Background: Compared with otherPopulusspecies,Populus euphraticaOliv. exhibits better tolerance to abiotic stress, especially those involving extreme temperatures. However, little is known about gene regulation and signaling pathways involved in low temperature stress responses in this species. Recent development of Illumina/Solexa-based deep-sequencing technologies has accelerated the study of global transcription profiling under specific conditions. To understand the gene network controlling low temperature perception inP. euphratica, we performed transcriptome sequencing using Solexa sequence analysis to generate a leaf transcriptome at a depth of 10 gigabases for each sample.Results: Using the Trinity method, 52,081,238 high-quality trimmed reads were assembled into a non-redundant set and 108,502 unigenes with an average length of 1,047 bp were generated. After performing functional annotations by aligning all-unigenes with public protein databases, 85,584 unigenes were annotated. Differentially expressed genes were investigated using the FPKM method by applying the Benjamini and Hochberg corrections. Overall, 2,858 transcripts were identified as differentially expressed unigenes in at least two samples and 131 were assigned as unigenes expressed differently in all three samples. In 4°C-treated sample and -4°C-treated sample, 1,661 and 866 differently expressed unigenes were detected at an estimated absolute log₂-fold change of > 1, respectively. Among them, the respective number of up-regulated unigenes in C4 and F4 sample was 1,113 and 630, while the respective number of down-regulated ungenes is 548 and 236. To increase our understanding of these differentially expressed genes, we performed gene ontology enrichment and metabolic pathway enrichment analyses. A large number of early cold (below or above freezing temperature)-responsive genes were identified, suggesting that a multitude of transcriptional cascades function in cold perception. Analyses of multiple cold-responsive genes, transcription factors, and some key transduction components involved in ABA and calcium signaling revealed their potential function in low temperature responses inP. euphratica.Conclusions: Our results provide a global transcriptome picture ofP. euphraticaunder low temperature stress. The potential cold stress related transcripts identified in this study provide valuable information for further understanding the molecular mechanisms of low temperature perception inP. euphratica.

Neuroblastoma is a common cancer in children, affected by a number of genes that interact with each other through intricate but coordinated networks. Traditional approaches can only reconstruct a single regulatory network that is topologically not informative enough to explain the complexity of neuroblastoma risk. We implemented and modified an advanced model for recovering informative, omnidirectional, dynamic, and personalized networks (idopNetworks) from static gene expression data for neuroblastoma risk. We analyzed 3439 immune genes of neuroblastoma for 217 high-risk patients and 30 low-risk patients by which to reconstruct large patient-specific idopNetworks. By converting these networks into risk-specific representations, we found that the shift in patients from a low to high risk or from a high to low risk might be due to the reciprocal change of hub regulators. By altering the directions of regulation exerted by these hubs, it may be possible to reduce a high risk to a low risk. Results from a holistic, systems-oriented paradigm through idopNetworks can potentially enable oncologists to experimentally identify the biomarkers of neuroblastoma and other cancers.

The fate of fetal germ cells (FGCs) in primordial follicles is largely determined by how they interact with the surrounding granulosa cells. However, the molecular mechanisms underlying this interactive process remain poorly understood. Here, we develop a computational model to characterize how individual genes program and rewire cellular crosstalk across FGCs and somas, how gene regulatory networks mediate signaling pathways that functionally link these two cell types, and how different FGCs diversify and evolve through cooperation and competition during embryo development. We analyze single-cell RNA-seq data of human female embryos using the new model, identifying previously uncharacterized mechanisms behind follicle development. The majority of genes (70%) promote FGC–soma synergism, only with a small portion (4%) that incur antagonism; hub genes function reciprocally between the FGC network and soma network; and germ cells tend to cooperate between different stages of development but compete in the same stage within a developmental embryo. Our network model could serve as a powerful tool to unravel the genomic signatures that mediate folliculogenesis from single-cell omics data.

RNA-seq, based on deep-sequencing techniques, has been widely employed to precisely measure levels of transcripts and their isoforms expressed under different conditions. However, robust statistical tools used to analyze these complex datasets are lacking. By grouping genes with similar expression profiles across treatments, cluster analysis provides insight into gene functions and networks that have become increasingly important. We proposed and verified a cluster algorithm based on a skellam model for grouping genes into distinct groups based on the pattern of gene expression in response to changing conditions or in different tissues. This algorithm capitalizes on the skellam distribution to capture the count property of RNA-seq data and clusters genes in different environments. A two-stage hierarchical expectation-maximization (EM) algorithm was implemented to estimate the optimal number of groups and mean expression levels of each group across two environments. A procedure was formulated to test whether and how a given group shows a plastic response to environmental changes. The model was used to analyze an RNA-seq dataset measured from reciprocal crosses of early Arabidopsis thaliana embryos that respond differently based on the extent of maternal and paternal genome contributions, from which genes associated with maternal and paternal contributions were identified. Simulation studies were also performed to validate the statistical behavior of the model. This model is a useful tool for clustering gene expression data by RNA-seq, thus facilitating our understanding of gene functions and networks.

Background: To maximize photosynthetic efficiency, plants have evolved a capacity by which leaf area scales allometrically with leaf mass through interactions with the environment. However, our understanding of genetic control of this allometric relationship remains limited.Results: We integrated allometric scaling laws expressed at static and ontogenetic levels into genetic mapping to identify the quantitative trait loci (QTLs) that mediate how leaf area scales with leaf mass and how such leaf allometry, under the control of these QTLs, varies as a response to environment change. A major QTL detected by the static model constantly affects the allometric growth of leaf area vs. leaf mass for the common bean (Phaseolus vulgaris) in two different environments. The ontogenetic model identified this QTL plus a few other QTLs that determine developmental trajectories of leaf allometry, whose expression is contingent heavily upon the environment.Conclusions: Our results gain new insight into the genetic mechanisms of how plants program their leaf morphogenesis to adapt to environmental perturbations.