Current Genomics - Volume 15, Issue 5, 2014

Heat stress, which strongly affects plant performance and often results in reduced vegetative growth and yields depression, has become an increasingly serious global problem. Populus euphratica Oliv. which has been considered as a tree model for the study of plant response to abiotic stresses, could be resistant to an extremely wide environmental temperature range (–40 °C to 45 °C). Previous study is mainly focused on its gene regulation upon drought and salt stress. However, little is known about gene regulation at the global transcriptome level upon heat stress. To understand the gene network controlling heat stress in P. euphratica, a transcriptome sequencing using Illumina Hiseq 2000 was performed to generate a 10 gigabases depth for each sample in the tissue of leaf. 119,573 unigeneswere generated with an average length of 474 bp. Approximately 49,605 (41.49%) unigenes exhibited significantly different expressions between two libraries. Among these unigenes, 11,165 (9.34%) were upregulated and 38,440 (32.15%) were down regulated. Heat shock proteins classified as molecular chaperones showed a significant percentage (1.13%) in the up regulated group. Heat responsive genes, such as polyubiquitins, were over expressed in heat treated sample. GO enrichment analysis revealed that the Go terms for differentially expressed unigenes were significantly enriched in hormone-mediated signal, biological process regulation and metabolic process regulation. Our data revealed a global transcriptome picture of P. euphratica in response to heat shock. The identified potential heat stress-related transcripts can be used to infer the gene regulation networks underlying the molecular mechanisms of heat response in P. euphratica.

Willows are one of the most important tree species for landscaping, biofuel and raw timber. Screening salttolerant willow varieties is an effective approach to balance wood supply and demand. However, more salt-tolerant willow varieties are required and little is known regarding the mechanism of salt tolerance at the gene expression level. In this paper, two willow varieties were studies in terms of their differences in salt-tolerances and mechanism of salt tolerance at the level of VP1 gene expression. The results showed that Salix L0911 (L0911) had higher biomass than Salix matsudana (SM), and salt injuries were less severe in L0911 than in SM. The activities of peroxidase and superoxide dismutase, as well as the contents of soluble protein and proline, were higher in L0911 than in SM, whereas the contents of Na+ and K+, as well as the Na+/K+ ratio, were lower in L0911 than in SM. Two VP1 genes (VP1.1 and VP1.2) cloned in L0911 and SM had similar sequences and structures. VP1.1 and VP1.2 belonged to different subgroups. Total expression levels of the VP1.1 gene in both roots and leaves of L0911 were higher than that in SM under normal conditions. Under salt stress, expression of VP1 in SM roots initially increased and then decreased, whereas the expression of VP1 in leaves of L0911 and SM, as well as in roots of L0911, decreased with increasing salt concentrations. This study increased our understanding of the salt-tolerance mechanism of willow and may facilitate the selection of salt-tolerant willow resources.

By measuring gene expression at an unprecedented resolution and throughput, RNA-seq has played a pivotal role in studying biological functions. Its typical application in clinical medicine is to identify the discrepancies of gene expression between two different types of cancer cells, sensitive and resistant to chemotherapeutic treatment, in a hope to predict drug response. Here we modified and used a mechanistic model to identify distinct patterns of gene expression in response of different types of breast cancer cell lines to chemotherapeutic treatment. This model was founded on a mixture likelihood of Poisson-distributed transcript read data, with each mixture component specified by the Skellam function. By estimating and comparing the amount of gene expression in each environment, the model can test how genes alter their expression in response to environment and how different genes interact with each other in the responsive process. Using the modified model, we identified the alternations of gene expression between two cell lines of breast cancer, resistant and sensitive to tamoxifen, which allows us to interpret the expression mechanism of how genes respond to metabolic differences between the two cell types. The model can have a general implication for studying the plastic pattern of gene expression across different environments measured by RNA-seq.

