Alterations in colorectal cancer virome and its persistence after surgery

Viruses are a key component of the colon microbiome, but the relationship between virome and colorectal cancer (CRC) remains poorly understood. We seek to identify alterations in the viral community that is characteristic of CRC and examine if they persist after surgery. Forty-nine fecal samples from 25 non-cancer (NC) individuals and 12 CRC patients, before and 6-months after surgery, were collected for metagenomic analysis. The fecal virome of CRC patients demonstrated an increased network connectivity as compared to NC individuals. Co-exclusion of influential viruses to bacterial species associated with healthy gut status was observed in CRC, suggesting an altered virome induced a change in the healthy gut bacteriome. Network analysis revealed lower connectivity within the virome and trans-kingdom interactions in NC. After surgery, the number of strong correlations decreased for trans-kingdom and within the bacteria and virome networks, indicating lower connectivity within the microbiome. Some co-occurrence patterns between dominant viruses and bacteria were also lost after surgery, suggesting a possible return to the healthy state of gut microbiome. Microbial signatures characteristic of CRC include an altered virome besides an altered bacterial composition. Elevated viral correlations and network connectivity were observed in CRC patients relative to healthy individuals, alongside distinct changes in the cross-kingdom correlation network unique to CRC patients. Some patterns of dysbiosis persist after surgery. Future studies should seek to verify if dysbiosis truly persists after surgery in a larger sample size with microbiome data collected at various time points after surgery to explore if there is field-change in the remaining colon, as well as to examine if persistent dysbiosis correlates with patient outcomes.

Annexin-A1 deficiency attenuates stress-induced tumor growth via fatty acid metabolism in mice: an Integrated multiple omics analysis on the stress- microbiome-metabolite-epigenetic-oncology (SMMEO) axis

Background: High emotional or psychophysical stress levels have been correlated with an increased risk and progression of various diseases. How stress impacts the gut microbiota to influence metabolism and subsequent cancer progression is unclear.

Methods: Feces and serum samples from BALB/c ANXA1^+/+ and ANXA1^-/- mice with or without chronic restraint stress were used for 16S rRNA gene sequencing and GC-MS metabolomics analysis to investigate the effect of stress on microbiome and metabolomics during stress and breast tumorigenesis. Breast tumors samples from stressed and non-stressed mice were used to perform Whole-Genome Bisulfite Sequencing (WGBS) and RNAseq analysis to construct the potential network from candidate hub genes. Finally, machine learning and integrated analysis were used to map the axis from chronic restraint stress to breast cancer development.

Results: We report that chronic stress promotes breast tumor growth via a stress-microbiome-metabolite-epigenetic-oncology (SMMEO) axis. Chronic restraint stress in mice alters the microbiome composition and fatty acids metabolism and induces an epigenetic signature in tumors xenografted after stress. Subsequent machine learning and systemic modeling analyses identified a significant correlation among microbiome composition, metabolites, and differentially methylated regions in stressed tumors. Moreover, silencing Annexin-A1 inhibits the changes in the gut microbiome and fatty acid metabolism after stress as well as basal and stress-induced tumor growth.

Conclusions: These data support a physiological axis linking the microbiome and metabolites to cancer epigenetics and inflammation. The identification of this axis could propel the next phase of experimental discovery in further understanding the underlying molecular mechanism of tumorigenesis caused by physiological stress.

MOLGENIS research: advanced bioinformatics data software for non-bioinformaticians

MOTIVATION

The volume and complexity of biological data increases rapidly. Many clinical professionals and biomedical researchers without a bioinformatics background are generating big '-omics' data, but do not always have the tools to manage, process or publicly share these data.

RESULTS

Here we present MOLGENIS Research, an open-source web-application to collect, manage, analyze, visualize and share large and complex biomedical datasets, without the need for advanced bioinformatics skills.

AVAILABILITY AND IMPLEMENTATION

MOLGENIS Research is freely available (open source software). It can be installed from source code (see http://github.com/molgenis), downloaded as a precompiled WAR file (for your own server), setup inside a Docker container (see http://molgenis.github.io), or requested as a Software-as-a-Service subscription. For a public demo instance and complete installation instructions see http://molgenis.org/research.

ZDOG: zooming in on dominating genes with mutations in cancer pathways

Background

Inference of cancer-causing genes and their biological functions are crucial but challenging due to the heterogeneity of somatic mutations. The heterogeneity of somatic mutations reveals that only a handful of oncogenes mutate frequently and a number of cancer-causing genes mutate rarely.

