The European Union (EU) initiative on the Digital Transformation of Health and Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and decentralized digital health infrastructure. Creating a European Health Research and Innovation Cloud (HRIC) within this environment should enable data sharing and analysis for health research across the EU, in compliance with data protection legislation while preserving the full trust of the participants. Such a HRIC should learn from and build on existing data infrastructures, integrate best practices, and focus on the concrete needs of the community in terms of technologies, governance, management, regulation, and ethics requirements. Here, we describe the vision and expected benefits of digital data sharing in health research activities and present a roadmap that fosters the opportunities while answering the challenges of implementing a HRIC. For this, we put forward five specific recommendations and action points to ensure that a European HRIC: i) is built on established standards and guidelines, providing cloud technologies through an open and decentralized infrastructure; ii) is developed and certified to the highest standards of interoperability and data security that can be trusted by all stakeholders; iii) is supported by a robust ethical and legal framework that is compliant with the EU General Data Protection Regulation (GDPR); iv) establishes a proper environment for the training of new generations of data and medical scientists; and v) stimulates research and innovation in transnational collaborations through public and private initiatives and partnerships funded by the EU through Horizon 2020 and Horizon Europe.

We here present a comparative genome, transcriptome and functional network analysis of three human cancer cell lines (A431, U251MG and U2OS), and investigate their relation to protein expression. Gene copy numbers significantly influenced corresponding transcript levels; their effect on protein levels was less pronounced. We focused on genes with altered mRNA and/or protein levels to identify those active in tumor maintenance. We provide comprehensive information for the three genomes and demonstrate the advantage of integrative analysis for identifying tumor-related genes amidst numerous background mutations by relating genomic variation to expression/protein abundance data and use gene networks to reveal implicated pathways.

BACKGROUND: Drug-induced liver injury (DILI) is one of the most common adverse reactions leading to product withdrawal post-marketing. Recently, genome-wide association studies have identified a number of human leukocyte antigen (HLA) alleles associated with DILI; however, the cellular and chemical mechanisms are not fully understood.

METHODS: To study these mechanisms, we established an HLA-typed cell archive from 400 healthy volunteers. In addition, we utilized HLA genotype data from more than four million individuals from publicly accessible repositories such as the Allele Frequency Net Database, Major Histocompatibility Complex Database and Immune Epitope Database to study the HLA alleles associated with DILI. We utilized novel in silico strategies to examine HLA haplotype relationships among the alleles associated with DILI by using bioinformatics tools such as NetMHCpan, PyPop, GraphViz, PHYLIP and TreeView.

RESULTS: We demonstrated that many of the alleles that have been associated with liver injury induced by structurally diverse drugs (flucloxacillin, co-amoxiclav, ximelagatran, lapatinib, lumiracoxib) reside on common HLA haplotypes, which were present in populations of diverse ethnicity.

CONCLUSIONS: Our bioinformatic analysis indicates that there may be a connection between the different HLA alleles associated with DILI caused by therapeutically and structurally different drugs, possibly through peptide binding of one of the HLA alleles that defines the causal haplotype. Further functional work, together with next-generation sequencing techniques, will be needed to define the causal alleles associated with DILI.

Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of "big data" for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health arid healthcare for all Europearis.

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Background: Parkinson's disease (PD) presently is conceptualized as a protein aggregation disease in which pathology involves both the enteric and the central nervous system, possibly spreading from one to another via the vagus nerves. As gastrointestinal dysfunction often precedes or parallels motor symptoms, the enteric system with its vast diversity of microorganisms may be involved in PD pathogenesis. Alterations in the enteric microbial taxonomic level of L-DOPA-naive PD patients might also serve as a biomarker.

Methods: We performed metagenomic shotgun analyses and compared the fecal microbiomes of 31 early stage, L-DOPA-naive PD patients to 28 age-matched controls.

