A new statistical method for curve group analysis of longitudinal gene expression data illustrated for breast cancer in the NOWAC postgenome cohort as a proof of principle.

BACKGROUND: The understanding of changes in temporal processes related to human carcinogenesis is limited. One approach for prospective functional genomic studies is to compile trajectories of differential expression of genes, based on measurements from many case-control pairs. We propose a new statistical method that does not assume any parametric shape for the gene trajectories. METHODS: The trajectory of a gene is defined as the curve representing the changes in gene expression levels in the blood as a function of time to cancer diagnosis. In a nested case-control design it consists of differences in gene expression levels between cases and controls. Genes can be grouped into curve groups, each curve group corresponding to genes with a similar development over time. The proposed new statistical approach is based on a set of hypothesis testing that can determine whether or not there is development in gene expression levels over time, and whether this development varies among different strata. Curve group analysis may reveal significant differences in gene expression levels over time among the different strata considered. This new method was applied as a "proof of concept" to breast cancer in the Norwegian Women and Cancer (NOWAC) postgenome cohort, using blood samples collected prospectively that were specifically preserved for transcriptomic analyses (PAX tube). Cohort members diagnosed with invasive breast cancer through 2009 were identified through linkage to the Cancer Registry of Norway, and for each case a random control from the postgenome cohort was also selected, matched by birth year and time of blood sampling, to create a case-control pair. After exclusions, 441 case-control pairs were available for analyses, in which we considered strata of lymph node status at time of diagnosis and time of diagnosis with respect to breast cancer screening visits. RESULTS: The development of gene expression levels in the NOWAC postgenome cohort varied in the last years before breast cancer diagnosis, and this development differed by lymph node status and participation in the Norwegian Breast Cancer Screening Program. The differences among the investigated strata appeared larger in the year before breast cancer diagnosis compared to earlier years. CONCLUSIONS: This approach shows good properties in term of statistical power and type 1 error under minimal assumptions. When applied to a real data set it was able to discriminate between groups of genes with non-linear similar patterns before diagnosis.

A statistical model of ChIA-PET data for accurate detection of chromatin 3D interactions.

Identification of three-dimensional (3D) interactions between regulatory elements across the genome is crucial to unravel the complex regulatory machinery that orchestrates proliferation and differentiation of cells. ChIA-PET is a novel method to identify such interactions, where physical contacts between regions bound by a specific protein are quantified using next-generation sequencing. However, determining the significance of the observed interaction frequencies in such datasets is challenging, and few methods have been proposed. Despite the fact that regions that are close in linear genomic distance have a much higher tendency to interact by chance, no methods to date are capable of taking such dependency into account. Here, we propose a statistical model taking into account the genomic distance relationship, as well as the general propensity of anchors to be involved in contacts overall. Using both real and simulated data, we show that the previously proposed statistical test, based on Fisher's exact test, leads to invalid results when data are dependent on genomic distance. We also evaluate our method on previously validated cell-line specific and constitutive 3D interactions, and show that relevant interactions are significant, while avoiding over-estimating the significance of short nearby interactions.

Age and pro-inflammatory gene polymorphisms influence adjacent segment disc degeneration more than fusion does in patients treated for chronic low back pain.

