TY  - JOUR
AB  - BACKGROUND: Previous case-control studies have reported aberrations of the gut microbiota in individuals with prediabetes. The primary objective of the present study was to explore the dynamics of the gut microbiota of individuals with prediabetes over 4 years with a secondary aim of relating microbiota dynamics to temporal changes of metabolic phenotypes. METHODS: The study included 486 European patients with prediabetes. Gut microbiota profiling was conducted using shotgun metagenomic sequencing and the same bioinformatics pipelines at study baseline and after 4 years. The same phenotyping protocols and core laboratory analyses were applied at the two timepoints. Phenotyping included anthropometrics and measurement of fasting plasma glucose and insulin levels, mean plasma glucose and insulin under an oral glucose tolerance test (OGTT), 2-h plasma glucose after an OGTT, oral glucose insulin sensitivity index, Matsuda insulin sensitivity index, body mass index, waist circumference, and systolic and diastolic blood pressure. Measures of the dynamics of bacterial microbiota were related to concomitant changes in markers of host metabolism. RESULTS: Over 4 years, significant declines in richness were observed in gut bacterial and viral species and microbial pathways accompanied by significant changes in the relative abundance and the genetic composition of multiple bacterial species. Additionally, bacterial-viral interactions diminished over time. Despite the overall reduction in bacterial richness and microbial pathway richness, 80 dominant core bacterial species and 78 core microbial pathways were identified at both timepoints in 99% of the individuals, representing a resilient component of the gut microbiota. Over the same period, individuals with prediabetes exhibited a significant increase in glycemia and insulinemia alongside a significant decline in insulin sensitivity. Estimates of the gut bacterial microbiota dynamics were significantly correlated with temporal impairments in host metabolic health. CONCLUSIONS: In this 4-year prospective study of European patients with prediabetes, the gut microbiota exhibited major changes in taxonomic composition, bacterial species genetics, and microbial functional potentials, many of which paralleled an aggravation of host metabolism. Whether the temporal gut microbiota changes represent an adaptation to the progression of metabolic abnormalities or actively contribute to these in prediabetes cases remains unsettled. TRIAL REGISTRATION: The Diabetes Research on Patient Stratification (DIRECT) study, an exploratory observational study initiated on October 15, 2012, was registered on ClinicalTrials.gov under the number NCT03814915.
AU  - Lyu, L.*
AU  - Fan, Y.*
AU  - Vogt, J.K.*
AU  - Clos-Garcia, M.*
AU  - Bonnefond, A.*
AU  - Pedersen, H.K.*
AU  - Dutta, A.*
AU  - Koivula, R.*
AU  - Sharma, S.
AU  - Allin, K.H.*
AU  - Brorsson, C.*
AU  - Cederberg, H.*
AU  - Chabanova, E.*
AU  - De Masi, F.*
AU  - Dermitzakis, E.*
AU  - Elders, P.J.M.*
AU  - Blom, M.T.*
AU  - Hollander, M.*
AU  - Eriksen, R.*
AU  - Forgie, I.*
AU  - Frost, G.*
AU  - Giordano, G.N.*
AU  - Grallert, H.
AU  - Haid, M.
AU  - Hansen, T.H.*
AU  - Jablonka, B.*
AU  - Kokkola, T.*
AU  - Mahajan, A.*
AU  - Mari, A.*
AU  - McDonald, T.J.*
AU  - Musholt, P.B.*
AU  - Pavo, I.*
AU  - Prehn, C.
AU  - Ridderstråle, M.*
AU  - Ruetten, H.*
AU  - Hart, L.M.'.*
AU  - Schwenk, J.M.*
AU  - Stankevic, E.*
AU  - Thomsen, H.S.*
AU  - Vangipurapu, J.*
AU  - Vestergaard, H.*
AU  - Viñuela, A.*
AU  - Walker, M.*
AU  - Hansen, T.*
AU  - Linneberg, A.*
AU  - Nielsen, H.B.*
AU  - Brunak, S.*
AU  - McCarthy, M.I.*
AU  - Froguel, P.*
AU  - Adamski, J.
AU  - Franks, P.W.*
AU  - Laakso, M.*
AU  - Beulens, J.W.J.*
AU  - Pearson, E.*
AU  - Pedersen, O.*
C1  - 75148
C2  - 57803
CY  - Campus, 4 Crinan St, London N1 9xw, England
TI  - The dynamics of the gut microbiota in prediabetes during a four-year follow-up among European patients-an IMI-DIRECT prospective study.
JO  - Genome Med.
VL  - 17
IS  - 1
PB  - Bmc
PY  - 2025
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - BACKGROUND: Rare oncogenic driver events, particularly affecting the expression or splicing of driver genes, are suspected to substantially contribute to the large heterogeneity of hematologic malignancies. However, their identification remains challenging. METHODS: To address this issue, we generated the largest dataset to date of matched whole genome sequencing and total RNA sequencing of hematologic malignancies from 3760 patients spanning 24 disease entities. Taking advantage of our dataset size, we focused on discovering rare regulatory aberrations. Therefore, we called expression and splicing outliers using an extension of the workflow DROP (Detection of RNA Outliers Pipeline) and AbSplice, a variant effect predictor that identifies genetic variants causing aberrant splicing. We next trained a machine learning model integrating these results to prioritize new candidate disease-specific driver genes. RESULTS: We found a median of seven expression outlier genes, two splicing outlier genes, and two rare splice-affecting variants per sample. Each category showed significant enrichment for already well-characterized driver genes, with odds ratios exceeding three among genes called in more than five samples. On held-out data, our integrative modeling significantly outperformed modeling based solely on genomic data and revealed promising novel candidate driver genes. Remarkably, we found a truncated form of the low density lipoprotein receptor LRP1B transcript to be aberrantly overexpressed in about half of hairy cell leukemia variant (HCL-V) samples and, to a lesser extent, in closely related B-cell neoplasms. This observation, which was confirmed in an independent cohort, suggests LRP1B as a novel marker for a HCL-V subclass and a yet unreported functional role of LRP1B within these rare entities. CONCLUSIONS: Altogether, our census of expression and splicing outliers for 24 hematologic malignancy entities and the companion computational workflow constitute unique resources to deepen our understanding of rare oncogenic events in hematologic cancers.
AU  - Cao, X.*
AU  - Huber, S.*
AU  - Ahari, A.J.*
AU  - Traube, F.R.*
AU  - Seifert, M.*
AU  - Oakes, C.C.*
AU  - Secheyko, P.*
AU  - Vilov, S.
AU  - Scheller, I.F.
AU  - Wagner, N.*
AU  - Yépez, V.A.*
AU  - Blombery, P.*
AU  - Haferlach, T.*
AU  - Heinig, M.
AU  - Wachutka, L.*
AU  - Hutter, S.*
AU  - Gagneur, J.
C1  - 70721
C2  - 55724
CY  - Campus, 4 Crinan St, London N1 9xw, England
TI  - Analysis of 3760 hematologic malignancies reveals rare transcriptomic aberrations of driver genes.
JO  - Genome Med.
VL  - 16
IS  - 1
PB  - Bmc
PY  - 2024
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.
AU  - Curion, F.