Results: We found increased Verrucomicrobiaceae (Akkermansia muciniphila) and unclassified Firmicutes, whereas Prevotellaceae (Prevotella copri) and Erysipelotrichaceae (Eubacterium biforme) were markedly lowered in PD samples. The observed differences could reliably separate PD from control with a ROC-AUC of 0.84. Functional analyses of the metagenomes revealed differences in microbiota metabolism in PD involving the beta-glucuronate and tryptophan metabolism. While the abundances of prophages and plasmids did not differ between PD and controls, total virus abundance was decreased in PD participants. Based on our analyses, the intake of either a MAO inhibitor, amantadine, or a dopamine agonist (which in summary relates to 90% of PD patients) had no overall influence on taxa abundance or microbial functions.

Conclusions: Our data revealed differences of colonic microbiota and of microbiota metabolism between PD patients and controls at an unprecedented detail not achievable through 16S sequencing. The findings point to a yet unappreciated aspect of PD, possibly involving the intestinal barrier function and immune function in PD patients. The influence of the parkinsonian medication should be further investigated in the future in larger cohorts.

Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient.

BACKGROUND: The COVID-19 pandemic, which has a prominent social and economic impact worldwide, shows a largely unexplained male bias for the severity and mortality of the disease. Loss of chromosome Y (LOY) is a risk factor candidate in COVID-19 due to its prior association with many chronic age-related diseases, and its impact on immune gene transcription.

METHODS: Publicly available scRNA-seq data of PBMC samples derived from male patients critically ill with COVID-19 were reanalyzed, and LOY status was added to the annotated cells. We further studied LOY in whole blood for 211 COVID-19 patients treated at intensive care units (ICU) from the first and second waves of the pandemic. Of these, 139 patients were subject to cell sorting for LOY analysis in granulocytes, low-density neutrophils (LDNs), monocytes, and PBMCs.

RESULTS: Reanalysis of available scRNA-seq data revealed LDNs and monocytes as the cell types most affected by LOY. Subsequently, DNA analysis indicated that 46%, 32%, and 29% of critically ill patients showed LOY above 5% cut-off in LDNs, granulocytes, and monocytes, respectively. Hence, the myeloid lineage that is crucial for the development of severe COVID-19 phenotype is affected by LOY. Moreover, LOY correlated with increasing WHO score (median difference 1.59%, 95% HDI 0.46% to 2.71%, p=0.025), death during ICU treatment (median difference 1.46%, 95% HDI 0.47% to 2.43%, p=0.0036), and history of vessel disease (median difference 2.16%, 95% HDI 0.74% to 3.7%, p=0.004), among other variables. In 16 recovered patients, sampled during ICU stay and 93-143 days later, LOY decreased significantly in whole blood and PBMCs. Furthermore, the number of LDNs at the recovery stage decreased dramatically (median difference 76.4 per 10,000 cell sorting events, 95% HDI 55.5 to 104, p=6e-11).

CONCLUSIONS: We present a link between LOY and an acute, life-threatening infectious disease. Furthermore, this study highlights LOY as the most prominent clonal mutation affecting the myeloid cell lineage during emergency myelopoiesis. The correlation between LOY level and COVID-19 severity might suggest that this mutation affects the functions of monocytes and neutrophils, which could have consequences for male innate immunity.

Background: The COVID-19 pandemic, which has a prominent social and economic impact worldwide, shows a largely unexplained male bias for the severity and mortality of the disease. Loss of chromosome Y (LOY) is a risk factor candidate in COVID-19 due to its prior association with many chronic age-related diseases, and its impact on immune gene transcription.

Methods: Publicly available scRNA-seq data of PBMC samples derived from male patients critically ill with COVID-19 were reanalyzed, and LOY status was added to the annotated cells. We further studied LOY in whole blood for 211 COVID-19 patients treated at intensive care units (ICU) from the first and second waves of the pandemic. Of these, 139 patients were subject to cell sorting for LOY analysis in granulocytes, low-density neutrophils (LDNs), monocytes, and PBMCs.

