Positive-unlabeled ensemble learning for kinase substrate prediction from dynamic phosphoproteomics data.

MOTIVATION: Protein phosphorylation is a post-translational modification that underlines various aspects of cellular signaling. A key step to reconstructing signaling networks involves identification of the set of all kinases and their substrates. Experimental characterization of kinase substrates is both expensive and time-consuming. To expedite the discovery of novel substrates, computational approaches based on kinase recognition sequence (motifs) from known substrates, protein structure, interaction and co-localization have been proposed. However, rarely do these methods take into account the dynamic responses of signaling cascades measured from in vivo cellular systems. Given that recent advances in mass spectrometry-based technologies make it possible to quantify phosphorylation on a proteome-wide scale, computational approaches that can integrate static features with dynamic phosphoproteome data would greatly facilitate the prediction of biologically relevant kinase-specific substrates. RESULTS: Here, we propose a positive-unlabeled ensemble learning approach that integrates dynamic phosphoproteomics data with static kinase recognition motifs to predict novel substrates for kinases of interest. We extended a positive-unlabeled learning technique for an ensemble model, which significantly improves prediction sensitivity on novel substrates of kinases while retaining high specificity. We evaluated the performance of the proposed model using simulation studies and subsequently applied it to predict novel substrates of key kinases relevant to insulin signaling. Our analyses show that static sequence motifs and dynamic phosphoproteomics data are complementary and that the proposed integrated model performs better than methods relying only on static information for accurate prediction of kinase-specific substrates. AVAILABILITY AND IMPLEMENTATION: Executable GUI tool, source code and documentation are freely available at https://github.com/PengyiYang/KSP-PUEL. CONTACT: pengyi.yang@nih.gov or jothi@mail.nih.gov SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transcriptome-wide targets of alternative splicing by RBM4 and possible role in cancer.

This study determined transcriptome-wide targets of the splicing factor RBM4 using Affymetrix GeneChip(®) Human Exon 1.0 ST Arrays and HeLa cells treated with RBM4-specific siRNA. This revealed 238 transcripts that were targeted for alternative splicing. Cross-linking and immunoprecipitation experiments identified 945 RBM4 targets in mouse HEK293 cells, 39% of which were ascribed to "alternative splicing" by in silico pathway analysis. Mouse embryonic stem cells transfected with Rbm4 siRNA hairpins exhibited reduced colony numbers and size consistent with involvement of RBM4 in cell proliferation. RBM4 cDNA probing of a cancer cDNA array involving 18 different tumor types from 13 different tissues and matching normal tissue found overexpression of RBM4 mRNA (p<0.01) in cervical, breast, lung, colon, ovarian and rectal cancers. Many RBM4 targets we identified have been implicated in these cancers. In conclusion, our findings reveal transcriptome-wide targets of RBM4 and point to potential cancer-related targets and mechanisms that may involve RBM4.

Molecular interaction networks for the analysis of human disease: utility, limitations, and considerations.

High-throughput '-omics' data can be combined with large-scale molecular interaction networks, for example, protein-protein interaction networks, to provide a unique framework for the investigation of human molecular biology. Interest in these integrative '-omics' methods is growing rapidly because of their potential to understand complexity and association with disease; such approaches have a focus on associations between phenotype and "network-type." The potential of this research is enticing, yet there remain a series of important considerations. Here, we discuss interaction data selection, data quality, the relative merits of using data from large high-throughput studies versus a meta-database of smaller literature-curated studies, and possible issues of sociological or inspection bias in interaction data. Other work underway, especially international consortia to establish data formats, quality standards and address data redundancy, and the improvements these efforts are making to the field, is also evaluated. We present options for researchers intending to use large-scale molecular interaction networks as a functional context for protein or gene expression data, including microRNAs, especially in the context of human disease.

Estimation of data-specific constitutive exons with RNA-Seq data.