PURPOSE: Does lumbar fusion lead to accelerated adjacent segment disc degeneration (ASDD) or is it explained by genetics and aging? The influence of genetics on ASDD remains to be explored. This study assesses whether the disc space height adjacent to a fused segment is associated with candidate gene single nucleotide polymorphisms (SNPs). METHODS: Patients with low back pain from four RCTs (N = 208 fusion; 77 non-operative treatment) underwent standing plain radiography and genetic analyses at 13 ± 4 years follow-up. Disc space height was measured using a validated computer-assisted distortion-compensated roentgen analysis technique and reported in standard deviations from normal values. Genetic association analyses included 34 SNPs in 25 structural, inflammatory, matrix degrading, apoptotic, vitamin D receptor and OA-related genes relevant to disc degeneration. These were analysed for their association with disc space height (after adjusting for age, gender, smoking, duration of follow-up and treatment group) first, separately, and then together in a stepwise multivariable model. RESULTS: Two SNPs from the IL18RAP gene (rs1420106 and rs917997) were each associated with a lower disc space height at the adjacent level (B = -0.34, p = 0.04 and B = -0.35, p = 0.04, respectively) and the MMP-9 gene SNP rs20544 was associated with a greater disc space height (B = 0.35, p = 0.04). Age (p < 0.001) and fusion (p < 0.008) were also significant variables in each analysis. The total explained variance in disc space height was for each SNP model 13-14 %, with 11-12 % of this being accounted for by the given SNP, 64-67 % by age and 19-22 % by fusion. In the multivariable regression analysis (with nine SNPs selected for entry, along with the covariates) the total explained variance in disc space height was 23 %, with the nine SNPs, age and fusion accounting for 45, 45 and 7 % of this, respectively. CONCLUSIONS: Age was the most significant determinant of adjacent segment disc space height followed by genetic factors, specifically inflammatory genes. Fusion explained a statistically significant but small proportion of the total variance. Much of the variance remained to be explained.

Multi-focus cluster labeling.

Document collections resulting from searches in the biomedical literature, for instance, in PubMed, are often so large that some organization of the returned information is necessary. Clustering is an efficient tool for organizing search results. To help the user to decide how to continue the search for relevant documents, the content of each cluster can be characterized by a set of representative keywords or cluster labels. As different users may have different interests, it can be desirable with solutions that make it possible to produce labels from a selection of different topical categories. We therefore introduce the concept of multi-focus cluster labeling to give users the possibility to get an overview of the contents through labels from multiple viewpoints. The concept for multi-focus cluster labeling has been established and has been demonstrated on three different document collections. We illustrate that multi-focus visualizations can give an overview of clusters along axes that general labels are not able to convey. The approach is generic and should be applicable to any biomedical (or other) domain with any selection of foci where appropriate focus vocabularies can be established. A user evaluation also indicates that such a multi-focus concept is useful.

The Genomic HyperBrowser: an analysis web server for genome-scale data.

The immense increase in availability of genomic scale datasets, such as those provided by the ENCODE and Roadmap Epigenomics projects, presents unprecedented opportunities for individual researchers to pose novel falsifiable biological questions. With this opportunity, however, researchers are faced with the challenge of how to best analyze and interpret their genome-scale datasets. A powerful way of representing genome-scale data is as feature-specific coordinates relative to reference genome assemblies, i.e. as genomic tracks. The Genomic HyperBrowser (http://hyperbrowser.uio.no) is an open-ended web server for the analysis of genomic track data. Through the provision of several highly customizable components for processing and statistical analysis of genomic tracks, the HyperBrowser opens for a range of genomic investigations, related to, e.g., gene regulation, disease association or epigenetic modifications of the genome.

Catechol-O-methyltransferase (COMT) gene polymorphisms are associated with baseline disability but not long-term treatment outcome in patients with chronic low back pain.

PURPOSE: To examine the association between COMT and OPRM1 gene polymorphisms and pain and disability at baseline and long-term follow-up in patients treated for chronic low back pain (LBP). METHODS: 371 of 767 unrelated European patients recruited from four randomised trials underwent genetic analyses at mean 11.4 years follow-up. 274 patients had fusion and 97 had non-operative treatment. Association analyses included disability, pain, five single nucleotide polymorphisms (SNPs) in the COMT gene, and one SNP in the OPRM1 gene. Analyses were adjusted for age, gender, smoking, analgesics and treatment. RESULTS: Disability at baseline was significantly associated with COMT SNPs rs4818 (p = 0.02), rs6269 (p = 0.007), rs4633 (p = 0.04) rs2075507 (p = 0.009), two haplotypes (p < 0.002), age, gender and smoking (p ≤ 0.002). No significant associations with clinical variables were observed for OPRM1, or for COMT at long-term follow-up. CONCLUSIONS: Results suggest that genetic factors are partly responsible for the variation in disability levels in patients presenting with chronic LBP being considered for surgery; in contrast, genetics has no influence on the long-term outcome of treatment.

