ALOHA: Aggregated local extrema splines for high-throughput dose-response analysis

Computational methods for genomic dose-response integrate dose-response modeling with bioinformatics tools to evaluate changes in molecular and cellular functions related to pathogenic processes. These methods use parametric models to describe each gene's dose-response, but such models may not adequately capture expression changes. Additionally, current approaches do not consider gene co-expression networks. When assessing co-expression networks, one typically does not consider the dose-response relationship, resulting in 'co-regulated' gene sets containing genes having different dose-response patterns. To avoid these limitations, we develop an analysis pipeline called Aggregated Local Extrema Splines for High-throughput Analysis (ALOHA), which computes individual genomic dose-response functions using a flexible class Bayesian shape constrained splines and clusters gene co-regulation based upon these fits. Using splines, we reduce information loss due to parametric lack-of-fit issues, and because we cluster on dose-response relationships, we better identify co-regulation clusters for genes that have co-expressed dose-response patterns from chemical exposure. The clustered pathways can then be used to estimate a dose associated with a pre-specified biological response, i.e., the benchmark dose (BMD), and approximate a point of departure dose corresponding to minimal adverse response in the whole tissue/organism. We compare our approach to current parametric methods and our biologically enriched gene sets to cluster on normalized expression data. Using this methodology, we can more effectively extract the underlying structure leading to more cohesive estimates of gene set potency.

The clinical significance of transcription factor WD repeat and HMG-box DNA binding protein 1 in laryngeal squamous cell carcinoma and its potential molecular mechanism

BACKGROUND

Currently, high expression of WD repeat and HMG-box DNA binding protein 1 (WDHD1) has been found in a variety of tumors; but there is no research has been conducted concerning the expression of WDHD1 in laryngeal squamous cell carcinoma (LSCC). Our purpose is to investigate the expression and the latent mechanism of WDHD1 in LSCC.

METHODS

Firstly, 9 data sets from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and ArrayExpress were statistically analyzed to explore the expression of WDHD1 in LSCC; immunohistochemistry was performed in 79 LSCC tissues and 44 non-cancer tissues to further verify the result. In addition, the target gene of WDHD1 was predicted and immunohistochemistry was used to detect the expression of the target gene. The potential mechanism of WDHD1 in LSCC was investigated by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses and protein-protein interaction network (PPI).

RESULTS

The WDHD1 mRNA was expressed at higher levels in the LSCC tissue than in the normal tissue (SMD=1.90, 95% CI=1.50-2.30); and the results of immunohistochemistry were consistent with the conclusion. Using chip-seq analysis, we found that S-phase kinase-associated protein 2 (Skp2) had a significant binding peak with WDHD1, and the expression of these two genes was significantly positively correlated. Immunohistochemistry showed that Skp2 was also highly expressed in LSCC. In addition, GO and KEGG analysis revealed the WDHD1 positively correlated genes was closely related to cell cycle, and PPI analysis identified 10 hub genes: COL7A1, COL4A2, COL4A1, COL4A6, COL11A1, COL5A2, COL1A1, COL13A1, COL8A1 and COL10A1, which may be critical to the progression of LSCC.

CONCLUSIONS

WDHD1 was overexpressed in LSCC tissues. Meanwhile, WDHD1 and its target gene Skp2 for transcriptional regulation may play a role in the progression of LSCC by regulating the cell cycle.

Unraveling molecular mechanism underlying biomaterial and stem cells interaction during cell fate commitment using high throughput data analysis

