A genome-wide computational approach to define microRNA-Polycomb/trithorax gene regulatory circuits in Drosophila.

Characterization of gene regulatory networks is fundamental to understanding homeostatic development. This process can be simplified by analyzing relatively simple genomes such as the genome of Drosophila melanogaster. In this work we have developed a computational framework in Drosophila to explore for the presence of gene regulatory circuits between two large groups of transcriptional regulators: the epigenetic group of the Polycomb/trithorax (PcG/trxG) proteins and the microRNAs (miRNAs). We have searched genome-wide for miRNA targets in PcG/trxG transcripts as well as for Polycomb Response Elements (PREs) in miRNA genes. Our results show that 10% of the analyzed miRNAs could be controlling PcG/trxG gene expression, while 40% of those miRNAs are putatively controlled by the selected set of PcG/trxG proteins. The integration of these analyses has resulted in the predicted existence of 3 classes of miRNA-PcG/trxG crosstalk interactions that define potential regulatory circuits. In the first class, miRNA-PcG circuits are defined by miRNAs that reciprocally crosstalk with PcG. In the second, miRNA-trxG circuits are defined by miRNAs that reciprocally crosstalk with trxG. In the third class, miRNA-PcG/trxG shared circuits are defined by miRNAs that crosstalk with both PcG and trxG regulators. These putative regulatory circuits may uncover a novel mechanism in Drosophila for the control of PcG/trxG and miRNAs levels of expression. The computational framework developed here for Drosophila melanogaster can serve as a model case for similar analyses in other species. Moreover, our work provides, for the first time, a new and useful resource for the Drosophila community to consult prior to experimental studies investigating the epigenetic regulatory networks of miRNA-PcG/trxG mediated gene expression.

Transcriptional regulation by NR5A2 links differentiation and inflammation in the pancreas.

Chronic inflammation increases the risk of developing one of several types of cancer. Inflammatory responses are currently thought to be controlled by mechanisms that rely on transcriptional networks that are distinct from those involved in cell differentiation. The orphan nuclear receptor NR5A2 participates in a wide variety of processes, including cholesterol and glucose metabolism in the liver, resolution of endoplasmic reticulum stress, intestinal glucocorticoid production, pancreatic development and acinar differentiation. In genome-wide association studies, single nucleotide polymorphisms in the vicinity of NR5A2 have previously been associated with the risk of pancreatic adenocarcinoma. In mice, Nr5a2 heterozygosity sensitizes the pancreas to damage, impairs regeneration and cooperates with mutant Kras in tumour progression. Here, using a global transcriptomic analysis, we describe an epithelial-cell-autonomous basal pre-inflammatory state in the pancreas of Nr5a2+/- mice that is reminiscent of the early stages of pancreatitis-induced inflammation and is conserved in histologically normal human pancreases with reduced expression of NR5A2 mRNA. In Nr5a2+/-mice, NR5A2 undergoes a marked transcriptional switch, relocating from differentiation-specific to inflammatory genes and thereby promoting gene transcription that is dependent on the AP-1 transcription factor. Pancreatic deletion of Jun rescues the pre-inflammatory phenotype, as well as binding of NR5A2 to inflammatory gene promoters and the defective regenerative response to damage. These findings support the notion that, in the pancreas, the transcriptional networks involved in differentiation-specific functions also suppress inflammatory programmes. Under conditions of genetic or environmental constraint, these networks can be subverted to foster inflammation.

3D chromatin connectivity underlies replication origin efficiency in mouse embryonic stem cells.

In mammalian cells, chromosomal replication starts at thousands of origins at which replisomes are assembled. Replicative stress triggers additional initiation events from 'dormant' origins whose genomic distribution and regulation are not well understood. In this study, we have analyzed origin activity in mouse embryonic stem cells in the absence or presence of mild replicative stress induced by aphidicolin, a DNA polymerase inhibitor, or by deregulation of origin licensing factor CDC6. In both cases, we observe that the majority of stress-responsive origins are also active in a small fraction of the cell population in a normal S phase, and stress increases their frequency of activation. In a search for the molecular determinants of origin efficiency, we compared the genetic and epigenetic features of origins displaying different levels of activation, and integrated their genomic positions in three-dimensional chromatin interaction networks derived from high-depth Hi-C and promoter-capture Hi-C data. We report that origin efficiency is directly proportional to the proximity to transcriptional start sites and to the number of contacts established between origin-containing chromatin fragments, supporting the organization of origins in higher-level DNA replication factories.