AU  - Theis, F.J.
C1  - 70829
C2  - 55758
CY  - Campus, 4 Crinan St, London N1 9xw, England
TI  - Machine learning integrative approaches to advance computational immunology.
JO  - Genome Med.
VL  - 16
IS  - 1
PB  - Bmc
PY  - 2024
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - BACKGROUND: Low-frequency variants play an important role in breast cancer (BC) susceptibility. Gene-based methods can increase power by combining multiple variants in the same gene and help identify target genes. METHODS: We evaluated the potential of gene-based aggregation in the Breast Cancer Association Consortium cohorts including 83,471 cases and 59,199 controls. Low-frequency variants were aggregated for individual genes' coding and regulatory regions. Association results in European ancestry samples were compared to single-marker association results in the same cohort. Gene-based associations were also combined in meta-analysis across individuals with European, Asian, African, and Latin American and Hispanic ancestry. RESULTS: In European ancestry samples, 14 genes were significantly associated (q < 0.05) with BC. Of those, two genes, FMNL3 (P = 6.11 × 10-6) and AC058822.1 (P = 1.47 × 10-4), represent new associations. High FMNL3 expression has previously been linked to poor prognosis in several other cancers. Meta-analysis of samples with diverse ancestry discovered further associations including established candidate genes ESR1 and CBLB. Furthermore, literature review and database query found further support for a biologically plausible link with cancer for genes CBLB, FMNL3, FGFR2, LSP1, MAP3K1, and SRGAP2C. CONCLUSIONS: Using extended gene-based aggregation tests including coding and regulatory variation, we report identification of plausible target genes for previously identified single-marker associations with BC as well as the discovery of novel genes implicated in BC development. Including multi ancestral cohorts in this study enabled the identification of otherwise missed disease associations as ESR1 (P = 1.31 × 10-5), demonstrating the importance of diversifying study cohorts.
AU  - Mueller, S.H.*
AU  - Lai, A.G.*
AU  - Valkovskaya, M.*
AU  - Michailidou, K.*
AU  - Bolla, M.K.*
AU  - Wang, Q.*
AU  - Dennis, J.*
AU  - Lush, M.*
AU  - Abu-Ful, Z.*
AU  - Ahearn, T.U.*
AU  - Andrulis, I.L.*
AU  - Anton-Culver, H.*
AU  - Antonenkova, N.N.*
AU  - Arndt, V.*
AU  - Aronson, K.J.*
AU  - Augustinsson, A.*
AU  - Baert, T.*
AU  - Freeman, L.E.B.*
AU  - Beckmann, M.W.*
AU  - Behrens, S.*
AU  - Benítez, J.*
AU  - Bermisheva, M.*
AU  - Blomqvist, C.*
AU  - Bogdanova, N.V.*
AU  - Bojesen, S.E.*
AU  - Bonanni, B.*
AU  - Brenner, H.*
AU  - Brucker, S.Y.*
AU  - Buys, S.S.*
AU  - Castelao, J.E.*
AU  - Chan, T.L.*
AU  - Chang-Claude, J.*
AU  - Chanock, S.J.*
AU  - Choi, J.Y.*
AU  - Chung, W.K.*
AU  - NBCS Collaborators*
AU  - Colonna, S.V.*
AU  - CTS Consortium*
AU  - Cornelissen, S.*
AU  - Couch, F.J.*
AU  - Czene, K.*
AU  - Daly, M.B.*
AU  - Devilee, P.*
AU  - Dörk, T.*
AU  - Dossus, L.*
AU  - Dwek, M.*
AU  - Eccles, D.M.*
AU  - Ekici, A.B.*
AU  - Eliassen, A.H.*
AU  - Engel, C.*
AU  - Evans, D.G.*
AU  - Fasching, P.A.*
AU  - Fletcher, O.*
AU  - Flyger, H.*
AU  - Gago-Dominguez, M.*
AU  - Gao, Y.T.*
AU  - Garcia-Closas, M.*
AU  - García-Sáenz, J.A.*
AU  - Genkinger, J.*
AU  - Gentry-Maharaj, A.*
AU  - Grassmann, F.*
AU  - Guénel, P.*
AU  - Gündert, M.
AU  - Haeberle, L.*
AU  - Hahnen, E.*
AU  - Haiman, C.A.*
AU  - Hakansson, N.*
AU  - Hall, P.*
AU  - Harkness, E.F.*
AU  - Harrington, P.A.*
AU  - Hartikainen, J.M.*
AU  - Hartman, M.*
AU  - Hein, A.*
AU  - Ho, W.K.*
AU  - Hooning, M.J.*
AU  - Hoppe, R.*
AU  - Hopper, J.L.*
AU  - Houlston, R.S.*
AU  - Howell, A.*
AU  - Hunter, D.J.*
AU  - Huo, D.*
AU  - ABCTB Investigators*
AU  - Ito, H.*
AU  - Iwasaki, M.*
AU  - Jakubowska, A.*
AU  - Janni, W.*
AU  - John, E.M.*
AU  - Jones, M.E.*
AU  - Jung, A.*
AU  - Kaaks, R.*
AU  - Kang, D.*
AU  - Khusnutdinova, E.K.*
AU  - Kim, S.W.*
AU  - Kitahara, C.M.*
AU  - Koutros, S.*
AU  - Kraft, P.*
AU  - Kristensen, V.N.*
AU  - Kubelka-Sabit, K.*
AU  - Kurian, A.W.*
AU  - Kwong, A.*
AU  - Lacey, J.V.*
AU  - Lambrechts, D.*
AU  - Le Marchand, L.*
AU  - Li, J.*
AU  - Linet, M.*
AU  - Lo, W.Y.*
AU  - Long, J.*
AU  - Lophatananon, A.*
AU  - Mannermaa, A.*
AU  - Manoochehri, M.*
AU  - Margolin, S.*
AU  - Matsuo, K.*
AU  - Mavroudis, D.*
AU  - Menon, U.*
AU  - Muir, K.*
AU  - Murphy, R.A.*
AU  - Nevanlinna, H.*
AU  - Newman, W.G.*
AU  - Niederacher, D.*
AU  - O'Brien, K.M.*
AU  - Obi, N.*
AU  - Offit, K.*
AU  - Olopade, O.I.*
AU  - Olshan, A.F.*
AU  - Olsson, H.*
AU  - Park, S.K.*
AU  - Patel, A.V.*
AU  - Patel, A.*
AU  - Perou, C.M.*
AU  - Peto, J.*
AU  - Pharoah, P.D.P.*
AU  - Plaseska-Karanfilska, D.*
AU  - Presneau, N.*
AU  - Rack, B.*
AU  - Radice, P.*
AU  - Ramachandran, D.*
AU  - Rashid, M.U.*
AU  - Rennert, G.*
AU  - Romero, A.*
AU  - Ruddy, K.J.*
AU  - Ruebner, M.*
AU  - Saloustros, E.*
AU  - Sandler, D.P.*
AU  - Sawyer, E.J.*
AU  - Schmidt, M.K.*
AU  - Schmutzler, R.K.*
AU  - Schneider, M.O.*
AU  - Scott, C.*
AU  - Shah, M.*
AU  - Sharma, P.*
AU  - Shen, C.Y.*
AU  - Shu, X.O.*
AU  - Simard, J.*
AU  - Surowy, H.*
AU  - Tamimi, R.M.*
AU  - Tapper, W.J.*
AU  - Taylor, J.A.*
AU  - Teo, S.H.*
AU  - Teras, L.R.*
AU  - Toland, A.E.*
AU  - Tollenaar, R.A.E.M.*
AU  - Torres, D.*
AU  - Torres-Mejía, G.*
AU  - Troester, M.A.*
AU  - Truong, T.*
AU  - Vachon, C.M.*
AU  - Vijai, J.*
AU  - Weinberg, C.R.*
AU  - Wendt, C.*
AU  - Winqvist, R.*
AU  - Wolk, A.*
AU  - Wu, A.H.*
AU  - Yamaji, T.*
AU  - Yang, X.R.*
AU  - Yu, J.C.*
AU  - Zheng, W.*
AU  - Ziogas, A.*
AU  - Ziv, E.*
AU  - Dunning, A.M.*
AU  - Easton, D.F.*
AU  - Hemingway, H.*
AU  - Hamann, U.*
AU  - Kuchenbaecker, K.B.*
C1  - 67348
C2  - 54185
CY  - Campus, 4 Crinan St, London N1 9xw, England
TI  - Aggregation tests identify new gene associations with breast cancer in populations with diverse ancestry.
