The placenta is an ephemeral but critical organ for the survival of all eutherian mammals and marsupials. It is the primary messenger system between the mother and fetus, where communicational signals, nutrients, waste, gases, and extrinsic factors are exchanged. Although the placenta may buffer the fetus from various environmental insults, placental dysfunction might also contribute to detrimental developmental origins of adult health and disease effects. The placenta of one sex over the other might possess greater ability to respond and buffer against environmental insults. Given the potential role of the placenta in effecting the lifetime health of the offspring, it is not surprising that there has been a resurging interest in this organ, including the Human Placental Project launched by the National Institutes of Child Health and Human Development. In this review, we will compare embryological development of the laboratory mouse and human chorioallantoic placentae. Next, evidence that various species, including humans, exhibit normal sex-dependent structural and functional placental differences will be examined followed by how in utero environmental changes (nutritional state, stress, and exposure to environmental chemicals) might interact with fetal sex to affect this organ. Recent data also suggest that paternal state impacts placental function in a sex-dependent manner. The research to date linking placental maladaptive responses and later developmental origins of adult health and disease effects will be explored. Finally, we will focus on how sex chromosomes and epimutations may contribute to sex-dependent differences in placental function, the unanswered questions, and future directions that warrant further consideration.

Soybean (Glycine max L.) is a major crop providing an important source of protein and oil, which can also be converted into biodiesel. A major milestone in soybean research was the recent sequencing of its genome. The sequence predicts 69 145 putative soybean genes, with 46 430 predicted with high confidence. In order to examine the expression of these genes, we utilized the Illumina Solexa platform to sequence cDNA derived from 14 conditions (tissues). The result is a searchable soybean gene expression atlas accessible through a browser (http://digbio.missouri.edu/soybean_atlas). The data provide experimental support for the transcription of 55 616 annotated genes and also demonstrate that 13 529 annotated soybean genes are putative pseudogenes, and 1736 currently unannotated sequences are transcribed. An analysis of this atlas reveals strong differences in gene expression patterns between different tissues, especially between root and aerial organs, but also reveals similarities between gene expression in other tissues, such as flower and leaf organs. In order to demonstrate the full utility of the atlas, we investigated the expression patterns of genes implicated in nodulation, and also transcription factors, using both the Solexa sequence data and large‐scale qRT‐PCR. The availability of the soybean gene expression atlas allowed a comparison with gene expression documented in the two model legume species, Medicago truncatula and Lotus japonicus, as well as data available for Arabidopsis thaliana, facilitating both basic and applied aspects of soybean research.

Mammals usually produce approximately equal numbers of sons and daughters, but there are exceptions to this general rule, as has been observed in rum

Motivation: Microarray gene expression data has increasingly become the common data source that can provide insights into biological processes at a system-wide level. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to a large number of genes, which makes the problem of inferring gene regulatory network an ill-posed one. On the other hand, gene expression data generated by different groups worldwide are increasingly accumulated on many species and can be accessed from public databases or individual websites, although each experiment has only a limited number of time-points. Results: This paper proposes a novel method to combine multiple time-course microarray datasets from different conditions for inferring gene regulatory networks. The proposed method is called GNR (Gene Network Reconstruction tool) which is based on linear programming and a decomposition procedure. The method theoretically ensures the derivation of the most consistent network structure with respect to all of the datasets, thereby not only significantly alleviating the problem of data scarcity but also remarkably improving the prediction reliability. We tested GNR using both simulated data and experimental data in yeast and Arabidopsis. The result demonstrates the effectiveness of GNR in terms of predicting new gene regulatory relationship in yeast and Arabidopsis. Availability: The software is available from Author Webpage, Author Webpage and Author Webpage or upon request from the authors. Contact:chen@eic.osaka-sandai.ac.jp, xudong@missouri.edu, zxs@amt.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.