Stem cell differentiation towards various somatic cells and body organs has proven to be an effective technique in the understanding and progression of regenerative medicine. Despite the advances made, concerns regarding the low efficiency of differentiation and the remaining differences between stem cell products and their in vivo counterparts must be addressed. Biomaterials that mimic endogenous growth conditions represent one recent method used to improve the quality and efficiency of stem cell differentiation, though the mechanisms of this improvement remain to be completely understood. The effectiveness of various biomaterials can be analyzed through a multidisciplinary approach involving bioinformatics and systems biology tools. Here, we aim to use bioinformatics to accomplish two aims: 1) determine the effect of different biomaterials on stem cell growth and differentiation, and 2) understand the effect of cell of origin on the differentiation potential of multipotent stem cells. First, we demonstrate that the dimensionality (2D versus 3D) and the degradability of biomaterials affects the way that the cells are able to grow and differentiate at the transcriptional level. Additionally, according to transcriptional state of the cells, the particular cell of origin is an important factor in determining the response of stem cells to same biomaterial. Our data demonstrates the ability of bioinformatics to understand novel molecular mechanisms and context by which stem cells are most efficiently able to differentiate. These results and strategies can be used to suggest proper combinations of biomaterials and stem cells to achieve high differentiation efficiency and functionality of desired cell types.

CEGSO: Boosting Essential Proteins Prediction by Integrating Protein Complex, Gene Expression, Gene Ontology, Subcellular Localization and Orthology Information

Essential proteins are assumed to be an indispensable element in sustaining normal physiological function and crucial to drug design and disease diagnosis. The discovery of essential proteins is of great importance in revealing the molecular mechanisms and biological processes. Owing to the tedious biological experiment, many numerical methods have been developed to discover key proteins by mining the features of the high throughput data. Appropriate integration of differential biological information based on protein-protein interaction (PPI) network has been proven useful in predicting essential proteins. The main intention of this research is to provide a comprehensive study and a review on identifying essential proteins by integrating multi-source data and provide guidance for researchers. Detailed analysis and comparison of current essential protein prediction algorithms have been carried out and tested on benchmark PPI networks. In addition, based on the previous method TEGS (short for the network Topology, gene Expression, Gene ontology, and Subcellular localization), we improve the performance of predicting essential proteins by incorporating known protein complex information, the gene expression profile, Gene Ontology (GO) terms information, subcellular localization information, and protein's orthology data into the PPI network, named CEGSO. The simulation results show that CEGSO achieves more accurate and robust results than other compared methods under different test datasets with various evaluation measurements.

Integrative analysis of layers of data in hepatocellular carcinoma reveals pathway dependencies

BACKGROUND

The broader use of high-throughput technologies has led to improved molecular characterization of hepatocellular carcinoma (HCC). 

AIM

To comprehensively analyze and characterize all publicly available genomic, gene expression, methylation, miRNA and proteomic data in HCC, covering 85 studies and 3355 patient sample profiles, to identify the key dysregulated genes and pathways they affect. 

METHODS

We collected and curated all well-annotated and publicly available high-throughput datasets from PubMed and Gene Expression Omnibus derived from human HCC tissue. Comprehensive pathway enrichment analysis was performed using pathDIP for each data type (genomic, gene expression, methylation, miRNA and proteomic), and the overlap of pathways was assessed to elucidate pathway dependencies in HCC.

RESULTS

We identified a total of 8733 abstracts retrieved by the search on PubMed on HCC for the different layers of data on human HCC samples, published until December 2016. The common key dysregulated pathways in HCC tissue across different layers of data included epidermal growth factor (EGFR) and β1-integrin pathways. Genes along these pathways were significantly and consistently dysregulated across the different types of high-throughput data and had prognostic value with respect to overall survival. Using CTD database, estradiol would best modulate and revert these genes appropriately.

CONCLUSION

By analyzing and integrating all available high-throughput genomic, transcriptomic, miRNA, methylation and proteomic data from human HCC tissue, we identified EGFR, β1-integrin and axon guidance as pathway dependencies in HCC. These are master regulators of key pathways in HCC, such as the mTOR, Ras/Raf/MAPK and p53 pathways. The genes implicated in these pathways had prognostic value in HCC, with Netrin and Slit3 being novel proteins of prognostic importance to HCC. Based on this integrative analysis, EGFR, and β1-integrin are master regulators that could serve as potential therapeutic targets in HCC.