JO  - Genome Med.
VL  - 15
IS  - 1
PB  - Bmc
PY  - 2023
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - BACKGROUND: Protein truncating variants in ATM, BRCA1, BRCA2, CHEK2, and PALB2 are associated with increased breast cancer risk, but risks associated with missense variants in these genes are uncertain. METHODS: We analyzed data on 59,639 breast cancer cases and 53,165 controls from studies participating in the Breast Cancer Association Consortium BRIDGES project. We sampled training (80%) and validation (20%) sets to analyze rare missense variants in ATM (1146 training variants), BRCA1 (644), BRCA2 (1425), CHEK2 (325), and PALB2 (472). We evaluated breast cancer risks according to five in silico prediction-of-deleteriousness algorithms, functional protein domain, and frequency, using logistic regression models and also mixture models in which a subset of variants was assumed to be risk-associated. RESULTS: The most predictive in silico algorithms were Helix (BRCA1, BRCA2 and CHEK2) and CADD (ATM). Increased risks appeared restricted to functional protein domains for ATM (FAT and PIK domains) and BRCA1 (RING and BRCT domains). For ATM, BRCA1, and BRCA2, data were compatible with small subsets (approximately 7%, 2%, and 0.6%, respectively) of rare missense variants giving similar risk to those of protein truncating variants in the same gene. For CHEK2, data were more consistent with a large fraction (approximately 60%) of rare missense variants giving a lower risk (OR 1.75, 95% CI (1.47-2.08)) than CHEK2 protein truncating variants. There was little evidence for an association with risk for missense variants in PALB2. The best fitting models were well calibrated in the validation set. CONCLUSIONS: These results will inform risk prediction models and the selection of candidate variants for functional assays and could contribute to the clinical reporting of gene panel testing for breast cancer susceptibility.
AU  - Dorling, L.*
AU  - Carvalho, S.*
AU  - Allen, J.*
AU  - Parsons, M.T.*
AU  - Fortuno, C.*
AU  - González-Neira, A.*
AU  - Heijl, S.M.*
AU  - Adank, M.A.*
AU  - Ahearn, T.U.*
AU  - Andrulis, I.L.*
AU  - Auvinen, P.*
AU  - Becher, H.*
AU  - Beckmann, M.W.*
AU  - Behrens, S.*
AU  - Bermisheva, M.*
AU  - Bogdanova, N.V.*
AU  - Bojesen, S.E.*
AU  - Bolla, M.K.*
AU  - Bremer, M.*
AU  - Briceno, I.*
AU  - Camp, N.J.*
AU  - Campbell, A.*
AU  - Castelao, J.E.*
AU  - Chang-Claude, J.*
AU  - Chanock, S.J.*
AU  - Chenevix-Trench, G.*
AU  - Collee, J.M.*
AU  - Czene, K.*
AU  - Dennis, J.*
AU  - Dörk, T.*
AU  - Eriksson, M.*
AU  - Evans, D.G.*
AU  - Fasching, P.A.*
AU  - Figueroa, J.*
AU  - Flyger, H.*
AU  - Gabrielson, M.*
AU  - Gago-Dominguez, M.*
AU  - Garcia-Closas, M.*
AU  - Giles, G.G.*
AU  - Glendon, G.*
AU  - Guénel, P.*
AU  - Gündert, M.
AU  - Hadjisavvas, A.*
AU  - Hahnen, E.*
AU  - Hall, P.*
AU  - Hamann, U.*
AU  - Harkness, E.F.*
AU  - Hartman, M.*
AU  - Hogervorst, F.B.L.*
AU  - Hollestelle, A.*
AU  - Hoppe, R.*
AU  - Howell, A.*
AU  - Jakubowska, A.*
AU  - Jung, A.*
AU  - Khusnutdinova, E.*
AU  - Kim, S.W.*
AU  - Ko, Y.D.*
AU  - Kristensen, V.N.*
AU  - Lakeman, I.M.M.*
AU  - Li, J.*
AU  - Lindblom, A.*
AU  - Loizidou, M.A.*
AU  - Lophatananon, A.*
AU  - Lubiński, J.*
AU  - Luccarini, C.*
AU  - Madsen, M.J.*
AU  - Mannermaa, A.*
AU  - Manoochehri, M.*
AU  - Margolin, S.*
AU  - Mavroudis, D.*
AU  - Milne, R.L.*
AU  - Mohd Taib, N.A.*
AU  - Muir, K.*
AU  - Nevanlinna, H.*
AU  - Newman, W.G.*
AU  - Oosterwijk, J.C.*
AU  - Park, S.K.*
AU  - Peterlongo, P.*
AU  - Radice, P.*
AU  - Saloustros, E.*
AU  - Sawyer, E.J.*
AU  - Schmutzler, R.K.*
AU  - Shah, M.*
AU  - Sim, X.*
AU  - Southey, M.C.*
AU  - Surowy, H.M.*
AU  - Suvanto, M.*
AU  - Tomlinson, I.*
AU  - Torres, D.*
AU  - Truong, T.*
AU  - van Asperen, C.J.*
AU  - Waltes, R.*
AU  - Wang, Q.*
AU  - Yang, X.R.*
AU  - Pharoah, P.D.P.*
AU  - Schmidt, M.K.*
AU  - Benítez, J.*
AU  - Vroling, B.*
AU  - Dunning, A.M.*
AU  - Teo, S.H.*
AU  - Kvist, A.*
AU  - de la Hoya, M.*
AU  - Devilee, P.*
AU  - Spurdle, A.B.*
AU  - Vreeswijk, M.P.G.*
AU  - Easton, D.F.*
C1  - 65083
C2  - 52128
TI  - Breast cancer risks associated with missense variants in breast cancer susceptibility genes.
JO  - Genome Med.