BACKGROUND: RNA-Seq has the potential to answer many diverse and interesting questions about the inner workings of cells. Estimating changes in the overall transcription of a gene is not straightforward. Changes in overall gene transcription can easily be confounded with changes in exon usage which alter the lengths of transcripts produced by a gene. Measuring the expression of constitutive exons--xons which are consistently conserved after splicing--ffers an unbiased estimation of the overall transcription of a gene. RESULTS: We propose a clustering-based method, exClust, for estimating the exons that are consistently conserved after splicing in a given data set. These are considered as the exons which are "constitutive" in this data. The method utilises information from both annotation and the dataset of interest. The method is implemented in an openly available R function package, sydSeq. CONCLUSION: When used on two real datasets exClust includes more than three times as many reads as the standard UI method, and improves concordance with qRT-PCR data. When compared to other methods, our method is shown to produce robust estimates of overall gene transcription.

Improved moderation for gene-wise variance estimation in RNA-Seq via the exploitation of external information.

BACKGROUND: The cost of RNA-Seq has been decreasing over the last few years. Despite this, experiments with four or less biological replicates are still quite common. Estimating the variances of gene expression estimates becomes both a challenging and interesting problem in these situations of low replication. However, with the wealth of microarray and other publicly available gene expression data readily accessible on public repositories, these sources of information can be leveraged to make improvements in variance estimation. RESULTS: We have proposed a novel approach called Tshrink+ for inferring differential gene expression through improved modelling of the gene-wise variances. Existing methods share information between genes of similar average expression by shrinking, or moderating, the gene-wise variances to a fitted common variance. We have been able to achieve improved estimation of the common variance by using gene-wise sample variances from external experiments, as well as gene length. CONCLUSIONS: Using biological data we show that utilising additional external information can improve the modelling of the common variance and hence the calling of differentially expressed genes. These sources of additional information include gene length and gene-wise sample variances from other RNA-Seq and microarray datasets, of both related and seemingly unrelated tissue types. The results of this are promising, with our differential expression test, Tshrink+, performing favourably when compared to existing methods such as DESeq and edgeR when considering both gene ranking and sensitivity. These improved variance models could easily be implemented in both DESeq and edgeR and highlight the need for a database that offers a profile of gene variances over a range of tissue types and organisms.

ZRANB2 localizes to supraspliceosomes and influences the alternative splicing of multiple genes in the transcriptome.

Alternative splicing is a major source of protein diversity in humans. The human splicing factor zinc finger, Ran-binding domain containing protein 2 (ZRANB2) is a splicing protein whose specific endogenous targets are unknown. Its upregulation in grade III ovarian serous papillary carcinoma could suggest a role in some cancers. To determine whether ZRANB2 is part of the supraspliceosome, nuclear supernatants from human embryonic kidney 293 cells were prepared and then fractioned on a glycerol gradient, followed by Western blotting. The same was done after treatment with a tyrosine kinase to induce phosphorylation. This showed for the first time that ZRANB2 is part of the supraspliceosome, and that phosphorylation affects its subcellular location. Studies were then performed to understand the splicing targets of ZRANB2 at the whole-transcriptome level. HeLa cells were transfected with a vector containing ZRANB2 or with a vector-only control. RNA was extracted, converted to cDNA and hybridized to Affymetrix GeneChip(®) Human Exon 1.0 ST Arrays. At the FDR ≤1.3 significance level we found that ZRANB2 influenced the alternative splicing of primary transcripts of CENTB1, WDR78, C10orf18, CABP4, SMARCC2, SPATA13, OR4C6, ZNF263, CAPN10, SALL1, ST18 and ZP2. Several of these have been implicated in tumor development. In conclusion ZRANB2 is part of the supraspliceosome and causes differential splicing of numerous primary transcripts, some of which might have a role in cancer.

The yeast homolog of heme oxygenase-1 affords cellular antioxidant protection via the transcriptional regulation of known antioxidant genes.