Automatic identification of informative regions with epigenomic changes associated to hematopoiesis.

Hematopoiesis is one of the best characterized biological systems but the connection between chromatin changes and lineage differentiation is not yet well understood. We have developed a bioinformatic workflow to generate a chromatin space that allows to classify 42 human healthy blood epigenomes from the BLUEPRINT, NIH ROADMAP and ENCODE consortia by their cell type. This approach let us to distinguish different cells types based on their epigenomic profiles, thus recapitulating important aspects of human hematopoiesis. The analysis of the orthogonal dimension of the chromatin space identify 32,662 chromatin determinant regions (CDRs), genomic regions with different epigenetic characteristics between the cell types. Functional analysis revealed that these regions are linked with cell identities. The inclusion of leukemia epigenomes in the healthy hematological chromatin sample space gives us insights on the healthy cell types that are more epigenetically similar to the disease samples. Further analysis of tumoral epigenetic alterations in hematopoietic CDRs points to sets of genes that are tightly regulated in leukemic transformations and commonly mutated in other tumors. Our method provides an analytical approach to study the relationship between epigenomic changes and cell lineage differentiation. Method availability: https://github.com/david-juan/ChromDet.

c-Myc downregulation is required for preacinar to acinar maturation and pancreatic homeostasis.

BACKGROUND AND AIMS: c-Myc is highly expressed in pancreatic multipotent progenitor cells (MPC) and in pancreatic cancer. The transition from MPC to unipotent acinar progenitors is associated with c-Myc downregulation; a role for c-Myc in this process, and its possible relationship to a role in cancer, has not been established. DESIGN: Using coimmunoprecipitation assays, we demonstrate that c-Myc and Ptf1a interact. Using reverse transcriptase qPCR, western blot and immunofluorescence, we show the erosion of the acinar programme. To analyse the genomic distribution of c-Myc and Ptf1a and the global transcriptomic profile, we used ChIP-seq and RNA-seq, respectively; validation was performed with ChIP-qPCR and RT-qPCR. Lineage-tracing experiments were used to follow the effect of c-Myc overexpression in preacinar cells on acinar differentiation. RESULTS: c-Myc binds and represses the transcriptional activity of Ptf1a. c-Myc overexpression in preacinar cells leads to a massive erosion of differentiation. In adult Ela1-Myc mice: (1) c-Myc binds to Ptf1a, and Tcf3 is downregulated; (2) Ptf1a and c-Myc display partially overlapping chromatin occupancy but do not bind the same E-boxes; (3) at the proximal promoter of genes coding for digestive enzymes, we find reduced PTF1 binding and increased levels of repressive chromatin marks and PRC2 complex components. Lineage tracing of committed acinar precursors reveals that c-Myc overexpression does not restore multipotency but allows the persistence of a preacinar-like cell population. In addition, mutant KRas can lead to c-Myc overexpression and acinar dysregulation. CONCLUSIONS: c-Myc repression during development is crucial for the maturation of preacinar cells, and c-Myc overexpression can contribute to pancreatic carcinogenesis through the induction of a dedifferentiated state.

GATA6 regulates EMT and tumour dissemination, and is a marker of response to adjuvant chemotherapy in pancreatic cancer.

BACKGROUND AND AIMS: The role of GATA factors in cancer has gained increasing attention recently, but the function of GATA6 in pancreatic ductal adenocarcinoma (PDAC) is controversial. GATA6 is amplified in a subset of tumours and was proposed to be oncogenic, but high GATA6 levels are found in well-differentiated tumours and are associated with better patient outcome. By contrast, a tumour-suppressive function of GATA6 was demonstrated using genetic mouse models. We aimed at clarifying GATA6 function in PDAC. DESIGN: We combined GATA6 silencing and overexpression in PDAC cell lines with GATA6 ChIP-Seq and RNA-Seq data, in order to understand the mechanism of GATA6 functions. We then confirmed some of our observations in primary patient samples, some of which were included in the ESPAC-3 randomised clinical trial for adjuvant therapy. RESULTS: GATA6 inhibits the epithelial-mesenchymal transition (EMT) in vitro and cell dissemination in vivo. GATA6 has a unique proepithelial and antimesenchymal function, and its transcriptional regulation is direct and implies, indirectly, the regulation of other transcription factors involved in EMT. GATA6 is lost in tumours, in association with altered differentiation and the acquisition of a basal-like molecular phenotype, consistent with an epithelial-to-epithelial (ET2) transition. Patients with basal-like GATA6low tumours have a shorter survival and have a distinctly poor response to adjuvant 5-fluorouracil (5-FU)/leucovorin. However, modulation of GATA6 expression in cultured cells does not directly regulate response to 5-FU. CONCLUSIONS: We provide mechanistic insight into GATA6 tumour-suppressive function, its role as a regulator of canonical epithelial differentiation, and propose that loss of GATA6 expression is both prognostic and predictive of response to adjuvant therapy.

