Extracellular vesicles from dental pulp mesenchymal stem cells modulate macrophage phenotype during acute and chronic cardiac inflammation in athymic nude rats with myocardial infarction.

BACKGROUND/AIMS: Extracellular vesicles (EVs) derived from dental pulp mesenchymal stem cells (DP-MSCs) are a promising therapeutic option for the treatment of myocardial ischemia. The aim of this study is to determine whether MSC-EVs could promote a pro-resolving environment in the heart by modulating macrophage populations. METHODS: EVs derived from three independent biopsies of DP-MSCs (MSC-EVs) were isolated by tangential flow-filtration and size exclusion chromatography and were characterized by omics analyses. Biological processes associated with these molecules were analyzed using String and GeneCodis platforms. The immunomodulatory capacity of MSC-EVs to polarize macrophages towards a pro-resolving or M2-like phenotype was assessed by evaluating surface markers, cytokine production, and efferocytosis. The therapeutic potential of MSC-EVs was evaluated in an acute myocardial infarction (AMI) model in nude rats. Infarct size and the distribution of macrophage populations in the infarct area were evaluated 7 and 21 days after intramyocardial injection of MSC-EVs. RESULTS: Lipidomic, proteomic, and miRNA-seq analysis of MSC-EVs revealed their association with biological processes involved in tissue regeneration and regulation of the immune system, among others. MSC-EVs promoted the differentiation of pro-inflammatory macrophages towards a pro-resolving phenotype, as evidenced by increased expression of M2 markers and decreased secretion of pro-inflammatory cytokines. Administration of MSC-EVs in rats with AMI limited the extent of the infarcted area at 7 and 21 days post-infarction. MSC-EV treatment also reduced the number of pro-inflammatory macrophages within the infarct area, promoting the resolution of inflammation. CONCLUSION: EVs derived from DP-MSCs exhibited similar characteristics at the omics level irrespective of the biopsy from which they were derived. All MSC-EVs exerted effective pro-resolving responses in a rat model of AMI, indicating their potential as therapeutic agents for the treatment of inflammation associated with AMI.

Machine learning approaches in microbiome research: challenges and best practices.

Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model interpretation, and the extraction of biological information from the results. To assist decision-making, we offer a set of recommendations on algorithm selection, pipeline creation and evaluation, stemming from the COST Action ML4Microbiome. We compared the suggested approaches on a multi-cohort shotgun metagenomics dataset of colorectal cancer patients, focusing on their performance in disease diagnosis and biomarker discovery. It is demonstrated that the use of compositional transformations and filtering methods as part of data preprocessing does not always improve the predictive performance of a model. In contrast, the multivariate feature selection, such as the Statistically Equivalent Signatures algorithm, was effective in reducing the classification error. When validated on a separate test dataset, this algorithm in combination with random forest modeling, provided the most accurate performance estimates. Lastly, we showed how linear modeling by logistic regression coupled with visualization techniques such as Individual Conditional Expectation (ICE) plots can yield interpretable results and offer biological insights. These findings are significant for clinicians and non-experts alike in translational applications.

STATegra: Multi-Omics Data Integration - A Conceptual Scheme With a Bioinformatics Pipeline.

Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. Therefore, it is an unmet need to conceptualize how to integrate such data and implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining available multi-omic anlaysis tools (machine learning component analysis, non-parametric data combination, and a multi-omics exploratory analysis) in a step-wise manner. While in several studies, we have previously combined those integrative tools, here, we provide a systematic description of the STATegra framework and its validation using two The Cancer Genome Atlas (TCGA) case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma (SKCM) cases, we demonstrate an enhanced capacity of the framework (and beyond the individual tools) to identify features and pathways compared to single-omics analysis. Such an integrative multi-omics analysis framework for identifying features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package.

A Multiomics Study To Unravel the Effects of Developmental Exposure to Endosulfan in Rats: Molecular Explanation for Sex-Dependent Effects.

Exposure to low levels of environmental contaminants, including pesticides, induces neurodevelopmental toxicity. Environmental and food contaminants can reach the brain of the fetus, affecting brain development and leading to neurological dysfunction. The pesticide endosulfan is a persistent pollutant, and significant levels still remain detectable in the environment although its use is banned in some countries. In rats, endosulfan exposure during brain development alters motor activity, coordination, learning, and memory, even several months after uptake, and does so in a sex-dependent way. However, the molecular mechanisms driving these effects have not been studied in detail. In this work, we performed a multiomics study in cerebellum from rats exposed to endosulfan during embryonic development. Pregnant rats were orally exposed to a low dose (0.5 mg/kg) of endosulfan, daily, from gestational day 7 to postnatal day 21. The progeny was evaluated for cognitive and motor functions at adulthood. Expression of messenger RNA and microRNA genes, as well as protein and metabolite levels, were measured on cerebellar samples from males and females. An integrative analysis was conducted to identify altered processes under endosulfan effect. Effects between males and females were compared. Pathways significantly altered by endosulfan exposure included the phosphatidylinositol signaling system, calcium signaling, the cGMP-PKG pathway, the inflammatory and immune system, protein processing in the endoplasmic reticulum, and GABA and taurine metabolism. Sex-dependent effects of endosulfan in the omics results that matched sex differences in cognitive and motor tests were found. These results shed light on the molecular basis of impaired neurodevelopment and contribute to the identification of new biomarkers of neurotoxicity.

