Epigenetic Mechanisms Regulate Stem Cell Expressed Genes Pou5f1 and Gfra1 in a Male Germ Cell Line

Male fertility is declining and an underlying cause may be due to environment-epigenetic interactions in developing sperm, yet nothing is known of how the epigenome controls gene expression in sperm development. Histone methylation and acetylation are dynamically regulated in spermatogenesis and are sensitive to the environment. Our objectives were to determine how histone H3 methylation and acetylation contribute to the regulation of key genes in spermatogenesis. A germ cell line, GC-1, was exposed to either the control, or the chromatin modifying drugs tranylcypromine (T), an inhibitor of the histone H3 demethylase KDM1 (lysine specific demethylase 1), or trichostatin (TSA), an inhibitor of histone deacetylases, (HDAC). Quantitative PCR (qPCR) was used to identify genes that were sensitive to treatment. As a control for specificity the Myod1 (myogenic differentiation 1) gene was analyzed. Chromatin immunoprecipitation (ChIP) followed by qPCR was used to measure histone H3 methylation and acetylation at the promoters of target genes and the control, Myod1. Remarkably, the chromatin modifying treatment specifically induced the expression of spermatogonia expressed genes Pou5f1 and Gfra1. ChIP-qPCR revealed that induction of gene expression was associated with a gain in gene activating histone H3 methylation and acetylation in Pou5f1 and Gfra1 promoters, whereas CpG DNA methylation was not affected. Our data implicate a critical role for histone H3 methylation and acetylation in the regulation of genes expressed by spermatogonia – here, predominantly mediated by HDAC-containing protein complexes

A predictive assessment of genetic correlations between traits in chickens using markers

International audienceAbstractBackgroundGenomic selection has been successfully implemented in plant and animal breeding programs to shorten generation intervals and accelerate genetic progress per unit of time. In practice, genomic selection can be used to improve several correlated traits simultaneously via multiple-trait prediction, which exploits correlations between traits. However, few studies have explored multiple-trait genomic selection. Our aim was to infer genetic correlations between three traits measured in broiler chickens by exploring kinship matrices based on a linear combination of measures of pedigree and marker-based relatedness. A predictive assessment was used to gauge genetic correlations.MethodsA multivariate genomic best linear unbiased prediction model was designed to combine information from pedigree and genome-wide markers in order to assess genetic correlations between three complex traits in chickens, i.e. body weight at 35 days of age (BW), ultrasound area of breast meat (BM) and hen-house egg production (HHP). A dataset with 1351 birds that were genotyped with the 600 K Affymetrix platform was used. A kinship kernel (K) was constructed as K = λG + (1 − λ)A, where A is the numerator relationship matrix, measuring pedigree-based relatedness, and G is a genomic relationship matrix. The weight (λ) assigned to each source of information varied over the grid λ = (0, 0.2, 0.4, 0.6, 0.8, 1). Maximum likelihood estimates of heritability and genetic correlations were obtained at each λ, and the “optimum” λ was determined using cross-validation.ResultsEstimates of genetic correlations were affected by the weight placed on the source of information used to build K. For example, the genetic correlation between BW–HHP and BM–HHP changed markedly when λ varied from 0 (only A used for measuring relatedness) to 1 (only genomic information used). As λ increased, predictive correlations (correlation between observed phenotypes and predicted breeding values) increased and mean-squared predictive error decreased. However, the improvement in predictive ability was not monotonic, with an optimum found at some 0 < λ < 1, i.e., when both sources of information were used together.ConclusionsOur findings indicate that multiple-trait prediction may benefit from combining pedigree and marker information. Also, it appeared that expected correlated responses to selection computed from standard theory may differ from realized responses. The predictive assessment provided a metric for performance evaluation as well as a means for expressing uncertainty of outcomes of multiple-trait selection

Regulator-dependent mechanisms of C3b processing by factor I allow differentiation of immune responses

The complement system labels microbes and host debris for clearance. Degradation of surface-bound C3b is pivotal to direct immune responses and protect host cells. How the serine protease factor I (FI), assisted by regulators, cleaves either two or three distant peptide bonds in the CUB domain of C3b remains unclear. We present a crystal structure of C3b in complex with FI and regulator factor H (FH; domains 1-4 with 19-20). FI binds C3b-FH between FH domains 2 and 3 and a reoriented C3b C-terminal domain and docks onto the first scissile bond, while stabilizing its catalytic domain for proteolytic activity. One cleavage in C3b does not affect its overall structure, whereas two cleavages unfold CUB and dislodge the thioester-containing domain (TED), affecting binding of regulators and thereby determining the number of cleavages. These data explain how FI generates late-stage opsonins iC3b or C3dg in a context-dependent manner, to react to foreign, danger or healthy self signals

How novel structures inform understanding of complement function

33 p.-3 fig.During the last decade, the complement field has experienced outstanding advancements in the mechanistic understanding of how complement activators are recognized, what C3 activation means, how protein complexes like the C3 convertases and the membrane attack complex are assembled, and how positive and negative complement regulators perform their function. All of this has been made possible mostly because of the contributions of structural biology to the study of the complement components. The wealth of novel structural data has frequently provided support to previously held knowledge, but often has added alternative and unexpected insights into complement function. Here we will review some of these findings focusing in the alternative and terminal complement pathways.SRdeC is supported by the Spanish “Ministerio de Economía y Competitividad-FEDER” (SAF2015-66287R), the Seventh Framework Programme European Union Project EURenOmics (305608) and the Autonomous Region of Madrid (S2010/BMD- 2316). SRdeC is member of the “CIB intramural Program “Molecular Machines for Better Life (MACBET)”. EGdeJ is supported by the Spanish “Ministerio de Economía y Competitividad-FEDER” (RYC-2013-13395 and SAF2014-52339P). OL is supported by the Spanish Ministry of Economy, Industry and Competitiveness (SAF2014-52301-R).AT and MS are supported by the Spanish “Ministerio de Economía y Competitividad-FEDER” (IJCI-2015-25222 and IJCI-2015-24388, respectively).Peer reviewe