Heme oxygenase-1 (HO-1) degrades heme and protects cells from oxidative challenge. This antioxidant activity is thought to result from the HO-1 enzymatic activity, manifested by a decrease in the concentration of the pro-oxidant substrate heme, and an increase in the antioxidant product bilirubin. Using a global transcriptional approach, and yeast as a model, we show that HO-1 affords cellular protection via up-regulation of transcripts encoding enzymes involved in cellular antioxidant defense, rather than via its oxygenase activity. Like mammalian cells, yeast responds to oxidative stress by expressing its HO-1 homolog and, compared with the wild type, heme oxygenase-null mutant cells have increased sensitivity toward oxidants that is rescued by overexpression of human HO-1 or its yeast homolog. Increased oxidant sensitivity of heme oxygenase-null mutant cells is explained by a decrease in the expression of the genes encoding γ-glutamylcysteine synthetase, glutathione peroxidase, catalase, and methionine sulfoxide reductase, because overexpression of any of these genes affords partial, and overexpression of all four genes provides complete, protection to the null mutant. Genes encoding antioxidant enzymes represent only a small portion of the 480 differentially expressed transcripts in heme oxygenase-null mutants. Transcriptional regulation may be explained by the nuclear localization of heme oxygenase observed in oxidant-challenged cells. Our results challenge the notion that HO-1 functions simply as a catabolic and antioxidant enzyme. They indicate much broader functions for HO-1, the unraveling of which may help explain the multiple biological responses reported in animals as a result of altered HO-1 expression.

Gene-gene interaction filtering with ensemble of filters.

BACKGROUND: Complex diseases are commonly caused by multiple genes and their interactions with each other. Genome-wide association (GWA) studies provide us the opportunity to capture those disease associated genes and gene-gene interactions through panels of SNP markers. However, a proper filtering procedure is critical to reduce the search space prior to the computationally intensive gene-gene interaction identification step. In this study, we show that two commonly used SNP-SNP interaction filtering algorithms, ReliefF and tuned ReliefF (TuRF), are sensitive to the order of the samples in the dataset, giving rise to unstable and suboptimal results. However, we observe that the 'unstable' results from multiple runs of these algorithms can provide valuable information about the dataset. We therefore hypothesize that aggregating results from multiple runs of the algorithm may improve the filtering performance. RESULTS: We propose a simple and effective ensemble approach in which the results from multiple runs of an unstable filter are aggregated based on the general theory of ensemble learning. The ensemble versions of the ReliefF and TuRF algorithms, referred to as ReliefF-E and TuRF-E, are robust to sample order dependency and enable a more informative investigation of data characteristics. Using simulated and real datasets, we demonstrate that both the ensemble of ReliefF and the ensemble of TuRF can generate a much more stable SNP ranking than the original algorithms. Furthermore, the ensemble of TuRF achieved the highest success rate in comparison to many state-of-the-art algorithms as well as traditional χ2-test and odds ratio methods in terms of retaining gene-gene interactions.

Global identification of the genes and pathways differentially expressed in hypothalamus in early and established neurogenic hypertension.

The hypothalamus has an important etiological role in the onset and maintenance of hypertension and stress responses in the Schlager high blood pressure (BP) (BPH/2J) mouse, a genetic model of neurogenic hypertension. Using Affymetrix GeneChip Mouse Gene 1.0 ST Arrays we identified 1,019 hypothalamic genes whose expression differed between 6 wk old BPH/2J and normal BP (BPN/3J) strains, and 466 for 26 wk old mice. Of these, 459 were in 21 mouse BP quantitative trait loci. We validated 46 genes by qPCR. Gene changes that would increase sympathetic outflow at both ages were: Dynll1 encoding dynein light chain LC8-type 1, which physically destabilizes neuronal nitric oxide synthase, decreasing neuronal nitric oxide, and Hcrt encoding hypocretin and Npsr1 encoding neuropeptide S receptor 1, each involved in sympathetic response to stress. At both ages we identified genes for inflammation, such as CC-chemokine ligand 19 (Ccl19), and oxidative stress. Via reactive oxygen species generation, these could contribute to oxidative damage. Other genes identified could be responding to such perturbations. Atp2b1, the major gene from genome-wide association studies of BP variation, was underexpressed in the early phase. Comparison of profiles of young and adult BPH/2J mice, after adjusting for maturation genes, pointed to the proopiomelanocortin-α gene (Pomc) and neuropeptide Y gene (Npy), among others, as potentially causative. The present study has identified a diversity of genes and possible mechanisms involved in hypertension etiology and maintenance in the hypothalamus of BPH/2J mice, highlighting both common and divergent processes in each phase of the condition.

