Current efforts to prevent dyslipidemia are focused on the development of functional products as an alternative for hypertriglyceridemia management. This study assessed the metabolic effect of the daily consumption of a bean and oats snack bar (BOSB) on hypertriglyceridemia biomarkers among Mexican women. An 8-weeks randomized parallel clinical trial (ID: NCT0496694, https://clinicaltrials.gov/ct2/show/NCT04966494) was conducted with 26 hypertriglyceridemic women allocated to BOSB group (TG = 208.18 ± 56.97 mg/dL) and control group (TG = 182.28 ± 51.39 mg/dL). Only the BOSB group consumed 50 g of the product per day. Fasting blood samples were taken from women with an adherence ≥ 90%. A targeted proteomic analysis with plasma samples of control and BOSB groups were conducted using a human obesity antibody array kit and bioinformatic tools provided by the Ingenuity Pathways Analysis (IPA) software. Serum TG levels in the BOSB group decreased by 37.80% (132.04 ± 27.83 mg/dL) compared with the control group (178.87 ± 32.01 mg/dL); glucose levels decreased by 5.69% in the BOSB group (87.55 ± 3.36 mg/dL). A modest body weight (5%) reduction was also found. Forty proteins were differentially modulated by the BOSB consumption (fold change > 1.2). The proteomic analysis revealed the involvement of BOSB bioactives in prevention of monocytes recruitment and localized inflammatory response, inhibition of pre-adipocyte maturation and adipogenesis, inhibition of hepatic b-oxidation, and potential satiety regulation. These results are promising since the mere intervention with the BOSB reduced serum TG without diet restriction, giving insights for further research in prevention of hypertriglyceridemia.

The relationship between the donor-derived cell-free DNA fraction (dd-cfDNA[%]) in plasma in kidney transplant recipients at time of indication biopsy and gene expression in the biopsied allograft has not been defined.
In the prospective, multicenter Trifecta study, we collected tissue from 300 biopsies from 289 kidney transplant recipients to compare genome-wide gene expression in biopsies with dd-cfDNA(%) in corresponding plasma samples drawn just before biopsy. Rejection was assessed with the microarray-based Molecular Microscope Diagnostic System using automatically assigned rejection archetypes and molecular report sign-outs, and histology assessments that followed Banff guidelines.
The median time of biopsy post-transplantation was 455 days (5 days to 32 years), with a case mix similar to that of previous studies: 180 (60%) no rejection, 89 (30%) antibody-mediated rejection (ABMR), and 31 (10%) T cell-mediated rejection (TCMR) and mixed. In genome-wide mRNA measurements, all 20 top probe sets correlating with dd-cfDNA(%) were previously annotated for association with ABMR and all types of rejection, either natural killer (NK) cell-expressed (e.g., GNLY, CCL4, TRDC, and S1PR5) or IFN-γ-inducible (e.g., PLA1A, IDO1, CXCL11, and WARS). Among gene set and classifier scores, dd-cfDNA(%) correlated very strongly with ABMR and all types of rejection, reasonably strongly with active TCMR, and weakly with inactive TCMR, kidney injury, and atrophy fibrosis. Active ABMR, mixed, and active TCMR had the highest dd-cfDNA(%), whereas dd-cfDNA(%) was lower in late-stage ABMR and less-active TCMR. By multivariate random forests and logistic regression, molecular rejection variables predicted dd-cfDNA(%) better than histologic variables.
The dd-cfDNA(%) at time of indication biopsy strongly correlates with active molecular rejection and has the potential to reduce unnecessary biopsies.
NCT04239703.

The rapid spread of SARS-CoV-2 led to the necessity of developing diagnostic tests for rapid virus detection. Many commercial platforms have appeared and have been approved for this purpose. In this study, 95 positive and 5 negative retrospective samples were analyzed by 4 different commercial RT-qPCR kits (TaqMan 2019nCoV Assay, Allplex™SARS-COV-2 Assay, FTD SARS-COV-2 Assay and qCOVID-19). The Hologic Aptima SARS-COV-2 and the Clart-COVID-19 system were also tested. serial dilutions of SARS-COV-2 standard control were included for sensitivity analysis. Among the qPCR tested qCOVID19 and Allplex™SARS-COV-2 Assay were both able to detect all the clinical samples included in the study. All four qPCR evaluated showed high sensitivity for samples with Ct<33. Clart-COVID-19 microarrays detected all samples and controls used in this study whereas Hologic Aptima Panther failed with one of the clinical samples. However, the main problem with this system was the number of invalidated samples despite avoiding the use of medium with guanidine isothiocyanate as recommended by the manufacturer. All the techniques tested were of value for SARS-CoV-2 detection.