Results

We develop a Cytoscape app, named ZDOG, for visualization of the extent to which mutated genes may affect cancer pathways using the dominating tree model. The dominator tree model allows us to examine conveniently the positional importance of a gene in cancer signalling pathways. This tool facilitates the identification of mutated “master” regulators even with low mutation frequency in deregulated signalling pathways.

Conclusions

We have presented a model for facilitating the examination of the extent to which mutation in a gene may affect downstream components in a signalling pathway through its positional information. The model is implemented in a user-friendly Cytoscape app which will be freely available upon publication.

Availability

Together with a user manual, the ZDOG app is freely available at GitHub (https://github.com/rudi2013/ZDOG). It is also available in the Cytoscape app store (http://apps.cytoscape.org/apps/ZDOG) and users can easily install it using the Cytoscape App Manager.

Gut microbiota composition and functional changes in inflammatory bowel disease and irritable bowel syndrome

Changes in the gut microbiota have been associated with two of the most common gastrointestinal diseases, inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS). Here, we performed a case-control analysis using shotgun metagenomic sequencing of stool samples from 1792 individuals with IBD and IBS compared with control individuals in the general population. Despite substantial overlap between the gut microbiome of patients with IBD and IBS compared with control individuals, we were able to use gut microbiota composition differences to distinguish patients with IBD from those with IBS. By combining species-level profiles and strain-level profiles with bacterial growth rates, metabolic functions, antibiotic resistance, and virulence factor analyses, we identified key bacterial species that may be involved in two common gastrointestinal diseases.

The 1000IBD project: multi-omics data of 1000 inflammatory bowel disease patients; data release 1

BACKGROUND

Inflammatory bowel disease (IBD) is a chronic complex disease of the gastrointestinal tract. Patients with IBD can experience a wide range of symptoms, but the pathophysiological mechanisms that cause these individual differences in clinical presentation remain largely unknown. In consequence, IBD is currently classified into subtypes using clinical characteristics. If we are to develop a more targeted treatment approach, molecular subtypes of IBD need to be discovered that can be used as new drug targets. To achieve this, we need multiple layers of molecular data generated from the same IBD patients.

CONSTRUCTION AND CONTENT

We initiated the 1000IBD project ( https://1000ibd.org ) to prospectively follow more than 1000 IBD patients from the Northern provinces of the Netherlands. For these patients, we have collected a uniquely large number of phenotypes and generated multi-omics profiles. To date, 1215 participants have been enrolled in the project and enrolment is on-going. Phenotype data collected for these participants includes information on dietary and environmental factors, drug responses and adverse drug events. Genome information has been generated using genotyping (ImmunoChip, Global Screening Array and HumanExomeChip) and sequencing (whole exome sequencing and targeted resequencing of IBD susceptibility loci), transcriptome information generated using RNA-sequencing of intestinal biopsies and microbiome information generated using both sequencing of the 16S rRNA gene and whole genome shotgun metagenomic sequencing.

UTILITY AND DISCUSSION

All molecular data generated within the 1000IBD project will be shared on the European Genome-Phenome Archive ( https://ega-archive.org , accession no: EGAS00001002702). The first data release, detailed in this announcement and released simultaneously with this publication, will contain basic phenotypes for 1215 participants, genotypes of 314 participants and gut microbiome data from stool samples (315 participants) and biopsies (107 participants) generated by tag sequencing the 16S gene. Future releases will comprise many more additional phenotypes and -omics data layers. 1000IBD data can be used by other researchers as a replication cohort, a dataset to test new software tools, or a dataset for applying new statistical models.

CONCLUSIONS

We report on the establishment and future development of the 1000IBD project: the first comprehensive multi-omics dataset aimed at discovering IBD biomarker profiles and treatment targets.

Genetic association analysis identifies variants associated with disease progression in primary sclerosing cholangitis

OBJECTIVE

Primary sclerosing cholangitis (PSC) is a genetically complex, inflammatory bile duct disease of largely unknown aetiology often leading to liver transplantation or death. Little is known about the genetic contribution to the severity and progression of PSC. The aim of this study is to identify genetic variants associated with PSC disease progression and development of complications.