Genome-wide analysis of primary CD4+ and CD8+ T cell transcriptomes shows evidence for a network of enriched pathways associated with HIV disease.

BACKGROUND: HIV preferentially infects CD4+ T cells, and the functional impairment and numerical decline of CD4+ and CD8+ T cells characterize HIV disease. The numerical decline of CD4+ and CD8+ T cells affects the optimal ratio between the two cell types necessary for immune regulation. Therefore, this work aimed to define the genomic basis of HIV interactions with the cellular transcriptome of both CD4+ and CD8+ T cells. RESULTS: Genome-wide transcriptomes of primary CD4+ and CD8+ T cells from HIV+ patients were analyzed at different stages of HIV disease using Illumina microarray. For each cell subset, pairwise comparisons were performed and differentially expressed (DE) genes were identified (fold change >2 and B-statistic >0) followed by quantitative PCR validation. Gene ontology (GO) analysis of DE genes revealed enriched categories of complement activation, actin filament, proteasome core and proton-transporting ATPase complex. By gene set enrichment analysis (GSEA), a network of enriched pathways functionally connected by mitochondria was identified in both T cell subsets as a transcriptional signature of HIV disease progression. These pathways ranged from metabolism and energy production (TCA cycle and OXPHOS) to mitochondria meditated cell apoptosis and cell cycle dysregulation. The most unique and significant feature of our work was that the non-progressing status in HIV+ long-term non-progressors was associated with MAPK, WNT, and AKT pathways contributing to cell survival and anti-viral responses. CONCLUSIONS: These data offer new comparative insights into HIV disease progression from the aspect of HIV-host interactions at the transcriptomic level, which will facilitate the understanding of the genetic basis of transcriptomic interaction of HIV in vivo and how HIV subverts the human gene machinery at the individual cell type level.

Expression profiling of cytogenetically normal acute myeloid leukemia identifies microRNAs that target genes involved in monocytic differentiation.

MicroRNAs are short ribonucleic acids (RNAs) that play an important role in many aspects of cellular biology such as differentiation and apoptosis, due to their role in the regulation of gene expression. Using microRNA microarrays, we characterized the microRNA gene expression of 27 patients with acute myeloid leukemia (AML) with normal cytogenetics, focusing on the microRNAs differentially expressed between the M1 and M5 French-American-British (FAB) subtypes. An accurate delineation of these two AML entities was observed based on the expression of 12 microRNAs. We hypothesized that these microRNAs may potentially be involved in the differentiation block of M1 blasts and consequently monocytic differentiation. Using publically available mRNA data and microRNA target prediction software, we identified several key myeloid factors that may be targeted by our candidate microRNAs. The expression changes of the candidate microRNAs during monocytic differentiation of AML cell lines treated with Vitamin D and phorbol 12-myristate 13-acetate were examined. All six candidate microRNAs were significantly down-regulated over the time course by quantitative reverse transcriptase polymerase chain reaction suggesting a link between these microRNAs and monocytic differentiation. To further characterize these microRNAs, we confirmed by luciferase assays that these microRNA target several key myeloid factors such as MAFB, IRF8, and KLF4 identifying a possible mechanism for the control of differentiation by these microRNAs.

Comparison study of microarray meta-analysis methods.

BACKGROUND: Meta-analysis methods exist for combining multiple microarray datasets. However, there are a wide range of issues associated with microarray meta-analysis and a limited ability to compare the performance of different meta-analysis methods. RESULTS: We compare eight meta-analysis methods, five existing methods, two naive methods and a novel approach (mDEDS). Comparisons are performed using simulated data and two biological case studies with varying degrees of meta-analysis complexity. The performance of meta-analysis methods is assessed via ROC curves and prediction accuracy where applicable. CONCLUSIONS: Existing meta-analysis methods vary in their ability to perform successful meta-analysis. This success is very dependent on the complexity of the data and type of analysis. Our proposed method, mDEDS, performs competitively as a meta-analysis tool even as complexity increases. Because of the varying abilities of compared meta-analysis methods, care should be taken when considering the meta-analysis method used for particular research.

