Meta-analysis of sample-level dbGaP data reveals novel shared genetic link between body height and Crohn's disease.

To further explore genetic links between complex traits, we developed a comprehensive framework to harmonize and integrate extensive genotype and phenotype data from the four well-characterized cohorts with the focus on cardiometabolic diseases deposited to the database of Genotypes and Phenotypes (dbGaP). We generated a series of polygenic risk scores (PRS) to investigate pleiotropic effects of loci that confer genetic risk for 19 common diseases and traits on body height, type 2 diabetes (T2D), and myocardial infarction (MI). In a meta-analysis of 20,021 subjects, we identified shared genetic determinants of Crohn's Disease (CD), a type of inflammatory bowel disease, and body height (p = 5.5 × 10-5). The association of PRS-CD with height was replicated in UK Biobank (p = 1.1 × 10-5) and an independent cohort of 510 CD cases and controls (1.57 cm shorter height per PRS-CD interquartile increase, p = 5.0 × 10-3 and a 28% reduction in CD risk per interquartile increase in PRS-height, p = 1.1 × 10-3, with the effect independent of CD diagnosis). A pathway analysis of the variants overlapping between PRS-height and PRS-CD detected significant enrichment of genes from the inflammatory, immune-mediated and growth factor regulation pathways. This finding supports the clinical observation of growth failure in patients with childhood-onset CD and demonstrates the value of using individual-level data from dbGaP in searching for shared genetic determinants. This information can help provide a refined insight into disease pathogenesis and may have major implications for novel therapies and drug repurposing.

Comparison of phosphorylation patterns across eukaryotes by discriminative N-gram analysis.

BACKGROUND: How protein phosphorylation relates to kingdom/phylum divergence is largely unknown and the amino acid residues surrounding the phosphorylation site have profound importance on protein kinase-substrate interactions. Standard motif analysis is not adequate for large scale comparative analysis because each phophopeptide is assigned to a unique motif and perform poorly with the unbalanced nature of the input datasets. RESULTS: First the discriminative n-grams of five species from five different kingdom/phyla were identified. A signature with 5540 discriminative n-grams that could be found in other species from the same kingdoms/phyla was created. Using a test data set, the ability of the signature to classify species in their corresponding kingdom/phylum was confirmed using classification methods. Lastly, ortholog proteins among proteins with n-grams were identified in order to determine to what degree was the identity of the detected n-grams a property of phosphosites rather than a consequence of species-specific or kingdom/phylum-specific protein inventory. The motifs were grouped in clusters of equal physico-chemical nature and their distribution was similar between species in the same kingdom/phylum while clear differences were found among species of different kingdom/phylum. For example, the animal-specific top discriminative n-grams contained many basic amino acids and the plant-specific motifs were mainly acidic. Secondary structure prediction methods show that the discriminative n-grams in the majority of the cases lack from a regular secondary structure as on average they had 88% of random coil compared to 66% found in the phosphoproteins they were derived from. CONCLUSIONS: The discriminative n-grams were able to classify organisms in their corresponding kingdom/phylum, they show different patterns among species of different kingdom/phylum and these regions can contribute to evolutionary divergence as they are in disordered regions that can evolve rapidly. The differences found possibly reflect group-specific differences in the kinomes of the different groups of species.

The MORPH-R web server and software tool for predicting missing genes in biological pathways.

A biological pathway is the set of molecular entities involved in a given biological process and the interrelations among them. Even though biological pathways have been studied extensively, discovering missing genes in pathways remains a fundamental challenge. Here, we present an easy-to-use tool that allows users to run MORPH (MOdule-guided Ranking of candidate PatHway genes), an algorithm for revealing missing genes in biological pathways, and demonstrate its capabilities. MORPH supports the analysis in tomato, Arabidopsis and the two new species: rice and the newly sequenced potato genome. The new tool, called MORPH-R, is available both as a web server (at http://bioinformatics.psb.ugent.be/webtools/morph/) and as standalone software that can be used locally. In the standalone version, the user can apply the tool to new organisms using any proprietary and public data sources.

Evaluation and integration of functional annotation pipelines for newly sequenced organisms: the potato genome as a test case.

BACKGROUND: For most organisms, even if their genome sequence is available, little functional information about individual genes or proteins exists. Several annotation pipelines have been developed for functional analysis based on sequence, 'omics', and literature data. However, researchers encounter little guidance on how well they perform. Here, we used the recently sequenced potato genome as a case study. The potato genome was selected since its genome is newly sequenced and it is a non-model plant even if there is relatively ample information on individual potato genes, and multiple gene expression profiles are available. RESULTS: We show that the automatic gene annotations of potato have low accuracy when compared to a "gold standard" based on experimentally validated potato genes. Furthermore, we evaluate six state-of-the-art annotation pipelines and show that their predictions are markedly dissimilar (Jaccard similarity coefficient of 0.27 between pipelines on average). To overcome this discrepancy, we introduce a simple GO structure-based algorithm that reconciles the predictions of the different pipelines. We show that the integrated annotation covers more genes, increases by over 50% the number of highly co-expressed GO processes, and obtains much higher agreement with the gold standard. CONCLUSIONS: We find that different annotation pipelines produce different results, and show how to integrate them into a unified annotation that is of higher quality than each single pipeline. We offer an improved functional annotation of both PGSC and ITAG potato gene models, as well as tools that can be applied to additional pipelines and improve annotation in other organisms. This will greatly aid future functional analysis of '-omics' datasets from potato and other organisms with newly sequenced genomes. The new potato annotations are available with this paper.