Identification of eight candidate target genes of the recurrent 3p12-p14 loss in cervical cancer by integrative genomic profiling.

The pathogenetic role, including its target genes, of the recurrent 3p12-p14 loss in cervical cancer has remained unclear. To determine the onset of the event during carcinogenesis, we used microarray techniques and found that the loss was the most frequent 3p event, occurring in 61% of 92 invasive carcinomas, in only 2% of 43 high-grade intraepithelial lesions (CIN2/3), and in 33% of 6 CIN3 lesions adjacent to invasive carcinomas, suggesting a role in acquisition of invasiveness or early during the invasive phase. We performed an integrative DNA copy number and expression analysis of 77 invasive carcinomas, where all genes within the recurrent region were included. We selected eight genes, THOC7, PSMD6, SLC25A26, TMF1, RYBP, SHQ1, EBLN2, and GBE1, which were highly down-regulated in cases with loss, as confirmed at the protein level for RYBP and TMF1 by immunohistochemistry. The eight genes were subjected to network analysis based on the expression profiles, revealing interaction partners of proteins encoded by the genes that were coordinately regulated in tumours with loss. Several partners were shared among the eight genes, indicating crosstalk in their signalling. Gene ontology analysis showed enrichment of biological processes such as apoptosis, proliferation, and stress response in the network and suggested a relationship between down-regulation of the eight genes and activation of tumourigenic pathways. Survival analysis showed prognostic impact of the eight-gene signature that was confirmed in a validation cohort of 74 patients and was independent of clinical parameters. These results support the role of the eight candidate genes as targets of the 3p12-p14 loss in cervical cancer and suggest that the strong selection advantage of the loss during carcinogenesis might be caused by a synergetic effect of several tumourigenic processes controlled by these targets.

Treatment outcome of chronic low back pain and radiographic lumbar disc degeneration are associated with inflammatory and matrix degrading gene variants: a prospective genetic association study.

BACKGROUND: Inflammatory and matrix degrading gene variants have been reported to be associated with disc degeneration. Some of these variants also modulate peripheral pain. This study examines the association of these genetic variants with radiographic lumbar disc degeneration and changes in pain and disability at long-term after surgical and cognitive behavioural management. METHODS: 93 unrelated patients with chronic low back pain (CLBP) for duration of >1 year and lumbar disc degeneration were treated with lumbar fusion or cognitive intervention and exercises. Standardised questionnaires included the Oswestry Disability Index (ODI) and Visual Analog Score (VAS) for CLBP, were filled in by patients both at baseline and at 9 years follow-up. Degenerative changes at baseline Magnetic Resonance Imaging and Computed Tomography scans, were graded as moderate and severe (N=79). Yield and quality of blood and saliva DNA was assessed by nano drop spectrophotometry. Eight SNPs in 5 inflammatory and matrix degrading genes were successfully genotyped. Single marker and haplotype association with severity of degeneration, number of discs involved, changes in ODI and VAS CLBP, was done using Haploview, linear regression and R-package Haplostats. RESULTS: Association analysis of individual SNPs revealed association of IL18RAP polymorphism rs1420100 with severe degeneration (p = 0.05) and more than one degenerated disc (p = 0.02). From the same gene two SNPs, rs917997 and rs1420106, were found to be in strong linkage disequilibrium (LD) and were associated with post treatment improvement in disability (p = 0.02). Haplotype association analysis of 5 SNPs spanning across IL18RAP, IL18R1 and IL1A genes revealed significant associations with improvement in disability (p=0.02) and reduction in pain (p=0.04). An association was found between MMP3 polymorphism rs72520913 and improvement in pain (p = 0.03) and with severe degeneration (p = 0.006). CONCLUSIONS: The findings of the current study suggest a role of variation at inflammatory and matrix degrading genes with severity of lumbar disc degeneration, pain and disability.

