochoa Center of Excellence (MEIC award SEV-2015-0505)

Contemporary Challenges and Solutions

CA18131 CP16/00163 NIS-3317 NIS-3318 decision 295741 C18/BM/12585940The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.publishersversionpublishe