Hickory (Carya cathayensis Sarg.), an important nut-producing species in Southeastern China, has high economic value, but so far there has been no cultivar bred under species although it is mostly propagated by seeding and some elite individuals have been found. It has been found recently that this species has a certain rate of apomixis and poor knowledge of its genetic background has influenced development of a feasible breeding strategy. Here in this paper we first release SSR (Simple sequence repeat) markers developed in this species and their transferability to other three species of the same genus, Carya. A total of 311 pairs of SSR primers in hickory were developed based on sequenced cDNAs of a fruit development-associated cDNA library and RNA-seq data of developing female floral buds and could be used to distinguish hickory, C. hunanensis Cheng et R. H. Chang ex R. H. Chang et Lu, C. illinoensis K. Koch (pecan) and C. dabieshanensis M. C. Liu et Z. J. Li, but they were monomorphic in both hickory and C. hunanensis although multi-alleles have been identified in all the four species. There is a transferability rate of 63.02% observed between hickory and pecan and the markers can be applied to study genetic diversity of accessions in pecan. When used in C. dabieshanensis, it was revealed that C. dabieshanensis had the number of alleles per locus ranging from 2 to 4, observed heterozygosity from 0 to 0.6667 and expected heterozygosity from 0.333 to 0.8667, respectively, which supports the existence of C. dabieshanensis as a separate species different from hickory and indicates that there is potential for selection and breeding in this species.

Controlling for the multiplicity effect is an essential part of determining statistical significance in large-scale single-locus association genome scans on Single Nucleotide Polymorphisms (SNPs). Bonferroni adjustment is a commonly used approach due to its simplicity, but is conservative and has low power for large-scale tests. The permutation test, which is a powerful and popular tool, is computationally expensive and may mislead in the presence of family structure. We propose a computationally efficient and powerful multiple testing correction approach for Linkage Disequilibrium (LD) based Quantitative Trait Loci (QTL) mapping on the basis of graphical weighted-Bonferroni methods. The proposed multiplicity adjustment method synthesizes weighted Bonferroni-based closed testing procedures into a powerful and versatile graphical approach. By tailoring different priorities for the two hypothesis tests involved in LD based QTL mapping, we are able to increase power and maintain computational efficiency and conceptual simplicity. The proposed approach enables strong control of the familywise error rate (FWER). The performance of the proposed approach as compared to the standard Bonferroni correction is illustrated by simulation and real data. We observe a consistent and moderate increase in power under all simulated circumstances, among different sample sizes, heritabilities, and number of SNPs. We also applied the proposed method to a real outbred mouse HDL cholesterol QTL mapping project where we detected the significant QTLs that were highlighted in the literature, while still ensuring strong control of the FWER.

Congenital heart diseases (CHDs) are recognized as the most common type of birth malformations. Although recent advances in pre- and neonatal diagnosis as well as in surgical procedures have reduced the morbidity and mortality for many CHD, the etiology for CHD remains undefined. In non-syndromic and isolated (without a familial history or a Mendelian inheritance) forms of CHDs, a multifactorial pathogenesis with interplay between inherited and non-inherited causes is recognized. In this paper, we discuss the current knowledge of the potential molecular mechanisms, mediating abnormal cardiac development in non-syndromic and isolated CHD, including mutations in cardiac transcription factors, the role of somatic mutations and epigenetic alterations as well as the influence of gene-environment interactions. In the near future, the advent of high-throughput genomic technologies with the integration of system biology will expand our understanding of isolated, non-syndromic CHDs for their prevention, early diagnosis and therapy.

Relationships between genes are best represented using networks constructed from information of different types, with metabolic information being the most valuable and widely used for genetic network reconstruction. Other types of information are usually also available, and it would be desirable to systematically include them in algorithms for network reconstruction. Here, we present an algorithm to construct a global metabolic network that uses all available enzymatic and metabolic information about the organism. We construct a global enzymatic network (GEN) with a total of 4226 nodes (EC numbers) and 42723 edges representing all known metabolic reactions. As an example we use microarray data for Arabidopsis thaliana and combine it with the metabolic network constructing a final gene interaction network for this organism with 8212 nodes (genes) and 4606,901 edges. All scripts are available to be used for any organism for which genomic data is available.

In polymicrobial communities where several species co-exist in a certain niche and consequently the possibility of interactions among species is very high, gene expression data sources can give better insights in to underlying adaptation mechanisms assumed by bacteria. Furthermore, several possible synergistic or antagonistic interactions among species can be investigated through gene expression comparisons. Lung is one of the habitats harboring several distinct pathogens during severe pulmonary disorders such as chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF). Expression data analysis of these lung residents can help to gain a better understanding on how these species interact with each other within the host cells. The first part of this paper deals with introducing available data sources for the major bacteria responsible for causing lung diseases and their genomic relations. In the second part, the main focus is on the studies concerning gene expression analyses of these species.