Stress associated gene expression in blood cells is related to outcome in radiotherapy treated head and neck cancer patients.

BACKGROUND: We previously observed that a radiotherapy-induced biochemical response in plasma was associated with favourable outcome in head and neck squamous carcinoma cancer (HNSCC) patients. The aim of the present study was to compare stress associated blood cell gene expression between two sub-groups of HNSCC patients with different biochemical responses to radiotherapy. METHODS: Out of 87 patients (histologically verified), 10 biochemical 'responders' having a high relative increase in plasma oxidative damage and a concomitant decrease in plasma antioxidants during radiotherapy and 10 'poor-responders' were selected for gene-expression analysis and compared using gene set enrichment analysis. RESULTS: There was a significant induction of stress-relevant gene-sets in the responders following radiotherapy compared to the poor-responders. The relevance of the involvement of similar stress associated gene expression for HNSCC cancer and radioresistance was verified using two publicly available data sets of 42 HNSCC cases and 14 controls (GEO GSE6791), and radiation resistant and radiation sensitive HNSCC xenografts (E-GEOD-9716). CONCLUSIONS: Radiotherapy induces a systemic stress response, as revealed by induction of stress relevant gene expression in blood cells, which is associated to favourable outcome in a cohort of 87 HNSCC patients. Whether these changes in gene expression reflects a systemic effect or are biomarkers of the tumour micro-environmental status needs further study. TRIAL REGISTRATION: Raw data are available at ArrayExpress under accession number E-MEXP-2460.

Genetic contribution of catechol-O-methyltransferase variants in treatment outcome of low back pain: a prospective genetic association study.

BACKGROUND: Treatment outcome of low back pain (LBP) is associated with inter-individual variations in pain relief and functional disability. Genetic variants of catechol-O-methyltransferase (COMT) gene have previously been shown to be associated with pain sensitivity and pain medication. This study examines the association between COMT polymorphisms and 7-11 year change in Oswestry Disability Index (ODI) and Visual Analog Score (VAS) for LBP as clinical outcome variables in patients treated with surgical instrumented lumbar fusion or cognitive intervention and exercise. METHODS: 93 unrelated patients with chronic LBP for duration of >1 year and lumbar disc degeneration (LDD) were treated with lumbar fusion (N = 60) or cognitive therapy and exercises (N = 33). Standardised questionnaires assessing the ODI, VAS LBP, psychological factors and use of analgesics, were answered by patients both at baseline and at 7-11 years follow-up. Four SNPs in the COMT gene were successfully genotyped. Single marker as well as haplotype association with change in ODI and VAS LBP, were analyzed using Haploview, linear regression and R-package Haplostats. P-values were not formally corrected for multiple testing as this was an explorative study. RESULTS: Association analysis of individual SNPs adjusted for covariates revealed association of rs4633 and rs4680 with post treatment improvement in VAS LBP (p = 0.02, mean difference (ß) = 13.5 and p = 0.02, ß = 14.2 respectively). SNPs, rs4633 and rs4680 were found to be genotypically similar and in strong linkage disequilibrium (LD). A significant association was found with covariates, analgesics (p = 0.001, ß = 18.6); anxiety and depression (p = 0.008, ß = 15.4) and age (p = 0.03, mean difference per year (ß) = 0.7) at follow-up. There was a tendency for better improvement among heterozygous patients compared to the homozygous. No association was observed for the analysis of the common haplotypes, these SNPs were situated on. CONCLUSIONS: Results suggest an influence of genetic variants of COMT gene in describing the variation in pain after treatment for low back pain. Replication in large samples with testing for other pain related genes is warranted.

Gene dosage, expression, and ontology analysis identifies driver genes in the carcinogenesis and chemoradioresistance of cervical cancer.

