MultiBaC: an R package to remove batch effects in multi-omic experiments.

MOTIVATION: Batch effects in omics datasets are usually a source of technical noise that masks the biological signal and hampers data analysis. Batch effect removal has been widely addressed for individual omics technologies. However, multi-omic datasets may combine data obtained in different batches where omics type and batch are often confounded. Moreover, systematic biases may be introduced without notice during data acquisition, which creates a hidden batch effect. Current methods fail to address batch effect correction in these cases. RESULTS: In this article, we introduce the MultiBaC R package, a tool for batch effect removal in multi-omics and hidden batch effect scenarios. The package includes a diversity of graphical outputs for model validation and assessment of the batch effect correction. AVAILABILITY AND IMPLEMENTATION: MultiBaC package is available on Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/MultiBaC.html) and GitHub (https://github.com/ConesaLab/MultiBaC.git). The data underlying this article are available in Gene Expression Omnibus repository (accession numbers GSE11521, GSE1002, GSE56622 and GSE43747). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Health-related quality of life and radiological and functional lung changes of patients with COVID-19 Pneumonia 3 and 10 months after discharge.

BACKGROUND: Few studies have evaluated the long-term impact on health-related quality of life (HRQoL) in patients who have been hospitalized for COVID-19 pneumonia. Specific follow-up should be carried out to detect and treat possible pulmonary abnormalities, and the worsening of HRQoL should be estimated to target necessary resources for care of these patients after acute phase. The objective was to know the impact on HRQoL of patients who have been admitted for COVID-19 pneumonia, and to evaluate the clinical-radiological and functional changes of patients who have overcome COVID-19 pneumonia at 3 and 10 months of follow-up. METHODS: Prospective observational study of patients who required hospitalization for COVID-19 pneumonia between April and December 2020. All patients filled out the EuroQol five-dimension (EQ-5D) questionnaire with the EuroQol Visual Analogue Scale (E-VAS) for self-assessment of health status. Respiratory function tests and chest X-ray were carried out at 3 and 10 months of follow-up. RESULTS: 61 patients were included in the study. The need for ventilatory support was associated with anxiety/depression on the EQ-5D scale, as well as patients admitted to the intensive care unit (ICU). The mean EQ-5D and E-VAS index scores decreased with hospitalization time, the number of days spent in intermediate respiratory care unit (IRCU) and the level of dyspnoea at the beginning of the hospitalization period. Pulmonary sequelae were observed in 25 patients (41%) at 3 months and 17 (27.9%) at 10 months. Patients improve their forced vital capacity (FVC) by 196 ml (p = 0.001) at 10 months as well as 9% in diffusing capacity of lung for carbon monoxide (DLCO) (p = 0.001) at 10 months. DLCO was found to be correlated to lymphopenia and time spent in IRCU. Low FVC values were detected 10 months after discharge for subjects exhibiting high levels of dyspnoea at 3 months after discharge. CONCLUSIONS: Hospitalization for COVID-19 pneumonia affects the HRQoL of patients, with greater anxiety/depression in those who were more serious affected and are younger. A significant percentage of patients present fibrotic abnormalities and lung function impairment at the first and second follow-up after discharge.

Upregulation of Orai Channels Contributes to Aging-Related Vascular Alterations in Rat Coronary Arteries.

Vascular territories display heterogeneous sensitivity to the impacts of aging. The relevance of the STIM/Orai system to vascular function depends on the vascular bed. We aimed to evaluate the contribution of the STIM/Orai system to aging-related vascular dysfunction in rat coronary circulation. Vascular function was evaluated according to myography in coronary arteries from young (three-month-old) and older (twenty-month-old) rats. The effects of aging and STIM/Orai inhibition on the contraction and relaxation of the coronary arteries and on the protein expression of STIM-1, Orai1, and Orai3 in these vessels were determined. Aging-related hypercontractility to serotonin and endothelin-1 in arteries from male rats was reversed by STIM/Orai inhibition with YM-58483 or by specifically blocking the Orai1 channel with Synta66. The inhibitory effects of Synta66 on coronary vasoconstriction were also observed in older female rats. YM-58483 relaxed serotonin- but not KCl-contracted arteries from males. STIM/Orai inhibition improved defective endothelial vasodilations in aged arteries, even in the presence of NO synthase and cyclooxygenase inhibitors, but not in KCl-contracted segments. YM-58483 significantly enhanced relaxations to calcium-activated potassium channel stimulation in aged vessels. Increased protein expression of Orai1 and Orai3 was detected in arterial homogenates and sections from older rats. Upregulation of the Orai channel contributes to aging-related coronary dysfunction, revealing a potential target in reducing CVD risk.

