Pathway analysis in microarray data: a comparison of two different pathway analysis devices in the same data set

RNA Sequencing Characterizes Transcriptomes Differences in Cold Response Between Northern and Southern Alternanthera philoxeroides and Highlight Adaptations Associated With Northward Expansion

Alternanthera philoxeroides recently expanded its range northwards in China. It is unknown if the range expansion has a genetic and/or epigenetic basis, or merely an environmental basis due to a warming climate. To test these possibilities, we used an RNAseq approach with a common greenhouse design to examine gene expression in individuals from the northern edge and central portion of alligator weed range from China to determine if there were differences in their responses to cold temperatures. We hypothesized that if the recent range expansion was primarily environmental, we would observe few differences or only differences unrelated to low-temperature adaptations. We assembled over 75,000 genes of which over 65,000 had long open reading frames with similarity to sequences from arabidopsis. Differences in expression between northern and southern populations that were both exposed to low temperatures showed similar expression among genes in the C-REPEAT/DRE BINDING FACTOR (CBF) regulon. However, gene set and sub-network enrichment analysis indicated differences in the response of photosynthetic processes and oxidative stress responses were different between the two populations and we relate these differences to cold adaptation. The transcriptome differences in response to cold between the individuals from the two populations is consistent with adaptations potentiating or resulting from selection after expansion into colder environments and may indicate that genetic changes have accompanied the recent northward expansion of A. philoxeroides in China. However, we cannot rule out the possibility of epigenetic changes may have a role in this expansion.

Weed presence altered biotic stress and light signaling in maize even when weeds were removed early in the critical weed-free period

Weed presence early in the life cycle of maize (typically, from emergence through the 8 to 12 leaf growth stage) can reduce crop growth and yield and is known as the critical weed-free period (CWFP). Even if weeds are removed during or just after the CWFP, crop growth and yield often are not recoverable. We compared transcriptome responses of field-grown hybrid maize at V8 in two consecutive years among plants grown under weed-free and two weed-stressed conditions (weeds removed at V4 or present through V8) using RNAseq analysis techniques. Compared with weed-free plant responses, physiological differences at V8 were identified in all weed-stressed plants and were most often associated with altered photosynthetic processes, hormone signaling, nitrogen use and transport, and biotic stress responses. Even when weeds were removed at V4 and tissues sampled at V8, carbon: nitrogen supply imbalance, salicylic acid signals, and growth responses differed between the weed-stressed and weed-free plants. These underlying processes and a small number of developmentally important genes are potential targets for decreasing the maize response to weed pressure. Expression differences of several novel, long noncoding RNAs resulting from exposure of maize to weeds during the CWFP were also observed and could open new avenues for investigation into the function of these transcription units.

[Microarrays as biomarkers in trauma]

BACKGROUND

The traditional hypothesis-driven scientific approach cannot so far sufficiently elucidate complex pathophysiologies, such as posttraumatic systemic inflammation and subsequent multiple organ failure. This complex system includes different biological and functional levels, the genome, the transcriptome, the proteome, the biome (cells), the organs and finally the whole organism.

METHODS

Microarray techniques enable a simultaneous search for these different biological levels and their functional relationships on a large scale and to discover new functional pathways and networks and potentially new biomarkers for different pathologies. Microarray technologies lead to a new paradigm in science, the hypothesis-generating approach.

AIM

This article reviews important microarray findings in trauma and systemic inflammation research and discusses potentials and limitations of these biotechnological screening methods.

Induction of DREB2A pathway with repression of E2F, jasmonic acid biosynthetic and photosynthesis pathways in cold acclimation-specific freeze-resistant wheat crown

Winter wheat lines can achieve cold acclimation (development of tolerance to freezing temperatures) and vernalization (delay in transition from vegetative to reproductive phase) in response to low non-freezing temperatures. To describe cold-acclimation-specific processes and pathways, we utilized cold acclimation transcriptomic data from two lines varying in freeze survival but not vernalization. These lines, designated freeze-resistant (FR) and freeze-susceptible (FS), were the source of crown tissue RNA. Well-annotated differentially expressed genes (p ≤ 0.005 and fold change ≥ 2 in response to 4 weeks cold acclimation) were used for gene ontology and pathway analysis. "Abiotic stimuli" was identified as the most enriched and unique for FR. Unique to FS was "cytoplasmic components." Pathway analysis revealed the "triacylglycerol degradation" pathway as significantly downregulated and common to both FR and FS. The most enriched of FR pathways was "neighbors of DREB2A," with the highest positive median fold change. The "13-LOX and 13-HPL" and the "E2F" pathways were enriched in FR only with a negative median fold change. The "jasmonic acid biosynthesis" pathway and four "photosynthetic-associated" pathways were enriched in both FR and FS but with a more negative median fold change in FR than in FS. A pathway unique to FS was "binding partners of LHCA1," which was enriched only in FS with a significant negative median fold change. We propose that the DREB2A, E2F, jasmonic acid biosynthesis, and photosynthetic pathways are critical for discrimination between cold-acclimated lines varying in freeze survival.

Non-gaussian distributions affect identification of expression patterns, functional annotation, and prospective classification in human cancer genomes

INTRODUCTION

Gene expression data is often assumed to be normally-distributed, but this assumption has not been tested rigorously. We investigate the distribution of expression data in human cancer genomes and study the implications of deviations from the normal distribution for translational molecular oncology research.

METHODS

We conducted a central moments analysis of five cancer genomes and performed empiric distribution fitting to examine the true distribution of expression data both on the complete-experiment and on the individual-gene levels. We used a variety of parametric and nonparametric methods to test the effects of deviations from normality on gene calling, functional annotation, and prospective molecular classification using a sixth cancer genome.

RESULTS

Central moments analyses reveal statistically-significant deviations from normality in all of the analyzed cancer genomes. We observe as much as 37% variability in gene calling, 39% variability in functional annotation, and 30% variability in prospective, molecular tumor subclassification associated with this effect.

CONCLUSIONS

Cancer gene expression profiles are not normally-distributed, either on the complete-experiment or on the individual-gene level. Instead, they exhibit complex, heavy-tailed distributions characterized by statistically-significant skewness and kurtosis. The non-Gaussian distribution of this data affects identification of differentially-expressed genes, functional annotation, and prospective molecular classification. These effects may be reduced in some circumstances, although not completely eliminated, by using nonparametric analytics. This analysis highlights two unreliable assumptions of translational cancer gene expression analysis: that "small" departures from normality in the expression data distributions are analytically-insignificant and that "robust" gene-calling algorithms can fully compensate for these effects.