VL  - 14
IS  - 1
PY  - 2022
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - BACKGROUND: Molecular measurements of the genome, the transcriptome, and the epigenome, often termed multi-omics data, provide an in-depth view on biological systems and their integration is crucial for gaining insights in complex regulatory processes. These data can be used to explain disease related genetic variants by linking them to intermediate molecular traits (quantitative trait loci, QTL). Molecular networks regulating cellular processes leave footprints in QTL results as so-called trans-QTL hotspots. Reconstructing these networks is a complex endeavor and use of biological prior information can improve network inference. However, previous efforts were limited in the types of priors used or have only been applied to model systems. In this study, we reconstruct the regulatory networks underlying trans-QTL hotspots using human cohort data and data-driven prior information. METHODS: We devised a new strategy to integrate QTL with human population scale multi-omics data. State-of-the art network inference methods including BDgraph and glasso were applied to these data. Comprehensive prior information to guide network inference was manually curated from large-scale biological databases. The inference approach was extensively benchmarked using simulated data and cross-cohort replication analyses. Best performing methods were subsequently applied to real-world human cohort data. RESULTS: Our benchmarks showed that prior-based strategies outperform methods without prior information in simulated data and show better replication across datasets. Application of our approach to human cohort data highlighted two novel regulatory networks related to schizophrenia and lean body mass for which we generated novel functional hypotheses. CONCLUSIONS: We demonstrate that existing biological knowledge can improve the integrative analysis of networks underlying trans associations and generate novel hypotheses about regulatory mechanisms.
AU  - Hawe, J.
AU  - Saha, A.*
AU  - Waldenberger, M.
AU  - Kunze, S.
AU  - Wahl, S.
AU  - Müller-Nurasyid, M.
AU  - Prokisch, H.*
AU  - Grallert, H.
AU  - Herder, C.*
AU  - Peters, A.
AU  - Strauch, K.
AU  - Theis, F.J.*
AU  - Gieger, C.
AU  - Chambers, J.*
AU  - Battle, A.*
AU  - Heinig, M.
C1  - 66596
C2  - 53233
TI  - Network reconstruction for trans acting genetic loci using multi-omics data and prior information.
JO  - Genome Med.
VL  - 14
IS  - 1
PY  - 2022
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - BACKGROUND: Lack of functional evidence hampers variant interpretation, leaving a large proportion of individuals with a suspected Mendelian disorder without genetic diagnosis after whole genome or whole exome sequencing (WES). Research studies advocate to further sequence transcriptomes to directly and systematically probe gene expression defects. However, collection of additional biopsies and establishment of lab workflows, analytical pipelines, and defined concepts in clinical interpretation of aberrant gene expression are still needed for adopting RNA sequencing (RNA-seq) in routine diagnostics. METHODS: We implemented an automated RNA-seq protocol and a computational workflow with which we analyzed skin fibroblasts of 303 individuals with a suspected mitochondrial disease that previously underwent WES. We also assessed through simulations how aberrant expression and mono-allelic expression tests depend on RNA-seq coverage. RESULTS: We detected on average 12,500 genes per sample including around 60% of all disease genes-a coverage substantially higher than with whole blood, supporting the use of skin biopsies. We prioritized genes demonstrating aberrant expression, aberrant splicing, or mono-allelic expression. The pipeline required less than 1 week from sample preparation to result reporting and provided a median of eight disease-associated genes per patient for inspection. A genetic diagnosis was established for 16% of the 205 WES-inconclusive cases. Detection of aberrant expression was a major contributor to diagnosis including instances of 50% reduction, which, together with mono-allelic expression, allowed for the diagnosis of dominant disorders caused by haploinsufficiency. Moreover, calling aberrant splicing and variants from RNA-seq data enabled detecting and validating splice-disrupting variants, of which the majority fell outside WES-covered regions. CONCLUSION: Together, these results show that streamlined experimental and computational processes can accelerate the implementation of RNA-seq in routine diagnostics.
AU  - Yépez, V.A.*
AU  - Gusic, M.
AU  - Kopajtich, R.
AU  - Mertes, C.*
AU  - Smith, N.H.*
AU  - Alston, C.L.*
AU  - Ban, R.
AU  - Beblo, S.*
AU  - Berutti, R.
AU  - Blessing, H.*
AU  - Ciara, E.*
AU  - Distelmaier, F.*
AU  - Freisinger, P.*
AU  - Häberle, J.*
AU  - Hayflick, S.J.*
AU  - Hempel, M.*
AU  - Itkis, Y.S.*
AU  - Kishita, Y.*
AU  - Klopstock, T.*
AU  - Krylova, T.D.*
AU  - Lamperti, C.*
AU  - Lenz, D.*
AU  - Makowski, C.*
AU  - Mosegaard, S.*
AU  - Müller, M.F.*
AU  - Muñoz-Pujol, G.*
AU  - Nadel, A.
AU  - Ohtake, A.*
AU  - Okazaki, Y.*
AU  - Procopio, E.*
AU  - Schwarzmayr, T.
AU  - Smet, J.*
AU  - Staufner, C.*
AU  - Stenton, S.
AU  - Strom, T.-M.
AU  - Terrile, C.
AU  - Tort, F.*
AU  - van Coster, R.*
AU  - Vanlander, A.*
AU  - Wagner, M.
AU  - Xu, M.
AU  - Fang, F.*
AU  - Ghezzi, D.*
AU  - Mayr, J.A.*
AU  - Piekutowska-Abramczuk, D.*
AU  - Ribes, A.*
AU  - Rötig, A.*
AU  - Taylor, R.W.*
AU  - Wortmann, S.B.*
AU  - Murayama, K.*
AU  - Meitinger, T.*
AU  - Gagneur, J.
AU  - Prokisch, H.
C1  - 64742
C2  - 51949
TI  - Clinical implementation of RNA sequencing for Mendelian disease diagnostics.
JO  - Genome Med.
VL  - 14
IS  - 1
PY  - 2022
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - BACKGROUND: The microbiome has emerged as an environmental factor contributing to obesity and type 2 diabetes (T2D). Increasing evidence suggests links between circulating bacterial components (i.e., bacterial DNA), cardiometabolic disease, and blunted response to metabolic interventions. In this aspect, thorough next-generation sequencing-based and contaminant-aware approaches are lacking. To address this, we tested whether bacterial DNA could be amplified in the blood of subjects with obesity and high metabolic risk under strict experimental and analytical control and whether a putative bacterial signature is related to metabolic improvement after bariatric surgery. METHODS: Subjects undergoing bariatric surgery were recruited into sex- and BMI-matched subgroups with (n = 24) or without T2D (n = 24). Bacterial DNA in the blood was quantified and prokaryotic 16S rRNA gene amplicons were sequenced. A contaminant-aware approach was applied to derive a compositional microbial signature from bacterial sequences in all subjects at baseline and at 3 and 12 months after surgery. We modeled associations between bacterial load and composition with host metabolic and anthropometric markers. We further tested whether compositional shifts were related to weight loss response and T2D remission. Lastly, bacteria were visualized in blood samples using catalyzed reporter deposition (CARD)-fluorescence in situ hybridization (FISH). RESULTS: The contaminant-aware blood bacterial signature was associated with metabolic health. Based on bacterial phyla and genera detected in the blood samples, a metabolic syndrome classification index score was derived and shown to robustly classify subjects along their actual clinical group. T2D was characterized by decreased bacterial richness and loss of genera associated with improved metabolic health. Weight loss and metabolic improvement following bariatric surgery were associated with an early and stable increase of these genera in parallel with improvements in key cardiometabolic risk parameters. CARD-FISH allowed the detection of living bacteria in blood samples in obesity. CONCLUSIONS: We show that the circulating bacterial signature reflects metabolic disease and its improvement after bariatric surgery. Our work provides contaminant-aware evidence for the presence of living bacteria in the blood and suggests a putative crosstalk between components of the blood and metabolism in metabolic health regulation.