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.

Asymmetric dimethylarginine (ADMA) elevation and arginase up-regulation contribute to endothelial dysfunction related to insulin resistance in rats and morbidly obese humans.

KEY POINTS: The presence of insulin resistance (IR) is determinant for endothelial dysfunction associated with obesity. Although recent studies have implicated the involvement of mitochondrial superoxide and inflammation in the defective nitric oxide (NO)-mediated responses and subsequent endothelial dysfunction in IR, other mechanisms could compromise this pathway. In the present study, we assessed the role of asymmetric dimethylarginine (ADMA) and arginase with respect to IR-induced impairment of endothelium-dependent vasodilatation in human morbid obesity and in a non-obese rat model of IR. We show that both increased ADMA and up-regulated arginase are determinant factors in the alteration of the l-arginine/NO pathway associated with IR in both models and also that acute treatment of arteries with arginase inhibitor or with l-arginine significantly alleviate endothelial dysfunction. These results help to expand our knowledge regarding the mechanisms of endothelial dysfunction that are related to obesity and IR and establish potential therapeutic targets for intervention. ABSTRACT: Insulin resistance (IR) is determinant for endothelial dysfunction in human obesity. Although we have previously reported the involvement of mitochondrial superoxide and inflammation, other mechanisms could compromise NO-mediated responses in IR. We evaluated the role of the endogenous NOS inhibitor asymmetric dimethylarginine (ADMA) and arginase with respect to IR-induced impairment of l-arginine/NO-mediated vasodilatation in human morbid obesity and in a non-obese rat model of IR. Bradykinin-induced vasodilatation was evaluated in microarteries derived from insulin-resistant morbidly obese (IR-MO) and non-insulin-resistant MO (NIR-MO) subjects. Defective endothelial vasodilatation in IR-MO was improved by l-arginine supplementation. Increased levels of ADMA were detected in serum and adipose tissue from IR-MO. Serum ADMA positively correlated with IR score and negatively with pD2 for bradykinin. Gene expression determination by RT-PCR revealed not only the decreased expression of ADMA degrading enzyme dimethylarginine dimethylaminohydrolase (DDAH)1/2 in IR-MO microarteries, but also increased expression of arginase-2. Arginase inhibition improved endothelial vasodilatation in IR-MO. Analysis of endothelial vasodilatation in a non-obese IR model (fructose-fed rat) confirmed an elevation of circulating and aortic ADMA concentrations, as well as reduced DDAH aortic content and increased aortic arginase activity in IR. Improvement of endothelial vasodilatation in IR rats by l-arginine supplementation and arginase inhibition provided functional corroboration. These results demonstrate that increased ADMA and up-regulated arginase contribute to endothelial dysfunction as determined by the presence of IR in human obesity, most probably by compromising arginine availability. The results provide novel insights regarding the mechanisms of endothelial dysfunction related to obesity and IR and establish potential therapeutic targets for intervention.

Enabling network inference methods to handle missing data and outliers.