Analysis of the prototypical Staphylococcus aureus multiresistance plasmid pSK1.

The Staphylococcus aureus multiresistance plasmid pSK1 is the prototype of a family of structurally related plasmids that were first identified in epidemic S. aureus strains isolated in Australia during the 1980s and subsequently in Europe. Here we present the complete 28.15kb nucleotide sequence of pSK1 and discuss the genetic content and evolution of the 14kb region that is conserved throughout the pSK1 plasmid family. In addition to the previously characterized plasmid maintenance functions, this backbone region encodes 12 putative gene products, including a lipoprotein, teichoic acid translocation permease, cell wall anchored surface protein and an Fst-like toxin as part of a Type I toxin-antitoxin system. Furthermore, transcriptional profiling has revealed that plasmid carriage most likely has a minimal impact on the host, a factor that may contribute to the ability of pSK1 family plasmids to carry multiple resistance determinants.

Reduced p16 and increased cyclin D1 and pRb expression are correlated with progression in cutaneous melanocytic tumors.

The immunohistochemical expression of cell cycle proteins p16, cyclin D1, and pRb was assessed in 112 benign and malignant melanocytic tumors and correlated with tumor progression, prognosis, and outcome. Comparing benign and malignant tumors, there were significant differences in the median score for all 3 proteins, with decreased p16 (P = .000001), increased cyclin D1 (P = .01), and increased pRb in melanomas (P = .01). There was a progressive loss of expression of p16 with progression from benign naevi to primary melanomas and to metastases. p16 was significantly decreased in primary tumors from melanoma patients who developed recurrent disease (P = .0000013). Cyclin D1 and pRb showed a progressive increase in expression from benign to malignant tumors but with relative decreases in the more advanced tumors (thick primaries and metastatic melanomas). Alterations in cell cycle proteins involved in G1/S transition are implicated in melanocytic tumor progression and have a potential role in diagnosis and prognostication.

Molecular analysis of gene expression in the developing pontocerebellar projection system.

As an approach toward understanding the molecular mechanisms of neuronal differentiation, we utilized DNA microarrays to elucidate global patterns of gene expression during pontocerebellar development. Through this analysis, we identified groups of genes specific to neuronal precursor cells, associated with axon outgrowth, and regulated in response to contact with synaptic target cells. In the cerebellum, we identified a phase of granule cell differentiation that is independent of interactions with other cerebellar cell types. Analysis of pontine gene expression revealed that distinct programs of gene expression, correlated with axon outgrowth and synapse formation, can be decoupled and are likely influenced by different cells in the cerebellar target environment. Our approach provides insight into the genetic programs underlying the differentiation of specific cell types in the pontocerebellar projection system.

Prediction of alternatively skipped exons and splicing enhancers from exon junction arrays.

BACKGROUND: Alternative splicing of exons in a pre-mRNA transcript is an important mechanism which contributes to protein diversity in human. Arrays for detecting alternative splicing are available using several different probe designs, including those based on exon-junctions. In this work, we introduce a new method for predicting alternatively skipped exons from exon-junction arrays. Predictions based on our method are compared against controls and their sequences are analyzed to identify motifs important for regulating alternative splicing. RESULTS: Our comparison of several alternative methods shows that an exon-skipping score based on neighboring junctions best discriminates between positive and negative controls. Sequence analysis of our predicted exons confirms the presence of known splicing regulatory sequences. In addition, we also derive a set of development-related alternatively spliced genes based on fetal versus adult tissue comparisons and find that our predictions are consistent with their functional annotations. Ab initio motif finding algorithms are applied to identify several motifs that may be relevant for splicing during development. CONCLUSION: This work describes a new method for analyzing exon-junction arrays, identifies sequence motifs that are specific for alternative and constitutive splicing and suggests a role for several known splicing factors and their motifs in developmental regulation.