Integrative analysis of gene dosage, expression, and ontology (GO) data was performed to discover driver genes in the carcinogenesis and chemoradioresistance of cervical cancers. Gene dosage and expression profiles of 102 locally advanced cervical cancers were generated by microarray techniques. Fifty-two of these patients were also analyzed with the Illumina expression method to confirm the gene expression results. An independent cohort of 41 patients was used for validation of gene expressions associated with clinical outcome. Statistical analysis identified 29 recurrent gains and losses and 3 losses (on 3p, 13q, 21q) associated with poor outcome after chemoradiotherapy. The intratumor heterogeneity, assessed from the gene dosage profiles, was low for these alterations, showing that they had emerged prior to many other alterations and probably were early events in carcinogenesis. Integration of the alterations with gene expression and GO data identified genes that were regulated by the alterations and revealed five biological processes that were significantly overrepresented among the affected genes: apoptosis, metabolism, macromolecule localization, translation, and transcription. Four genes on 3p (RYBP, GBE1) and 13q (FAM48A, MED4) correlated with outcome at both the gene dosage and expression level and were satisfactorily validated in the independent cohort. These integrated analyses yielded 57 candidate drivers of 24 genetic events, including novel loci responsible for chemoradioresistance. Further mapping of the connections among genetic events, drivers, and biological processes suggested that each individual event stimulates specific processes in carcinogenesis through the coordinated control of multiple genes. The present results may provide novel therapeutic opportunities of both early and advanced stage cervical cancers.

The differential disease regulome.

BACKGROUND: Transcription factors in disease-relevant pathways represent potential drug targets, by impacting a distinct set of pathways that may be modulated through gene regulation. The influence of transcription factors is typically studied on a per disease basis, and no current resources provide a global overview of the relations between transcription factors and disease. Furthermore, existing pipelines for related large-scale analysis are tailored for particular sources of input data, and there is a need for generic methodology for integrating complementary sources of genomic information. RESULTS: We here present a large-scale analysis of multiple diseases versus multiple transcription factors, with a global map of over-and under-representation of 446 transcription factors in 1010 diseases. This map, referred to as the differential disease regulome, provides a first global statistical overview of the complex interrelationships between diseases, genes and controlling elements. The map is visualized using the Google map engine, due to its very large size, and provides a range of detailed information in a dynamic presentation format.The analysis is achieved through a novel methodology that performs a pairwise, genome-wide comparison on the cartesian product of two distinct sets of annotation tracks, e.g. all combinations of one disease and one TF.The methodology was also used to extend with maps using alternative data sets related to transcription and disease, as well as data sets related to Gene Ontology classification and histone modifications. We provide a web-based interface that allows users to generate other custom maps, which could be based on precisely specified subsets of transcription factors and diseases, or, in general, on any categorical genome annotation tracks as they are improved or become available. CONCLUSION: We have created a first resource that provides a global overview of the complex relations between transcription factors and disease. As the accuracy of the disease regulome depends mainly on the quality of the input data, forthcoming ChIP-seq based binding data for many TFs will provide improved maps. We further believe our approach to genome analysis could allow an advance from the current typical situation of one-time integrative efforts to reproducible and upgradable integrative analysis. The differential disease regulome and its associated methodology is available at http://hyperbrowser.uio.no.

The blood transcriptome prior to ovarian cancer diagnosis: A case-control study in the NOWAC postgenome cohort.