BACKGROUND: The inference of complex networks from data is a challenging problem in biological sciences, as well as in a wide range of disciplines such as chemistry, technology, economics, or sociology. The quantity and quality of the data greatly affect the results. While many methodologies have been developed for this task, they seldom take into account issues such as missing data or outlier detection and correction, which need to be properly addressed before network inference. RESULTS: Here we present an approach to (i) handle missing data and (ii) detect and correct outliers based on multivariate projection to latent structures. The method, called trimmed scores regression (TSR), enables network inference methods to analyse incomplete datasets by imputing the missing values coherently with the latent data structure. Furthermore, it substitutes the faulty values in a dataset by proper estimations. We provide an implementation of this approach, and show how it can be integrated with any network inference method as a preliminary data curation step. This functionality is demonstrated with a state of the art network inference method based on mutual information distance and entropy reduction, MIDER. CONCLUSION: The methodology presented here enables network inference methods to analyse a large number of incomplete and faulty datasets that could not be reliably analysed so far. Our comparative studies show the superiority of TSR over other missing data approaches used by practitioners. Furthermore, the method allows for outlier detection and correction.

Automatic individual arterial input functions calculated from PCA outperform manual and population-averaged approaches for the pharmacokinetic modeling of DCE-MR images.

BACKGROUND: To introduce a segmentation method to calculate an automatic arterial input function (AIF) based on principal component analysis (PCA) of dynamic contrast enhanced MR (DCE-MR) imaging and compare it with individual manually selected and population-averaged AIFs using calculated pharmacokinetic parameters. METHODS: The study included 65 individuals with prostate examinations (27 tumors and 38 controls). Manual AIFs were individually extracted and also averaged to obtain a population AIF. Automatic AIFs were individually obtained by applying PCA to volumetric DCE-MR imaging data and finding the highest correlation of the PCs with a reference AIF. Variability was assessed using coefficients of variation and repeated measures tests. The different AIFs were used as inputs to the pharmacokinetic model and correlation coefficients, Bland-Altman plots and analysis of variance tests were obtained to compare the results. RESULTS: Automatic PCA-based AIFs were successfully extracted in all cases. The manual and PCA-based AIFs showed good correlation (r between pharmacokinetic parameters ranging from 0.74 to 0.95), with differences below the manual individual variability (RMSCV up to 27.3%). The population-averaged AIF showed larger differences (r from 0.30 to 0.61). CONCLUSION: The automatic PCA-based approach minimizes the variability associated to obtaining individual volume-based AIFs in DCE-MR studies of the prostate.

Differential expression in RNA-seq: a matter of depth.

Next-generation sequencing (NGS) technologies are revolutionizing genome research, and in particular, their application to transcriptomics (RNA-seq) is increasingly being used for gene expression profiling as a replacement for microarrays. However, the properties of RNA-seq data have not been yet fully established, and additional research is needed for understanding how these data respond to differential expression analysis. In this work, we set out to gain insights into the characteristics of RNA-seq data analysis by studying an important parameter of this technology: the sequencing depth. We have analyzed how sequencing depth affects the detection of transcripts and their identification as differentially expressed, looking at aspects such as transcript biotype, length, expression level, and fold-change. We have evaluated different algorithms available for the analysis of RNA-seq and proposed a novel approach--NOISeq--that differs from existing methods in that it is data-adaptive and nonparametric. Our results reveal that most existing methodologies suffer from a strong dependency on sequencing depth for their differential expression calls and that this results in a considerable number of false positives that increases as the number of reads grows. In contrast, our proposed method models the noise distribution from the actual data, can therefore better adapt to the size of the data set, and is more effective in controlling the rate of false discoveries. This work discusses the true potential of RNA-seq for studying regulation at low expression ranges, the noise within RNA-seq data, and the issue of replication.

Near infrared hyperspectral imaging for forensic analysis of document forgery.

Hyperspectral images in the near infrared range (HSI-NIR) were evaluated as a nondestructive method to detect fraud in documents. Three different types of typical forgeries were simulated by (a) obliterating text, (b) adding text and (c) approaching the crossing lines problem. The simulated samples were imaged in the range of 928-2524 nm with spectral and spatial resolutions of 6.3 nm and 10 µm, respectively. After data pre-processing, different chemometric techniques were evaluated for each type of forgery. Principal component analysis (PCA) was performed to elucidate the first two types of adulteration, (a) and (b). Moreover, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) was used in an attempt to improve the results of the type (a) obliteration and type (b) adding text problems. Finally, MCR-ALS and Partial Least Squares-Discriminant Analysis (PLS-DA), employed as a variable selection tool, were used to study the type (c) forgeries, i.e. crossing lines problem. Type (a) forgeries (obliterating text) were successfully identified in 43% of the samples using both the chemometric methods (PCA and MCR-ALS). Type (b) forgeries (adding text) were successfully identified in 82% of the samples using both the methods (PCA and MCR-ALS). Finally, type (c) forgeries (crossing lines) were successfully identified in 85% of the samples. The results demonstrate the potential of HSI-NIR associated with chemometric tools to support document forgery identification.

