No abstract

Techniques in molecular biology have permitted the gathering of an extremely large amount of information relating organisms and their genes. The current challenge is assigning a putative function to thousands of genes that have been detected in different organisms. One of the most informative types of genomic data to achieve a better knowledge of protein function is gene expression data. Based on gene expression data and assuming that genes involved in the same function should have a similar or correlated expression pattern, a function can be attributed to those genes with unknown functions when they appear to be linked in a gene co-expression network (GCN). Several tools for the construction of GCNs have been proposed and applied to plant gene expression data. Here, we review recent methodologies used for plant gene expression data and compare the results, advantages and disadvantages in order to help researchers in their choice of a method for the construction of GCNs.

Gene co-expression networks (GCNs) are graphic representations that depict the coordinated transcription of genes in response to certain stimuli. GCNs provide functional annotations of genes whose function is unknown and are further used in studies of translational functional genomics among species. In this work, a methodology for the reconstruction and comparison of GCNs is presented. This approach was applied using gene expression data that were obtained from immunity experiments in Arabidopsis thaliana, rice, soybean, tomato and cassava. After the evaluation of diverse similarity metrics for the GCN reconstruction, we recommended the mutual information coefficient measurement and a clustering coefficient-based method for similarity threshold selection. To compare GCNs, we proposed a multivariate approach based on the Principal Component Analysis (PCA). Branches of plant immunity that were exemplified by each experiment were analyzed in conjunction with the PCA results, suggesting both the robustness and the dynamic nature of the cellular responses. The dynamic of molecular plant responses produced networks with different characteristics that are differentiable using our methodology. The comparison of GCNs from plant pathosystems, showed that in response to similar pathogens plants could activate conserved signaling pathways. The results confirmed that the closeness of GCNs projected on the principal component space is an indicative of similarity among GCNs. This also can be used to understand global patterns of events triggered during plant immune responses.

In temperate latitudes, many insects enter diapause (dormancy) during the cold season, a period during which developmental processes come to a standstill. The wood white (Leptidea sinapis) is a butterfly species distributed across western Eurasia that shows photoperiod-induced diapause with variation in critical day-length across populations at different latitudes. We assembled transcriptomes and estimated gene expression levels at different developmental stages in experimentally induced directly developing and diapausing cohorts of a single Swedish population of L. sinapis to investigate the regulatory mechanisms underpinning diapause initiation. Different day lengths resulted in expression changes of developmental genes and affected the rate of accumulation of signal molecules, suggesting that diapause induction might be controlled by increased activity of monoamine neurotransmitters in larvae reared under short-day light conditions. Expression differences between light treatment groups of two monoamine regulator genes (DDC and ST) were observed already in instar III larvae. Once developmental pathways were irreversibly set at instar V, a handful of genes related to dopamine production were differentially expressed leading to a significant decrease in expression of global metabolic genes and increase in expression of genes related to fatty acid synthesis and sequestration. This is in line with a time-dependent (hour-glass) model of diapause regulation where a gradual shift in the concentration of monoamine neurotransmitters and their metabolites during development of larvae under short-day conditions leads to increased storage of fat, decreased energy expenditures, and ultimately developmental stasis at the pupal stage.

Adipsin is a protease produced at high levels by adipose tissue. It is involved in complement activation and metabolic control. The objective of this study was to determine the changes in adipsin levels during different stages of normal pregnancy, and its association with obstetric outcomes, such as preeclampsia. This nested case-control study in a longitudinal cohort included normal pregnant (n = 54) and preeclamptic (n = 18) women, both followed throughout pregnancy. Additionally, some of the normal pregnant women were followed up three months postpartum (n = 18). Healthy non-pregnant women were also studied during their menstrual cycle (n = 20). The results of this study show that in healthy non-pregnant women, adipsin levels did not change significantly during the menstrual cycle. In normal pregnant women, adipsin levels were lower (p < 0.01) when compared with non-pregnant healthy women, but these serum levels increased again during postpartum (p < 0.001). Adipsin levels were significantly elevated in preeclamptic women in late pregnancy (P < 0.01). A significant correlation was not found between leptin and adipsin during the three periods of gestation studied in healthy pregnant and preeclamptic women. Our results suggest that adipsin may be involved in pregnancy-associated metabolic changes. Moreover, the increase of adipsin levels towards late gestation in preeclamptic women could be related to the pathophysiology of this disease.