Epithelial ovarian cancer (EOC) has a 5-year relative survival of 50%, partly because markers of early-stage disease are not available in current clinical diagnostics. The aim of the present study was to investigate whether EOC is associated with transcriptional profiles in blood collected up to 7 years before diagnosis. For this, we used RNA-stabilized whole blood, which contains circulating immune cells, from a sample of EOC cases from the population-based Norwegian Women and Cancer (NOWAC) postgenome cohort. We explored case-control differences in gene expression in all EOC (66 case-control pairs), as well as associations between gene expression and metastatic EOC (56 pairs), serous EOC (45 pairs, 44 of which were metastatic), and interval from blood sample collection to diagnosis (≤3 or >3 years; 34 and 31 pairs, respectively). Lastly, we assessed differential expression of genes associated with EOC in published functional genomics studies that used blood samples collected from newly diagnosed women. After adjustment for multiple testing, this nested case-control study revealed no significant case-control differences in gene expression in all EOC (false discovery rate q>0.96). With the exception of a few probes, the log2 fold change values obtained in gene-wise linear models were below ±0.2. P-values were lowest in analyses of metastatic EOC (80% of which were serous EOC). No common transcriptional profile was indicated by interval to diagnosis; when comparing the 100 genes with the lowest p-values in gene-wise tests in samples collected ≤3 and >3 years before EOC diagnosis, no overlap in these genes was observed. Among 86 genes linked to ovarian cancer in previous publications, our data contained expression values for 42, and of these, tests of LIME1, GPR162, STAB1, and SKAP1, resulted in unadjusted p<0.05. Although limited by sample size, our findings indicated less variation in blood gene expression between women with similar tumor characteristics.

Transcriptomic signals in blood prior to lung cancer focusing on time to diagnosis and metastasis.

Recent studies have indicated that there are functional genomic signals that can be detected in blood years before cancer diagnosis. This study aimed to assess gene expression in prospective blood samples from the Norwegian Women and Cancer cohort focusing on time to lung cancer diagnosis and metastatic cancer using a nested case-control design. We employed several approaches to statistically analyze the data and the methods indicated that the case-control differences were subtle but most distinguishable in metastatic case-control pairs in the period 0-3 years prior to diagnosis. The genes of interest along with estimated blood cell populations could indicate disruption of immunological processes in blood. The genes identified from approaches focusing on alterations with time to diagnosis were distinct from those focusing on the case-control differences. Our results support that explorative analyses of prospective blood samples could indicate circulating signals of disease-related processes.

Global blood gene expression profiles following a breast cancer diagnosis-Clinical follow-up in the NOWAC post-genome cohort.

OBJECTIVE: This explorative study aimed to assess if there are any time-dependent blood gene expression changes during the first one to eight years after breast cancer diagnosis, which can be linked to the clinical outcome of the disease. MATERIAL AND METHODS: A random distribution of follow-up time from breast cancer diagnosis till blood sampling was obtained by a nested, matched case-control design in the Norwegian Women and Cancer Post-genome Cohort. From 2002-5, women were invited to donate blood samples, regardless of any cancer diagnosis. At end of the study period in 2015, any cancer diagnoses in the 50 000 participants were obtained via linkage to the Norwegian Cancer Registry. For each breast cancer patient (n = 415), an age- and storage time-matched control was drawn. The design gave a uniform, random length of follow-up time, independent of cancer stage. Differences in blood gene expression between breast cancer cases and controls were identified using the Bioconductor R-package limma, using a moving window in time, to handle the varying time elapsed from diagnosis to blood sample. RESULTS: The number of differentially expressed genes between cases and controls were close to 2,000 in the first year after diagnosis, but fell sharply the second year. During the next years, a transient second increase was observed, but only in women with metastatic disease who later died, both compared to invasive cases that survived (p<0,001) and to metastatic cases that survived (p = 0.024). Among the differentially expressed genes there was an overrepresentation of heme metabolism and T cell-related processes. CONCLUSION: This explorative analysis identified changing trajectories in the years after diagnosis, depending on clinical stage. Hypothetically, this could represent the escape of the metastatic cancer from the immune system.

User-Intended Doppler Measurement Type Prediction Combining CNNs With Smart Post-Processing.