ARSyN: a method for the identification and removal of systematic noise in multifactorial time course microarray experiments.

Transcriptomic profiling experiments that aim to the identification of responsive genes in specific biological conditions are commonly set up under defined experimental designs that try to assess the effects of factors and their interactions on gene expression. Data from these controlled experiments, however, may also contain sources of unwanted noise that can distort the signal under study, affect the residuals of applied statistical models, and hamper data analysis. Commonly, normalization methods are applied to transcriptomics data to remove technical artifacts, but these are normally based on general assumptions of transcript distribution and greatly ignore both the characteristics of the experiment under consideration and the coordinative nature of gene expression. In this paper, we propose a novel methodology, ARSyN, for the preprocessing of microarray data that takes into account these 2 last aspects. By combining analysis of variance (ANOVA) modeling of gene expression values and multivariate analysis of estimated effects, the method identifies the nonstructured part of the signal associated to the experimental factors (the noise within the signal) and the structured variation of the ANOVA errors (the signal of the noise). By removing these noise fractions from the original data, we create a filtered data set that is rich in the information of interest and includes only the random noise required for inferential analysis. In this work, we focus on multifactorial time course microarray (MTCM) experiments with 2 factors: one quantitative such as time or dosage and the other qualitative, as tissue, strain, or treatment. However, the method can be used in other situations such as experiments with only one factor or more complex designs with more than 2 factors. The filtered data obtained after applying ARSyN can be further analyzed with the appropriate statistical technique to obtain the biological information required. To evaluate the performance of the filtering strategy, we have applied different statistical approaches for MTCM analysis to several real and simulated data sets, studying also the efficiency of these techniques. By comparing the results obtained with the original and ARSyN filtered data and also with other filtering techniques, we can conclude that the proposed method increases the statistical power to detect biological signals, especially in cases where there are high levels of structural noise. Software for ARSyN is freely available at http://www.ua.es/personal/mj.nueda.

Process understanding and monitoring: A glimpse into data strategies for miniaturized NIR spectrometers.

BACKGROUND: The implementation of process analytical technologies (PAT) has gained attention since 2004 when its formal introduction through the U.S. Food and Drug Administration was introduced. Manufacturers that need to evaluate the employment of new monitoring systems could face different challenges: identification of suitable sensors, verification of data meaning, evaluation of several statistical strategies to obtain insights about data and achieve process understanding and finally, the actual possibilities for monitoring. Kefir fermentations were chosen as an example because of the chemical and physical transformations that occurred during the process, which could be common to several other fermentation processes. In order to pave the way for monitoring establish the information contained in the data and find the right tools for extracting them is of extreme importance. Strategies to identify different experimental conditions in the spectra acquired with a miniaturized NIR (1350-2550 nm) during process occurrence were addressed. RESULTS: The study aims to offer insights into good practices and steps to pave the way for process monitoring with handheld NIR data. The main aspects of interest for batch processes in preliminary evaluations were investigated and discussed. On the one hand, process understanding and, on the other, the possibilities for process monitoring and endpoint determination were examined. The combination of different statistical tools allowed the extraction of information from the data and the identification of the link between them and the chemical and physical changes during the process. In addition, insights into the spectra characteristics in the studied spectroscopic range for kefir fermentation were reported. SIGNIFICANCE: The capabilities for miniaturized NIR spectra to represent and statistical strategies to characterize different experimental conditions in a real case fermentation occurrence were proved. The strengths and limitations of some of the common approaches to catch changes in fermentation condition were highlighted. For the various statistical approaches, the chances offered in the research and development stages and to set the scene for monitoring and end-point detection were explored.