Results: Reanalysis of available scRNA-seq data revealed LDNs and monocytes as the cell types most affected by LOY. Subsequently, DNA analysis indicated that 46%, 32%, and 29% of critically ill patients showed LOY above 5% cut-off in LDNs, granulocytes, and monocytes, respectively. Hence, the myeloid lineage that is crucial for the development of severe COVID-19 phenotype is affected by LOY. Moreover, LOY correlated with increasing WHO score (median difference 1.59%, 95% HDI 0.46% to 2.71%, p=0.025), death during ICU treatment (median difference 1.46%, 95% HDI 0.47% to 2.43%, p=0.0036), and history of vessel disease (median difference 2.16%, 95% HDI 0.74% to 3.7%, p=0.004), among other variables. In 16 recovered patients, sampled during ICU stay and 93–143 days later, LOY decreased significantly in whole blood and PBMCs. Furthermore, the number of LDNs at the recovery stage decreased dramatically (median difference 76.4 per 10,000 cell sorting events, 95% HDI 55.5 to 104, p=6e−11).

Conclusions: We present a link between LOY and an acute, life-threatening infectious disease. Furthermore, this study highlights LOY as the most prominent clonal mutation affecting the myeloid cell lineage during emergency myelopoiesis. The correlation between LOY level and COVID-19 severity might suggest that this mutation affects the functions of monocytes and neutrophils, which could have consequences for male innate immunity.

Systems biology has matured considerably as a discipline over the last decade, yet some of the key challenges separating current research efforts in systems biology and clinically useful results are only now becoming apparent. As these gaps are better defined, the new discipline of systems medicine is emerging as a translational extension of systems biology. How is systems medicine defined? What are relevant ontologies for systems medicine? What are the key theoretic and methodologic challenges facing computational disease modeling? How are inaccurate and incomplete data, and uncertain biologic knowledge best synthesized in useful computational models? Does network analysis provide clinically useful insight? We discuss the outstanding difficulties in translating a rapidly growing body of data into knowledge usable at the bedside. Although core-specific challenges are best met by specialized groups, it appears fundamental that such efforts should be guided by a roadmap for systems medicine drafted by a coalition of scientists from the clinical, experimental, computational, and theoretic domains.

Breast cancer is the most common cancer in women worldwide, and the development of new technologies for better understanding of the molecular changes involved in breast cancer progression is essential. Metabolic changes precede overt phenotypic changes, because cellular regulation ultimately affects the use of small-molecule substrates for cell division, growth or environmental changes such as hypoxia. Differences in metabolism between normal cells and cancer cells have been identified. Because small alterations in enzyme concentrations or activities can cause large changes in overall metabolite levels, the metabolome can be regarded as the amplified output of a biological system. The metabolome coverage in human breast cancer tissues can be maximized by combining different technologies for metabolic profiling. Researchers are investigating alterations in the steady state concentrations of metabolites that reflect amplified changes in genetic control of metabolism. Metabolomic results can be used to classify breast cancer on the basis of tumor biology, to identify new prognostic and predictive markers and to discover new targets for future therapeutic interventions. Here, we examine recent results, including those from the European FP7 project METAcancer consortium, that show that integrated metabolomic analyses can provide information on the stage, subtype and grade of breast tumors and give mechanistic insights. We predict an intensified use of metabolomic screens in clinical and preclinical studies focusing on the onset and progression of tumor development.

Significant advances have been made over the past 5 years in mapping and characterizing structural variation in the human genome. Despite this progress, our understanding of inversion variants is still very restricted. While unbalanced variants such as copy number variations can be mapped using array-based approaches, strategies for characterization of inversion variants have been limited and underdeveloped. Traditional cytogenetic approaches have long been able to identify microscopic inversion events, but discovery of submicroscopic events has remained elusive and largely ignored. With the advent of paired-end sequencing approaches, it is now possible to map inversions across the human genome. Based on the paired-end sequencing studies published to date, it is now feasible to make a first map of inversions across the human genome and to use this map to explore the characteristics and distribution of this form of variation. The current map of inversions indicates that many remain to be identified, especially in the smaller size ranges. This review provides an overview of the current knowledge about human inversions and their contribution to human phenotypes. Further characterization of inversions should be considered as an important step towards a deeper understanding of human variation and genome dynamics.

