Melatonin decreases human adipose tissue insulin sensitivity.

Melatonin is a pineal hormone that modulates the circadian system and exerts soporific and phase-shifting effects. It is also involved in many other physiological processes, such as those implicated in cardiovascular, endocrine, immune, and metabolic functions. However, the role of melatonin in glucose metabolism remains contradictory, and its action on human adipose tissue (AT) explants has not been demonstrated. We aimed to assess whether melatonin (a pharmacological dose) influences insulin sensitivity in human AT. This will help better understand melatonin administration's effect on glucose metabolism. Abdominal AT (subcutaneous and visceral) biopsies were obtained from 19 participants with severe obesity (age: 42.84 ± 12.48 years; body mass index: 43.14 ± 8.26 kg/m2) who underwent a laparoscopic gastric bypass. AT biopsies were exposed to four different treatments: control (C), insulin alone (I) (10 nM), melatonin alone (M) (5000 pg/mL), and insulin plus melatonin combined (I + M). All four conditions were repeated in both subcutaneous and visceral AT, and all were performed in the morning at 8 a.m. (n = 19) and the evening at 8 p.m. (in a subsample of n = 12). We used western blot analysis to determine insulin signaling (using the pAKT/tAKT ratio). Furthermore, RNAseq analyses were performed to better understand the metabolic pathways involved in the effect of melatonin on insulin signaling. As expected, insulin treatment (I) increased the pAKT/tAKT ratio compared with control (p < .0001). Furthermore, the addition of melatonin (I + M) resulted in a decrease in insulin signaling as compared with insulin alone (I); this effect was significant only during the evening time (not in the morning time). Further, RNAseq analyses in visceral AT during the evening condition (at 8 p.m.) showed that melatonin resulted in a prompt transcriptome response (around 1 h after melatonin addition), particularly by downregulating the insulin signaling pathway. Our results show that melatonin reduces insulin sensitivity in human AT during the evening. These results may partly explain the previous studies showing a decrease in glucose tolerance after oral melatonin administration in the evening or when eating late when endogenous melatonin is present.

Ultrasound Therapy With High-Pressure Pulses Is Effective to Reduce the Effects of Collagenase-Induced Tendinopathy in Rat's Achilles Tendon.

OBJECTIVE: We propose an ultrasonic treatment for collagenase-induced tendinopathy in rat's Achilles tendon using pulses with a low number of cycles, high acoustic pressure and very low duty cycle. METHODS: Twenty rats were used to perform the experiment. Four experimental groups of calcaneal tendons were studied: control (n = 6), sham (n = 4), collagenase-induced tendinopathy (n = 8) and ultrasound-treated collagenase-induced tendinopathy (n = 8). Surgical intervention was performed to expose the tendons prior to collagenase injection. A 1 MHz ultrasonic tansducer with a focusing lens was used. Ultrasonic treatments were used with an average total treatment time of 2.5 min, 20-cycle pulses, pressure amplitude p = 7 MPa, and 0.02% duty cycle. Histopathology of the samples was performed to evaluate nuclear density, acute inflammation, and signs of neovascularization. Collagen (types I and III), elastic fibers, and glycosaminoglycans were also analyzed. RESULTS: No tendon involvement was found by the surgical process. Ultrasonic treatment is safe, as it does not affect healthy tendons. When collagenase infiltrated animals were treated with US, a clear predominance of type I collagen fibers and a similar collagen ratio profile to that observed in the control and sham groups was observed, with a higher density of elastic fibers compared to the control and sham groups and a significant increase in the density of glycosaminoglycans. CONCLUSION: The ultrasound treatment proposed reduces the effects of the artificial collagenase lesion to reach the basal level after 45 d.

Salivary biomarkers as pioneering indicators for diagnosis and severity stratification of pediatric long COVID.

Introduction: Long COVID, or post-acute sequelae of SARS-CoV-2 infection (PASC), manifests as persistent and often debilitating symptoms enduring well beyond the initial COVID-19 infection. This disease is especially worrying in children since it can seriously alter their development. Presently, a specific diagnostic test or definitive biomarker set for confirming long COVID is lacking, relying instead on the protracted presence of symptoms post-acute infection. Methods: We measured the levels of 13 biomarkers in 105 saliva samples (49 from children with long COVID and 56 controls), and the Pearson correlation coefficient was used to analyse the correlations between the levels of the different salivary biomarkers. Multivariate logistic regression analyses were performed to determine which of the 13 analysed salivary biomarkers were useful to discriminate between children with long COVID and controls, as well as between children with mild and severe long COVID symptoms. Results: Pediatric long COVID exhibited increased oxidant biomarkers and decreased antioxidant, immune response, and stress-related biomarkers. Correlation analyses unveiled distinct patterns between biomarkers in long COVID and controls. Notably, a multivariate logistic regression pinpointed TOS, ADA2, total proteins, and AOPP as pivotal variables, culminating in a remarkably accurate predictive model distinguishing long COVID from controls. Furthermore, total proteins and ADA1 were instrumental in discerning between mild and severe long COVID symptoms. Discussion: This research sheds light on the potential clinical utility of salivary biomarkers in diagnosing and categorizing the severity of pediatric long COVID. It also lays the groundwork for future investigations aimed at unravelling the prognostic value of these biomarkers in predicting the trajectory of long COVID in affected individuals.

