It has been long thought that neuronal loss in Parkinson’s disease (PD) is related to reactive oxygen species from mitochondrial dysfunction. However, there have been few investigations surveying both transcriptome and proteome in PD. This review focuses on recent work using microarrays and mass spectrometry to examine neurotoxicological models of PD in the mouse. Molecular pathways involved in oxidative phosphorylation, oxidative stress, apoptosis/cell death, signal transduction and neurotransmission were highlighted. Analysis of tyrosine nitration suggested that this important post-translational modification, due to conjugation of reactive oxygen species with nitric oxide, may play an important role in signal transduction as well as the molecular pathology of PD. Thus, the combined investigations highlight known pathways in PD but also point to new directions for research, implicating particularly the role of relatively understudied classes of post-translational modifications in normal cell signaling and neurological disorders.

Interaction networks can be charted by seeking gene pairs that are amplified and/or deleted in tandem, even when located at a distance on the genome. Our experience with radiation hybrid (RH) panels, a library of cell clones that have been used for genetic mapping, have shown this tool can pinpoint statistically significant patterns of co-inherited gene pairs. In fact, we were able to identify gene pairs specifically associated with the mechanism of cell survival at single gene resolution. Further, the RH network can be used to provide single gene specificity for cancer networks constructed from correlated copy number alterations (CNAs). In a survival network for glioblastoma, we found that the epidermal growth factor receptor (EGFR) oncogene interacted with 46 genes. Of these genes, ten (22%) happened to be targets for existing drugs. Here, we highlight the potential of CNA networks to guide combinatorial drug treatment in cancer, autoimmunity and atherosclerosis.

In the current era of tumor genome sequencing, single amino acid missense variants in the von Hippel-Lindau (VHL) tumor suppressor gene are frequently identified in clear cell renal carcinoma (ccRCC). Due to the incomplete knowledge of the structural architecture of VHL protein, the functional significance of many missense mutations cannot be assigned. L169P is one such missense mutation identified in the case of aggressive, metastatic ccRCC. Here, we characterized the biochemical activity, transcriptomic hypoxia signature and biological functions of the L169P variant. Lentiviral vector expressing either wildtype (WT) or L169P VHL were used to transduce two VHL-deficient human ccRCC cell lines, 786-O and RCC4. The stability of the VHL protein and the expression level of VHL, HIF1α and HIF2α were analyzed. The impact of restoring L169P or WT VHL on the hypoxia gene expression program in 786-O cells was assessed by mRNA sequencing (RNAseq) and computed hypoxic scores. The impact of restoring VHL expression on the growth of ccRCC models was assessed in cell cultures and in chorioallantoic membrane (CAM) xenografts. In the 786-O cells, the protein stability of L169P VHL was comparable to WT VHL. No obvious difference in the capability of degrading HIF1α and HIF2α was observed between WT and L169P VHL in the 786-O or RCC4 cells. The hypoxic scores were not significantly different in the 786-O cells expressing either wildtype or L169P VHL. From the cellular function perspective, both WT and L169P VHL slowed cell proliferation in vitro and in vivo. The L169P VHL variant is comparable to WT VHL in terms of protein stability, ability to degrade HIF1α factors and ability to regulate hypoxia gene expression, as well as in the suppression of ccRCC tumor cell growth. Taken together, our data indicate that the L169P VHL variant alone is unlikely to drive the oncogenesis of sporadic ccRCC.

Comprehensive maps of genetic interactions in mammalian cells are daunting to construct because of the large number of potential interactions, ~ 2 × 10⁸ for protein coding genes. We previously used co-inheritance of distant genes from published radiation hybrid (RH) datasets to identify genetic interactions. However, it was necessary to combine six legacy datasets from four species to obtain adequate statistical power. Mapping resolution was also limited by the low density PCR genotyping. Here, we employ shallow sequencing of nascent human RH clones as an economical approach to constructing interaction maps. In this initial study, 15 clones were analyzed, enabling construction of a network with 225 genes and 2,359 interactions (FDR < 0.05). Despite its small size, the network showed significant overlap with the previous RH network and with a protein-protein interaction network. Consumables were ≲$50 per clone, showing that affordable, high quality genetic interaction maps are feasible in mammalian cells.