Metabolic disorders in children after hematopoietic stem cell transplantation (HSCT) are poorly characterized. However, it is known that dyslipidemia and insulin resistance are particularly common in these patients. We conducted a prospective study of 27 patients treated with HSCT to assess the possibility of predicting these abnormalities. We measured gene expressions using a microarray technique to identify differences in expression of genes associated with lipid metabolism before and after HSCT. In patients treated with HSCT, total cholesterol levels were significantly higher after the procedure compared with the values before HSCT. Microarray analysis revealed statistically significant differences in expressions of three genes, DPP4, PLAG1, and SCD, after applying the Benjamini-Hochberg procedure (pBH < 0.05). In multiple logistic regression, the increase of DPP4 gene expression before HCST (as well as its change between pre- and post-HSCT status) was associated with dyslipidemia. In children treated with HSCT, the burden of lipid disorders in short-term follow-up seems to be lower than before the procedure. The expression pattern of DPP4 is linked with dyslipidemia after the transplantation.

Many scientific studies, especially in the biomedical sciences, generate data measured simultaneously over a multitude of units, over a period of time, and under different conditions or combinations of factors. Often, an important question of interest asked relates to which units behave similarly under different conditions, but measuring the variation over time complicates the analysis significantly. In this article we address such a problem arising from a gene expression study relating to bone aging, and develop a Bayesian statistical method that can simultaneously detect and uncover signals on three levels within such data: factorial, longitudinal, and transcriptional. Our model framework considers both cluster and time-point-specific parameters and these parameters uniquely determine the shapes of the temporal gene expression profiles, allowing the discovery and characterization of latent gene clusters based on similar underlying biological mechanisms. Our methodology was successfully applied to discover transcriptional networks in a microarray data set comparing the transcriptomic changes that occurred during bone aging in male and female mice expressing one or both copies of the bromodomain (Brd2) gene, a transcriptional regulator which exhibits an age-dependent sex-linked bone loss phenotype.

From a general structural perspective, a mucosal tissue is constituted by two main matrices: the tissue and the secreted mucus. Jointly, they fulfill a wide range of functions including the protection of the epithelial layer. In this study, we simultaneously analyzed the epithelial tissue and the secreted mucus response using a holistic interactome-based multi-omics approach. The effect of the gilthead sea bream (Sparus aurata) skin mucosa to a dietary inclusion of spray-dried porcine plasma (SDPP) was evaluated. The epithelial skin microarrays-based transcriptome data showed 194 differentially expressed genes, meanwhile the exuded mucus proteome analysis 35 differentially synthesized proteins. Separately, the skin transcripteractome revealed an expression profile that favored biological mechanisms associated to gene expression, biogenesis, vesicle function, protein transport and localization to the membrane. Mucus proteome showed an enhanced protective role with putatively higher antioxidant and antimicrobial properties. The integrated skin mucosa multi-interactome analysis evidenced the interrelationship and synergy between the metabolism and the exuded mucus functions improving specifically the tissue development, innate defenses, and environment recognition. Histologically, the skin increased in thickness and in number of mucous cells. A positive impact on animal performance, growth and feed efficiency was also registered. Collectively, the results suggest an intimate crosstalk between skin tissue and its exuded mucus in response to the nutritional stimulus (SDPP supplementation) that favors the stimulation of cell protein turnover and the activation of the exudation machinery in the skin mucosa. Thus, the multi-omics-based interactome analysis provides a comprehensive understanding of the biological context of response that takes place in a mucosal tissue. In perspective, this strategy is applicable for evaluating the effect of any experimental variable on any mucosal tissue functionality, including the benefits this assessment may provide on the study of the mammalian mucosa.

Regulatory B cells do not constitute a distinct cell lineage because no unique marker or set of markers can exclusively identify neither murine nor human regulatory B cells, and efficient IL-10 production is their only known distinguishing feature. After purification of IL-10-secreting B cells, one may want to characterize them by analyzing their gene expression profile. This goal can be achieved by using different technologies: RT-qPCR, microarrays, Nanostring\'s nCounter technology, Biomark HD are techniques that will allow you to analyze their gene expression, whether in a targeted (RT-qPCR), extended but targeted (Nanostring\'s nCounter technology, Biomark HD) or exhaustive (Microarray) way. Aim of this chapter is the description of these techniques in the view of their application to the study and characterization of regulatory B cells.

