Exploring the transcription factor activity in high-throughput gene expression data using RLQ analysis

Plant trait-environment trends and their conservation implications for riparian wetlands in the Yellow River

Determining the relationship between plant functional traits and the environment are key for the protection and sustainable utilization of riparian wetlands. In the middle and lower reaches of the Yellow River, riparian wetlands are divided into seasonal floodplain wetlands (natural) and pond-like wetlands or paddy fields (artificial). Here, species composition differences were catalogued based on plant functional traits including origin, life history, and wetland affinity in natural and artificial wetlands. Wetland physicochemical characteristics and regional socio-economic parameters collected as indicators of environmental variables were used to analyze the plant functional trait-environment relationship. The results reveal that plant functional traits in the seasonal floodplain wetland are impacted by physicochemical characteristics of habitat. The abundance of annual plants tends to decrease with concentration of heavy metals, while species diversity is mainly determined by soil physical and chemical properties, especially soil pH and temperature. Specifically, wetland-obligate species (not in water) are more resistant to heavy metal content in water than species with other types of wetland affinity. Life history strategies of species in artificial sites tend to be significantly associated with animal husbandry and artificial populations, while the wetland affinity of species is mainly determined by regional agriculture, especially the installation of agricultural covered areas. Furthermore, water quality and nutrients in suspended sediments from the Yellow River affected species diversity and life history strategies by affecting water and soil conditions of surrounding wetlands, especially conductivity and phosphorus levels.

covRNA: discovering covariate associations in large-scale gene expression data

OBJECTIVE

The biological interpretation of gene expression measurements is a challenging task. While ordination methods are routinely used to identify clusters of samples or co-expressed genes, these methods do not take sample or gene annotations into account. We aim to provide a tool that allows users of all backgrounds to assess and visualize the intrinsic correlation structure of complex annotated gene expression data and discover the covariates that jointly affect expression patterns.

RESULTS

The Bioconductor package covRNA provides a convenient and fast interface for testing and visualizing complex relationships between sample and gene covariates mediated by gene expression data in an entirely unsupervised setting. The relationships between sample and gene covariates are tested by statistical permutation tests and visualized by ordination. The methods are inspired by the fourthcorner and RLQ analyses used in ecological research for the analysis of species abundance data, that we modified to make them suitable for the distributional characteristics of both, RNA-Seq read counts and microarray intensities, and to provide a high-performance parallelized implementation for the analysis of large-scale gene expression data on multi-core computational systems. CovRNA provides additional modules for unsupervised gene filtering and plotting functions to ensure a smooth and coherent analysis workflow.

Potential role of the HOXD8 transcription factor in cisplatin resistance and tumour metastasis in advanced epithelial ovarian cancer

Few studies have examined the potential transcription factor (TF) simultaneously associated with cisplatin resistance and metastasis in ovarian cancer. To assess a related mechanism, a 345-channel protein/DNA array and transcriptional activity ELISA were performed to compare the TF activities in the cisplatin-sensitive SKOV3 and cisplatin-resistant SKOV3-DDP cells and in HO-8910 and the homologous highly metastatic HO-8910PM cells. In SKOV3-DDP vs. SKOV3 cells, 43 TFs were up-regulated, while 31 were down-regulated. In HO-8910PM vs. HO-8910 cells, 13 TFs were up-regulated, while 18 were down-regulated. In these two models, 4 TFs (HOXD8(1), HOXD8(2), RB, RFX1/2/3) were simultaneously up-regulated, and 9 TFs (SRE, FKHR, Angiotensinogen ANG-IRE, Pax2, CD28RC/NF-IL2B, HLF, CPE, CBFB and c-Ets-1) were down-regulated. HOXD8 mRNA and protein expression levels measured by reverse transcription polymerase chain reaction and ELISA, respectively, were significantly higher in SKOV3-DDP and HO-8910PM than in their corresponding cell lines (both p < 0.05). In 52 cases of different ovarian disease, the patients with recurrent and cisplatin-resistant ovarian cancer had higher expression levels of HOXD8 than patients with primary malignant tumours (p = 0.018, p = 0.001) or benign tumours (p = 0.001, p < 0.001). Taken together, these results suggest that HOXD8 is potentially associated with both cisplatin resistance and metastasis in advanced ovarian cancer.