DESIGN

We collected standardised PSC subphenotypes in a large cohort of 3402 patients with PSC. After quality control, we combined 130 422 single nucleotide polymorphisms of all patients-obtained using the Illumina immunochip-with their disease subphenotypes. Using logistic regression and Cox proportional hazards models, we identified genetic variants associated with binary and time-to-event PSC subphenotypes.

RESULTS

We identified genetic variant rs853974 to be associated with liver transplant-free survival (p=6.07×10^-9). Kaplan-Meier survival analysis showed a 50.9% (95% CI 41.5% to 59.5%) transplant-free survival for homozygous AA allele carriers of rs853974 compared with 72.8% (95% CI 69.6% to 75.7%) for GG carriers at 10 years after PSC diagnosis. For the candidate gene in the region, RSPO3, we demonstrated expression in key liver-resident effector cells, such as human and murine cholangiocytes and human hepatic stellate cells.

CONCLUSION

We present a large international PSC cohort, and report genetic loci associated with PSC disease progression. For liver transplant-free survival, we identified a genome-wide significant signal and demonstrated expression of the candidate gene RSPO3 in key liver-resident effector cells. This warrants further assessments of the role of this potential key PSC modifier gene.

Genomic and Expression Analyses Identify a Disease-Modifying Variant for Fibrostenotic Crohn's Disease

BACKGROUND AND AIMS

Crohn's disease [CD] is a chronic inflammatory disease with unpredictable behaviour. More than half of CD patients eventually develop complications such as stenosis, for which they then require endoscopic dilatation or surgery, as no anti-fibrotic drugs are currently available. We aim to identify disease-modifying genes associated with fibrostenotic CD.

METHODS

We performed a within-case analysis comparing 'extreme phenotypes' using the Immunochip and replication of the top single nucleotide polymorphisms [SNPs] with Agena Bioscience in two independent case-control cohorts totalling 322 cases with fibrostenotis [recurrent after surgery] and 619 cases with purely inflammatory CD.

RESULTS

Combined meta-analysis resulted in a genome-wide significant signal for SNP rs11861007 [p = 6.0910-11], located on chromosome 16, in lncRNA RP11-679B19.1, an lncRNA of unknown function, and close to exon 9 of the WWOX gene, which codes for WW domain-containing oxidoreductase. We analysed mRNA expression of TGF-β and downstream genes in ileocecal resection material from ten patients with and without the WWOX risk allele. Patients carrying the risk allele [A] showed enhanced colonic expression of TGF-β compared to patients homozygous for the wild-type [G] allele [p = 0.0079].

CONCLUSION

We have identified a variant in WWOX and in lncRNA RP11-679B19.1 as a disease-modifying genetic variant associated with recurrent fibrostenotic CD and replicated this association in an independent cohort. WWOX can potentially play a crucial role in fibrostenosis in CD, being positioned at the crossroads of inflammation and fibrosis.

Interplay of host genetics and gut microbiota underlying the onset and clinical presentation of inflammatory bowel disease

OBJECTIVE

Patients with IBD display substantial heterogeneity in clinical characteristics. We hypothesise that individual differences in the complex interaction of the host genome and the gut microbiota can explain the onset and the heterogeneous presentation of IBD. Therefore, we performed a case-control analysis of the gut microbiota, the host genome and the clinical phenotypes of IBD.

DESIGN

Stool samples, peripheral blood and extensive phenotype data were collected from 313 patients with IBD and 582 truly healthy controls, selected from a population cohort. The gut microbiota composition was assessed by tag-sequencing the 16S rRNA gene. All participants were genotyped. We composed genetic risk scores from 11 functional genetic variants proven to be associated with IBD in genes that are directly involved in the bacterial handling in the gut: NOD2, CARD9, ATG16L1, IRGM and FUT2.

RESULTS

Strikingly, we observed significant alterations of the gut microbiota of healthy individuals with a high genetic risk for IBD: the IBD genetic risk score was significantly associated with a decrease in the genus Roseburia in healthy controls (false discovery rate 0.017). Moreover, disease location was a major determinant of the gut microbiota: the gut microbiota of patients with colonic Crohn's disease (CD) is different from that of patients with ileal CD, with a decrease in alpha diversity associated to ileal disease (p=3.28×10^-13).

CONCLUSIONS

We show for the first time that genetic risk variants associated with IBD influence the gut microbiota in healthy individuals. Roseburia spp are acetate-to-butyrate converters, and a decrease has already been observed in patients with IBD.