Fatty liver and fibrosis in glycine N-methyltransferase knockout mice is prevented by nicotinamide.

UNLABELLED: Deletion of glycine N-methyltransferase (GNMT), the main gene involved in liver S-adenosylmethionine (SAM) catabolism, leads to the hepatic accumulation of this molecule and the development of fatty liver and fibrosis in mice. To demonstrate that the excess of hepatic SAM is the main agent contributing to liver disease in GNMT knockout (KO) mice, we treated 1.5-month-old GNMT-KO mice for 6 weeks with nicotinamide (NAM), a substrate of the enzyme NAM N-methyltransferase. NAM administration markedly reduced hepatic SAM content, prevented DNA hypermethylation, and normalized the expression of critical genes involved in fatty acid metabolism, oxidative stress, inflammation, cell proliferation, and apoptosis. More importantly, NAM treatment prevented the development of fatty liver and fibrosis in GNMT-KO mice. Because GNMT expression is down-regulated in patients with cirrhosis, and because some subjects with GNMT mutations have spontaneous liver disease, the clinical implications of the present findings are obvious, at least with respect to these latter individuals. Because NAM has been used for many years to treat a broad spectrum of diseases (including pellagra and diabetes) without significant side effects, it should be considered in subjects with GNMT mutations. CONCLUSION: The findings of this study indicate that the anomalous accumulation of SAM in GNMT-KO mice can be corrected by NAM treatment leading to the normalization of the expression of many genes involved in fatty acid metabolism, oxidative stress, inflammation, cell proliferation, and apoptosis, as well as reversion of the appearance of the pathologic phenotype.

Genome Scale Modeling to Study the Metabolic Competition between Cells in the Tumor Microenvironment.

The tumor's physiology emerges from the dynamic interplay of numerous cell types, such as cancer cells, immune cells and stromal cells, within the tumor microenvironment. Immune and cancer cells compete for nutrients within the tumor microenvironment, leading to a metabolic battle between these cell populations. Tumor cells can reprogram their metabolism to meet the high demand of building blocks and ATP for proliferation, and to gain an advantage over the action of immune cells. The study of the metabolic reprogramming mechanisms underlying cancer requires the quantification of metabolic fluxes which can be estimated at the genome-scale with constraint-based or kinetic modeling. Constraint-based models use a set of linear constraints to simulate steady-state metabolic fluxes, whereas kinetic models can simulate both the transient behavior and steady-state values of cellular fluxes and concentrations. The integration of cell- or tissue-specific data enables the construction of context-specific models that reflect cell-type- or tissue-specific metabolic properties. While the available modeling frameworks enable limited modeling of the metabolic crosstalk between tumor and immune cells in the tumor stroma, future developments will likely involve new hybrid kinetic/stoichiometric formulations.

Systems Pharmacology Identifies an Arterial Wall Regulatory Gene Network Mediating Coronary Artery Disease Side Effects of Antiretroviral Therapy.

BACKGROUND: Antiretroviral therapy (ART) for HIV infection increases risk for coronary artery disease (CAD), presumably by causing dyslipidemia and increased atherosclerosis. We applied systems pharmacology to identify and validate specific regulatory gene networks through which ART drugs may promote CAD. METHODS: Transcriptional responses of human cell lines to 15 ART drugs retrieved from the Library of Integrated Cellular Signatures (overall 1127 experiments) were used to establish consensus ART gene/transcriptional signatures. Next, enrichments of differentially expressed genes and gene-gene connectivity within these ART-consensus signatures were sought in 30 regulatory gene networks associated with CAD and CAD-related phenotypes in the Stockholm Atherosclerosis Gene Expression study. RESULTS: Ten of 15 ART signatures were significantly enriched both for differential expression and connectivity in a specific atherosclerotic arterial wall regulatory gene network (AR-RGN) causal for CAD involving RNA processing genes. An atherosclerosis in vitro model of cholestryl ester-loaded foam cells was then used for experimental validation. Treatments of these foam cells with ritonavir, nelfinavir, and saquinavir at least doubled cholestryl ester accumulation ( P=0.02, 0.0009, and 0.02, respectively), whereas RNA silencing of the AR-RGN top key driver, PQBP1 (polyglutamine binding protein 1), significantly curbed cholestryl ester accumulation following treatment with any of these ART drugs by >37% ( P<0.05). CONCLUSIONS: By applying a novel systems pharmacology data analysis framework, 3 commonly used ARTs (ritonavir, nelfinavir, and saquinavir) were found altering the activity of AR-RGN, a regulatory gene network promoting foam cell formation and risk of CAD. Targeting AR-RGN or its top key driver PQBP1 may help reduce CAD side effects of these ART drugs.

