Mining pathway signatures from microarray data and relevant biological knowledge

Comprehensive transcriptomic profiling reveals complex molecular mechanisms in the regulation of style-length dimorphism in Guettarda speciosa (Rubiaceae), a species with "anomalous" distyly

BACKGROUND

The evolution of heterostyly, a genetically controlled floral polymorphism, has been a hotspot of research since the 19th century. In recent years, studies on the molecular mechanism of distyly (the most common form of heterostyly) revealed an evolutionary convergence in genes for brassinosteroids (BR) degradation in different angiosperm groups. This floral polymorphism often exhibits considerable variability that some taxa have significant stylar dimorphism, but anther height differs less. This phenomenon has been termed "anomalous" distyly, which is usually regarded as a transitional stage in evolution. Compared to "typical" distyly, the genetic regulation of "anomalous" distyly is almost unknown, leaving a big gap in our understanding of this special floral adaptation strategy.

METHODS

Here we performed the first molecular-level study focusing on this floral polymorphism in Guettarda speciosa (Rubiaceae), a tropical tree with "anomalous" distyly. Comprehensive transcriptomic profiling was conducted to examine which genes and metabolic pathways were involved in the genetic control of style dimorphism and if they exhibit similar convergence with "typical" distylous species.

RESULTS

"Brassinosteroid homeostasis" and "plant hormone signal transduction" was the most significantly enriched GO term and KEGG pathway in the comparisons between L- and S-morph styles, respectively. Interestingly, homologs of all the reported S-locus genes either showed very similar expressions between L- and S-morph styles or no hits were found in G. speciosa. BKI1, a negative regulator of brassinosteroid signaling directly repressing BRI1 signal transduction, was identified as a potential gene regulating style length, which significantly up-regulated in the styles of S-morph.

DISCUSSION

These findings supported the hypothesis that style length in G. speciosa was regulated through a BR-related signaling network in which BKI1 may be one key gene. Our data suggested, in species with "anomalous" distyly, style length was regulated by gene differential expressions, instead of the "hemizygous" S-locus genes in "typical" distylous flowers such as Primula and Gelsemium, representing an "intermediate" stage in the evolution of distyly. Genome-level analysis and functional studies in more species with "typical" and "anomalous" distyly would further decipher this "most complex marriage arrangement" in angiosperms and improve our knowledge of floral evolution.

Global transcriptome and gene co-expression network analyses on the development of distyly in Primula oreodoxa

Distyly is a genetically controlled flower polymorphism that has intrigued both botanists and evolutionary biologists ever since Darwin's time. Despite extensive reports on the pollination and evolution of distylous systems, the genetic basis and mechanism of molecular regulation remain unclear. In the present study, comparative transcriptome profiling was conducted in primrose (Primula oreodoxa), the prime research model for heterostyly. Thirty-six transcriptomes were sequenced for styles at different stages and corolla tube in the three morphs of P. oreodoxa. Large numbers of differentially expressed genes (DEGs) were detected in the transcriptomes of styles across different morphs. Several transcription factors (TFs) and phytohormone metabolism-related genes were highlighted in S-morphs. A growing number of genes showed differential expression patterns along with the development of styles, suggesting that the genetic control of distyly may be more complicated than ever expected. Analysis of co-expression networks and module-trait relationships identified modules significantly associated with style development. CYP734A50, a key S-locus gene whose products degrade brassinosteroids, was co-expressed with many genes in the module and showed significant negative association with style length. In addition, crucial TFs involved in phytohormone signaling pathways were found to be connected with CYP734A50 in the co-expression module. Our global transcriptomic analysis has identified DEGs that are potentially involved in regulation of style length in P. oreodoxa, and may shed light on the evolution and broad biological processes of heterostyly.

The Genetic Basis of Peyronie Disease: A Review

INTRODUCTION

Peyronie disease (PD) is a progressive fibrotic disorder of the penile tunica albuginea that results in fibrotic penile plaques and can lead to penile deformity. Characterized by aberrant fibrosis resulting in part from the persistence of myofibroblasts and altered gene expression, the molecular factors underpinning PD and other related fibrotic diatheses are just being elucidated. A genetic link to PD was first identified three decades ago using pedigree analyses. However, the specific genetic factors that predispose patients to aberrant fibrosis remain unknown, and the relations between these fibrotic conditions and other heritable diseases, including malignancy, are uncharacterized.

AIM

To review the current landscape linking molecular and genetic factors to aberrant fibrosis in PD and related fibrotic diatheses, including Dupuytren disease.

METHODS

Review and evaluation of the literature from 1970 to the present for genetic factors associated with PD were performed.

MAIN OUTCOME MEASURES

Data describing the genetic factors associated with PD were obtained.

RESULTS

We describe the known structural chromosomal abnormalities and single-nucleotide polymorphisms associated with fibrotic diatheses and discuss the spectrum of differential gene expression data comparing normal tissues with those derived from men with PD or Dupuytren disease. We discuss epigenetic mechanisms that might regulate gene expression and alter predisposition to fibrosis.

CONCLUSION

Although the current understanding of the genetic factors associated with PD is limited, significant advances have been made during the past three decades. Further research is necessary to provide a more comprehensive understanding of the landscape of genetic factors responsible for the development of PD.