Recruitment of the Mammalian Histone-modifying EMSY Complex to Target Genes Is Regulated by ZNF131.

Recent work from others and us revealed interactions between the Sin3/HDAC complex, the H3K4me3 demethylase KDM5A, GATAD1, and EMSY. Here, we characterize the EMSY/KDM5A/SIN3B complex in detail by quantitative interaction proteomics and ChIP-sequencing. We identify a novel substoichiometric interactor of the complex, transcription factor ZNF131, which recruits EMSY to a large number of active, H3K4me3 marked promoters. Interestingly, using an EMSY knock-out line and subsequent rescue experiments, we show that EMSY is in most cases positively correlated with transcriptional activity of its target genes and stimulates cell proliferation. Finally, by immunohistochemical staining of primary breast tissue microarrays we find that EMSY/KDM5A/SIN3B complex subunits are frequently overexpressed in primary breast cancer cases in a correlative manner. Taken together, these data open venues for exploring the possibility that sporadic breast cancer patients with EMSY amplification might benefit from epigenetic combination therapy targeting both the KDM5A demethylase and histone deacetylases.

Most highly expressed protein-coding genes have a single dominant isoform.

Although eukaryotic cells express a wide range of alternatively spliced transcripts, it is not clear whether genes tend to express a range of transcripts simultaneously across cells, or produce dominant isoforms in a manner that is either tissue-specific or regardless of tissue. To date, large-scale investigations into the pattern of transcript expression across distinct tissues have produced contradictory results. Here, we attempt to determine whether genes express a dominant splice variant at the protein level. We interrogate peptides from eight large-scale human proteomics experiments and databases and find that there is a single dominant protein isoform, irrespective of tissue or cell type, for the vast majority of the protein-coding genes in these experiments, in partial agreement with the conclusions from the most recent large-scale RNAseq study. Remarkably, the dominant isoforms from the experimental proteomics analyses coincided overwhelmingly with the reference isoforms selected by two completely orthogonal sources, the consensus coding sequence variants, which are agreed upon by separate manual genome curation teams, and the principal isoforms from the APPRIS database, predicted automatically from the conservation of protein sequence, structure, and function.

The UBC-40 Urothelial Bladder Cancer cell line index: a genomic resource for functional studies.

BACKGROUND: Urothelial bladder cancer is a highly heterogeneous disease. Cancer cell lines are useful tools for its study. This is a comprehensive genomic characterization of 40 urothelial bladder carcinoma (UBC) cell lines including information on origin, mutation status of genes implicated in bladder cancer (FGFR3, PIK3CA, TP53, and RAS), copy number alterations assessed using high density SNP arrays, uniparental disomy (UPD) events, and gene expression. RESULTS: Based on gene mutation patterns and genomic changes we identify lines representative of the FGFR3-driven tumor pathway and of the TP53/RB tumor suppressor-driven pathway. High-density array copy number analysis identified significant focal gains (1q32, 5p13.1-12, 7q11, and 7q33) and losses (i.e. 6p22.1) in regions altered in tumors but not previously described as affected in bladder cell lines. We also identify new evidence for frequent regions of UPD, often coinciding with regions reported to be lost in tumors. Previously undescribed chromosome X losses found in UBC lines also point to potential tumor suppressor genes. Cell lines representative of the FGFR3-driven pathway showed a lower number of UPD events. CONCLUSIONS: Overall, there is a predominance of more aggressive tumor subtypes among the cell lines. We provide a cell line classification that establishes their relatedness to the major molecularly-defined bladder tumor subtypes. The compiled information should serve as a useful reference to the bladder cancer research community and should help to select cell lines appropriate for the functional analysis of bladder cancer genes, for example those being identified through massive parallel sequencing.

KLF6 loss of function in human prostate cancer progression is implicated in resistance to androgen deprivation.

