Brown adipose tissue metabolism in women is dependent on ovarian status.

In rodents, loss of estradiol (E2) reduces brown adipose tissue (BAT) metabolic activity. Whether E2 impacts BAT activity in women is not known. BAT oxidative metabolism was measured in premenopausal (n = 27; 35 ± 9 yr; body mass index = 26.0 ± 5.3 kg/m2) and postmenopausal (n = 25; 51 ± 8 yr; body mass index = 28.0 ± 5.0 kg/m2) women at room temperature and during acute cold exposure using [11C]acetate with positron emission tomography coupled with computed tomograph. BAT glucose uptake was also measured during acute cold exposure using 2-deoxy-2-[18F]fluoro-d-glucose. To isolate the effects of ovarian hormones from biological aging, measurements were repeated in a subset of premenopausal women (n = 8; 40 ± 4 yr; BMI = 28.0 ± 7.2 kg/m2) after 6 mo of gonadotropin-releasing hormone agonist therapy to suppress ovarian hormones. At room temperature, there was no difference in BAT oxidative metabolism between premenopausal (0.56 ± 0.31 min-1) and postmenopausal women (0.63 ± 0.28 min-1). During cold exposure, BAT oxidative metabolism (1.28 ± 0.85 vs. 0.91 ± 0.63 min-1, P = 0.03) and net BAT glucose uptake (84.4 ± 82.5 vs. 29.7 ± 31.4 nmol·g-1·min-1, P < 0.01) were higher in premenopausal than postmenopausal women. In premenopausal women who underwent gonadotropin-releasing hormone agonist, cold-stimulated BAT oxidative metabolism was reduced to a similar level (from 1.36 ± 0.66 min-1 to 0.91 ± 0.41 min-1) to that observed in postmenopausal women (0.91 ± 0.63 min-1). These results provide the first evidence in humans that reproductive hormones are associated with BAT oxidative metabolism and suggest that BAT may be a target to attenuate age-related reduction in energy expenditure and maintain metabolic health in postmenopausal women.NEW & NOTEWORTHY In rodents, loss of estrogen reduces brown adipose tissue (BAT) activity. Whether this is true in humans is not known. We found that BAT oxidative metabolism and glucose uptake were lower in postmenopausal compared to premenopausal women. In premenopausal women who underwent ovarian suppression to reduce circulating estrogen, BAT oxidative metabolism was reduced to postmenopausal levels. Thus the loss of ovarian function in women leads to a reduction in BAT metabolic activity independent of age.

Dynamic transcriptomic analysis of Ischemic Injury in a Porcine Pre-Clinical Model mimicking Donors Deceased after Circulatory Death.

Due to organ shortage, clinicians are prone to consider alternative type of organ donors among them donors deceased after circulatory death (DCD). However, especially using these organs which are more prone to graft dysfunction, there is a need to better understand mechanistic events ocuring during ischemia phase and leading to ischemia/reperfusion injuries (IRI). The aim of this study is to provide a dynamic transcriptomic analysis of preclinical porcine model kidneys subjected to ischemic stress mimicking DCD donor. We compared cortex and corticomedullary junction (CMJ) tissues from porcine kidneys submitted to 60 min warm ischemia (WI) followed by 0, 6 or 24 hours of cold storage in University of Wisconsin solution versus control non-ischemic kidneys (n = 5 per group). 29 cortex genes and 113 CMJ genes were significantly up or down-regulated after WI versus healthy kidneys, and up to 400 genes were regulated after WI followed by 6 or 24 hours of cold storage (p < 0.05). Functionnal enrichment analysis (home selected gene kinetic classification, Gene-ontology-biological processes and Gene-ontology-molecular-function) revealed relevant genes implication during WI and cold storage. We uncovered targets which we will further validate as biomarkers and new therapeutic targets to optimize graft kidney quality before transplantation and improve whole transplantation outcome.

Optimization of an RNA-Seq Differential Gene Expression Analysis Depending on Biological Replicate Number and Library Size.

RNA-Seq is a widely used technology that allows an efficient genome-wide quantification of gene expressions for, for example, differential expression (DE) analysis. After a brief review of the main issues, methods and tools related to the DE analysis of RNA-Seq data, this article focuses on the impact of both the replicate number and library size in such analyses. While the main drawback of previous relevant studies is the lack of generality, we conducted both an analysis of a two-condition experiment (with eight biological replicates per condition) to compare the results with previous benchmark studies, and a meta-analysis of 17 experiments with up to 18 biological conditions, eight biological replicates and 100 million (M) reads per sample. As a global trend, we concluded that the replicate number has a larger impact than the library size on the power of the DE analysis, except for low-expressed genes, for which both parameters seem to have the same impact. Our study also provides new insights for practitioners aiming to enhance their experimental designs. For instance, by analyzing both the sensitivity and specificity of the DE analysis, we showed that the optimal threshold to control the false discovery rate (FDR) is approximately 2-r, where r is the replicate number. Furthermore, we showed that the false positive rate (FPR) is rather well controlled by all three studied R packages: DESeq, DESeq2, and edgeR. We also analyzed the impact of both the replicate number and library size on gene ontology (GO) enrichment analysis. Interestingly, we concluded that increases in the replicate number and library size tend to enhance the sensitivity and specificity, respectively, of the GO analysis. Finally, we recommend to RNA-Seq practitioners the production of a pilot data set to strictly analyze the power of their experimental design, or the use of a public data set, which should be similar to the data set they will obtain. For individuals working on tomato research, on the basis of the meta-analysis, we recommend at least four biological replicates per condition and 20 M reads per sample to be almost sure of obtaining about 1000 DE genes if they exist.