Pooled Resequencing of 122 Ulcerative Colitis Genes in a Large Dutch Cohort Suggests Population-Specific Associations of Rare Variants in MUC2

Genome-wide association studies have revealed several common genetic risk variants for ulcerative colitis (UC). However, little is known about the contribution of rare, large effect genetic variants to UC susceptibility. In this study, we performed a deep targeted re-sequencing of 122 genes in Dutch UC patients in order to investigate the contribution of rare variants to the genetic susceptibility to UC. The selection of genes consists of 111 established human UC susceptibility genes and 11 genes that lead to spontaneous colitis when knocked-out in mice. In addition, we sequenced the promoter regions of 45 genes where known variants exert cis-eQTL-effects. Targeted pooled re-sequencing was performed on DNA of 790 Dutch UC cases. The Genome of the Netherlands project provided sequence data of 500 healthy controls. After quality control and prioritization based on allele frequency and pathogenicity probability, follow-up genotyping of 171 rare variants was performed on 1021 Dutch UC cases and 1166 Dutch controls. Single-variant association and gene-based analyses identified an association of rare variants in the MUC2 gene with UC. The associated variants in the Dutch population could not be replicated in a German replication cohort (1026 UC cases, 3532 controls). In conclusion, this study has identified a putative role for MUC2 on UC susceptibility in the Dutch population and suggests a population-specific contribution of rare variants to UC.

Performance of the Montreal classification for inflammatory bowel diseases

AIM: To validate the Montreal classification system for Crohn’s disease (CD) and ulcerative colitis (UC) within the Netherlands.

METHODS: A selection of 20 de-identified medical records with an appropriate representation of the inflammatory bowel disease (IBD) sub phenotypes were scored by 30 observers with different professions (gastroenterologist specialist in IBD, gastroenterologist in training and IBD-nurses) and experience level with IBD patient care. Patients were classified according to the Montreal classification. In addition, participants were asked to score extra-intestinal manifestations (EIM) and disease severity in CD based on their clinical judgment. The inter-observer agreement was calculated by percentages of correct answers (answers identical to the “expert evaluation”) and Fleiss-kappa (κ). Kappa cut-offs: < 0.4-poor; 0.41-0.6-moderate; 0.61-0.8-good; > 0.8 excellent.

RESULTS: The inter-observer agreement was excellent for diagnosis (κ = 0.96), perianal disease (κ = 0.92) and disease location in CD (κ = 0.82) and good for age of onset (κ = 0.67), upper gastrointestinal disease (κ = 0.62), disease behaviour in CD (κ = 0.79) and disease extent in UC (κ = 0.65). Disease severity in UC was scored poor (κ = 0.23). The additional items resulted in a good inter-observer agreement for EIM (κ = 0.68) and a moderate agreement for disease severity in CD (κ = 0.44). Percentages of correct answers over all Montreal items give a good reflection of the inter-observer agreement (> 80%), except for disease severity (48%-74%). IBD-nurses were significantly worse in scoring upper gastrointestinal disease in CD compared to gastroenterologists (P = 0.008) and gastroenterologists in training (P = 0.040). Observers with less than 10 years of experience were significantly better at scoring UC severity than observers with 10-20 years (P = 0.003) and more than 20 years (P = 0.003) of experience with IBD patient care. Observers with 10-20 years of experience with IBD patient care were significantly better at scoring upper gastrointestinal disease in CD than observers with less than 10 years (P = 0.007) and more than 20 years (P = 0.007) of experience with IBD patient care.

CONCLUSION: We found a good to excellent inter-observer agreement for all Montreal items except for disease severity in UC (poor).

Analysis of the lung transcriptome in Mycobacterium tuberculosis-infected mice reveals major differences in immune response pathways between TB-susceptible and resistant hosts

Using whole genome microarrays, we compared changes in gene expression patterns in the lungs of TB-resistant A/Sn and TB-susceptible I/St mice at day 14 following infection with Mycobacterium tuberculosis H37Rv. Analyses of differentially expressed genes for representation of gene ontology terms and activation of regulatory pathways revealed interstrain differences in antigen presentation, NK, T and B cell activation pathways. In general, resistant A/Sn mice exhibited a more complex pattern and stronger activation of host defense pathways compared to the TB-susceptible I/St mouse strain. In addition, in I/St mice elevated activation of genes involved in neutrophil response was observed and confirmed by quantitative RT-PCR and histopathology. Furthermore, a specific post infection upregulation of cysteine protease inhibitors was found in susceptible I/St mice.