Advances in analytical instrumentation have provided the possibility of examining thousands of genes, peptides, or metabolites in parallel. However, the cost and time-consuming data acquisition process causes a generalized lack of samples. From a data analysis perspective, omics data are characterized by high dimensionality and small sample counts. In many scenarios, the analytical aim is to differentiate between two different conditions or classes combining an analytical method plus a tailored qualitative predictive model using available examples collected in a dataset. For this purpose, partial least squares-discriminant analysis (PLS-DA) is frequently employed in omics research. Recently, there has been growing concern about the uncritical use of this method, since it is prone to overfitting and may aggravate problems of false discoveries. In many applications involving a small number of subjects or samples, predictive model performance estimation is only based on cross-validation (CV) results with a strong preference for reporting results using leave one out (LOO). The combination of PLS-DA for high dimensionality data and small sample conditions, together with a weak validation methodology is a recipe for unreliable estimations of model performance. In this work, we present a systematic study about the impact of the dataset size, the dimensionality, and the CV technique used on PLS-DA overoptimism when performance estimation is done in cross-validation. Firstly, by using synthetic data generated from a same probability distribution and with assigned random binary labels, we have obtained a dataset where the true classification rate (CR) is 50%. As expected, our results confirm that internal validation provides overoptimistic estimations of the classification accuracy (i.e., overfitting). We have characterized the CR estimator in terms of bias and variance depending on the internal CV technique used and sample to dimensionality ratio. In small sample conditions, due to the large bias and variance of the estimator, the occurrence of extremely good CRs is common. We have found that overfitting peaks when the sample size in the training subset approaches the feature vector dimensionality minus one. In these conditions, the models are neither under- or overdetermined with a unique solution. This effect is particularly intense for LOO and peaks higher in small sample conditions. Overoptimism is decreased beyond this point where the abundance of noisy produces a regularization effect leading to less complex models. In terms of overfitting, our study ranks CV methods as follows: Bootstrap produces the most accurate estimator of the CR, followed by bootstrapped Latin partitions, random subsampling, K-Fold, and finally, the very popular LOO provides the worst results. Simulation results are further confirmed in real datasets from mass spectrometry and microarrays.

OBJECTIVE: Epidemiological and intervention studies have attempted to link the health effects of a diet rich in fruits and vegetables with the consumption of polyphenols and their impact in neurodegenerative diseases. Studies have shown that polyphenols can cross the intestinal barrier and reach concentrations in the bloodstream able to exert effects in vivo. However, the effective uptake of polyphenols into the brain is still regarded with some reservations. Here we describe a combination of approaches to examine the putative transport of blackberry-digested polyphenols (BDP) across the blood-brain barrier (BBB) and ultimate evaluation of their neuroprotective effects.
METHODS: BDP was obtained by in vitro digestion of blackberry extract and BDP major aglycones (hBDP) were obtained by enzymatic hydrolysis. Chemical characterization and BBB transport of extracts were evaluated by LC-MSn. BBB transport and cytoprotection of both extracts was assessed in HBMEC monolayers. Neuroprotective potential of BDP was assessed in NT2-derived 3D co-cultures of neurons and astrocytes and in primary mouse cerebellar granule cells. BDP-modulated genes were evaluated by microarray analysis.
RESULTS: Components from BDP and hBDP were shown to be transported across the BBB. Physiologically relevant concentrations of both extracts were cytoprotective at endothelial level and BDP was neuroprotective in primary neurons and in an advanced 3D cell model. The major canonical pathways involved in the neuroprotective effect of BDP were unveiled, including mTOR signaling and the unfolded protein response pathway. Genes such as ASNS and ATF5 emerged as novel BDP-modulated targets.
CONCLUSIONS: BBB transport of BDP and hBDP components reinforces the health benefits of a diet rich in polyphenols in neurodegenerative disorders. Our results suggest some novel pathways and genes that may be involved in the neuroprotective mechanism of the BDP polyphenol components.

Understanding molecular basis involved in overweight is an important first step in developing therapeutic pathways against excess in body weight gain.
The purpose of our pilot study was to evaluate the gene expression profiles in the peripheral blood of obese patients without other metabolic complications.
A sample of 17 obese patients without metabolic syndrome and 15 non obese control subjects was evaluated in a prospective way. Following \'One-Color Microarray-Based Gene Expression Analysis\' protocol Version 5.7 (Agilent p/n 4140-90040), cRNA was hybridized with Whole Human Genome Oligo Microarray Kit (Agilent p/n G2519F-014850) containing 41,000+ unique human genes and transcripts.
The average age of the study group was 43.6 ± 19.7 years with a sex distribution of 64.7% females and 35.3% males. No statistical differences were detected with healthy controls 41.9 ± 12.3 years with a sex distribution of 70% females and 30% males. Obese patients showed 1436 genes that were differentially expressed compared to control group. Ingenuity Pathway Analysis showed that these genes participated in 13 different categories related to metabolism and cellular functions. In the gene set of cellular function, the most important genes were C-terminal region of Nel-like molecule 1 protein (NELL1) and Pigment epithelium-derived factor (SPEDF), both genes were over-expressed. In the gene set of metabolism, insulin growth factor type 1 (IGF1), ApoA5 (apolipoprotein subtype 5), Foxo4 (Forkhead transcription factor 4), ADIPOR1 (receptor of adiponectin type 1) and AQP7 (aquaporin channel proteins7) were over expressed. Moreover, PIKFYVE (PtdIns(3) P 5-kinase), and ROCK-2 (rho-kinase II) were under expressed.
We showed that PBMCs from obese subjects presented significant changes in gene expression, exhibiting 1436 differentially expressed genes compared to PBMCs from non-obese subjects. Furthermore, our data showed a number of genes involved in relevant processes implicated in metabolism, with genes presenting high fold-change values (up-regulation and down regulation) associated with lipid, carbohydrate and protein metabolism.