Genome-wide identification and characterization of tRNA-derived RNA fragments in land plants

The manuscript by Alves et al. entitled "Genome-wide identification and characterization of tRNA-derived RNA fragments in land plants" describes the identification and characterization of tRNAderived sRNA fragments in plants. By combining bioinformatic analysis and genetic and molecular approaches, we show that tRF biogenesis does not rely on canonical microRNA/siRNA processing machinery (i.e., independent of DICER-LIKE proteins). Moreover, we provide evidences that the Arabidopsis S-like Ribonuclease 1 (RNS1) might be involved in the biogenesis of tRFs. Detailed analyses showed that plant tRFs are sorted into different types of ARGONAUTE proteins and that they have potential target candidate genes. Our work advances the understanding of the tRF biology in plants by providing evidences that plant and animal tRFs shared common features and raising the hypothesis that an interplay between tRFs and other sRNAs might be important to fine-tune gene expression and protein biosynthesis in plant cells. Small RNA (sRNA) fragments derived from tRNAs (3'-loop, 5'-loop, anti-codon loop), named tRFs, have been reported in several organisms, including humans and plants. Although they may interfere with gene expression, their biogenesis and biological functions in plants remain poorly understood. Here, we capitalized on small RNA sequencing data from distinct species such as Arabidopsis thaliana, Oryza sativa, and Physcomitrella patens to examine the diversity of plant tRFs and provide insight into their properties. In silico analyzes of 19 to 25-nt tRFs derived from 5' (tRF-5s) and 3'CCA (tRF-3s) tRNA loops in these three evolutionary distant species showed that they are conserved and their abundance did not correlate with the number of genomic copies of the parental tRNAs. Moreover, tRF-5 is the most abundant variant in all three species. In silico and in vivo expression analyses unraveled differential accumulation of tRFs in Arabidopsis tissues/organs, suggesting that they are not byproducts of tRNA degradation. We also verified that the biogenesis of most Arabidopsis 19-25 nt tRF-5s and tRF-3s is not primarily dependent on DICER-LIKE proteins, though they seem to be associated with ARGONAUTE proteins and have few potential targets. Finally, we provide evidence that Arabidopsis ribonuclease RNS1 might be involved in the processing and/or degradation of tRFs. Our data support the notion that an interplay between tRFs and other sRNAs might be important to fine tune gene expression and protein biosynthesis in plant cells.

Next generation sequencing technology and genomewide data analysis: Perspectives for retinal research

The advent of high throughput next generation sequencing (NGS) has accelerated the pace of discovery of disease-associated genetic variants and genomewide profiling of expressed sequences and epigenetic marks, thereby permitting systems-based analyses of ocular development and disease. Rapid evolution of NGS and associated methodologies presents significant challenges in acquisition, management, and analysis of large data sets and for extracting biologically or clinically relevant information. Here we illustrate the basic design of commonly used NGS-based methods, specifically whole exome sequencing, transcriptome, and epigenome profiling, and provide recommendations for data analyses. We briefly discuss systems biology approaches for integrating multiple data sets to elucidate gene regulatory or disease networks. While we provide examples from the retina, the NGS guidelines reviewed here are applicable to other tissues/cell types as well.

Gene regulatory network modeling using literature curated and high throughput data

Building on the linear matrix inequality (LMI) formulation developed recently by Zavlanos et al. (Automatica: Special Issue Syst Biol 47(6):1113-1122, 2011), we present a theoretical framework and algorithms to derive a class of ordinary differential equation (ODE) models of gene regulatory networks using literature curated data and microarray data. The solution proposed by Zavlanos et al. (Automatica: Special Issue Syst Biol 47(6):1113-1122, 2011) requires that the microarray data be obtained as the outcome of a series of controlled experiments in which the network is perturbed by over-expressing one gene at a time. We note that this constraint may be relaxed for some applications and, in addition, demonstrate how the conservatism in these algorithms may be reduced by using the Perron-Frobenius diagonal dominance conditions as the stability constraints. Due to the LMI formulation, it follows that the bounded real lemma may easily be used to make use of additional information. We present case studies that illustrate how these algorithms can be used on datasets to derive ODE models of the underlying regulatory networks.