An amendment to this paper has been published and can be accessed via the original article.

Background

Genomic medicine has paved the way for identifying biomarkers and therapeutically actionable targets for complex diseases, but is complicated by the involvement of thousands of variably expressed genes across multiple cell types. Single-cell RNA-sequencing study (scRNA-seq) allows the characterization of such complex changes in whole organs.

Methods

The study is based on applying network tools to organize and analyze scRNA-seq data from a mouse model of arthritis and human rheumatoid arthritis, in order to find diagnostic biomarkers and therapeutic targets. Diagnostic validation studies were performed using expression profiling data and potential protein biomarkers from prospective clinical studies of 13 diseases. A candidate drug was examined by a treatment study of a mouse model of arthritis, using phenotypic, immunohistochemical, and cellular analyses as read-outs.

Results

We performed the first systematic analysis of pathways, potential biomarkers, and drug targets in scRNA-seq data from a complex disease, starting with inflamed joints and lymph nodes from a mouse model of arthritis. We found the involvement of hundreds of pathways, biomarkers, and drug targets that differed greatly between cell types. Analyses of scRNA-seq and GWAS data from human rheumatoid arthritis (RA) supported a similar dispersion of pathogenic mechanisms in different cell types. Thus, systems-level approaches to prioritize biomarkers and drugs are needed. Here, we present a prioritization strategy that is based on constructing network models of disease-associated cell types and interactions using scRNA-seq data from our mouse model of arthritis, as well as human RA, which we term multicellular disease models (MCDMs). We find that the network centrality of MCDM cell types correlates with the enrichment of genes harboring genetic variants associated with RA and thus could potentially be used to prioritize cell types and genes for diagnostics and therapeutics. We validated this hypothesis in a large-scale study of patients with 13 different autoimmune, allergic, infectious, malignant, endocrine, metabolic, and cardiovascular diseases, as well as a therapeutic study of the mouse arthritis model.

Conclusions

Overall, our results support that our strategy has the potential to help prioritize diagnostic and therapeutic targets in human disease.

BACKGROUND: Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects.

METHODS: Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals.

RESULTS: We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations.

CONCLUSIONS: Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions.

MATLAB code is available at https://github.com/assafgo/warfarin-cohort.

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.

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.

Background: Translational research typically aims to identify and functionally validate individual, disease-specific genes. However, reaching this aim is complicated by the involvement of thousands of genes in common diseases, and that many of those genes are pleiotropic, that is, shared by several diseases.

Methods: We integrated genomic meta-analyses with prospective clinical studies to systematically investigate the pathogenic, diagnostic and therapeutic roles of pleiotropic genes. In a novel approach, we first used pathway analysis of all published genome-wide association studies (GWAS) to find a cell type common to many diseases.

Results: The analysis showed over-representation of the T helper cell differentiation pathway, which is expressed in T cells. This led us to focus on expression profiling of CD4(+) T cells from highly diverse inflammatory and malignant diseases. We found that pleiotropic genes were highly interconnected and formed a pleiotropic module, which was enriched for inflammatory, metabolic and proliferative pathways. The general relevance of this module was supported by highly significant enrichment of genetic variants identified by all GWAS and cancer studies, as well as known diagnostic and therapeutic targets. Prospective clinical studies of multiple sclerosis and allergy showed the importance of both pleiotropic and disease specific modules for clinical stratification.

Conclusions: In summary, this translational genomics study identified a pleiotropic module, which has key pathogenic, diagnostic and therapeutic roles.