Distinct gene loci control the host response to influenza H1N1 virus infection in a time-dependent manner

Background

There is strong but mostly circumstantial evidence that genetic factors modulate the severity of influenza infection in humans. Using genetically diverse but fully inbred strains of mice it has been shown that host sequence variants have a strong influence on the severity of influenza A disease progression. In particular, C57BL/6J, the most widely used mouse strain in biomedical research, is comparatively resistant. In contrast, DBA/2J is highly susceptible.

Results

To map regions of the genome responsible for differences in influenza susceptibility, we infected a family of 53 BXD-type lines derived from a cross between C57BL/6J and DBA/2J strains with influenza A virus (PR8, H1N1). We monitored body weight, survival, and mean time to death for 13 days after infection. Qivr5 (quantitative trait for influenza virus resistance on chromosome 5) was the largest and most significant QTL for weight loss. The effect of Qivr5 was detectable on day 2 post infection, but was most pronounced on days 5 and 6. Survival rate mapped to Qivr5, but additionally revealed a second significant locus on chromosome 19 (Qivr19). Analysis of mean time to death affirmed both Qivr5 and Qivr19. In addition, we observed several regions of the genome with suggestive linkage. There are potentially complex combinatorial interactions of the parental alleles among loci. Analysis of multiple gene expression data sets and sequence variants in these strains highlights about 30 strong candidate genes across all loci that may control influenza A susceptibility and resistance.

Conclusions

We have mapped influenza susceptibility loci to chromosomes 2, 5, 16, 17, and 19. Body weight and survival loci have a time-dependent profile that presumably reflects the temporal dynamic of the response to infection. We highlight candidate genes in the respective intervals and review their possible biological function during infection.

Expression QTL mapping in regulatory and helper T cells from the BXD family of strains reveals novel cell-specific genes, gene-gene interactions and candidate genes for auto-immune disease

BACKGROUND

Regulatory T cells (Tregs) play an essential role in the control of the immune response. Treg cells represent important targets for therapeutic interventions of the immune system. Therefore, it will be very important to understand in more detail which genes are specifically activated in Treg cells versus T helper (Th) cells, and which gene regulatory circuits may be involved in specifying and maintaining Treg cell homeostasis.

RESULTS

We isolated Treg and Th cells from a genetically diverse family of 31 BXD type recombinant inbred strains and the fully inbred parental strains of this family--C57BL/6J and DBA/2J. Subsequently genome-wide gene expression studies were performed from the isolated Treg and Th cells. A comparative analysis of the transcriptomes of these cell populations allowed us to identify many novel differentially expressed genes. Analysis of cis- and trans-expression Quantitative Trait Loci (eQTLs) highlighted common and unique regulatory mechanisms that are active in the two cell types. Trans-eQTL regions were found for the Treg functional genes Nrp1, Stat3 and Ikzf4. Analyses of the respective QTL intervals suggested several candidate genes that may be involved in regulating these genes in Treg cells. Similarly, possible candidate genes were found which may regulate the expression of F2rl1, Ctla4, Klrb1f. In addition, we identified a focused group of candidate genes that may be important for the maintenance of self-tolerance and the prevention of allergy.

CONCLUSIONS

Variation of expression across the strains allowed us to find many novel gene-interaction networks in both T cell subsets. In addition, these two data sets enabled us to identify many differentially expressed genes and to nominate candidate genes that may have important functions for the maintenance of self-tolerance and the prevention of allergy.

Bioinformatics tools and database resources for systems genetics analysis in mice--a short review and an evaluation of future needs

During a meeting of the SYSGENET working group ‘Bioinformatics’, currently available software tools and databases for systems genetics in mice were reviewed and the needs for future developments discussed. The group evaluated interoperability and performed initial feasibility studies. To aid future compatibility of software and exchange of already developed software modules, a strong recommendation was made by the group to integrate HAPPY and R/qtl analysis toolboxes, GeneNetwork and XGAP database platforms, and TIQS and xQTL processing platforms. R should be used as the principal computer language for QTL data analysis in all platforms and a ‘cloud’ should be used for software dissemination to the community. Furthermore, the working group recommended that all data models and software source code should be made visible in public repositories to allow a coordinated effort on the use of common data structures and file formats.