AU  - Chakaroun, R.M.*
AU  - Massier, L.*
AU  - Heintz-Buschart, A.*
AU  - Said, N.*
AU  - Fallmann, J.*
AU  - Crane, A.*
AU  - Schütz, T.*
AU  - Dietrich, A.*
AU  - Blüher, M.
AU  - Stumvoll, M.*
AU  - Musat, N.*
AU  - Kovacs, P.*
C1  - 62470
C2  - 50742
CY  - Campus, 4 Crinan St, London N1 9xw, England
TI  - Circulating bacterial signature is linked to metabolic disease and shifts with metabolic alleviation after bariatric surgery.
JO  - Genome Med.
VL  - 13
IS  - 1
PB  - Bmc
PY  - 2021
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - BACKGROUND: ATPase family AAA-domain containing protein 3A (ATAD3A) is a nuclear-encoded mitochondrial membrane-anchored protein involved in diverse processes including mitochondrial dynamics, mitochondrial DNA organization, and cholesterol metabolism. Biallelic deletions (null), recessive missense variants (hypomorph), and heterozygous missense variants or duplications (antimorph) in ATAD3A lead to neurological syndromes in humans. METHODS: To expand the mutational spectrum of ATAD3A variants and to provide functional interpretation of missense alleles in trans to deletion alleles, we performed exome sequencing for identification of single nucleotide variants (SNVs) and copy number variants (CNVs) in ATAD3A in individuals with neurological and mitochondrial phenotypes. A Drosophila Atad3a Gal4 knockin-null allele was generated using CRISPR-Cas9 genome editing technology to aid the interpretation of variants. RESULTS: We report 13 individuals from 8 unrelated families with biallelic ATAD3A variants. The variants included four missense variants inherited in trans to loss-of-function alleles (p.(Leu77Val), p.(Phe50Leu), p.(Arg170Trp), p.(Gly236Val)), a homozygous missense variant p.(Arg327Pro), and a heterozygous non-frameshift indel p.(Lys568del). Affected individuals exhibited findings previously associated with ATAD3A pathogenic variation, including developmental delay, hypotonia, congenital cataracts, hypertrophic cardiomyopathy, and cerebellar atrophy. Drosophila studies indicated that Phe50Leu, Gly236Val, Arg327Pro, and Lys568del are severe loss-of-function alleles leading to early developmental lethality. Further, we showed that Phe50Leu, Gly236Val, and Arg327Pro cause neurogenesis defects. On the contrary, Leu77Val and Arg170Trp are partial loss-of-function alleles that cause progressive locomotion defects and whose expression leads to an increase in autophagy and mitophagy in adult muscles. CONCLUSION: Our findings expand the allelic spectrum of ATAD3A variants and exemplify the use of a functional assay in Drosophila to aid variant interpretation.
AU  - Yap, Z.Y.*
AU  - Park, Y.H.*
AU  - Wortmann, S.B.*
AU  - Gunning, A.C.*
AU  - Ezer, S.*
AU  - Lee, S.*
AU  - Duraine, L.*
AU  - Wilichowski, E.*
AU  - Wilson, K.*
AU  - Mayr, J.A.*
AU  - Wagner, M.
AU  - Li, H.*
AU  - Kini, U.*
AU  - Black, E.D.*
AU  - Monaghan, K.G.*
AU  - Lupski, J.R.*
AU  - Ellard, S.*
AU  - Westphal, D.S.*
AU  - Harel, T.*
AU  - Yoon, W.H.*
C1  - 61780
C2  - 50452
CY  - Campus, 4 Crinan St, London N1 9xw, England
TI  - Functional interpretation of ATAD3A variants in neuro-mitochondrial phenotypes.
JO  - Genome Med.
VL  - 13
IS  - 1
PB  - Bmc
PY  - 2021
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - Background During aging, there is a physiological decline, an increase of morbidity and mortality, and a natural change in the gut microbiome. In this study, we investigated the influence of the gut microbiome on different metabolic parameters in adult and aged mice. Methods Fecal and blood samples from adult (n = 42, 100-300 days) and aging (n = 32, 550-750 days) mice were collected. Microbiome analysis was done using QIIME2. Mouse weight and body composition were measured using NMR, and insulin and leptin levels in the blood were measured with Mouse Adipokine Magnetic Bead Panel kit. Fecal microbiota transplantation experiments from adult and aged mice into young germ-free mice were carried out in order to examine the effect of the gut microbiome of adult and aging mice on weight, body composition, insulin, and leptin. Results We demonstrate that the microbiomes from adult and aged mice are distinguishable. We also report changes in metabolic parameters as we observed significantly higher weight and fat mass and low lean mass in aged compared to adult mice along with high insulin and leptin levels in the blood. The transplanted gut microbiome from aged mice transferred part of the phenotypes seen in aged mice. Fat body mass and insulin levels were higher in the mice who received feces from aged mice than mice receiving feces from adult mice. In addition, they consumed more food and had a higher respiratory quotient compared to mice receiving adult feces. Conclusions We conclude that aged mice have a gut microbiota with obesogenic characteristics. In addition, the gut bacterial population itself is sufficient to induce some of the manifestations of obesity.
AU  - Binyamin, D.*
AU  - Werbner, N.*
AU  - Nuriel-Ohayon, M.*
AU  - Uzan, A.*
AU  - Mor, H.*
AU  - Abbas, A.*
AU  - Ziv, O.*
AU  - Teperino, R.
AU  - Gutmann, R.*
AU  - Koren, O.*
C1  - 60275
C2  - 49089
CY  - Campus, 4 Crinan St, London N1 9xw, England
TI  - The aging mouse microbiome has obesogenic characteristics.
JO  - Genome Med.