Feedforward regulation of Myc coordinates lineage-specific with housekeeping gene expression during B cell progenitor cell differentiation.

The differentiation of self-renewing progenitor cells requires not only the regulation of lineage- and developmental stage-specific genes but also the coordinated adaptation of housekeeping functions from a metabolically active, proliferative state toward quiescence. How metabolic and cell-cycle states are coordinated with the regulation of cell type-specific genes is an important question, because dissociation between differentiation, cell cycle, and metabolic states is a hallmark of cancer. Here, we use a model system to systematically identify key transcriptional regulators of Ikaros-dependent B cell-progenitor differentiation. We find that the coordinated regulation of housekeeping functions and tissue-specific gene expression requires a feedforward circuit whereby Ikaros down-regulates the expression of Myc. Our findings show how coordination between differentiation and housekeeping states can be achieved by interconnected regulators. Similar principles likely coordinate differentiation and housekeeping functions during progenitor cell differentiation in other cell lineages.

Tumor microenvironment-targeted poly-L-glutamic acid-based combination conjugate for enhanced triple negative breast cancer treatment.

The intrinsic characteristics of the tumor microenvironment (TME), including acidic pH and overexpression of hydrolytic enzymes, offer an exciting opportunity for the rational design of TME-drug delivery systems (DDS). We developed and characterized a pH-responsive biodegradable poly-L-glutamic acid (PGA)-based combination conjugate family with the aim of optimizing anticancer effects. We obtained combination conjugates bearing Doxorubicin (Dox) and aminoglutethimide (AGM) with two Dox loadings and two different hydrazone pH-sensitive linkers that promote the specific release of Dox from the polymeric backbone within the TME. Low Dox loading coupled with a short hydrazone linker yielded optimal effects on primary tumor growth, lung metastasis (∼90% reduction), and toxicological profile in a preclinical metastatic triple-negative breast cancer (TNBC) murine model. The use of transcriptomic analysis helped us to identify the molecular mechanisms responsible for such results including a differential immunomodulation and cell death pathways among the conjugates. This data highlights the advantages of targeting the TME, the therapeutic value of polymer-based combination approaches, and the utility of -omics-based analysis to accelerate anticancer DDS.

Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package.

As the use of RNA-seq has popularized, there is an increasing consciousness of the importance of experimental design, bias removal, accurate quantification and control of false positives for proper data analysis. We introduce the NOISeq R-package for quality control and analysis of count data. We show how the available diagnostic tools can be used to monitor quality issues, make pre-processing decisions and improve analysis. We demonstrate that the non-parametric NOISeqBIO efficiently controls false discoveries in experiments with biological replication and outperforms state-of-the-art methods. NOISeq is a comprehensive resource that meets current needs for robust data-aware analysis of RNA-seq differential expression.

Hypoxia promotes efficient differentiation of human embryonic stem cells to functional endothelium.

Early development of mammalian embryos occurs in an environment of relative hypoxia. Nevertheless, human embryonic stem cells (hESC), which are derived from the inner cell mass of blastocyst, are routinely cultured under the same atmospheric conditions (21% O(2)) as somatic cells. We hypothesized that O(2) levels modulate gene expression and differentiation potential of hESC, and thus, we performed gene profiling of hESC maintained under normoxic or hypoxic (1% or 5% O(2)) conditions. Our analysis revealed that hypoxia downregulates expression of pluripotency markers in hESC but increases significantly the expression of genes associated with angio- and vasculogenesis including vascular endothelial growth factor and angiopoitein-like proteins. Consequently, we were able to efficiently differentiate hESC to functional endothelial cells (EC) by varying O(2) levels; after 24 hours at 5% O(2), more than 50% of cells were CD34+. Transplantation of resulting endothelial-like cells improved both systolic function and fractional shortening in a rodent model of myocardial infarction. Moreover, analysis of the infarcted zone revealed that transplanted EC reduced the area of fibrous scar tissue by 50%. Thus, use of hypoxic conditions to specify the endothelial lineage suggests a novel strategy for cellular therapies aimed at repair of damaged vasculature in pathologies such as cerebral ischemia and myocardial infarction.