Relative expression analysis for identifying perturbed pathways

What mRNA Abundances Can Tell us about Metabolism

Inferring decreased or increased metabolic functions from transcript profiles is at first sight a bold and speculative attempt because of the functional layers in between: proteins, enzymatic activities, and reaction fluxes. However, the growing interest in this field can easily be explained by two facts: the high quality of genome-scale metabolic network reconstructions and the highly developed technology to obtain genome-covering RNA profiles. Here, an overview of important algorithmic approaches is given by means of criteria by which published procedures can be classified. The frontiers of the methods are sketched and critical voices are being heard. Finally, an outlook for the prospects of the field is given.

Pairwise protein expression classifier for candidate biomarker discovery for early detection of human disease prognosis

BACKGROUND

An approach to molecular classification based on the comparative expression of protein pairs is presented. The method overcomes some of the present limitations in using peptide intensity data for class prediction for problems such as the detection of a disease, disease prognosis, or for predicting treatment response. Data analysis is particularly challenging in these situations due to sample size (typically tens) being much smaller than the large number of peptides (typically thousands). Methods based upon high dimensional statistical models, machine learning or other complex classifiers generate decisions which may be very accurate but can be complex and difficult to interpret in simple or biologically meaningful terms. A classification scheme, called ProtPair, is presented that generates simple decision rules leading to accurate classification which is based on measurement of very few proteins and requires only relative expression values, providing specific targeted hypotheses suitable for straightforward validation.

RESULTS

ProtPair has been tested against clinical data from 21 patients following a bone marrow transplant, 13 of which progress to idiopathic pneumonia syndrome (IPS). The approach combines multiple peptide pairs originating from the same set of proteins, with each unique peptide pair providing an independent measure of discriminatory power. The prediction rate of the ProtPair for IPS study as measured by leave-one-out CV is 69.1%, which can be very beneficial for clinical diagnosis as it may flag patients in need of closer monitoring. The "top ranked" proteins provided by ProtPair are known to be associated with the biological processes and pathways intimately associated with known IPS biology based on mouse models.

CONCLUSIONS

An approach to biomarker discovery, called ProtPair, is presented. ProtPair is based on the differential expression of pairs of peptides and the associated proteins. Using mass spectrometry data from "bottom up" proteomics methods, functionally related proteins/peptide pairs exhibiting co-ordinated changes expression profile are discovered, which represent a signature for patients progressing to various disease conditions. The method has been tested against clinical data from patients progressing to idiopthatic pneumonia syndrome (IPS) following a bone marrow transplant. The data indicates that patients with improper regulation in the concentration of specific acute phase response proteins at the time of bone marrow transplant are highly likely to develop IPS within few weeks. The results lead to a specific set of protein pairs that can be efficiently verified by investigating the pairwise abundance change in independent cohorts using ELISA or targeted mass spectrometry techniques. This generalized classifier can be extended to other clinical problems in a variety of contexts.

Research resource: whole transcriptome RNA sequencing detects multiple 1α,25-dihydroxyvitamin D(3)-sensitive metabolic pathways in developing zebrafish

The biological role of vitamin D receptors (VDR), which are abundantly expressed in developing zebrafish (Danio rerio) as early as 48 h after fertilization, and before the development of a mineralized skeleton and mature intestine and kidney, is unknown. We probed the role of VDR in developing zebrafish biology by examining changes in expression of RNA by whole transcriptome shotgun sequencing (RNA-seq) in fish treated with picomolar concentrations of the VDR ligand and hormonal form of vitamin D(3), 1α,25-dihydroxyvitamin D(3) [1α,25(OH)(2)D(3))].We observed significant changes in RNAs of transcription factors, leptin, peptide hormones, and RNAs encoding proteins of fatty acid, amino acid, xenobiotic metabolism, receptor-activator of NFκB ligand (RANKL), and calcitonin-like ligand receptor pathways. Early highly restricted, and subsequent massive changes in more than 10% of expressed cellular RNA were observed. At days post fertilization (dpf) 2 [24 h 1α,25(OH)(2)D(3)-treatment], only four RNAs were differentially expressed (hormone vs. vehicle). On dpf 4 (72 h treatment), 77 RNAs; on dpf 6 (120 h treatment) 1039 RNAs; and on dpf 7 (144 h treatment), 2407 RNAs were differentially expressed in response to 1α,25(OH)(2)D(3). Fewer RNAs (n = 481) were altered in dpf 7 larvae treated for 24 h with 1α,25(OH)(2)D(3) vs. those treated with hormone for 144 h. At dpf 7, in 1α,25(OH)(2)D(3)-treated larvae, pharyngeal cartilage was larger and mineralization was greater. Changes in expression of RNAs for transcription factors, peptide hormones, and RNAs encoding proteins integral to fatty acid, amino acid, leptin, calcitonin-like ligand receptor, RANKL, and xenobiotic metabolism pathways, demonstrate heretofore unrecognized mechanisms by which 1α,25(OH)(2)D(3) functions in vivo in developing eukaryotes.

Gene interaction enrichment and network analysis to identify dysregulated pathways and their interactions in complex diseases

BACKGROUND

The molecular behavior of biological systems can be described in terms of three fundamental components: (i) the physical entities, (ii) the interactions among these entities, and (iii) the dynamics of these entities and interactions. The mechanisms that drive complex disease can be productively viewed in the context of the perturbations of these components. One challenge in this regard is to identify the pathways altered in specific diseases. To address this challenge, Gene Set Enrichment Analysis (GSEA) and others have been developed, which focus on alterations of individual properties of the entities (such as gene expression). However, the dynamics of the interactions with respect to disease have been less well studied (i.e., properties of components ii and iii).

RESULTS

Here, we present a novel method called Gene Interaction Enrichment and Network Analysis (GIENA) to identify dysregulated gene interactions, i.e., pairs of genes whose relationships differ between disease and control. Four functions are defined to model the biologically relevant gene interactions of cooperation (sum of mRNA expression), competition (difference between mRNA expression), redundancy (maximum of expression), or dependency (minimum of expression) among the expression levels. The proposed framework identifies dysregulated interactions and pathways enriched in dysregulated interactions; points out interactions that are perturbed across pathways; and moreover, based on the biological annotation of each type of dysregulated interaction gives clues about the regulatory logic governing the systems level perturbation. We demonstrated the potential of GIENA using published datasets related to cancer.

CONCLUSIONS

We showed that GIENA identifies dysregulated pathways that are missed by traditional enrichment methods based on the individual gene properties and that use of traditional methods combined with GIENA provides coverage of the largest number of relevant pathways. In addition, using the interactions detected by GIENA, specific gene networks both within and across pathways associated with the relevant phenotypes are constructed and analyzed.