VL  - 12
IS  - 1
PB  - Bmc
PY  - 2020
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - BACKGROUND: The concentrations of the highly atherogenic lipoprotein(a) [Lp(a)] are mainly genetically determined by the LPA gene locus. However, up to 70% of the coding sequence is located in the complex so-called kringle IV type 2 (KIV-2) copy number variation, a region hardly accessible by common genotyping and sequencing technologies. Despite its size, little is known about genetic variants in this complex region. The R21X variant is a functional variant located in this region, but it has never been analyzed in large cohorts. METHODS: We typed R21X in 10,910 individuals from three European populations using a newly developed high-throughput allele-specific qPCR assay. R21X allelic location was determined by separating the LPA alleles using pulsed-field gel electrophoresis (PFGE) and typing them separately. Using GWAS data, we identified a proxy SNP located outside of the KIV-2. Linkage disequilibrium was determined both statistically and by long-range haplotyping using PFGE. Worldwide frequencies were determined by reanalyzing the sequencing data of the 1000 Genomes Project with a dedicated pipeline. RESULTS: R21X carriers (frequency 0.016-0.021) showed significantly lower mean Lp(a) concentrations (- 11.7 mg/dL [- 15.5; - 7.82], p = 3.39e-32). The variant is located mostly on medium-sized LPA alleles. In the 1000 Genome data, R21X mostly occurs in Europeans and South Asians, is absent in Africans, and shows varying frequencies in South American populations (0 to 0.022). Of note, the best proxy SNP was another LPA null mutation (rs41272114, D' = 0.958, R2 = 0.281). D' was very high in all 1000G populations (0.986-0.996), although rs41272114 frequency varies considerably (0-0.182). Co-localization of both null mutations on the same allele was confirmed by PFGE-based long-range haplotyping. CONCLUSIONS: We performed the largest epidemiological study on an LPA KIV-2 variant so far, showing that it is possible to assess LPA KIV-2 mutations on a large scale. Surprisingly, in all analyzed populations, R21X was located on the same haplotype as the splice mutation rs41272114, creating "double-null" LPA alleles. Despite being a nonsense variant, the R21X status does not provide additional information beyond the rs41272114 genotype. This has important implications for studies using LPA loss-of-function mutations as genetic instruments and emphasizes the complexity of LPA genetics.
AU  - Di Maio, S.*
AU  - Grüneis, R.*
AU  - Streiter, G.*
AU  - Lamina, C.*
AU  - Maglione, M.*
AU  - Schoenherr, S.*
AU  - Öfner, D.*
AU  - Thorand, B.
AU  - Peters, A.
AU  - Eckardt, K.U.*
AU  - Köttgen, A.*
AU  - Kronenberg, F.*
AU  - Coassin, S.*
C1  - 59939
C2  - 48966
TI  - Investigation of a nonsense mutation located in the complex KIV-2 copy number variation region of apolipoprotein(a) in 10,910 individuals.
JO  - Genome Med.
VL  - 12
IS  - 1
PY  - 2020
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - Background: The rising prevalence of type 2 diabetes (T2D) poses a major global challenge. It remains unresolved to what extent transcriptomic signatures of metabolic dysregulation and T2D can be observed in easily accessible tissues such as blood. Additionally, large-scale human studies are required to further our understanding of the putative inflammatory component of insulin resistance and T2D. Here we used transcriptomics data from individuals with (n = 789) and without (n = 2127) T2D from the IMI-DIRECT cohorts to describe the co-expression structure of whole blood that mainly reflects processes and cell types of the immune system, and how it relates to metabolically relevant clinical traits and T2D. Methods: Clusters of co-expressed genes were identified in the non-diabetic IMI-DIRECT cohort and evaluated with regard to stability, as well as preservation and rewiring in the cohort of individuals with T2D. We performed functional and immune cell signature enrichment analyses, and a genome-wide association study to describe the genetic regulation of the modules. Phenotypic and trans-omics associations of the transcriptomic modules were investigated across both IMI-DIRECT cohorts. Results: We identified 55 whole blood co-expression modules, some of which clustered in larger super-modules. We identified a large number of associations between these transcriptomic modules and measures of insulin action and glucose tolerance. Some of the metabolically linked modules reflect neutrophil-lymphocyte ratio in blood while others are independent of white blood cell estimates, including a module of genes encoding neutrophil granule proteins with antibacterial properties for which the strongest associations with clinical traits and T2D status were observed. Through the integration of genetic and multi-omics data, we provide a holistic view of the regulation and molecular context of whole blood transcriptomic modules. We furthermore identified an overlap between genetic signals for T2D and co-expression modules involved in type II interferon signaling. Conclusions: Our results offer a large-scale map of whole blood transcriptomic modules in the context of metabolic disease and point to novel biological candidates for future studies related to T2D.
AU  - Gudmundsdottir, V.*
AU  - Pedersen, H.K.*
AU  - Mazzoni, G.*
AU  - Allin, K.H.*
AU  - Artati, A.
AU  - Beulens, J.W.*
AU  - Banasik, K.*
AU  - Brorsson, C.*
AU  - Cederberg, H.*
AU  - Chabanova, E.*
AU  - De Masi, F.*
AU  - Elders, P.J.M.*
AU  - Forgie, I.*
AU  - Giordano, G.N.*
AU  - Grallert, H.
AU  - Gupta, R.*
AU  - Haid, M.
AU  - Hansen, T.*
AU  - Hansen, T.H.*
AU  - Hattersley, A.T.*
AU  - Heggie, A.*
AU  - Hong, M.G.*
AU  - Jones, A.G.*
AU  - Koivula, R.W.*
AU  - Kokkola, T.*
AU  - Laakso, M.*
AU  - Løngreen, P.*
AU  - Mahajan, A.*
AU  - Mari, A.*
AU  - McDonald, T.J.*
AU  - McEvoy, D.*
AU  - Musholt, P.B.*
AU  - Pavo, I.*
AU  - Prehn, C.
AU  - Ruetten, H.*
AU  - Ridderstråle, M.*
AU  - Rutters, F.*
AU  - Sharma, S.
AU  - Slieker, R.C.*
AU  - Syed, A.*
AU  - Tajes, J.F.*
AU  - Thomas, C.E.*
AU  - Thomsen, H.S.*
AU  - Vangipurapu, J.*
AU  - Vestergaard, H.*
AU  - Viñuela, A.*
AU  - Wesolowska-Andersen, A.*
AU  - Walker, M.*
AU  - Adamski, J.
AU  - Schwenk, J.M.*
AU  - McCarthy, M.I.*
AU  - Pearson, E.*
AU  - Dermitzakis, E.*
AU  - Franks, P.W.*
AU  - Pedersen, O.*
AU  - Brunak, S.*
C1  - 60712
C2  - 49587
CY  - Campus, 4 Crinan St, London N1 9xw, England
TI  - Whole blood co-expression modules associate with metabolic traits and type 2 diabetes: An IMI-DIRECT study.
JO  - Genome Med.
VL  - 12
IS  - 1
PB  - Bmc
PY  - 2020
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - Background: One of the major challenges in obesity treatment is to explain the high variability in the individual’s response to specific dietary and physical activity interventions. With this study, we tested the hypothesis that specific DNA methylation changes reflect individual responsiveness to lifestyle intervention and may serve as epigenetic predictors for a successful weight-loss. Methods: We conducted an explorative genome-wide DNA methylation analysis in blood samples from 120 subjects (90% men, mean ± SD age = 49 ± 9 years, body mass-index (BMI) = 30.2 ± 3.3 kg/m2) from the 18-month CENTRAL randomized controlled trial who underwent either Mediterranean/low-carbohydrate or low-fat diet with or without physical activity. Results: Analyses comparing male subjects with the most prominent body weight-loss (responders, mean weight change − 16%) vs. non-responders (+ 2.4%) (N = 10 each) revealed significant variation in DNA methylation of several genes including LRRC27, CRISP2, and SLFN12 (all adj. P < 1 × 10−5). Gene ontology analysis indicated that biological processes such as cell adhesion and molecular functions such as calcium ion binding could have an important role in determining the success of interventional therapies in obesity. Epigenome-wide association for relative weight-loss (%) identified 15 CpGs being negatively correlated with weight change after intervention (all combined P < 1 × 10− 4) including new and also known obesity candidates such as NUDT3 and NCOR2. A baseline DNA methylation score better predicted successful weight-loss [area under the curve (AUC) receiver operating characteristic (ROC) = 0.95–1.0] than predictors such as age and BMI (AUC ROC = 0.56). Conclusions: Body weight-loss following 18-month lifestyle intervention is associated with specific methylation signatures. Moreover, methylation differences in the identified genes could serve as prognostic biomarkers to predict a successful weight-loss therapy and thus contribute to advances in patient-tailored obesity treatment.