Analysis of 16S rRNA Gene Sequence of Nasopharyngeal Exudate Reveals Changes in Key Microbial Communities Associated with Aging.

Functional or compositional perturbations of the microbiome can occur at different sites, of the body and this dysbiosis has been linked to various diseases. Changes in the nasopharyngeal microbiome are associated to patient's susceptibility to multiple viral infections, supporting the idea that the nasopharynx may be playing an important role in health and disease. Most studies on the nasopharyngeal microbiome have focused on a specific period in the lifespan, such as infancy or the old age, or have other limitations such as low sample size. Therefore, detailed studies analyzing the age- and sex-associated changes in the nasopharyngeal microbiome of healthy people across their whole life are essential to understand the relevance of the nasopharynx in the pathogenesis of multiple diseases, particularly viral infections. One hundred twenty nasopharyngeal samples from healthy subjects of all ages and both sexes were analyzed by 16S rRNA sequencing. Nasopharyngeal bacterial alpha diversity did not vary in any case between age or sex groups. Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes were the predominant phyla in all the age groups, with several sex-associated. Acinetobacter, Brevundimonas, Dolosigranulum, Finegoldia, Haemophilus, Leptotrichia, Moraxella, Peptoniphilus, Pseudomonas, Rothia, and Staphylococcus were the only 11 bacterial genera that presented significant age-associated differences. Other bacterial genera such as Anaerococcus, Burkholderia, Campylobacter, Delftia, Prevotella, Neisseria, Propionibacterium, Streptococcus, Ralstonia, Sphingomonas, and Corynebacterium appeared in the population with a very high frequency, suggesting that their presence might be biologically relevant. Therefore, in contrast to other anatomical areas such as the gut, bacterial diversity in the nasopharynx of healthy subjects remains stable and resistant to perturbations throughout the whole life and in both sexes. Age-associated abundance changes were observed at phylum, family, and genus levels, as well as several sex-associated changes probably due to the different levels of sex hormones present in both sexes at certain ages. Our results provide a complete and valuable dataset that will be useful for future research aiming for studying the relationship between changes in the nasopharyngeal microbiome and susceptibility to or severity of multiple diseases.

DEVEA: an interactive shiny application for Differential Expression analysis, data Visualization and Enrichment Analysis of transcriptomics data.

We are at a time of considerable growth in the use and development of transcriptomics studies and subsequent in silico analysis. RNA sequencing is one of the most widely used approaches, now integrated in many studies.  The processing of these data may typically require a noteworthy number of steps, statistical knowledge, and coding skills which is not accessible to all scientists. Despite the undeniable development of software applications over the years to address this concern, it is still possible to improve.  Here we present DEVEA, an R shiny application tool developed to perform differential expression analysis, data visualization and enrichment pathway analysis mainly from transcriptomics data, but also from simpler gene lists with or without statistical values.  Its intuitive and easy-to-manipulate interface facilitates gene expression exploration through numerous interactive figures and tables, statistical comparisons of expression profile levels between groups and further meta-analysis such as enrichment analysis, without bioinformatics expertise. DEVEA performs a thorough analysis from multiple and flexible input data representing distinct analysis stages. From them, it produces dynamic graphs and tables, to explore the expression levels and statistical differential expression analysis results. Moreover, it generates a comprehensive pathway analysis to extend biological insights. Finally, a complete and customizable HTML report can be extracted for further result exploration outside the application. DEVEA is accessible at https://shiny.imib.es/devea/ and the source code is available on our GitHub repository https://github.com/MiriamRiquelmeP/DEVEA.

Global Methylome Scores Correlate with Histological Subtypes of Colorectal Carcinoma and Show Different Associations with Common Clinical and Molecular Features.