PLATERO: A calibration protocol for plate reader green fluorescence measurements.

One of the most common sources of information in Synthetic Biology is the data coming from plate reader fluorescence measurements. These experiments provide a measure of the light emitted by a certain fluorescent molecule, such as the Green Fluorescent Protein (GFP). However, these measurements are generally expressed in arbitrary units and are affected by the measurement device gain. This limits the range of measurements in a single experiment and hampers the comparison of results among experiments. In this work, we describe PLATERO, a calibration protocol to express fluorescence measures in concentration units of a reference fluorophore. The protocol removes the gain effect of the measurement device on the acquired data. In addition, the fluorescence intensity values are transformed into units of concentration using a Fluorescein calibration model. Both steps are expressed in a single mathematical expression that returns normalized, gain-independent, and comparable data, even if the acquisition was done at different device gain levels. Most important, the PLATERO embeds a Linearity and Bias Analysis that provides an assessment of the uncertainty of the model estimations, and a Reproducibility and Repeatability analysis that evaluates the sources of variability originating from the measurements and the equipment. All the functions used to build the model, exploit it with new data, and perform the uncertainty and variability assessment are available in an open access repository.

New bladder cancer non-invasive surveillance method based on voltammetric electronic tongue measurement of urine.

Bladder cancer (BC) is the sixth leading cause of death by cancer. Depending on the invasiveness of tumors, patients with BC will undergo surgery and surveillance lifelong, owing the high rate of recurrence and progression. In this context, the development of strategies to support non-invasive BC diagnosis is focusing attention. Voltammetric electronic tongue (VET) has been demonstrated to be of use in the analysis of biofluids. Here, we present the implementation of a VET to study 207 urines to discriminate BC and non-BC for diagnosis and surveillance to detect recurrences. Special attention has been paid to the experimental setup to improve reproducibility in the measurements. PLSDA analysis together with variable selection provided a model with high sensitivity, specificity, and area under the ROC curve AUC (0.844, 0.882, and 0.917, respectively). These results pave the way for the development of non-invasive low-cost and easy-to-use strategies to support BC diagnosis and follow-up.

Diagnosis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome With Partial Least Squares Discriminant Analysis: Relevance of Blood Extracellular Vesicles.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), a chronic disease characterized by long-lasting persistent debilitating widespread fatigue and post-exertional malaise, remains diagnosed by clinical criteria. Our group and others have identified differentially expressed miRNA profiles in the blood of patients. However, their diagnostic power individually or in combinations seems limited. A Partial Least Squares-Discriminant Analysis (PLS-DA) model initially based on 817 variables: two demographic, 34 blood analytic, 136 PBMC miRNAs, 639 Extracellular Vesicle (EV) miRNAs, and six EV features, selected an optimal number of five components, and a subset of 32 regressors showing statistically significant discriminant power. The presence of four EV-features (size and z-values of EVs prepared with or without proteinase K treatment) among the 32 regressors, suggested that blood vesicles carry relevant disease information. To further explore the features of ME/CFS EVs, we subjected them to Raman micro-spectroscopic analysis, identifying carotenoid peaks as ME/CFS fingerprints, possibly due to erythrocyte deficiencies. Although PLS-DA analysis showed limited capacity of Raman fingerprints for diagnosis (AUC = 0.7067), Raman data served to refine the number of PBMC miRNAs from our previous model still ensuring a perfect classification of subjects (AUC=1). Further investigations to evaluate model performance in extended cohorts of patients, to identify the precise ME/CFS EV components detected by Raman and to reveal their functional significance in the disease are warranted.

Machine-learning-derived predictive score for early estimation of COVID-19 mortality risk in hospitalized patients.