Robust gene signatures from microarray data using genetic algorithms enriched with biological pathway keywords

Genetic algorithms are widely used in the estimation of expression profiles from microarrays data. However, these techniques are unable to produce stable and robust solutions suitable to use in clinical and biomedical studies. This paper presents a novel two-stage evolutionary strategy for gene feature selection combining the genetic algorithm with biological information extracted from the KEGG database. A comparative study is carried out over public data from three different types of cancer (leukemia, lung cancer and prostate cancer). Even though the analyses only use features having KEGG information, the results demonstrate that this two-stage evolutionary strategy increased the consistency, robustness and accuracy of a blind discrimination among relapsed and healthy individuals. Therefore, this approach could facilitate the definition of gene signatures for the clinical prognosis and diagnostic of cancer diseases in a near future. Additionally, it could also be used for biological knowledge discovery about the studied disease.

Biological network inference for drug discovery

A better understanding of the pathophysiology should help deliver drugs whose targets are involved in the causative processes underlying a disease. Biological network inference uses computational methods for deducing from high-throughput experimental data, the topology and the causal structure of the interactions among the drugs and their targets. Therefore, biological network inference can support and contribute to the experimental identification of both gene and protein networks causing a disease as well as the biochemical networks of drugs metabolism and mechanisms of action. The resulting high-level networks serve as a foundational basis for more detailed mechanistic models and are increasingly used in drug discovery by pharmaceutical and biotechnology companies. We review and compare recent computational technologies for network inference applied to drug discovery.

Multi-membership gene regulation in pathway based microarray analysis

Background

Gene expression analysis has been intensively researched for more than a decade. Recently, there has been elevated interest in the integration of microarray data analysis with other types of biological knowledge in a holistic analytical approach. We propose a methodology that can be facilitated for pathway based microarray data analysis, based on the observation that a substantial proportion of genes present in biochemical pathway databases are members of a number of distinct pathways. Our methodology aims towards establishing the state of individual pathways, by identifying those truly affected by the experimental conditions based on the behaviour of such genes. For that purpose it considers all the pathways in which a gene participates and the general census of gene expression per pathway.

Results

We utilise hill climbing, simulated annealing and a genetic algorithm to analyse the consistency of the produced results, through the application of fuzzy adjusted rand indexes and hamming distance. All algorithms produce highly consistent genes to pathways allocations, revealing the contribution of genes to pathway functionality, in agreement with current pathway state visualisation techniques, with the simulated annealing search proving slightly superior in terms of efficiency.

Conclusions

We show that the expression values of genes, which are members of a number of biochemical pathways or modules, are the net effect of the contribution of each gene to these biochemical processes. We show that by manipulating the pathway and module contribution of such genes to follow underlying trends we can interpret microarray results centred on the behaviour of these genes.

Applications of different weighting schemes to improve pathway-based analysis

Conventionally, pathway-based analysis assumes that genes in a pathway equally contribute to a biological function, thus assigning uniform weight to genes. However, this assumption has been proved incorrect, and applying uniform weight in the pathway analysis may not be an appropriate approach for the tasks like molecular classification of diseases, as genes in a functional group may have different predicting power. Hence, we propose to use different weights to genes in pathway-based analysis and devise four weighting schemes. We applied them in two existing pathway analysis methods using both real and simulated gene expression data for pathways. Among all schemes, random weighting scheme, which generates random weights and selects optimal weights minimizing an objective function, performs best in terms of P value or error rate reduction. Weighting changes pathway scoring and brings up some new significant pathways, leading to the detection of disease-related genes that are missed under uniform weight.

Sparse canonical correlation analysis for identifying, connecting and completing gene-expression networks

BACKGROUND

We generalized penalized canonical correlation analysis for analyzing microarray gene-expression measurements for checking completeness of known metabolic pathways and identifying candidate genes for incorporation in the pathway. We used Wold's method for calculation of the canonical variates, and we applied ridge penalization to the regression of pathway genes on canonical variates of the non-pathway genes, and the elastic net to the regression of non-pathway genes on the canonical variates of the pathway genes.

RESULTS

We performed a small simulation to illustrate the model's capability to identify new candidate genes to incorporate in the pathway: in our simulations it appeared that a gene was correctly identified if the correlation with the pathway genes was 0.3 or more. We applied the methods to a gene-expression microarray data set of 12, 209 genes measured in 45 patients with glioblastoma, and we considered genes to incorporate in the glioma-pathway: we identified more than 25 genes that correlated > 0.9 with canonical variates of the pathway genes.

CONCLUSION

We concluded that penalized canonical correlation analysis is a powerful tool to identify candidate genes in pathway analysis.

Gene module level analysis: identification to networks and dynamics

Nature exhibits modular design in biological systems. Gene module level analysis is based on this module concept, aiming to understand biological network design and systems behavior in disease and development by emphasizing on modules of genes rather than individual genes. Module level analysis has been extensively applied in genome wide level analysis, exploring the organization of biological systems from identifying modules to reconstructing module networks and analyzing module dynamics. Such module level perspective provides a high level representation of the regulatory scenario and design of biological systems, promising to revolutionize our view of systems biology, genetic engineering as well as disease mechanisms and molecular medicine.