Longitudinal Human Milk miRNA Composition over the First 3 mo of Lactation in a Cohort of Healthy Mothers Delivering Term Infants.

BACKGROUND: MicroRNAs (miRNAs) are small noncoding RNAs involved in posttranscriptional regulation. miRNAs can be secreted and found in many body fluids, and although they are particularly abundant in breastmilk, their functions remain elusive. Human milk (HM) miRNAs start to raise considerable interest, but a comprehensive understanding of the repertoire and expression profiles along lactation has not been well characterized. OBJECTIVES: This study aimed to characterize the longitudinal profile of HM miRNA between the second week and third month postpartum. METHODS: We used a new sensitive technology to measure HM miRNAs in a cohort of 44 French mothers [mean ± SD age: 31 ± 3.5; BMI (in kg/m2) 21.8 ± 2.3] who delivered at term and provided HM samples at 3 time points (17 ± 3 d, 60 ± 3 d, and 90 ± 3 d) during follow-up visits. RESULTS: We detected 685 miRNAs, of which 35 showed a high and stable expression along the lactation period analyzed. We also described for the first time a set of 11 miRNAs with a dynamic expression profile. To gain insight into the potential functional relevance of this set of miRNAs, we selected miR-3126 and miR-3184 to treat undifferentiated Caco-2 human intestinal cells and then assessed differentially expressed genes and modulation of related biological pathways. CONCLUSIONS: Overall, our study provides new insights into HM miRNA composition and, to our knowledge, the first description of its longitudinal dynamics in mothers who delivered at term. Our in vitro results obtained in undifferentiated Caco-2 human intestinal cells transfected with HM miRNAs also provide further support to the hypothesized mother-to-neonate signaling role of HM miRNAs. This trial was registered at clinicaltrials.gov as NCT01894893.

Sialylated human milk oligosaccharides program cognitive development through a non-genomic transmission mode.

Breastmilk contains bioactive molecules essential for brain and cognitive development. While sialylated human milk oligosaccharides (HMOs) have been implicated in phenotypic programming, their selective role and underlying mechanisms remained elusive. Here, we investigated the long-term consequences of a selective lactational deprivation of a specific sialylated HMO in mice. We capitalized on a knock-out (KO) mouse model (B6.129-St6gal1tm2Jxm/J) lacking the gene responsible for the synthesis of sialyl(alpha2,6)lactose (6'SL), one of the two sources of sialic acid (Neu5Ac) to the lactating offspring. Neu5Ac is involved in the formation of brain structures sustaining cognition. To deprive lactating offspring of 6'SL, we cross-fostered newborn wild-type (WT) pups to KO dams, which provide 6'SL-deficient milk. To test whether lactational 6'SL deprivation affects cognitive capabilities in adulthood, we assessed attention, perseveration, and memory. To detail the associated endophenotypes, we investigated hippocampal electrophysiology, plasma metabolomics, and gut microbiota composition. To investigate the underlying molecular mechanisms, we assessed gene expression (at eye-opening and in adulthood) in two brain regions mediating executive functions and memory (hippocampus and prefrontal cortex, PFC). Compared to control mice, WT offspring deprived of 6'SL during lactation exhibited consistent alterations in all cognitive functions addressed, hippocampal electrophysiology, and in pathways regulating the serotonergic system (identified through gut microbiota and plasma metabolomics). These were associated with a site- (PFC) and time-specific (eye-opening) reduced expression of genes involved in central nervous system development. Our data suggest that 6'SL in maternal milk adjusts cognitive development through a short-term upregulation of genes modulating neuronal patterning in the PFC.

Automatic module selection from several microarray gene expression studies.

Independence of genes is commonly but incorrectly assumed in microarray data analysis; rather, genes are activated in co-regulated sets referred to as modules. In this article, we develop an automatic method to define modules common to multiple independent studies. We use an empirical Bayes procedure to estimate a sparse correlation matrix for all studies, identify modules by clustering, and develop an extreme-value-based method to detect so-called scattered genes, which do not belong to any module. The resulting algorithm is very fast and produces accurate modules in simulation studies. Application to real data identifies modules with significant enrichment and results in a huge dimension reduction, which can alleviate the computational burden of further analyses.

Meta-analysis of incomplete microarray studies.

Meta-analysis of microarray studies to produce an overall gene list is relatively straightforward when complete data are available. When some studies lack information-providing only a ranked list of genes, for example-it is common to reduce all studies to ranked lists prior to combining them. Since this entails a loss of information, we consider a hierarchical Bayes approach to meta-analysis using different types of information from different studies: the full data matrix, summary statistics, or ranks. The model uses an informative prior for the parameter of interest to aid the detection of differentially expressed genes. Simulations show that the new approach can give substantial power gains compared with classical meta-analysis and list aggregation methods. A meta-analysis of 11 published studies with different data types identifies genes known to be involved in ovarian cancer and shows significant enrichment.

A Comparative Study of Nine SARS-CoV-2 IgG Lateral Flow Assays Using Both Post-Infection and Post-Vaccination Samples.