Spectral Doppler measurements are an important part of the standard echocardiographic examination. These measurements give insight into myocardial motion and blood flow, providing clinicians with parameters for diagnostic decision making. Many of these measurements are performed automatically with high accuracy, increasing the efficiency of the diagnostic pipeline. However, full automation is not yet available because the user must manually select which measurement should be performed on each image. In this work, we develop a pipeline based on convolutional neural networks (CNNs) to automatically classify the measurement type from cardiac Doppler scans. We show how the multi-modal information in each spectral Doppler recording can be combined using a meta parameter post-processing mapping scheme and heatmaps to encode coordinate locations. Additionally, we experiment with several architectures to examine the tradeoff between accuracy, speed, and memory usage for resource-constrained environments. Finally, we propose a confidence metric using the values in the last fully connected layer of the network and show that our confidence metric can prevent many misclassifications. Our algorithm enables a fully automatic pipeline from acquisition to Doppler spectrum measurements. We achieve 96% accuracy on a test set drawn from separate clinical sites, indicating that the proposed method is suitable for clinical adoption.

Blood cell gene expression associated with cellular stress defense is modulated by antioxidant-rich food in a randomised controlled clinical trial of male smokers.

BACKGROUND: Plant-based diets rich in fruit and vegetables can prevent development of several chronic age-related diseases. However, the mechanisms behind this protective effect are not elucidated. We have tested the hypothesis that intake of antioxidant-rich foods can affect groups of genes associated with cellular stress defence in human blood cells. TRIAL REGISTRATION NUMBER: NCT00520819 http://clinicaltrials.gov. METHODS: In an 8-week dietary intervention study, 102 healthy male smokers were randomised to either a diet rich in various antioxidant-rich foods, a kiwifruit diet (three kiwifruits/d added to the regular diet) or a control group. Blood cell gene expression profiles were obtained from 10 randomly selected individuals of each group. Diet-induced changes on gene expression were compared to controls using a novel application of the gene set enrichment analysis (GSEA) on transcription profiles obtained using Affymetrix HG-U133-Plus 2.0 whole genome arrays. RESULTS: Changes were observed in the blood cell gene expression profiles in both intervention groups when compared to the control group. Groups of genes involved in regulation of cellular stress defence, such as DNA repair, apoptosis and hypoxia, were significantly upregulated (GSEA, FDR q-values < 5%) by both diets compared to the control group. Genes with common regulatory motifs for aryl hydrocarbon receptor (AhR) and AhR nuclear translocator (AhR/ARNT) were upregulated by both interventions (FDR q-values < 5%). Plasma antioxidant biomarkers (polyphenols/carotenoids) increased in both groups. CONCLUSIONS: The observed changes in the blood cell gene expression profiles suggest that the beneficial effects of a plant-based diet on human health may be mediated through optimization of defence processes.

Distinct Subsets of Noncoding RNAs Are Strongly Associated With BMD and Fracture, Studied in Weight-Bearing and Non-Weight-Bearing Human Bone.

We investigated mechanisms resulting in low bone mineral density (BMD) and susceptibility to fracture by comparing noncoding RNAs (ncRNAs) in biopsies of non-weight-bearing (NWB) iliac (n = 84) and weight bearing (WB) femoral (n = 18) postmenopausal bone across BMDs varying from normal (T-score > -1.0) to osteoporotic (T-score ≤ -2.5). Global bone ncRNA concentrations were determined by PCR and microchip analyses. Association with BMD or fracture, adjusted by age and body mass index, were calculated using linear and logistic regression and least absolute shrinkage and selection operator (Lasso) analysis. At 10% false discovery rate (FDR), 75 iliac bone ncRNAs and 94 femoral bone ncRNAs were associated with total hip BMD. Eight of the ncRNAs were common for the two sites, but five of them (miR-484, miR-328-3p, miR-27a-5p, miR-28-3p, and miR-409-3p) correlated positively to BMD in femoral bone, but negatively in iliac bone. Of predicted pathways recognized in bone metabolism, ECM-receptor interaction and proteoglycans in cancer emerged at both sites, whereas fatty acid metabolism and focal adhesion were only identified in iliac bone. Lasso analysis and cross-validations identified sets of nine bone ncRNAs correlating strongly with adjusted total hip BMD in both femoral and iliac bone. Twenty-eight iliac ncRNAs were associated with risk of fracture (FDR < 0.1). The small nucleolar RNAs, RNU44 and RNU48, have a function in stabilization of ribosomal RNAs (rRNAs), and their association with fracture and BMD suggest that aberrant processing of rRNAs may be involved in development of osteoporosis. Cis-eQTL (expressed quantitative trait loci) analysis of the iliac bone biopsies identified two loci associated with microRNAs (miRNAs), one previously identified in a heel-BMD genomewide association study (GWAS). In this comprehensive investigation of the skeletal genetic background in postmenopausal women, we identified functional bone ncRNAs associated to fracture and BMD, representing distinct subsets in WB and NWB skeletal sites. © 2020 The Authors. Journal of Bone and Mineral Research published by American Society for Bone and Mineral Research.

