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Summary The influence of the Wenchuan earthquake on semen quality of adult male survivors is unclear. We investigated the semen quality included 673 male survivors from the worse-affected counties in the earthquake between Aug 2008 and July 2013. Semen parameters including pH, volume, concentration, motility and morphology were measured according to the World Health Organization (WHO) criteria. Kruskal–Wallis analysis of variance was used to examine the statistical differences between years, and a logistic regression was used to analyse the impacts caused by earthquake on the changes of semen quality. We found the medians (5th and 95th) were 2.5 ml (0.6–5.5) for semen volume, 59.0 × 106 ml−1 [(13.0–133.0)] × 106 ml−1 for semen concentration, 46% (13–64%) for sperm progressive motility and 3.0% (0–17.5%) for normal morphology for adult male survivors. Semen concentration, the percentage of sperm progressive motility, total motility and sperm normal morphology were all decreased in the first 3 years, and the differences among years 1, 2 and 3 were significant except the percentage of sperm progressive motility (P

The trends in semen quality are conflicting. Although many previous surveys on semen quality indicated a decline, the trends in semen quality in Sichuan area of south-west China are not clear. We analysed the semen parameters in a cohort of 28,213 adult males close to general population in Sichuan between July 2007 and June 2012, and investigated the changes on semen quality. The semen parameters including pH, volume, concentration, motility, morphology were measured according to the World Health Organization (WHO) criteria. Kruskal-Wallis analysis of variance was used to examine the statistical differences of semen quality between groups. We found that the medians (5th and 95th percentiles) were 2.4 ml (1.0-5.0) for semen volume, 62.0 × 10(6) ml(-1) (15.0-142.0) for semen concentration, 39% (18-60%) for sperm progressive motility and 10.5% (1.0-34.5%) for normal morphology. In these 5 years, sperm concentration and the percentage of sperm normal morphology were decreased from 66.0 × 10(6 ) ml(-1) to 49.0 × 10(6) ml(-1) and from 13.5% to 4.5%, respectively; among different reproductive history groups, sperm concentration and the percentage of sperm normal morphology were also decreased in these 5 years. And the incidence of azoospermia was increasing. These may imply that there is a decline in semen quality of adult males in Sichuan area.

It is difficult to derive all qualitative proteomic and metabolomic experimental data in male (pollen tube) and female (pistil) reproductive tissues during pollination because of the limited sensitivity of current technology. In this study, genome-scale enzyme correlation network models for plants (Arabidopsis/maize) were constructed by analyzing the enzymes and metabolic routes from a global perspective. Then, we developed a data-driven computational pipeline using the “guilt by association” principle to analyze the transcriptional coexpression profiles of enzymatic genes in the consecutive steps for metabolic routes in the fast-growing pollen tube and stigma during pollination. The analysis identified an inferred pattern of pollen tube-stigma ethanol coupling. When the pollen tube elongates in the transmitting tissue (TT) of the pistil, this elongation triggers the mobilization of energy from glycolysis in the TT cells of the pistil. Energy-rich metabolites (ethanol) are secreted that can be taken up by the pollen tube, where these metabolites are incorporated into the pollen tube's tricarboxylic acid (TCA) cycle, which leads to enhanced ATP production for facilitating pollen tube growth. In addition, our analysis also provided evidence for the cooperation of kaempferol, dTDP-alpha-L-rhamnose and cell-wall-related proteins; phosphatidic-acid-mediated Ca2+ oscillations and cytoskeleton; and glutamate degradation IV for γ-aminobutyric acid (GABA) signaling activation in Arabidopsis and maize stigmas to provide the signals and materials required for pollen tube tip growth. In particular, the “guilt by association” computational pipeline and the genome-scale enzyme correlation network models (GECN) developed in this study was initiated with experimental “omics” data, followed by data analysis and data integration to determine correlations, and could provide a new platform to assist inachieving a deeper understanding of the co-regulation and inter-regulation model in plant research.