AU  - Keller, M.
AU  - Yaskolka Meir, A.*
AU  - Bernhart, S.H.*
AU  - Gepner, Y.*
AU  - Shelef, I.*
AU  - Schwarzfuchs, D.*
AU  - Tsaban, G.*
AU  - Zelicha, H.*
AU  - Hopp, L.*
AU  - Müller, L.*
AU  - Rohde-Zimmermann, K.
AU  - Böttcher, Y.*
AU  - Stumvoll, M.
AU  - Blüher, M.
AU  - Kovacs, P.*
AU  - Shai, I.*
C1  - 60554
C2  - 49493
CY  - Campus, 4 Crinan St, London N1 9xw, England
TI  - DNA methylation signature in blood mirrors successful weight-loss during lifestyle interventions: The CENTRAL trial.
JO  - Genome Med.
VL  - 12
IS  - 1
PB  - Bmc
PY  - 2020
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - BACKGROUND: NF-κB is widely involved in lymphoid malignancies; however, the functional roles and specific transcriptomes of NF-κB dimers with distinct subunit compositions have been unclear. METHODS: Using combined ChIP-sequencing and microarray analyses, we determined the cistromes and target gene signatures of canonical and non-canonical NF-κB species in Hodgkin lymphoma (HL) cells. RESULTS: We found that the various NF-κB subunits are recruited to regions with redundant κB motifs in a large number of genes. Yet canonical and non-canonical NF-κB dimers up- and downregulate gene sets that are both distinct and overlapping, and are associated with diverse biological functions. p50 and p52 are formed through NIK-dependent p105 and p100 precursor processing in HL cells and are the predominant DNA binding subunits. Logistic regression analyses of combinations of the p50, p52, RelA, and RelB subunits in binding regions that have been assigned to genes they regulate reveal a cross-contribution of p52 and p50 to canonical and non-canonical transcriptomes. These analyses also indicate that the subunit occupancy pattern of NF-κB binding regions and their distance from the genes they regulate are determinants of gene activation versus repression. The pathway-specific signatures of activated and repressed genes distinguish HL from other NF-κB-associated lymphoid malignancies and inversely correlate with gene expression patterns in normal germinal center B cells, which are presumed to be the precursors of HL cells. CONCLUSIONS: We provide insights that are relevant for lymphomas with constitutive NF-κB activation and generally for the decoding of the mechanisms of differential gene regulation through canonical and non-canonical NF-κB signaling.
AU  - de Oliveira, K.A.*
AU  - Kaergel, E.*
AU  - Heinig, M.
AU  - Fontaine, J.F.*
AU  - Patone, G.*
AU  - Muro, E.M.*
AU  - Mathas, S.*
AU  - Hummel, M.*
AU  - Andrade-Navarro, M.A.*
AU  - Hubner, N.*
AU  - Scheidereit, C.*
C1  - 48171
C2  - 39941
CY  - London
TI  - A roadmap of constitutive NF-κB activity in Hodgkin lymphoma: Dominant roles of p50 and p52 revealed by genome-wide analyses.
JO  - Genome Med.
VL  - 8
IS  - 1
PB  - Biomed Central Ltd
PY  - 2016
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - The advent of mitochondrial replacement techniques poses many scientific, regulatory, and ethical questions. Previous studies suggest good safety and efficacy profiles of these techniques, but challenges remain for clinical implementation and international consensus is needed on the regulation of these approaches.
AU  - Klopstock, T.*
AU  - Klopstock, B.*
AU  - Prokisch, H.
C1  - 50054
C2  - 42182
CY  - London
TI  - Mitochondrial replacement approaches: Challenges for clinical implementation.
JO  - Genome Med.
VL  - 8
PB  - Biomed Central Ltd
PY  - 2016
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - The cause of a complex disease cannot be pinpointed to a single origin; rather, a highly complex network of many factors that interact on different levels over time and space is disturbed. This complexity requires novel approaches to diagnosis, treatment, and prevention. To foster the necessary shift to a pro-active systems medicine, proof-of-concept studies are needed. Here, we highlight several systems approaches that have been shown to work within the field of respiratory medicine, and we propose the next steps for broader implementation.
AU  - Kirschner, M.*
AU  - Bauch, A.*
AU  - Agusti, A.*
AU  - Hilke, S.*
AU  - Merk, S.*
AU  - Pison, C.*
AU  - Roldan, J.*
AU  - Seidenath, B.*
AU  - Wilken, M.*
AU  - Wouters, E.F.*
AU  - Mewes, H.-W.
AU  - Heumann, K.*
AU  - Maier, D.*
C1  - 46943
C2  - 39070
TI  - Implementing systems medicine within healthcare.
JO  - Genome Med.
VL  - 7
IS  - 1
PY  - 2015
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - New methods for epigenome editing now make it possible to manipulate the epigenome in living cells with unprecedented specificity and efficiency. These ground-breaking approaches are beginning to yield novel insights into the function of individual chromatin marks in the context of cellular phenotype.
AU  - Köferle, A.*
AU  - Stricker, S.H.
AU  - Beck, S.*
C1  - 45355
C2  - 39398
CY  - London
TI  - Brave new epigenomes: The dawn of epigenetic engineering.
JO  - Genome Med.
VL  - 7
IS  - 1
PB  - Biomed Central Ltd
PY  - 2015
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - This corrects the article DOI: 10.1186/s13073-015-0185-8.
AU  - Köferle, A.*
AU  - Stricker, S.H.
AU  - Beck, S.*
C1  - 46548
C2  - 37717
CY  - London
TI  - Erratum to: Brave new epigenomes: The dawn of epigenetic engineering.
JO  - Genome Med.