Genome-wide analysis of the mouse lung transcriptome reveals novel molecular gene interaction networks and cell-specific expression signatures

BACKGROUND

The lung is critical in surveillance and initial defense against pathogens. In humans, as in mice, individual genetic differences strongly modulate pulmonary responses to infectious agents, severity of lung disease, and potential allergic reactions. In a first step towards understanding genetic predisposition and pulmonary molecular networks that underlie individual differences in disease vulnerability, we performed a global analysis of normative lung gene expression levels in inbred mouse strains and a large family of BXD strains that are widely used for systems genetics. Our goal is to provide a key community resource on the genetics of the normative lung transcriptome that can serve as a foundation for experimental analysis and allow predicting genetic predisposition and response to pathogens, allergens, and xenobiotics.

METHODS

Steady-state polyA+ mRNA levels were assayed across a diverse and fully genotyped panel of 57 isogenic strains using the Affymetrix M430 2.0 array. Correlations of expression levels between genes were determined. Global expression QTL (eQTL) analysis and network covariance analysis was performed using tools and resources in GeneNetwork http://www.genenetwork.org.

RESULTS

Expression values were highly variable across strains and in many cases exhibited a high heritability factor. Several genes which showed a restricted expression to lung tissue were identified. Using correlations between gene expression values across all strains, we defined and extended memberships of several important molecular networks in the lung. Furthermore, we were able to extract signatures of immune cell subpopulations and characterize co-variation and shared genetic modulation. Known QTL regions for respiratory infection susceptibility were investigated and several cis-eQTL genes were identified. Numerous cis- and trans-regulated transcripts and chromosomal intervals with strong regulatory activity were mapped. The Cyp1a1 P450 transcript had a strong trans-acting eQTL (LOD 11.8) on Chr 12 at 36 ± 1 Mb. This interval contains the transcription factor Ahr that has a critical mis-sense allele in the DBA/2J haplotype and evidently modulates transcriptional activation by AhR.

CONCLUSIONS

Large-scale gene expression analyses in genetic reference populations revealed lung-specific and immune-cell gene expression profiles and suggested specific gene regulatory interactions.

QTLminer: identifying genes regulating quantitative traits

Background

Quantitative trait locus (QTL) mapping identifies genomic regions that likely contain genes regulating a quantitative trait. However, QTL regions may encompass tens to hundreds of genes. To find the most promising candidate genes that regulate the trait, the biologist typically collects information from multiple resources about the genes in the QTL interval. This process is very laborious and time consuming.

Results

QTLminer is a bioinformatics tool that automatically performs QTL region analysis. It is available in GeneNetwork and it integrates information such as gene annotation, gene expression and sequence polymorphisms for all the genes within a given genomic interval.

Conclusions

QTLminer substantially speeds up discovery of the most promising candidate genes within a QTL region.

Data-driven assessment of eQTL mapping methods

Background

The analysis of expression quantitative trait loci (eQTL) is a potentially powerful way to detect transcriptional regulatory relationships at the genomic scale. However, eQTL data sets often go underexploited because legacy QTL methods are used to map the relationship between the expression trait and genotype. Often these methods are inappropriate for complex traits such as gene expression, particularly in the case of epistasis.

Results

Here we compare legacy QTL mapping methods with several modern multi-locus methods and evaluate their ability to produce eQTL that agree with independent external data in a systematic way. We found that the modern multi-locus methods (Random Forests, sparse partial least squares, lasso, and elastic net) clearly outperformed the legacy QTL methods (Haley-Knott regression and composite interval mapping) in terms of biological relevance of the mapped eQTL. In particular, we found that our new approach, based on Random Forests, showed superior performance among the multi-locus methods.

Conclusions

Benchmarks based on the recapitulation of experimental findings provide valuable insight when selecting the appropriate eQTL mapping method. Our battery of tests suggests that Random Forests map eQTL that are more likely to be validated by independent data, when compared to competing multi-locus and legacy eQTL mapping methods.

XGAP: a uniform and extensible data model and software platform for genotype and phenotype experiments

XGAP, a software platform for the integration and analysis of genotype and phenotype data.