We sought exonic transcriptional regulatory elements by shotgun cloning human cDNA fragments into luciferase reporter vectors and measuring the resulting expression levels in liver cells. We uncovered seven regulatory elements within coding regions and three within 3' untranslated regions (UTRs). Two of the putative regulatory elements were enhancers and eight were silencers. The regulatory elements were generally but not consistently evolutionarily conserved and also showed a trend toward decreased population diversity. Furthermore, the exonic regulatory elements were enriched in known transcription factor binding sites (TFBSs) and were associated with several histone modifications and transcriptionally relevant chromatin. Evidence was obtained for bidirectional cis-regulation of a coding region element within a tubulin gene, TUBA1B, by the transcription factors PPARA and RORA. We estimate that hundreds of exonic transcriptional regulatory elements exist, an unexpected finding that highlights a surprising multi-functionality of sequences in the human genome.

Abstract Background The relationships between the gene functional similarity and gene expression profile, and between gene function annotation and gene sequence have been studied extensively. However, not much work has considered the connection between gene functions and location of a gene's expression in the mammalian tissues. On the other hand, although unsupervised learning methods have been commonly used in functional genomics, supervised learning cannot be directly applied to a set of normal genes without having a target (class) attribute. Results Here, we propose a supervised learning methodology to predict pair-wise gene functional similarity from multiplex gene expression maps that provide information about the location of gene expression. The features are extracted from expression maps and the labels denote the functional similarities of pairs of genes. We make use of wavelet features, original expression values, difference and average values of neighboring voxels and other features to perform boosting analysis. The experimental results show that with increasing similarities of gene expression maps, the functional similarities are increased too. The model predicts the functional similarities between genes to a certain degree. The weights of the features in the model indicate the features that are more significant for this prediction. Conclusions By considering pairs of genes, we propose a supervised learning methodology to predict pair-wise gene functional similarity from multiplex gene expression maps. We also explore the relationship between similarities of gene maps and gene functions. By using AdaBoost coupled with our proposed weak classifier we analyze a large-scale gene expression dataset and predict gene functional similarities. We also detect the most significant single voxels and pairs of neighboring voxels and visualize them in the expression map image of a mouse brain. This work is very important for predicting functions of unknown genes. It also has broader applicability since the methodology can be applied to analyze any large-scale dataset without a target attribute and is not restricted to gene expressions.

Genetic screens in mammalian cells commonly focus on loss-of-function approaches. To evaluate the phenotypic consequences of extra gene copies, we used bulk segregant analysis (BSA) of radiation hybrid (RH) cells. We constructed six pools of RH cells, each consisting of ∼2500 independent clones, and placed the pools under selection in media with or without paclitaxel. Low pass sequencing identified 859 growth loci, 38 paclitaxel loci, 62 interaction loci, and three loci for mitochondrial abundance at genome-wide significance. Resolution was measured as ∼30 kb, close to single-gene. Divergent properties were displayed by the RH-BSA growth genes compared to those from loss-of-function screens, refuting the balance hypothesis. In addition, enhanced retention of human centromeres in the RH pools suggests a new approach to functional dissection of these chromosomal elements. Pooled analysis of RH cells showed high power and resolution and should be a useful addition to the mammalian genetic toolkit.

Background

Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological disease. Researchers have previously used voxelation in combination with microarrays for acquisition of genome-wide atlases of expression patterns in the mouse brain. On the other hand, some work has been performed on studying gene functions, without taking into account the location information of a gene's expression in a mouse brain. In this paper, we present an approach for identifying the relation between gene expression maps obtained by voxelation and gene functions.

Results

To analyze the dataset, we chose typical genes as queries and aimed at discovering similar gene groups. Gene similarity was determined by using the wavelet features extracted from the left and right hemispheres averaged gene expression maps, and by the Euclidean distance between each pair of feature vectors. We also performed a multiple clustering approach on the gene expression maps, combined with hierarchical clustering. Among each group of similar genes and clusters, the gene function similarity was measured by calculating the average gene function distances in the gene ontology structure. By applying our methodology to find similar genes to certain target genes we were able to improve our understanding of gene expression patterns and gene functions. By applying the clustering analysis method, we obtained significant clusters, which have both very similar gene expression maps and very similar gene functions respectively to their corresponding gene ontologies. The cellular component ontology resulted in prominent clusters expressed in cortex and corpus callosum. The molecular function ontology gave prominent clusters in cortex, corpus callosum and hypothalamus. The biological process ontology resulted in clusters in cortex, hypothalamus and choroid plexus. Clusters from all three ontologies combined were most prominently expressed in cortex and corpus callosum.

Conclusion

The experimental results confirm the hypothesis that genes with similar gene expression maps might have similar gene functions. The voxelation data takes into account the location information of gene expression level in mouse brain, which is novel in related research. The proposed approach can potentially be used to predict gene functions and provide helpful suggestions to biologists.