Stepwise normalization of two-channel spotted microarrays.

Intensities measurements of spotted microarrays embody many undesirable systematic variations. Very commonly, varying amounts and types of such variations are observed in different arrays. Although various normalization methods have been proposed to remove such systematic effects, it has not been well studied how to assess or select the most appropriate method for different arrays and data sets. To address this issue, we present a novel normalization technique, STEPNORM, for data-dependent and adaptive normalization of two-channel spotted microarrays. STEPNORM performs a stepwise interrogation of a range of different normalization models and selects the appropriate method based on formal model selection criteria. In addition, we evaluate the effectiveness of STEPNORM and other commonly used normalization methods utilizing a set of specially constructed splicing arrays.

Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.

There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is often used to force the distribution of the intensity log ratios to have a median of zero for each slide. However, such global normalization approaches are not adequate in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. This article proposes normalization methods that are based on robust local regression and account for intensity and spatial dependence in dye biases for different types of cDNA microarray experiments. The selection of appropriate controls for normalization is discussed and a novel set of controls (microarray sample pool, MSP) is introduced to aid in intensity-dependent normalization. Lastly, to allow for comparisons of expression levels across slides, a robust method based on maximum likelihood estimation is proposed to adjust for scale differences among slides.

MicroRNA and mRNA expression profiling in metastatic melanoma reveal associations with BRAF mutation and patient prognosis.

The role of microRNAs (miRNAs) in melanoma is unclear. We examined global miRNA expression profiles in fresh-frozen metastatic melanomas in relation to clinical outcome and BRAF mutation, with validation in independent cohorts of tumours and sera. We integrated miRNA and mRNA information from the same samples and elucidated networks associated with outcome and mutation. Associations with prognosis were replicated for miR-150-5p, miR-142-3p and miR-142-5p. Co-analysis of miRNA and mRNA uncovered a network associated with poor prognosis (PP) that paradoxically favoured expression of miRNAs opposing tumorigenesis. These miRNAs are likely part of an autoregulatory response to oncogenic drivers, rather than drivers themselves. Robust association of miR-150-5p and the miR-142 duplex with good prognosis and earlier stage metastatic melanoma supports their potential as biomarkers. miRNAs overexpressed in association with PP in an autoregulatory fashion will not be suitable therapeutic targets.

Differential gene expression by integrin beta 7+ and beta 7- memory T helper cells.

BACKGROUND: The cell adhesion molecule integrin alpha 4 beta 7 helps direct the migration of blood lymphocytes to the intestine and associated lymphoid tissues. We hypothesized that beta 7+ and beta 7- blood memory T helper cells differ in their expression of genes that play a role in the adhesion or migration of T cells. RESULTS: RNA was prepared from beta 7+ and beta 7- CD4+ CD45RA- blood T cells from nine normal human subjects and analyzed using oligonucleotide microarrays. Of 21357 genes represented on the arrays, 16 were more highly expressed in beta 7+ cells and 18 were more highly expressed in beta 7- cells (>/=1.5 fold difference and adjusted P < 0.05). Several of the differentially expressed transcripts encode proteins with established or putative roles in lymphocyte adhesion and chemotaxis, including the chemokine receptors CCR9 and CCR10, the integrin alpha 4 subunit, L-selectin, KLRB1 (CD161), NT5E (CD73), LGALS1 and LGALS2 (galectin-1 and -2), and RGS1. Flow cytometry was used to determine whether differences in levels of transcripts encoding cell surface proteins were associated with differential expression of those proteins. Using this approach, we found that surface expression of KLRB1, LAIR1, and NT5E proteins was higher on beta 7+ memory/effector T cells than on beta 7- cells. CONCLUSIONS: Memory/effector T cells that express integrin beta 7 have a distinct pattern of expression of a set of gene transcripts. Several of these molecules can affect cell adhesion or chemotaxis and are therefore likely to modulate the complex multistep process that regulates trafficking of CD4+ memory T cell subsets with different homing behaviors.