We present an extensible software model for the genotype and phenotype community, XGAP. Readers can download a standard XGAP (http://www.xgap.org) or auto-generate a custom version using MOLGENIS with programming interfaces to R-software and web-services or user interfaces for biologists. XGAP has simple load formats for any type of genotype, epigenotype, transcript, protein, metabolite or other phenotype data. Current functionality includes tools ranging from eQTL analysis in mouse to genome-wide association studies in humans.

Gene expression changes in the host response between resistant and susceptible inbred mouse strains after influenza A infection

Inbred mouse strains exhibit differences in susceptibility to influenza A infections. However, the molecular mechanisms underlying these differences are unknown. Therefore, we infected a highly susceptible mouse strain (DBA/2J) and a resistant strain (C57BL/6J) with influenza A H1N1 (PR8) and performed genome-wide expression analysis. We found genes expressed in lung epithelium that were specifically down-regulated in DBA/2J mice, whereas a cluster of genes on chromosome 3 was only down-regulated in C57BL/6J. In both mouse strains, chemokines, cytokines and interferon-response genes were up-regulated, indicating that the main innate immune defense pathways were activated. However, many immune response genes were up-regulated in DBA/2J much stronger than in C57BL/6J, and several immune response genes were exclusively regulated in DBA/2J. Thus, susceptible DBA/2J mice showed a hyper-inflammatory response. This response is similar to infections with highly pathogenic influenza virus and may serve as a paradigm for a hyper-inflammatory host response to influenza A virus.

Towards the integration of mouse databases - definition and implementation of solutions to two use-cases in mouse functional genomics

Background

The integration of information present in many disparate biological databases represents a major challenge in biomedical research. To define the problems and needs, and to explore strategies for database integration in mouse functional genomics, we consulted the biologist user community and implemented solutions to two user-defined use-cases.

Results

We organised workshops, meetings and used a questionnaire to identify the needs of biologist database users in mouse functional genomics. As a result, two use-cases were developed that can be used to drive future designs or extensions of mouse databases. Here, we present the use-cases and describe some initial computational solutions for them. The application for the gene-centric use-case, "MUSIG-Gen" starts from a list of gene names and collects a wide range of data types from several distributed databases in a "shopping cart"-like manner. The iterative user-driven approach is a response to strongly articulated requests from users, especially those without computational biology backgrounds. The application for the phenotype-centric use-case, "MUSIG-Phen", is based on a similar concept and starting from phenotype descriptions retrieves information for associated genes.

Conclusion

The use-cases created, and their prototype software implementations should help to better define biologists' needs for database integration and may serve as a starting point for future bioinformatics solutions aimed at end-user biologists.

affyGG: computational protocols for genetical genomics with Affymetrix arrays

MOTIVATION

Affymetrix arrays use multiple probes per gene to measure mRNA abundances. Standard software takes averages over probes. Important information may be lost if polymorphisms in the mRNA affect the hybridization of individual probes.

RESULTS

We present custom software to analyze genetical genomics experiments in human, mouse and other organisms: (i) an R package providing functions for QTL analysis at the individual probe level and (ii) Perl scripts providing custom tracks in the UCSC Genome Browser to check for sequence polymorphisms in probe regions.

AVAILABILITY

http://gbic.biol.rug.nl/supplementary.

Sequence polymorphisms cause many false cis eQTLs

Many investigations have reported the successful mapping of quantitative trait loci (QTLs) for gene expression phenotypes (eQTLs). Local eQTLs, where expression phenotypes map to the genes themselves, are of especially great interest, because they are direct candidates for previously mapped physiological QTLs. Here we show that many mapped local eQTLs in genetical genomics experiments do not reflect actual expression differences caused by sequence polymorphisms in cis-acting factors changing mRNA levels. Instead they indicate hybridization differences caused by sequence polymorphisms in the mRNA region that is targeted by the microarray probes. Many such polymorphisms can be detected by a sensitive and novel statistical approach that takes the individual probe signals into account. Applying this approach to recent mouse and human eQTL data, we demonstrate that indeed many local eQTLs are falsely reported as “cis-acting” or “cis” and can be successfully detected and eliminated with this approach.

A verification protocol for the probe sequences of Affymetrix genome arrays reveals high probe accuracy for studies in mouse, human and rat

Background

The Affymetrix GeneChip technology uses multiple probes per gene to measure its expression level. Individual probe signals can vary widely, which hampers proper interpretation. This variation can be caused by probes that do not properly match their target gene or that match multiple genes. To determine the accuracy of Affymetrix arrays, we developed an extensive verification protocol, for mouse arrays incorporating the NCBI RefSeq, NCBI UniGene Unique, NIA Mouse Gene Index, and UCSC mouse genome databases.