GSEA-SNP: applying gene set enrichment analysis to SNP data from genome-wide association studies.

The power of genome-wide SNP association studies is limited, among others, by the large number of false positive test results. To provide a remedy, we combined SNP association analysis with the pathway-driven gene set enrichment analysis (GSEA), recently developed to facilitate handling of genome-wide gene expression data. The resulting GSEA-SNP method rests on the assumption that SNPs underlying a disease phenotype are enriched in genes constituting a signaling pathway or those with a common regulation. Besides improving power for association mapping, GSEA-SNP may facilitate the identification of disease-associated SNPs and pathways, as well as the understanding of the underlying biological mechanisms. GSEA-SNP may also help to identify markers with weak effects, undetectable in association studies without pathway consideration. The program is freely available and can be downloaded from our website.

Validation of oligoarrays for quantitative exploration of the transcriptome.

BACKGROUND: Oligoarrays have become an accessible technique for exploring the transcriptome, but it is presently unclear how absolute transcript data from this technique compare to the data achieved with tag-based quantitative techniques, such as massively parallel signature sequencing (MPSS) and serial analysis of gene expression (SAGE). By use of the TransCount method we calculated absolute transcript concentrations from spotted oligoarray intensities, enabling direct comparisons with tag counts obtained with MPSS and SAGE. The tag counts were converted to number of transcripts per cell by assuming that the sum of all transcripts in a single cell was 5.105. Our aim was to investigate whether the less resource demanding and more widespread oligoarray technique could provide data that were correlated to and had the same absolute scale as those obtained with MPSS and SAGE. RESULTS: A number of 1,777 unique transcripts were detected in common for the three technologies and served as the basis for our analyses. The correlations involving the oligoarray data were not weaker than, but, similar to the correlation between the MPSS and SAGE data, both when the entire concentration range was considered and at high concentrations. The data sets were more strongly correlated at high transcript concentrations than at low concentrations. On an absolute scale, the number of transcripts per cell and gene was generally higher based on oligoarrays than on MPSS and SAGE, and ranged from 1.6 to 9,705 for the 1,777 overlapping genes. The MPSS data were on same scale as the SAGE data, ranging from 0.5 to 3,180 (MPSS) and 9 to1,268 (SAGE) transcripts per cell and gene. The sum of all transcripts per cell for these genes was 3.8.105 (oligoarrays), 1.1.105 (MPSS) and 7.6.104 (SAGE), whereas the corresponding sum for all detected transcripts was 1.1.106 (oligoarrays), 2.8.105 (MPSS) and 3.8.105 (SAGE). CONCLUSION: The oligoarrays and TransCount provide quantitative transcript concentrations that are correlated to MPSS and SAGE data, but, the absolute scale of the measurements differs across the technologies. The discrepancy questions whether the sum of all transcripts within a single cell might be higher than the number of 5.105 suggested in the literature and used to convert tag counts to transcripts per cell. If so, this may explain the apparent higher transcript detection efficiency of the oligoarrays, and has to be clarified before absolute transcript concentrations can be interchanged across the technologies. The ability to obtain transcript concentrations from oligoarrays opens up the possibility of efficient generation of universal transcript databases with low resource demands.