The clinical course of COVID-19 is highly variable. It is therefore essential to predict as early and accurately as possible the severity level of the disease in a COVID-19 patient who is admitted to the hospital. This means identifying the contributing factors of mortality and developing an easy-to-use score that could enable a fast assessment of the mortality risk using only information recorded at the hospitalization. A large database of adult patients with a confirmed diagnosis of COVID-19 (n = 15,628; with 2,846 deceased) admitted to Spanish hospitals between December 2019 and July 2020 was analyzed. By means of multiple machine learning algorithms, we developed models that could accurately predict their mortality. We used the information about classifiers' performance metrics and about importance and coherence among the predictors to define a mortality score that can be easily calculated using a minimal number of mortality predictors and yielded accurate estimates of the patient severity status. The optimal predictive model encompassed five predictors (age, oxygen saturation, platelets, lactate dehydrogenase, and creatinine) and yielded a satisfactory classification of survived and deceased patients (area under the curve: 0.8454 with validation set). These five predictors were additionally used to define a mortality score for COVID-19 patients at their hospitalization. This score is not only easy to calculate but also to interpret since it ranges from zero to eight, along with a linear increase in the mortality risk from 0% to 80%. A simple risk score based on five commonly available clinical variables of adult COVID-19 patients admitted to hospital is able to accurately discriminate their mortality probability, and its interpretation is straightforward and useful.

Early manifestation of aging-related vascular dysfunction in human penile vasculature-A potential explanation for the role of erectile dysfunction as a harbinger of systemic vascular disease.

Advanced age is related to functional alterations of human vasculature, but erectile dysfunction precedes systemic manifestations of vascular disease. The current study aimed to simultaneously evaluate the influence of aging on vascular function (relaxation and contraction responses) in systemic human vascular territories: aorta (HA) and resistance mesenteric arteries (HMA) and human corpus cavernosum (HCC) and penile resistance arteries (HPRA). Associations of oxidative stress and inflammation circulating biomarkers with age and functional responses were also determined. Vascular specimens were obtained from 76 organ donors (age range 18-87). Four age-groups were established: < 40, 40-55, 56-65 and > 65 years old. Increasing age was associated with a decline in endothelium-dependent relaxation induced by BK in HMA (r = -0.597, p = 0.0001), or by ACh in HCC (r = -0.505, p = 0.0022), and HPRA (r = -0.601, p = 0.0012). Significant impairment was detected at > 65 years old in HMA but earlier in penile vasculature (> 55 years old). Age-related reduction to H2O2-vasodilatory response started before in HCC (56-65 years old) than in HA (> 65 years old). In contrast to relaxation responses, aging-related hypercontractility to adrenergic stimulation was homogeneous: contractions significantly increased in subjects > 55 years old in all tested vessels. Although not significantly age related, circulating levels of ADMA (r = -0.681, p = 0.0052) and TNF-α (r = -0.537, p = 0.0385) were negatively correlated with endothelial vasodilation in HMA but not in HCC or HPRA. Penile vasculature exhibits an early impairment of endothelium-dependent and H2O2-induced vasodilations when compared to mesenteric microcirculation and aorta. Therefore, functional susceptibility of penile vasculature to the aging process may account for anticipation of erectile dysfunction to systemic manifestations of vascular disease.

Functional Role of STIM-1 and Orai1 in Human Microvascular Aging.

The impact of aging on vascular function is heterogeneous depending on the vascular territories. Calcium regulation plays a key role in vascular function and has been implicated in aging-related hypercontractility of corpus cavernosum. We aimed to evaluate stromal interaction molecule (STIM)/Orai system involvement in aging-related vascular alterations in the human macro and microvasculature. Aortae specimens and mesenteric arteries (MA), obtained from 45 organ donors, were functionally evaluated in organ chambers and wire myographs. Subjects were divided into groups either younger or older than 65-years old. The expressions of STIM-1, Orai1, and Orai3 were determined by immunofluorescence in the aorta and MA, and by Western blot in the aorta homogenates. The inhibition of STIM/Orai with YM-58483 (20 µM) reversed adrenergic hypercontractility in MA from older subjects but did not modify aging-related hypercontractility in the aortic strips. Aging was related to an increased expression of Orai1 in human aorta, while Orai1 and STIM-1 were upregulated in MA. STIM-1 and Orai1 protein expressions were inversely correlated to endothelial function in MA. Circulating levels of Orai1 were correlated with the inflammatory factor TNF-α and with the endothelial dysfunction marker asymmetric dimethylarginine. Aging is associated with an increased expression of the STIM/Orai system in human vessels with functional relevance only in the microvascular territory, suggesting its role in aging-related microvascular dysfunction.