Phytophthora infestans specific phosphorylation patterns and new putative control targets.

In this study we applied biomathematical searches of gene regulatory mechanisms to learn more about oomycete biology and to identify new putative targets for pesticides or biological control against Phytophthora infestans. First, oomycete phylum-specific phosphorylation motifs were found by discriminative n-gram analysis. We found 11.600 P. infestans specific n-grams, mapping 642 phosphoproteins. The most abundant group among these related to phosphatidylinositol metabolism. Due to the large number of possible targets found and our hypothesis that multi-level control is a sign of usefulness as targets for intervention, we identified overlapping targets with a second screen. This was performed to identify proteins dually regulated by small RNA and phosphorylation. We found 164 proteins to be regulated by both sRNA and phosphorylation and the dominating functions where phosphatidylinositol signalling/metabolism, endocytosis, and autophagy. Furthermore we performed a similar regulatory study and discriminative n-gram analysis of proteins with no clear orthologs in other species and proteins that are known to be unique to P. infestans such as the RxLR effectors, Crinkler (CRN) proteins and elicitins. We identified CRN proteins with specific phospho-motifs present in all life stages. PITG_12626, PITG_14042 and PITG_23175 are CRN proteins that have species-specific phosphorylation motifs and are subject to dual regulation.

A novel workflow correlating RNA-seq data to Phythophthora infestans resistance levels in wild Solanum species and potato clones.

Comparative transcriptomics between species can provide valuable understanding of plant-pathogen interactions. Here, we focus on wild Solanum species and potato clones with varying degree of resistance against Phytophthora infestans, which causes the devastating late blight disease in potato. The transcriptomes of three wild Solanum species native to Southern Sweden, Solanum dulcamara, Solanum nigrum, and Solanum physalifolium were compared to three potato clones, Desiree (cv.), SW93-1015 and Sarpo Mira. Desiree and S. physalifolium are susceptible to P. infestans whereas the other four have different degrees of resistance. By building transcript families based on de novo assembled RNA-seq across species and clones and correlating these to resistance phenotypes, we created a novel workflow to identify families with expanded or depleted number of transcripts in relation to the P. infestans resistance level. Analysis was facilitated by inferring functional annotations based on the family structure and semantic clustering. More transcript families were expanded in the resistant clones and species and the enriched functions of these were associated to expected gene ontology (GO) terms for resistance mechanisms such as hypersensitive response, host programmed cell death and endopeptidase activity. However, a number of unexpected functions and transcripts were also identified, for example transmembrane transport and protein acylation expanded in the susceptible group and a cluster of Zinc knuckle family proteins expanded in the resistant group. Over 400 expressed putative resistance (R-)genes were identified and resistant clones Sarpo Mira and SW93-1015 had ca 25% more expressed putative R-genes than susceptible cultivar Desiree. However, no differences in numbers of susceptibility (S-)gene homologs were seen between species and clones. In addition, we identified P. infestans transcripts including effectors in the early stages of P. infestans-Solanum interactions.

Integrative genomic signatures of hepatocellular carcinoma derived from nonalcoholic Fatty liver disease.

Nonalcoholic fatty liver disease (NAFLD) is a risk factor for Hepatocellular carcinoma (HCC), but he transition from NAFLD to HCC is poorly understood. Feature selection algorithms in human and genetically modified mice NAFLD and HCC microarray data were applied to generate signatures of NAFLD progression and HCC differential survival. These signatures were used to study the pathogenesis of NAFLD derived HCC and explore which subtypes of cancers that can be investigated using mouse models. Our findings show that: (I) HNF4 is a common potential transcription factor mediating the transcription of NAFLD progression genes (II) mice HCC derived from NAFLD co-cluster with a less aggressive human HCC subtype of differential prognosis and mixed etiology (III) the HCC survival signature is able to correctly classify 95% of the samples and gives Fgf20 and Tgfb1i1 as the most robust genes for prediction (IV) the expression values of genes composing the signature in an independent human HCC dataset revealed different HCC subtypes showing differences in survival time by a Logrank test. In summary, we present marker signatures for NAFLD derived HCC molecular pathogenesis both at the gene and pathway level.

Overview on techniques in cluster analysis.

Clustering is the unsupervised, semisupervised, and supervised classification of patterns into groups. The clustering problem has been addressed in many contexts and disciplines. Cluster analysis encompasses different methods and algorithms for grouping objects of similar kinds into respective categories. In this chapter, we describe a number of methods and algorithms for cluster analysis in a stepwise framework. The steps of a typical clustering analysis process include sequentially pattern representation, the choice of the similarity measure, the choice of the clustering algorithm, the assessment of the output, and the representation of the clusters.