Results

Applying this protocol to Affymetrix Mouse Genome arrays (the earlier U74Av2 and the newer 430 2.0 array), the number of sequence-verified probes with perfect matches was no less than 85% and 95%, respectively; and for 74% and 85% of the probe sets all probes were sequence verified. The latter percentages increased to 80% and 94% after discarding one or two unverifiable probes per probe set, and even further to 84% and 97% when, in addition, allowing for one or two mismatches between probe and target gene. Similar results were obtained for other mouse arrays, as well as for human and rat arrays. Based on these data, refined chip definition files for all arrays are provided online. Researchers can choose the version appropriate for their study to (re)analyze expression data.

Conclusion

The accuracy of Affymetrix probe sequences is higher than previously reported, particularly on newer arrays. Yet, refined probe set definitions have clear effects on the detection of differentially expressed genes. We demonstrate that the interpretation of the results of Affymetrix arrays is improved when the new chip definition files are used.

A statistical multiprobe model for analyzing cis and trans genes in genetical genomics experiments with short-oligonucleotide arrays

Short-oligonucleotide arrays typically contain multiple probes per gene. In genetical genomics applications a statistical model for the individual probe signals can help in separating "true" differential mRNA expression from "ghost" effects caused by polymorphisms, misdesigned probes, and batch effects. It can also help in detecting alternative splicing, start, or termination.

Uncovering regulatory pathways that affect hematopoietic stem cell function using 'genetical genomics'

We combined large-scale mRNA expression analysis and gene mapping to identify genes and loci that control hematopoietic stem cell (HSC) function. We measured mRNA expression levels in purified HSCs isolated from a panel of densely genotyped recombinant inbred mouse strains. We mapped quantitative trait loci (QTLs) associated with variation in expression of thousands of transcripts. By comparing the physical transcript position with the location of the controlling QTL, we identified polymorphic cis-acting stem cell genes. We also identified multiple trans-acting control loci that modify expression of large numbers of genes. These groups of coregulated transcripts identify pathways that specify variation in stem cells. We illustrate this concept with the identification of candidate genes involved with HSC turnover. We compared expression QTLs in HSCs and brain from the same mice and identified both shared and tissue-specific QTLs. Our data are accessible through WebQTL, a web-based interface that allows custom genetic linkage analysis and identification of coregulated transcripts.

Combining microarrays and genetic analysis

Gene expression can be studied at a genome-wide scale with the aid of modern microarray technologies. Expression profiling of tens to hundreds of individuals in a genetic population can reveal the consequences of genetic variation. In this paper it is argued that the design and analysis of such a study is not a matter of simply applying the existing and more-or-less standard computational tools for microarrays to a new type of experimental data. It is shown how to fully exploit the power of genetics through optimal experimental design and analysis for two major microarray technologies, cDNA two-colour arrays and Affymetrix short oligonucleotide arrays.

Effect of aging on tensile mechanical properties of the rabbit distal femoral growth plate

Tensile tests to failure were performed on the distal femoral growth plate of rabbits. The measured tensile forces, together with animal weight and dimensional measurements of the femur, were used to develop a noninvasive method of predicting growth failure loads and to examine the effects of age on the tensile strength of the growth plate. Growth plate failure load correlated with rabbit weight, (r = 0.93), femoral length, (r = 0.91), and contoured growth plate surface area, (r = 0.74). Regression analysis for these relationships allowed for failure load prediction if animal weight, femoral length, or growth plate surface area was known. Nominal stress (failure load/surface area) was calculated and correlated with animal weight (age) for each distal femoral growth plate utilizing two surface area estimating techniques: growth plate surface contour and growth plate cross-sectional enlargement. A strong association was found with either technique. The growth and maturation of growth plate cartilage is of major importance in pediatric orthopaedics. This study demonstrate that the age-dependent increases in tensile mechanical properties of the growth plate do not result merely from growth plate enlrgement or conformational changes. The changing tensile mechanical properties of the growth plate may explain why transphyseal distraction used clinically has produced varied and somewhat unpredictable results. Our study suggests that dose-response investigations are needed to define safe and optimal transphyseal distraction tensions before further clinical application is pursued.