Background: Translational research typically aims to identify and functionally validate individual, disease-specific genes. However, reaching this aim is complicated by the involvement of thousands of genes in common diseases, and that many of those genes are pleiotropic, that is, shared by several diseases. Methods: We integrated genomic meta-analyses with prospective clinical studies to systematically investigate the pathogenic, diagnostic and therapeutic roles of pleiotropic genes. In a novel approach, we first used pathway analysis of all published genome-wide association studies (GWAS) to find a cell type common to many diseases. Results: The analysis showed over-representation of the T helper cell differentiation pathway, which is expressed in T cells. This led us to focus on expression profiling of CD4(+) T cells from highly diverse inflammatory and malignant diseases. We found that pleiotropic genes were highly interconnected and formed a pleiotropic module, which was enriched for inflammatory, metabolic and proliferative pathways. The general relevance of this module was supported by highly significant enrichment of genetic variants identified by all GWAS and cancer studies, as well as known diagnostic and therapeutic targets. Prospective clinical studies of multiple sclerosis and allergy showed the importance of both pleiotropic and disease specific modules for clinical stratification. Conclusions: In summary, this translational genomics study identified a pleiotropic module, which has key pathogenic, diagnostic and therapeutic roles.

Many common diseases, such as asthma, diabetes or obesity, involve altered interactions between thousands of genes. High-throughput techniques (omics) allow identification of such genes and their products, but functional understanding is a formidable challenge. Network-based analyses of omics data have identified modules of disease-associated genes that have been used to obtain both a systems level and a molecular understanding of disease mechanisms. For example, in allergy a module was used to find a novel candidate gene that was validated by functional and clinical studies. Such analyses play important roles in systems medicine. This is an emerging discipline that aims to gain a translational understanding of the complex mechanisms underlying common diseases. In this review, we will explain and provide examples of how network-based analyses of omics data, in combination with functional and clinical studies, are aiding our understanding of disease, as well as helping to prioritize diagnostic markers or therapeutic candidate genes. Such analyses involve significant problems and limitations, which will be discussed. We also highlight the steps needed for clinical implementation.

BACKGROUND: The advent of direct-to-consumer (DTC) genetic testing (GT) has sparked a number of debates regarding the scientific validity of tests, their broad health and ethical implications for society as well as their legal status. To date, relatively few empirical studies have been published regarding this phenomenon. We conducted a survey of European clinical geneticists to gauge their awareness of, experiences with, and attitudes towards DTC GT.

METHODS: We invited 300 clinical geneticists from 28 European countries to complete an online questionnaire. Statistical analyses of closed-ended questions were performed using the STATISTICA software package. Answers to open-ended questions were analysed for recurring themes.

RESULTS: One hundred and thirty-one clinical geneticists answered our survey (response rate, 44%). Eighty-six percent (110/128) of respondents were aware of DTC GT, and over one-third had been contacted by at least one patient regarding these services. The majority (84%) of respondents did not agree with telephone medical supervision outside of an established doctor-patient relationship. The majority of clinical geneticists also found it unacceptable to provide non-face-to-face medical supervision for: (i) a presymptomatic test for a condition with very high penetrance; (ii) a predictive test for a condition that has a 'medium' penetrance of 50% to 60%; and (iii) carrier testing. For conditions that are neither treatable nor preventable and for disorders with serious health consequences, clinical geneticists were almost unanimous in expressing the unacceptability of offering such genetic tests outside of the traditional healthcare setting, without an established physician-patient relationship and without face-to-face medical supervision.

CONCLUSION: A high percentage of European clinical geneticists are aware of DTC GT and the majority do not agree with the model of provision used by many commercial companies for certain severe or actionable health conditions. Despite this disagreement with the DTC model of provision, >85% of respondents said that they would offer genetic counselling to patients who asked for a consultation after having undergone DTC genetic testing. The understanding of the views and opinions of this expert stakeholder group should be considered in the attempts to shape responsible policy and guidelines for these services.