VL  - 7
IS  - 1
PB  - Biomed Central Ltd
PY  - 2015
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - Background Emerging technologies based on mass spectrometry or nuclear magnetic resonance enable the monitoring of hundreds of small metabolites from tissues or body fluids. Profiling of metabolites can help elucidate causal pathways linking established genetic variants to known disease risk factors such as blood lipid traits. Methods We applied statistical methodology to dissect causal relationships between single nucleotide polymorphisms, metabolite concentrations and serum lipid traits, focusing on 95 genetic loci reproducibly associated with the four main serum lipids (total-, low-density lipoprotein- and high-density lipoprotein- cholesterol and triglycerides). The dataset used included 2,973 individuals from two independent population-based cohorts with data for 151 small molecule metabolites and four main serum lipids. Three statistical approaches, namely conditional analysis, Mendelian Randomization and Structural Equation Modelling, were compared to investigate causal relationship at sets of a single nucleotide polymorphism, a metabolite and a lipid trait associated with one another. Results A subset of three lipid-associated loci (FADS1, GCKR and LPA) have a statistically significant association with at least one main lipid and one metabolite concentration in our data, defining a total of 38 cross-associated sets of a single nucleotide polymorphism, a metabolite and a lipid trait. Structural Equation Modelling provided sufficient discrimination to indicate that the association of a single nucleotide polymorphism with a lipid trait was mediated through a metabolite at 15 of the 38 sets, and involving variants at the FADS1 and GCKR loci. Conclusions These data provide a framework for evaluating the causal role of components of the metabolome (or other intermediate factors) in mediating the association between established genetic variants and diseases or traits.
AU  - Shin, S.-Y.*
AU  - Petersen, A.-K.
AU  - Wahl, S.
AU  - Zhai, G.*
AU  - Römisch-Margl, W.
AU  - Small, K.S.*
AU  - Döring, A.
AU  - Kato, B.S.*
AU  - Peters, A.
AU  - Grundberg, E.*
AU  - Prehn, C.
AU  - Wang-Sattler, R.
AU  - Wichmann, H.-E.
AU  - Hrabě de Angelis, M.
AU  - Illig, T.*
AU  - Adamski, J.
AU  - Deloukas, P.*
AU  - Spector, T.D.*
AU  - Suhre, K.
AU  - Gieger, C.
AU  - Soranzo, N.*
C1  - 30949
C2  - 34030
CY  - London
TI  - Interrogating causal pathways linking genetic variants, small molecule metabolites and circulating lipids.
JO  - Genome Med.
VL  - 6
IS  - 3
PB  - Biomed Central Ltd
PY  - 2014
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - Background: The incomplete understanding of disease causes and drug mechanisms of action often leads to ineffective drug therapies or side effects. Therefore, new approaches are needed to improve treatment decisions and to elucidate molecular mechanisms underlying pathologies and unwanted drug effects. Methods: We present here the first analysis of phenotypically related drug-disease pairs. The phenotypic similarity between 4,869 human diseases and 1,667 drugs was evaluated using an ontology-based semantic similarity approach to compare disease symptoms with drug side effects. We assessed and visualized the enrichment over random of clinical and molecular relationships among drug-disease pairs that share phenotypes using lift plots. To determine the associations between drug and disease classes enriched among phenotypically related pairs we employed a network-based approach combined with Fisher's exact test. Results: We observed that molecularly and clinically related (for example, indication or contraindication) drugs and diseases are likely to share phenotypes. An analysis of the relations between drug mechanisms of action (MoAs) and disease classes among highly similar pairs revealed known and suspected MoA-disease relationships. Interestingly, we found that contraindications associated with high phenotypic similarity often involve diseases that have been reported as side effects of the drug, probably due to common mechanisms. Based on this, we propose a list of 752 precautions or potential contraindications for 486 drugs. Conclusions: Phenotypic similarity between drugs and diseases facilitates the proposal of contraindications and the mechanistic understanding of diseases and drug side effects.
AU  - Vogt, I.
AU  - Prinz, J.
AU  - Campillos, M.
C1  - 32268
C2  - 35002
CY  - London
TI  - Molecularly and clinically related drugs and diseases are enriched in phenotypically similar drug-disease pairs.
JO  - Genome Med.
VL  - 6
IS  - 7
PB  - Biomed Central Ltd
PY  - 2014
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - Nuclear magnetic resonance spectroscopy (NMR) provides robust readouts of many metabolic parameters in one experiment. However, identification of clinically relevant markers in 1H NMR spectra is a major challenge. Association of NMR derived quantities with genetic variants can uncover biologically relevant metabolic traits. Using NMR data of plasma samples from 1,757 individuals from the KORA study together with 655,658 genetic variants, we show that ratios between NMR intensities at two chemical shift positions can provide informative and robust biomarkers. We report seven loci of genetic association with NMR derived traits (APOA1, CETP, CPS1, GCKR, FADS1, LIPC, PYROXD2) and characterize these traits biochemically using mass spectrometry. These ratios may now be used in clinical studies.
AU  - Raffler, J.
AU  - Römisch-Margl, W.
AU  - Petersen, A.-K.
AU  - Pagel, P.*
AU  - Blöchl, F.*
AU  - Hengstenberg, C.*
AU  - Illig, T.
AU  - Meisinger, C.
AU  - Stark, K.*
AU  - Wichmann, H.-E.
AU  - Adamski, J.
AU  - Gieger, C.
AU  - Kastenmüller, G.
AU  - Suhre, K.
C1  - 22785
C2  - 30941
TI  - Identification and MS-assisted interpretation of genetically influenced NMR signals in human plasma.
JO  - Genome Med.
VL  - 5
IS  - 2
PB  - BioMed Central
PY  - 2013
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - ABSTRACT: Genome-wide association studies (GWAS) analyze the genetic component of a phenotype or the etiology of a disease. Despite the success of many GWAS, little progress has been made in uncovering the underlying mechanisms for many diseases. The use of metabolomics as a readout of molecular phenotypes has enabled the discovery of previously undetected associations between diseases and signaling and metabolic pathways. In addition, combining GWAS and metabolomic information allows the simultaneous analysis of the genetic and environmental impacts on homeostasis. Most success has been seen in metabolic diseases such as diabetes, obesity and dyslipidemia. Recently, associations between loci such as FADS1, ELOVL2 or SLC16A9 and lipid concentrations have been explained by GWAS with metabolomics. Combining GWAS with metabolomics (mGWAS) provides the robust and quantitative information required for the development of specific diagnostics and targeted drugs. This review discusses the limitations of GWAS and presents examples of how metabolomics can overcome these limitations with the focus on metabolic diseases.
AU  - Adamski, J.
C1  - 7488
C2  - 29748
TI  - Genome-wide association studies with metabolomics.
JO  - Genome Med.
VL  - 4
IS  - 4
PB  - BioMed Central
PY  - 2012
SN  - 1756-994X
ER  - 

TY  - JOUR
AB  - ABSTRACT: Chronic lung diseases (CLDs), including chronic obstructive pulmonary disease (COPD), are the second leading cause of death worldwide. The first report of database-driven drug discovery in carefully phenotyped COPD specimens has now been published in Genome Medicine, combining gene expression data in defined emphysematous areas with connectivity-map-based compound discovery. This joint effort may lead the way to novel and potentially more efficient concepts of personalized drug discovery for COPD in particular, and CLD in general.See research article http://genomemedicine.com/content/4/8/67/abstract.
AU  - Meiners, S.
AU  - Eickelberg, O.
C1  - 11149
C2  - 30520
TI  - Next-generation personalized drug discovery: The tripeptide GHK hits center stage in chronic obstructive pulmonary disease.
JO  - Genome Med.
VL  - 4
IS  - 8
PB  - BioMed Central
PY  - 2012
SN  - 1756-994X
ER  -