CAZyChip: dynamic assessment of exploration of glycoside hydrolases in microbial ecosystems.

BACKGROUND: Microorganisms constitute a reservoir of enzymes involved in environmental carbon cycling and degradation of plant polysaccharides through their production of a vast variety of Glycoside Hydrolases (GH). The CAZyChip was developed to allow a rapid characterization at transcriptomic level of these GHs and to identify enzymes acting on hydrolysis of polysaccharides or glycans. RESULTS: This DNA biochip contains the signature of 55,220 bacterial GHs available in the CAZy database. Probes were designed using two softwares, and microarrays were directly synthesized using the in situ ink-jet technology. CAZyChip specificity and reproducibility was validated by hybridization of known GHs RNA extracted from recombinant E. coli strains, which were previously identified by a functional metagenomic approach. The GHs arsenal was also studied in bioprocess conditions using rumen derived microbiota. CONCLUSIONS: The CAZyChip appears to be a user friendly tool for profiling the expression of a large variety of GHs. It can be used to study temporal variations of functional diversity, thereby facilitating the identification of new efficient candidates for enzymatic conversions from various ecosystems.

Targeted resequencing identifies PTCH1 as a major contributor to ocular developmental anomalies and extends the SOX2 regulatory network.

Ocular developmental anomalies (ODA) such as anophthalmia/microphthalmia (AM) or anterior segment dysgenesis (ASD) have an estimated combined prevalence of 3.7 in 10,000 births. Mutations in SOX2 are the most frequent contributors to severe ODA, yet account for a minority of the genetic drivers. To identify novel ODA loci, we conducted targeted high-throughput sequencing of 407 candidate genes in an initial cohort of 22 sporadic ODA patients. Patched 1 (PTCH1), an inhibitor of sonic hedgehog (SHH) signaling, harbored an enrichment of rare heterozygous variants in comparison to either controls, or to the other candidate genes (four missense and one frameshift); targeted resequencing of PTCH1 in a second cohort of 48 ODA patients identified two additional rare nonsynonymous changes. Using multiple transient models and a CRISPR/Cas9-generated mutant, we show physiologically relevant phenotypes altering SHH signaling and eye development upon abrogation of ptch1 in zebrafish for which in vivo complementation assays using these models showed that all six patient missense mutations affect SHH signaling. Finally, through transcriptomic and ChIP analyses, we show that SOX2 binds to an intronic domain of the PTCH1 locus to regulate PTCH1 expression, findings that were validated both in vitro and in vivo. Together, these results demonstrate that PTCH1 mutations contribute to as much as 10% of ODA, identify the SHH signaling pathway as a novel effector of SOX2 activity during human ocular development, and indicate that ODA is likely the result of overactive SHH signaling in humans harboring mutations in either PTCH1 or SOX2.

Fuzzy logic selection as a new reliable tool to identify molecular grade signatures in breast cancer--the INNODIAG study.

BACKGROUND: Personalized medicine has become a priority in breast cancer patient management. In addition to the routinely used clinicopathological characteristics, clinicians will have to face an increasing amount of data derived from tumor molecular profiling. The aims of this study were to develop a new gene selection method based on a fuzzy logic selection and classification algorithm, and to validate the gene signatures obtained on breast cancer patient cohorts. METHODS: We analyzed data from four published gene expression datasets for breast carcinomas. We identified the best discriminating genes by comparing molecular expression profiles between histologic grade 1 and 3 tumors for each of the training datasets. The most pertinent probes were selected and used to define fuzzy molecular grade 1-like (good prognosis) and fuzzy molecular grade 3-like (poor prognosis) profiles. To evaluate the prognostic performance of the fuzzy grade signatures in breast cancer tumors, a Kaplan-Meier analysis was conducted to compare the relapse-free survival deduced from histologic grade and fuzzy molecular grade classification. RESULTS: We applied the fuzzy logic selection on breast cancer databases and obtained four new gene signatures. Analysis in the training public sets showed good performance of these gene signatures for grade (sensitivity from 90% to 95%, specificity 67% to 93%). To validate these gene signatures, we designed probes on custom microarrays and tested them on 150 invasive breast carcinomas. Good performance was obtained with an error rate of less than 10%. For one gene signature, among 74 histologic grade 3 and 18 grade 1 tumors, 88 cases (96%) were correctly assigned. Interestingly histologic grade 2 tumors (n = 58) were split in these two molecular grade categories. CONCLUSION: We confirmed the use of fuzzy logic selection as a new tool to identify gene signatures with good reliability and increased classification power. This method based on artificial intelligence algorithms was successfully applied to breast cancers molecular grade classification allowing histologic grade 2 classification into grade 1 and grade 2 like to improve patients prognosis. It opens the way to further development for identification of new biomarker combinations in other applications such as prediction of treatment response.