BACKGROUND: The typical methylation patterns associated with cancer are hypermethylation at gene promoters and global genome hypomethylation. Aberrant CpG island hypermethylation at promoter regions and global genome hypomethylation have not been associated with histological colorectal carcinomas (CRC) subsets. Using Illumina's 450 k Infinium Human Methylation beadchip, the methylome of 82 CRCs were analyzed, comprising different histological subtypes: 40 serrated adenocarcinomas (SAC), 32 conventional carcinomas (CC) and 10 CRCs showing histological and molecular features of microsatellite instability (hmMSI-H), and, additionally, 35 normal adjacent mucosae. Scores reflecting the overall methylation at 250 bp, 1 kb and 2 kb from the transcription starting site (TSS) were studied. RESULTS: SAC has an intermediate methylation pattern between CC and hmMSI-H for the three genome locations. In addition, the shift from promoter hypermethylation to genomic hypomethylation occurs at a small sequence between 250 bp and 1 Kb from the gene TSS, and an asymmetric distribution of methylation was observed between both sides of the CpG islands (N vs. S shores). CONCLUSION: These findings show that different histological subtypes of CRC have a particular global methylation pattern depending on sequence distance to TSS and highlight the so far underestimated importance of CpGs aberrantly hypomethylated in the clinical phenotype of CRCs.

Proteomics in Liver Transplantation: A Systematic Review.

Background: Although proteomics has been employed in the study of several models of liver injury, proteomic methods have only recently been applied not only to biomarker discovery and validation but also to improve understanding of the molecular mechanisms involved in transplantation. Methods: The study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology and the guidelines for performing systematic literature reviews in bioinformatics (BiSLR). The PubMed, ScienceDirect, and Scopus databases were searched for publications through April 2020. Proteomics studies designed to understand liver transplant outcomes, including ischemia-reperfusion injury (IRI), rejection, or operational tolerance in human or rat samples that applied methodologies for differential expression analysis were considered. Results: The analysis included 22 studies after application of the inclusion and exclusion criteria. Among the 497 proteins annotated, 68 were shared between species and 10 were shared between sample sources. Among the types of studies analyzed, IRI and rejection shared a higher number of proteins. The most enriched pathway for liver biopsy samples, IRI, and rejection was metabolism, compared to cytokine-cytokine receptor interactions for tolerance. Conclusions: Proteomics is a promising technique to detect large numbers of proteins. However, our study shows that several technical issues such as the identification of proteoforms or the dynamic range of protein concentration in clinical samples hinder the successful identification of biomarkers in liver transplantation. In addition, there is a need to minimize the experimental variability between studies, increase the sample size and remove high-abundance plasma proteins.

Culture Medium and Sex Drive Epigenetic Reprogramming in Preimplantation Bovine Embryos.

Assisted reproductive technologies impact transcriptome and epigenome of embryos and can result in long-term phenotypic consequences. Whole-genome DNA methylation profiles from individual bovine blastocysts in vivo- and in vitro-derived (using three sources of protein: reproductive fluids, blood serum and bovine serum albumin) were generated. The impact of in vitro culture on DNA methylation was analyzed, and sex-specific methylation differences at blastocyst stage were uncovered. In vivo embryos showed the highest levels of methylation (29.5%), close to those produced in vitro with serum, whilst embryos produced in vitro with reproductive fluids or albumin showed less global methylation (25-25.4%). During repetitive element analysis, the serum group was the most affected. DNA methylation differences between in vivo and in vitro groups were more frequent in the first intron than in CpGi in promoters. Moreover, hierarchical cluster analysis showed that sex produced a stronger bias in the results than embryo origin. For each group, distance between male and female embryos varied, with in vivo blastocyst showing a lesser distance. Between the sexually dimorphic methylated tiles, which were biased to X-chromosome, critical factors for reproduction, developmental process, cell proliferation and DNA methylation machinery were included. These results support the idea that blastocysts show sexually-dimorphic DNA methylation patterns, and the known picture about the blastocyst methylome should be reconsidered.

gcProfileMakeR: An R Package for Automatic Classification of Constitutive and Non-Constitutive Metabolites.

