Analysis of blood-based gene expression in idiopathic Parkinson disease.

OBJECTIVE: To examine whether gene expression analysis of a large-scale Parkinson disease (PD) patient cohort produces a robust blood-based PD gene signature compared to previous studies that have used relatively small cohorts (≤220 samples). METHODS: Whole-blood gene expression profiles were collected from a total of 523 individuals. After preprocessing, the data contained 486 gene profiles (n = 205 PD, n = 233 controls, n = 48 other neurodegenerative diseases) that were partitioned into training, validation, and independent test cohorts to identify and validate a gene signature. Batch-effect reduction and cross-validation were performed to ensure signature reliability. Finally, functional and pathway enrichment analyses were applied to the signature to identify PD-associated gene networks. RESULTS: A gene signature of 100 probes that mapped to 87 genes, corresponding to 64 upregulated and 23 downregulated genes differentiating between patients with idiopathic PD and controls, was identified with the training cohort and successfully replicated in both an independent validation cohort (area under the curve [AUC] = 0.79, p = 7.13E-6) and a subsequent independent test cohort (AUC = 0.74, p = 4.2E-4). Network analysis of the signature revealed gene enrichment in pathways, including metabolism, oxidation, and ubiquitination/proteasomal activity, and misregulation of mitochondria-localized genes, including downregulation of COX4I1, ATP5A1, and VDAC3. CONCLUSIONS: We present a large-scale study of PD gene expression profiling. This work identifies a reliable blood-based PD signature and highlights the importance of large-scale patient cohorts in developing potential PD biomarkers.

Parallel profiling of the transcriptome, cistrome, and epigenome in the cellular response to ionizing radiation.

The DNA damage response (DDR) is a vast signaling network that is robustly activated by DNA double-strand breaks, the critical lesion induced by ionizing radiation (IR). Although much of this response operates at the protein level, a critical component of the network sustains many DDR branches by modulating the cellular transcriptome. Using deep sequencing, we delineated three layers in the transcriptional response to IR in human breast cancer cells: changes in the expression of genes encoding proteins or long noncoding RNAs, alterations in genomic binding by key transcription factors, and dynamics of epigenetic markers of active promoters and enhancers. We identified protein-coding and previously unidentified noncoding genes that were responsive to IR, and demonstrated that IR-induced transcriptional dynamics was mediated largely by the transcription factors p53 and nuclear factor κB (NF-κB) and was primarily dependent on the kinase ataxia-telangiectasia mutated (ATM). The resultant data set provides a rich resource for understanding a basic, underlying component of a critical cellular stress response.

Dissection of regulatory networks that are altered in disease via differential co-expression.

Comparing the gene-expression profiles of sick and healthy individuals can help in understanding disease. Such differential expression analysis is a well-established way to find gene sets whose expression is altered in the disease. Recent approaches to gene-expression analysis go a step further and seek differential co-expression patterns, wherein the level of co-expression of a set of genes differs markedly between disease and control samples. Such patterns can arise from a disease-related change in the regulatory mechanism governing that set of genes, and pinpoint dysfunctional regulatory networks. Here we present DICER, a new method for detecting differentially co-expressed gene sets using a novel probabilistic score for differential correlation. DICER goes beyond standard differential co-expression and detects pairs of modules showing differential co-expression. The expression profiles of genes within each module of the pair are correlated across all samples. The correlation between the two modules, however, differs markedly between the disease and normal samples. We show that DICER outperforms the state of the art in terms of significance and interpretability of the detected gene sets. Moreover, the gene sets discovered by DICER manifest regulation by disease-specific microRNA families. In a case study on Alzheimer's disease, DICER dissected biological processes and protein complexes into functional subunits that are differentially co-expressed, thereby revealing inner structures in disease regulatory networks.

Bridging expressed sequence alignments through targeted cDNA sequencing.

One of the major challenges in genome research is the identification of the complete set of genes in a genome. Alignments of expressed sequences (RNA and EST) with genomic sequences have been used to characterize genes. However, the number of alignments far exceeds the likely number of genes in a genome, suggesting that, for many genes, two or more alignments can be joined through overlapping sequences to yield accurate gene structures. High-throughput EST sequencing becomes less efficient in closing those alignment gaps due to its nonselective nature. We sought to bridge these alignments through a novel approach: targeted cDNA sequencing. Human expressed sequences from GenBank version 124 were aligned with the genomic sequence from NCBI build 24 using LEADS, Compugen's EST and RNA clustering and assembly software system. Nine hundred forty-eight pairs of alignments were selected based on EST clone information and/or their homology to the same known proteins. Reverse transcriptase PCR and sequencing yielded sequences for 363 of those pairs. These sequences helped characterize over 60 novel or otherwise incomplete genes in the recent UniGene build 153, which included over 1 million additional ESTs. These results indicate that this integrated and targeted strategy, combining computational prediction and experimental cDNA sequencing, can efficiently generate the overlapping sequences and enable the full characterization of genomes. Additional information about the contig pairs, the resultant overlapping sequences, tissue sources, and tissue profiles are available in a supplemental file.

A novel algorithm for computational identification of contaminated EST libraries.

A key goal of the Human Genome Project was to understand the complete set of human proteins, the proteome. Since the genome sequence by itself is not sufficient for predicting new genes and alternative splicing events that lead to new proteins, expressed sequence tags (ESTs) are used as the primary tool for these purposes. The high prevalence of artifacts in dbEST, however, often leads to invalid predictions. Here we describe a novel method for recognizing genomic DNA contamination and other artifacts that cannot be identified using current EST cleaning techniques. Our method uses the alignment of the entire set of ESTs to the human genome to identify highly contaminated EST libraries. We discovered 53 highly contaminated libraries and a subset of 24 766 ESTs from these libraries that probably represent contamination with genomic DNA, pre-mRNA, and ESTs that span non-canonical introns. Although this is only a small fraction of the entire EST dataset, each contaminating sequence could create a spurious transcript prediction. Indeed, in the clustering and assembly tool that we used, these sequences would have caused incorrect inference of 9575 new splice variants and 6370 new genes. Conclusions based on EST analysis, including prediction of alternative splicing, should be re-evaluated in light of these results. Our method, along with the identified set of contaminated sequences, will be essential for applications that depend on large EST datasets.