BACKGROUND: Since the SARS-CoV-2 pandemic, lateral flow assays (LFA) detecting specific antibodies have entered the market in abundance. Despite being CE-IVD-labeled, the antigenic compounds of the assays are often unknown, the performance characteristics provided by the manufacturer are often incomplete, and the samples used to obtain the data are not detailed. OBJECTIVE: To perform a comparative evaluation of nine lateral flow assays to detect IgG responses against SARS-CoV-2. For the evaluation, a carefully designed serum panel containing post-infection samples and post-vaccination (both mRNA vaccine and inactivated virus vaccine) samples was used. RESULTS: The sensitivity of the assays overall ranged from 9 to 90.3% and the specificity ranged from 94.2 to 100%. Spike protein-containing assays performed generally better than the assays with only nucleocapsid protein. The sensitivity of some assays was higher on post-infection samples, while other assays had a higher sensitivity to post-vaccination samples. CONCLUSION: A comparative approach in the verification of LFAs with an adequately designed serum panel enabled the identification of the antigens used in the assays. Sensitivities differed between post-infection and post-vaccination samples, depending on the assays used. This demonstrates that the verification of assays must be performed with samples representative of the intended use of the assay.

Nicotinamide riboside kinase 1 protects against diet and age-induced pancreatic ß-cell failure.

OBJECTIVE: Disturbances in NAD+ metabolism have been described as a hallmark for multiple metabolic and age-related diseases, including type 2 diabetes. While alterations in pancreatic ß-cell function are critical determinants of whole-body glucose homeostasis, the role of NAD+ metabolism in the endocrine pancreas remains poorly explored. Here, we aimed to evaluate the role of nicotinamide riboside (NR) metabolism in maintaining NAD+ levels and pancreatic ß-cell function in pathophysiological conditions. METHODS: Whole body and pancreatic ß-cell-specific NRK1 knockout (KO) mice were metabolically phenotyped in situations of high-fat feeding and aging. We also analyzed pancreatic ß-cell function, ß-cell mass and gene expression. RESULTS: We first demonstrate that NRK1, the essential enzyme for the utilization of NR, is abundantly expressed in pancreatic ß-cells. While NR treatment did not alter glucose-stimulated insulin secretion in pancreatic islets from young healthy mice, NRK1 knockout mice displayed glucose intolerance and compromised ß-cells response to a glucose challenge upon high-fat feeding or aging. Interestingly, ß cell dysfunction stemmed from the functional failure of other organs, such as liver and kidney, and the associated changes in circulating peptides and hormones, as mice lacking NRK1 exclusively in ß-cells did not show altered glucose homeostasis. CONCLUSIONS: This work unveils a new physiological role for NR metabolism in the maintenance of glucose tolerance and pancreatic ß-cell function in high-fat feeding or aging conditions.

Prevalence Estimate of Blood Doping in Elite Track and Field Athletes During Two Major International Events.

In elite sport, the Athlete Biological Passport (ABP) was invented to tackle cheaters by monitoring closely changes in biological parameters, flagging atypical variations. The hematological module of the ABP was indeed adopted in 2011 by World Athletics (WA). This study estimates the prevalence of blood doping based on hematological parameters in a large cohort of track and field athletes measured at two international major events (2011 and 2013 WA World Championships) with a hypothesized decrease in prevalence due to the ABP introduction. A total of 3683 blood samples were collected and analyzed from all participating athletes originating from 209 countries. The estimate of doping prevalence was obtained by using a Bayesian network with seven variables, as well as "blood doping" as a variable mimicking doping with low-doses of recombinant human erythropoietin (rhEPO), to generate reference cumulative distribution functions (CDFs) for the Abnormal Blood Profile Score (ABPS) from the ABP. Our results from robust hematological parameters indicate an estimation of an overall blood doping prevalence of 18% in 2011 and 15% in 2013 (non-significant difference) in average in endurance athletes [95% Confidence Interval (CI) 14-22 and 12-19% for 2011 and 2013, respectively]. A higher prevalence was observed in female athletes (22%, CI 16-28%) than in male athletes (15%, CI 9-20%) in 2011. In conclusion, this study presents the first comparison of blood doping prevalence in elite athletes based on biological measurements from major international events that may help scientists and experts to use the ABP in a more efficient and deterrent way.

ABPS: An R Package for Calculating the Abnormal Blood Profile Score.

The Abnormal Blood Profile Score (ABPS) is used to identify blood doping in sport. It combines seven hematological markers, including hemoglobin level, reticulocytes percent, and haematocrit level, using two different machine learning algorithms in order to create a single score that has a better ability to identify doping than each parameter taken alone. The resulting score allows the detection of several types of doping using a single score and is part of the current Athlete Biological Passport program managed by World Anti-Doping Agency (WADA). We describe ≪ ABPS ≫, an R package that allows the calculation of this score. This is the first software implementation calculating this score that is released publicly. The package also contains functions to calculate the OFF-score (another score used for detection of doping), as well as several test datasets. The package is useful for laboratories conducting anti-doping analyses and for researchers working on anti-doping research projects. In particular, it has been successfully used in projects estimating the prevalence of blood doping.