Metabolomes comprise constitutive and non-constitutive metabolites produced due to physiological, genetic or environmental effects. However, finding constitutive metabolites and non-constitutive metabolites in large datasets is technically challenging. We developed gcProfileMakeR, an R package using standard Excel output files from an Agilent Chemstation GC-MS for automatic data analysis using CAS numbers. gcProfileMakeR has two filters for data preprocessing removing contaminants and low-quality peaks. The first function NormalizeWithinFiles, samples assigning retention times to CAS. The second function NormalizeBetweenFiles, reaches a consensus between files where compounds in close retention times are grouped together. The third function getGroups, establishes what is considered as Constitutive Profile, Non-constitutive by Frequency i.e., not present in all samples and Non-constitutive by Quality. Results can be plotted with the plotGroup function. We used it to analyse floral scent emissions in four snapdragon genotypes. These included a wild type, Deficiens nicotianoides and compacta affecting floral identity and RNAi:AmLHY targeting a circadian clock gene. We identified differences in scent constitutive and non-constitutive profiles as well as in timing of emission. gcProfileMakeR is a very useful tool to define constitutive and non-constitutive scent profiles. It also allows to analyse genotypes and circadian datasets to identify differing metabolites.

Efficiency of Machine Learning Algorithms for the Determination of Macrovesicular Steatosis in Frozen Sections Stained with Sudan to Evaluate the Quality of the Graft in Liver Transplantation.

Liver transplantation is the only curative treatment option in patients diagnosed with end-stage liver disease. The low availability of organs demands an accurate selection procedure based on histological analysis, in order to evaluate the allograft. This assessment, traditionally carried out by a pathologist, is not exempt from subjectivity. In this sense, new tools based on machine learning and artificial vision are continuously being developed for the analysis of medical images of different typologies. Accordingly, in this work, we develop a computer vision-based application for the fast and automatic objective quantification of macrovesicular steatosis in histopathological liver section slides stained with Sudan stain. For this purpose, digital microscopy images were used to obtain thousands of feature vectors based on the RGB and CIE L*a*b* pixel values. These vectors, under a supervised process, were labelled as fat vacuole or non-fat vacuole, and a set of classifiers based on different algorithms were trained, accordingly. The results obtained showed an overall high accuracy for all classifiers (>0.99) with a sensitivity between 0.844 and 1, together with a specificity >0.99. In relation to their speed when classifying images, KNN and Naïve Bayes were substantially faster than other classification algorithms. Sudan stain is a convenient technique for evaluating ME in pre-transplant liver biopsies, providing reliable contrast and facilitating fast and accurate quantification through the machine learning algorithms tested.

Correction to: DNA methylation changes during preimplantation development reveal interspecies differences and reprogramming events at imprinted genes.

An amendment to this paper has been published and can be accessed via the original article.

DNA methylation changes during preimplantation development reveal inter-species differences and reprogramming events at imprinted genes.

Preimplantation embryos experience profound resetting of epigenetic information inherited from the gametes. Genome-wide analysis at single-base resolution has shown similarities but also species differences between human and mouse preimplantation embryos in DNA methylation patterns and reprogramming. Here, we have extended such analysis to two key livestock species, the pig and the cow. We generated genome-wide DNA methylation and whole-transcriptome datasets from gametes to blastocysts in both species. In oocytes from both species, a distinctive bimodal methylation landscape is present, with hypermethylated domains prevalent over hypomethylated domains, similar to human, while in the mouse the proportions are reversed.An oocyte-like pattern of methylation persists in the cleavage stages, albeit with some reduction in methylation level, persisting to blastocysts in cow, while pig blastocysts have a highly hypomethylated landscape. In the pig, there was evidence of transient de novo methylation at the 8-16 cell stages of domains unmethylated in oocytes, revealing a complex dynamic of methylation reprogramming. The methylation datasets were used to identify germline differentially methylated regions (gDMRs) of known imprinted genes and for the basis of detection of novel imprinted loci. Strikingly in the pig, we detected a consistent reduction in gDMR methylation at the 8-16 cell stages, followed by recovery to the blastocyst stage, suggesting an active period of imprint stabilization in preimplantation embryos. Transcriptome analysis revealed absence of expression in oocytes of both species of ZFP57, a key factor in the mouse for gDMR methylation maintenance, but presence of the alternative imprint regulator ZNF445. In conclusion, our study reveals species differences in DNA methylation reprogramming and suggests that porcine or bovine models may be closer to human in key aspects than in the mouse model.

ColPortal, an integrative multiomic platform for analysing epigenetic interactions in colorectal cancer.

Colorectal cancer (CRC) is the third leading cause of cancer mortality worldwide. Different pathological pathways and molecular drivers have been described and some of the associated markers are used to select effective anti-neoplastic therapy. More recent evidence points to a causal role of microbiota and altered microRNA expression in CRC carcinogenesis, but their relationship with pathological drivers or molecular phenotypes is not clearly established. Joint analysis of clinical and omics data can help clarify such relations. We present ColPortal, a platform that integrates transcriptomic, microtranscriptomic, methylomic and microbiota data of patients with colorectal cancer. ColPortal also includes detailed information of histological features and digital histological slides from the study cases, since histology is a morphological manifestation of a complex molecular change. The current cohort consists of Caucasian patients from Europe. For each patient, demographic information, location, histology, tumor staging, tissue prognostic factors, molecular biomarker status and clinical outcomes are integrated with omics data. ColPortal allows one to perform multiomics analyses for groups of patients selected by their clinical data.