High-throughput alternative splicing detection using dually constrained correspondence analysis (DCCA)

Alternative splicing is an important component of tumorigenesis. Recent advent of exon array technology enables the detection of alternative splicing at a genome-wide scale. The analysis of high-throughput alternative splicing is not yet standard and methodological developments are still needed. We propose a novel statistical approach-Dually Constrained Correspondence Analysis-for the detection of splicing changes in exon array data. Using this methodology, we investigated the genome-wide alteration of alternative splicing in patients with non-small cell lung cancer treated by bevacizumab/erlotinib. Splicing candidates reveal a series of genes related to carcinogenesis (SFTPB), cell adhesion (STAB2, PCDH15, HABP2), tumor aggressiveness (ARNTL2), apoptosis, proliferation and differentiation (PDE4D, FLT3, IL1R2), cell invasion (ETV1), as well as tumor growth (OLFM4, FGF14), tumor necrosis (AFF3) or tumor suppression (TUSC3, CSMD1, RHOBTB2, SERPINB5), with indication of known alternative splicing in a majority of genes. DCCA facilitates the identification of putative biologically relevant alternative splicing events in high-throughput exon array data.

A high performance prediction of HPV genotypes by Chaos game representation and singular value decomposition

BACKGROUND

Human Papillomavirus (HPV) genotyping is an important approach to fight cervical cancer due to the relevant information regarding risk stratification for diagnosis and the better understanding of the relationship of HPV with carcinogenesis. This paper proposed two new feature extraction techniques, i.e. ChaosCentroid and ChaosFrequency, for predicting HPV genotypes associated with the cancer. The additional diversified 12 HPV genotypes, i.e. types 6, 11, 16, 18, 31, 33, 35, 45, 52, 53, 58, and 66, were studied in this paper. In our proposed techniques, a partitioned Chaos Game Representation (CGR) is deployed to represent HPV genomes. ChaosCentroid captures the structure of sequences in terms of centroid of each sub-region with Euclidean distances among the centroids and the center of CGR as the relations of all sub-regions. ChaosFrequency extracts the statistical distribution of mono-, di-, or higher order nucleotides along HPV genomes and forms a matrix of frequency of dots in each sub-region. For performance evaluation, four different types of classifiers, i.e. Multi-layer Perceptron, Radial Basis Function, K-Nearest Neighbor, and Fuzzy K-Nearest Neighbor Techniques were deployed, and our best results from each classifier were compared with the NCBI genotyping tool.

RESULTS

The experimental results obtained by four different classifiers are in the same trend. ChaosCentroid gave considerably higher performance than ChaosFrequency when the input length is one but it was moderately lower than ChaosFrequency when the input length is two. Both proposed techniques yielded almost or exactly the best performance when the input length is more than three. But there is no significance between our proposed techniques and the comparative alignment method.

CONCLUSIONS

Our proposed alignment-free and scale-independent method can successfully transform HPV genomes with 7,000 - 10,000 base pairs into features of 1 - 11 dimensions. This signifies that our ChaosCentroid and ChaosFrequency can be served as the effective feature extraction techniques for predicting the HPV genotypes.

Transcriptome-wide signatures of tumor stage in kidney renal clear cell carcinoma: connecting copy number variation, methylation and transcription factor activity

BACKGROUND

Comparative analysis of expression profiles between early and late stage cancers can help to understand cancer progression and metastasis mechanisms and to predict the clinical aggressiveness of cancer. The observed stage-dependent expression changes can be explained by genetic and epigenetic alterations as well as transcription dysregulation. Unlike genetic and epigenetic alterations, however, activity changes of transcription factors, generally occurring at the post-transcriptional or post-translational level, are hard to detect and quantify.

METHODS

Here we developed a statistical framework to infer the activity changes of transcription factors by simultaneously taking into account the contributions of genetic and epigenetic alterations to mRNA expression variations.

RESULTS

Applied to kidney renal clear cell carcinoma (KIRC), the model underscored the role of methylation as a significant contributor to stage-dependent expression alterations and identified key transcription factors as potential drivers of cancer progression.

CONCLUSIONS

Integrating copy number, methylation, and transcription factor activity signatures to explain stage-dependent expression alterations presented a precise and comprehensive view on the underlying mechanisms during KIRC progression.