Background:

The Ras pathway genes KRAS, BRAF, or ERBBs have somatic mutations in similar to 60% of human colorectal carcinomas. At present, it is unknown whether the remaining cases lack mutations activating the Ras pathway or whether they have acquired mutations in genes hitherto unknown to belong to the pathway.

Methods:

To address the second possibility and extend the compendium of Ras pathway genes, we used genome-wide transposon mutagenesis of two human colorectal cancer cell systems deprived of their activating KRAS or BRAF allele to identify genes enabling growth in low glucose, a Ras pathway phenotype, when targeted.

Results:

Of the 163 recurrently targeted genes in the two different genetic backgrounds, one-third were known cancer genes and one-fifth had links to the EGFR/Ras/MAPK pathway. When compared to cancer genome sequencing datasets, nine genes also mutated in human colorectal cancers were identified. Among these, stable knockdown of FOXO3, NCOA3, and TCF7L2 restored growth in low glucose but reduced MEK/MAPK phosphorylation, reduced anchorage-independent growth, and modulated expressions of GLUT1 and Ras pathway related proteins. Knockdown of NCOA3 and FOXO3 significantly decreased the sensitivity to cetuximab of KRAS mutant but not wild-type cells.

Conclusions:

This work establishes a proof-of-concept that human cell-based genome-wide forward genetic screens can assign genes to pathways with clinical importance in human colorectal cancer.

BACKGROUND

A function for the microRNA (miRNA) pathway in vascular development and angiogenesis has been firmly established. miRNAs with selective expression in the vasculature are attractive as possible targets in miRNA-based therapies. However, little is known about the expression of miRNAs in microvessels in vivo. Here, we identified candidate microvascular-selective miRNAs by screening public miRNA expression datasets.

METHODS

Bioinformatics predictions of microvascular-selective expression were validated with real-time quantitative reverse transcription PCR on purified microvascular fragments from mouse. Pericyte expression was shown with in situ hybridization on tissue sections. Target sites were identified with 3' UTR luciferase assays, and migration was tested in a microfluid chemotaxis chamber.

RESULTS

miR-145, miR-126, miR-24, and miR-23a were selectively expressed in microvascular fragments isolated from a range of tissues. In situ hybridization and analysis of Pdgfb retention motif mutant mice demonstrated predominant expression of miR-145 in pericytes. We identified the Ets transcription factor Friend leukemia virus integration 1 (Fli1) as a miR-145 target, and showed that elevated levels of miR-145 reduced migration of microvascular cells in response to growth factor gradients in vitro.

CONCLUSIONS

miR-126, miR-24 and miR-23a are selectively expressed in microvascular endothelial cells in vivo, whereas miR-145 is expressed in pericytes. miR-145 targets the hematopoietic transcription factor Fli1 and blocks migration in response to growth factor gradients. Our findings have implications for vascular disease and provide necessary information for future drug design against miRNAs with selective expression in the microvasculature.

Background Medical digital twins are computational disease models for drug discovery and treatment. Unresolved problems include how to organize and prioritize between disease-associated changes in digital twins, on cellulome- and genome-wide scales. We present a dynamic framework that can be used to model such changes and thereby prioritize upstream regulators (URs) for biomarker- and drug discovery. Methods We started with seasonal allergic rhinitis (SAR) as a disease model, by analyses of in vitro allergen-stimulated peripheral blood mononuclear cells (PBMC) from SAR patients. Time-series a single-cell RNA-sequencing (scRNA-seq) data of these cells were used to construct multicellular network models (MNMs) at each time point of molecular interactions between cell types. We hypothesized that predicted molecular interactions between cell types in the MNMs could be traced to find an UR gene, at an early time point. We performed bioinformatic and functional studies of the MNMs to develop a scalable framework to prioritize UR genes. This framework was tested on a single-cell and bulk-profiling data from SAR and other inflammatory diseases. Results Our scRNA-seq-based time-series MNMs of SAR showed thousands of differentially expressed genes (DEGs) across multiple cell types, which varied between time points. Instead of a single-UR gene in each MNM, we found multiple URs dispersed across the cell types. Thus, at each time point, the MNMs formed multi-directional networks. The absence of linear hierarchies and time-dependent variations in MNMs complicated the prioritization of URs. For example, the expression and functions of Th2 cytokines, which are approved drug targets in allergies, varied across cell types, and time points. Our analyses of bulk- and single-cell data from other inflammatory diseases also revealed multi-directional networks that showed stage-dependent variations. We therefore developed a quantitative approach to prioritize URs: we ranked the URs based on their predicted effects on downstream target cells. Experimental and bioinformatic analyses supported that this kind of ranking is a tractable approach for prioritizing URs. Conclusions We present a scalable framework for modeling dynamic changes in digital twins, on cellulome- and genome-wide scales, to prioritize UR genes for biomarker and drug discovery.