The Snapdragon LATE ELONGATED HYPOCOTYL Plays A Dual Role in Activating Floral Growth and Scent Emission.

The plant circadian clock controls a large number of internal processes, including growth and metabolism. Scent emission displays a circadian pattern in many species such as the snapdragon. Here we show that knocking down LATE ELONGATED HYPOCOTYL in Antirrhinum majus affects growth and scent emission. In order to gain an understanding of the growth kinetics, we took a phenomic approach using in-house artificial vision systems, obtaining time-lapse videos. Wild type flowers showed a higher growth speed than knockdown plants. The maximal growth rate was decreased by 22% in plants with lower LHY expression. Floral volatiles were differentially affected as RNAi plants showed advanced emission of compounds synthesized from cinnamic acid and delayed emission of metabolites of benzoic acid. The monoterpenes myrcene and ocimene were delayed, whereas the sesquiterpene farnesene was advanced. Overall, transgenic lines showed an altered volatile emission pattern and displayed a modified scent profile. Our results show that AmLHY plays an important role in the quantitative and qualitative control of floral growth and scent emission.

The Petunia CHANEL Gene is a ZEITLUPE Ortholog Coordinating Growth and Scent Profiles.

The floral perianth, comprising sepals and petals, conceals the sexual organs and attracts pollinators. The coordination of growth and scent emission is not fully understood. We have analyzed the effect of knocking down CHANEL (PhCHL), the ZEITLUPE ortholog in petunia (PhCHL) by hairpin RNAs. Plants with low PhCHL mRNA had overall decreased size. Growth evaluation using time lapse image analysis showed that early leaf movement was not affected by RNAi:PhCHL, but flower angle movement was modified, moving earlier during the day in knockdown plants than in wild types. Despite differences in stem length, growth rate was not significantly affected by loss of PhCHL. In contrast, petal growth displayed lower growth rate in RNAi:PhCHL. Decreased levels of PhCHL caused strongly modified scent profiles, including changes in composition and timing of emission resulting in volatile profiles highly divergent from the wild type. Our results show a role of PhCHL in controlling growth and development of vegetative and reproductive organs in petunia. The different effects of PhCHL on organ development indicate an organ-specific interpretation of the down regulation of PhCHL. Through the control of both timing and quantitative volatile emissions, PhCHL appears to be a major coordinator of scent profiles.

Tolerance Biomarkers in Liver Transplantation: Independent External Validation of the Predictive Strength of SENP6 and FEM1C Gene Expression.

BACKGROUND: Numerous studies have emphasized the genetic and phenotypic profiles of tolerant transplant patients. Moreover, different groups have defined several biomarkers, trying to distinguish patients who are going to be tolerant from those who are going to reject. However, most of these biomarkers have not been validated by other groups or even established for clinical practice. METHODS: We reanalyzed and stratified the predictive capacity of 20 previously described biomarkers for liver transplantation tolerance in a cohort of 17 liver transplant patients subjected to an independent, nonrandomized, prospective study of immunosuppression drug withdrawal. RESULTS: Only 4 of the 20 studied biomarkers (expression of SENP6, FEM1C, miR31, and miR95) showed a strong predictive capacity in the present study. miR31 and FEM1C presented an area under the ROC curve of 96.7%, followed by SENP1 with 93.3%. Finally, miR95 had an area under the ROC curve value <86.7%. CONCLUSIONS: Even though this independent analysis seems to confirm the predictive strength of SENP6 and FEM1C in liver transplantation tolerance, there are also risks in establishing biomarkers for clinical phenotypes without an understanding of how they are biologically relevant. Future collaborations between groups should be promoted so that the most promising biomarkers can be validated and implemented in daily clinical practice.

Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms.

The study of phenomes or phenomics has been a central part of biology. The field of automatic phenotype acquisition technologies based on images has seen an important advance in the last years. As with other high-throughput technologies, it addresses a common set of problems, including data acquisition and analysis. In this review, we give an overview of the main systems developed to acquire images. We give an in-depth analysis of image processing with its major issues and the algorithms that are being used or emerging as useful to obtain data out of images in an automatic fashion.