Inactivation of the transcription factor/tumor suppressor Krüppel-like factor 6 (KLF6) has been described in prostate cancer (PC). This study investigated the prevalence and significance of KLF6 exon 2 mutations and splice variants (SVs) in different stages of human PC progression. By using laser-capture microdissection and recombinant clone isolation of DNA sequences to enhance sensitivity, base changes were found in 20 (24.7%) of 81 PC tissues versus 1 (4%) of 25 normal prostate tissues (P = 0.02). Of 26 base changes, 54% produced nonsynonymous mutations. Only three mutations had driver characteristics (PCs, 4%; NPs, 0%). By using microdissection of fresh-frozen tissues and recombinant isolation of RNA sequences, SVs were found in 39 (75%) of 52 PCs and in 10 (45%) of 22 NPs (P = 0.01). Sixteen different SVs, including 13 unique SVs, were identified that used cryptic splicing sites and encoded nonfunctional KLF6 proteins. PCs that had survived hormone (androgen)-deprivation therapy (n = 21) had a significantly higher (P < 0.05) incidence, number, and expression level of nonfunctional SVs than either NPs (n = 22) or hormone-naïve PCs (n = 25). Forced expression of nonfunctional SVs conferred a survival advantage of androgen-dependent LNCaP cells under castration-simulated culture conditions. Together, these data suggest that decreased availability of functional KLF6 contributes to clinical PC progression. This decrease arises infrequently by somatic mutation and more commonly by the acquisition of SVs that provide a survival advantage under castrate conditions, enabling resistance to hormone therapy.

Citizen science helps to raise awareness about gut microbiome health in people at risk of developing non-communicable diseases.

Citizens lack knowledge about the impact of gut microbiota on health and how lifestyle and dietary choices can influence it, leading to Non-Communicable Diseases (NCDs) and affecting overall well-being. Participatory action research (PAR) is a promising approach to enhance communication and encourage individuals to adopt healthier behaviors and improve their health. In this study, we explored the feasibility of integrating the photovoice method with citizen science approaches to assess the impact of social and environmental factors on gut microbiota health. In this context, citizen science approaches entailed the involvement of participants in the collection of samples for subsequent analysis, specifically gut microbiome assessment via 16S rRNA gene sequencing. We recruited 70 volunteers and organized six photovoice groups based on age and educational background. Participants selected 64 photographs that represented the influence of daily habits on gut microbiota health and created four photovoice themes. Analysis of the gut microbiome using 16S rRNA gene sequencing identified 474 taxa, and in-depth microbial analysis revealed three clusters of people based on gut microbiome diversity and body mass index (BMI). Our findings indicate that participants enhanced their knowledge of gut microbiome health through PAR activities, and we found a correlation between lower microbial diversity, higher BMI, and better achievement of learning outcomes. Using PAR as a methodology is an effective way to increase citizens' awareness and engagement in self-care, maintain healthy gut microbiota, and prevent NCD development. These interventions are particularly beneficial for individuals at higher risk of developing NCDs.

FooDrugs: a comprehensive food-drug interactions database with text documents and transcriptional data.

Food-drug interactions (FDIs) occur when a food item alters the pharmacokinetics or pharmacodynamics of a drug. FDIs can be clinically relevant, as they can hamper or enhance the therapeutic effects of a drug and impact both their efficacy and their safety. However, knowledge of FDIs in clinical practice is limited. This is partially due to the lack of resources focused on FDIs. Here, we describe FooDrugs, a database that centralizes FDI knowledge retrieved from two different approaches: a natural processing language pipeline that extracts potential FDIs from scientific documents and clinical trials and a molecular similarity approach based on the comparison of gene expression alterations caused by foods and drugs. FooDrugs database stores a total of 3 430 062 potential FDIs, with 1 108 429 retrieved from scientific documents and 2 321 633 inferred from molecular data. This resource aims to provide researchers and clinicians with a centralized repository for potential FDI information that is free and easy to use. Database URL:  https://zenodo.org/records/8192515 Database DOI:  https://doi.org/10.5281/zenodo.6638469.

AI4FoodDB: a database for personalized e-Health nutrition and lifestyle through wearable devices and artificial intelligence.