Comprehensive functional core microbiome comparison in genetically obese and lean hosts under the same environment.

Our study provides an exhaustive comparison of the microbiome core functionalities (captured by 3,936 microbial gene abundances) between hosts with divergent genotypes for intramuscular lipid deposition. After 10 generations of divergent selection for intramuscular fat in rabbits and 4.14 phenotypic standard deviations (SD) of selection response, we applied a combination of compositional and multivariate statistical techniques to identify 122 cecum microbial genes with differential abundances between the lines (ranging from -0.75 to +0.73 SD). This work elucidates that microbial biosynthesis lipopolysaccharides, peptidoglycans, lipoproteins, mucin components, and NADH reductases, amongst others, are influenced by the host genetic determination for lipid accretion in muscle. We also differentiated between host-genetically influenced microbial mechanisms regulating lipid deposition in body or intramuscular reservoirs, with only 28 out of 122 MGs commonly contributing to both. Importantly, the results of this study are of relevant interest for the efficient development of strategies fighting obesity.

Ageing-induced hypercontractility is related to functional enhancement of STIM/Orai and upregulation of Orai 3 in rat and human penile tissue.

The role of STIM/Orai calcium entry system on vascular ageing has not been elucidated. We aimed to evaluate the influence of ageing on STIM/Orai signalling and its role on ageing-induced alterations of contractile function in rat corpus cavernosum (RCC) and human penile resistance arteries (HPRA) and corpus cavernosum (HCC). RCC was obtained from 3 months-old and 20 months-old animals. HPRA and HCC were obtained from organ donors of varied ages without history of erectile dysfunction. Aging was associated with enhanced norepinephrine (NE)- and thromboxane analogue (U46619)-induced contractions in RCC which were significantly inhibited by the STIM/Orai inhibitor, YM-58483 (20 µM). Other STIM/Orai inhibitor, 2-aminoethyldiphenylborate also reduced NE-induced contractions in RCC from aged rats. YM-58483 significantly reduced neurogenic contractions and potentiated neurogenic relaxations in RCC from aged rats. In HCC and HPRA, NE-induced contractions were significantly enhanced in older subjects (>65 years-old) but YM-58483 completely reversed ageing-related hypercontractility. Ageing did not modify STIM-1 and Orai1 protein expressions but Orai3 was significantly overexpressed in cavernosal tissue from old rats and older subjects. Contribution of STIM/Orai to cavernosal contraction increases with ageing together with increased expression of Orai3. Orai inhibition could be a potential therapeutic strategy to reduce ageing-related impact on vascular/erectile function.

MultiBaC: A strategy to remove batch effects between different omic data types.

Diversity of omic technologies has expanded in the last years together with the number of omic data integration strategies. However, multiomic data generation is costly, and many research groups cannot afford research projects where many different omic techniques are generated, at least at the same time. As most researchers share their data in public repositories, different omic datasets of the same biological system obtained at different labs can be combined to construct a multiomic study. However, data obtained at different labs or moments in time are typically subjected to batch effects that need to be removed for successful data integration. While there are methods to correct batch effects on the same data types obtained in different studies, they cannot be applied to correct lab or batch effects across omics. This impairs multiomic meta-analysis. Fortunately, in many cases, at least one omics platform-i.e. gene expression- is repeatedly measured across labs, together with the additional omic modalities that are specific to each study. This creates an opportunity for batch analysis. We have developed MultiBaC (multiomic Multiomics Batch-effect Correction correction), a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. Our strategy is based on the existence of at least one shared data type which allows data prediction across omics. We validate this approach both on simulated data and on a case where the multiomic design is fully shared by two labs, hence batch effect correction within the same omic modality using traditional methods can be compared with the MultiBaC correction across data types. Finally, we apply MultiBaC to a true multiomic data integration problem to show that we are able to improve the detection of meaningful biological effects.