The increasing prevalence of diet-related diseases calls for an improvement in nutritional advice. Personalized nutrition aims to solve this problem by adapting dietary and lifestyle guidelines to the unique circumstances of each individual. With the latest advances in technology and data science, researchers can now automatically collect and analyze large amounts of data from a variety of sources, including wearable and smart devices. By combining these diverse data, more comprehensive insights of the human body and its diseases can be achieved. However, there are still major challenges to overcome, including the need for more robust data and standardization of methodologies for better subject monitoring and assessment. Here, we present the AI4Food database (AI4FoodDB), which gathers data from a nutritional weight loss intervention monitoring 100 overweight and obese participants during 1 month. Data acquisition involved manual traditional approaches, novel digital methods and the collection of biological samples, obtaining: (i) biological samples at the beginning and the end of the intervention, (ii) anthropometric measurements every 2 weeks, (iii) lifestyle and nutritional questionnaires at two different time points and (iv) continuous digital measurements for 2 weeks. To the best of our knowledge, AI4FoodDB is the first public database that centralizes food images, wearable sensors, validated questionnaires and biological samples from the same intervention. AI4FoodDB thus has immense potential for fostering the advancement of automatic and novel artificial intelligence techniques in the field of personalized care. Moreover, the collected information will yield valuable insights into the relationships between different variables and health outcomes, allowing researchers to generate and test new hypotheses, identify novel biomarkers and digital endpoints, and explore how different lifestyle, biological and digital factors impact health. The aim of this article is to describe the datasets included in AI4FoodDB and to outline the potential that they hold for precision health research. Database URL https://github.com/AI4Food/AI4FoodDB.

A toolbox of machine learning software to support microbiome analysis.

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.

A methodology for detecting the orthology signal in a PPI network at a functional complex level.

BACKGROUND: Stable evolutionary signal has been observed in a yeast protein-protein interaction (PPI) network. These finding suggests more connected regions of a PPI network to be potential mediators of evolutionary information. Because more connected regions of PPI networks contain functional complexes, we are motivated to exploit the orthology relation for identifying complexes that can be clearly attributed to such evolutionary signal. RESULTS: We proposed a computational methodology for detecting the orthology signal present in a PPI network at a functional complex level. Specifically, we examined highly functionally coherent putative protein complexes as detected by a clustering technique in the complete yeast PPI network, in the yeast sub-network which spans only ortholog proteins as determined by a given second organism, and in yeast sub-networks induced by a set of proteins randomly selected. We proposed a filtering technique for extracting orthology-driven clusters with unique functionalities, that is, neither enriched by clusters identified using the complete yeast PPI network nor identified using random sampling. Moreover, we extracted functional categories that can be clearly attributed to the presence of evolutionary signal as described by these clusters. CONCLUSIONS: Application of the proposed methodology to the yeast PPI network indicated that evolutionary information at a functional complex level can be retrieved from the structure of the network. In particular, we detected protein complexes whose functionality could be uniquely attributed to the evolutionary signal. Moreover, we identified functions that are over-represented in these complexes due the evolutionary signal.

5-Hydroxymethylcytosine: a new kid on the epigenetic block?

The discovery of the Ten-Eleven-Translocation (TET) oxygenases that catalyze the hydroxylation of 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC) has triggered an avalanche of studies aiming to resolve the role of 5hmC in gene regulation if any. Hitherto, TET1 is reported to bind to CpG-island (CGI) and bivalent promoters in mouse embryonic stem cells, whereas binding at DNAseI hypersensitive sites (HS) had escaped previous analysis. Significant enrichment/accumulation of 5hmC but not 5mC can indeed be detected at bivalent promoters and at DNaseI-HS. Surprisingly, however, 5hmC is not detected or present at very low levels at CGI promoters notwithstanding the presence of TET1. Our meta-analysis of DNA methylation profiling points to potential issues with regard to the various methodologies that are part of the toolbox used to detect 5mC and 5hmC. Discrepancies between published studies and technical limitations prevent an unambiguous assignment of 5hmC as a 'true' epigenetic mark, that is, read and interpreted by other factors and/or as a transiently accumulating intermediary product of the conversion of 5mC to unmodified cytosines.

A comprehensive map of microbial biomarkers along the gastrointestinal tract for celiac disease patients.

Dysbiosis of the microbiome has been related to Celiac disease (CeD) progress, an autoimmune disease characterized by gluten intolerance developed in genetically susceptible individuals under certain environmental factors. The microbiome contributes to CeD pathophysiology, modulating the immune response by the action of short-chain fatty acids (SCFA), affecting gut barrier integrity allowing the entrance of gluten-derived proteins, and degrading immunogenic peptides of gluten through endoprolyl peptidase enzymes. Despite the evidence suggesting the implication of gut microbiome over CeD pathogenesis, there is no consensus about the specific microbial changes observed in this pathology. Here, we compiled the largest dataset of 16S prokaryotic ribosomal RNA gene high-throughput sequencing for consensus profiling. We present for the first time an integrative analysis of metataxonomic data from patients with CeD, including samples from different body sites (saliva, pharynx, duodenum, and stool). We found the presence of coordinated changes through the gastrointestinal tract (GIT) characterized by an increase in Actinobacteria species in the upper GIT (pharynx and duodenum) and an increase in Proteobacteria in the lower GIT (duodenum and stool), as well as site-specific changes evidencing a dysbiosis in patients with CeD' microbiota. Moreover, we described the effect of adherence to a gluten-free diet (GFD) evidenced by an increase in beneficial bacteria and a decrease in some Betaproteobacteriales but not fully restoring CeD-related dysbiosis. Finally, we built a Random Forest model to classify patients based on the lower GIT composition achieving good performance.

Involvement of microbiota and short-chain fatty acids on non-alcoholic steatohepatitis when induced by feeding a hypercaloric diet rich in saturated fat and fructose.

Consumption of high-energy-yielding diets, rich in fructose and lipids, is a factor contributing to the current increase in non-alcoholic fatty liver disease prevalence. Gut microbiota composition and short-chain fatty acids (SCFAs) production alterations derived from unhealthy diets are considered putative underlying mechanisms. This study aimed to determine relationships between changes in gut microbiota composition and SCFA levels by comparing rats featuring diet-induced steatohepatitis with control counterparts fed a standard diet. A high-fat high-fructose (HFHF) feeding induced higher body, liver and mesenteric adipose tissue weights, increased liver triglyceride content and serum transaminase, glucose, non-HDL-c and MCP-1 levels. Greater liver malondialdehyde levels and glutathione peroxidase activity were also observed after feeding the hypercaloric diet. Regarding gut microbiota composition, a lowered diversity and increased abundances of bacteria from the Clostridium sensu stricto 1, Blautia, Eubacterium coprostanoligenes group, Flavonifractor, and UBA1819 genera were found in rats featuring diet-induced steatohepatitis, as well as higher isobutyric, valeric and isovaleric acids concentrations. These results suggest that hepatic alterations produced by a hypercaloric HFHF diet may be related to changes in overall gut microbiota composition and abundance of specific bacteria. The shift in SCFA levels produced by this unbalanced diet cannot be discarded as potential mediators of the reported hepatic and metabolic alterations.

Effectiveness of PRECEDE model for health education on changes and level of control of HbA1c, blood pressure, lipids, and body mass index in patients with type 2 diabetes mellitus.

BACKGROUND: Individual health education is considered to be essential in the overall care of patients with type 2 diabetes (DM2), although there is some uncertainty regarding its metabolic control benefits. There have been very few randomized studies on the effects of individual education on normal care in DM2 patients with a control group, and none of these have assessed the long-term results. Therefore, this study aims to use this design to assess the effectiveness of the PRECEDE (Predisposing, Reinforcing, Enabling, Causes in Educational Diagnosis, and Evaluation) education model in the metabolic control and the reduction of cardiovascular risk factors, in patients with type 2 diabetes. METHODS: An open community effectiveness study was carried out in 8 urban community health centers in the North-East Madrid Urban Area (Spain). Six hundred patients with DM2 were randomized in two groups: PRECEDE or conventional model for health promotion education. The main outcome measures were glycated hemoglobin A1c, body mass index (BMI), blood pressure, lipids and control criteria during the 2-year follow-up period. RESULTS: Glycated hemoglobin A1c and systolic blood pressure (SBP) levels decreased significantly in the PRECEDE group (multivariate analysis of covariance, with baseline glycated hemoglobin A1c, SBP, and variables showing statistically significant differences between groups at baseline visits). The decrease levels in diastolic blood pressure (DBP), triglycerides and LDL cholesterol were nonsignificant. PRECEDE increased compliance in all control criteria, except for LDL cholesterol. BMI did not change during the study in either of the two models analyzed. CONCLUSIONS: PRECEDE health education model is a useful method in the overall treatment in patients with type 2 diabetes, which contributes to decrease glycated hemoglobin A1c and SBP levels and increase the compliance in all the control criteria, except for LDL cholesterol. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov NCT01316367.