BackgroundSince different types of genetic variants, from single nucleotide variants (SNVs) to large chromosomal rearrangements, underlie intellectual disability, we evaluated the use of whole-genome sequencing (WGS) rather than chromosomal microarray analysis (CMA) as a first-line genetic diagnostic test.MethodsWe analyzed three cohorts with short-read WGS: (i) a retrospective cohort with validated copy number variants (CNVs) (cohort 1, n=68), (ii) individuals referred for monogenic multi-gene panels (cohort 2, n=156), and (iii) 100 prospective, consecutive cases referred to our center for CMA (cohort 3). Bioinformatic tools developed include FindSV, SVDB, Rhocall, Rhoviz, and vcf2cytosure.ResultsFirst, we validated our structural variant (SV)-calling pipeline on cohort 1, consisting of three trisomies and 79 deletions and duplications with a median size of 850kb (min 500bp, max 155Mb). All variants were detected. Second, we utilized the same pipeline in cohort 2 and analyzed with monogenic WGS panels, increasing the diagnostic yield to 8%. Next, cohort 3 was analyzed by both CMA and WGS. The WGS data was processed for large (>10kb) SVs genome-wide and for exonic SVs and SNVs in a panel of 887 genes linked to intellectual disability as well as genes matched to patient-specific Human Phenotype Ontology (HPO) phenotypes. This yielded a total of 25 pathogenic variants (SNVs or SVs), of which 12 were detected by CMA as well. We also applied short tandem repeat (STR) expansion detection and discovered one pathologic expansion in ATXN7. Finally, a case of Prader-Willi syndrome with uniparental disomy (UPD) was validated in the WGS data.Important positional information was obtained in all cohorts. Remarkably, 7% of the analyzed cases harbored complex structural variants, as exemplified by a ring chromosome and two duplications found to be an insertional translocation and part of a cryptic unbalanced translocation, respectively.ConclusionThe overall diagnostic rate of 27% was more than doubled compared to clinical microarray (12%). Using WGS, we detected a wide range of SVs with high accuracy. Since the WGS data also allowed for analysis of SNVs, UPD, and STRs, it represents a powerful comprehensive genetic test in a clinical diagnostic laboratory setting.

Background: Since different types of genetic variants, from single nucleotide variants (SNVs) to large chromosomal rearrangements, underlie intellectual disability, we evaluated the use of whole-genome sequencing (WGS) rather than chromosomal microarray analysis (CMA) as a first-line genetic diagnostic test.

Methods: We analyzed three cohorts with short-read WGS: (i) a retrospective cohort with validated copy number variants (CNVs) (cohort 1, n=68), (ii) individuals referred for monogenic multi-gene panels (cohort 2, n=156), and (iii) 100 prospective, consecutive cases referred to our center for CMA (cohort 3). Bioinformatic tools developed include FindSV, SVDB, Rhocall, Rhoviz, and vcf2cytosure.

Results: First, we validated our structural variant (SV)-calling pipeline on cohort 1, consisting of three trisomies and 79 deletions and duplications with a median size of 850kb (min 500bp, max 155Mb). All variants were detected. Second, we utilized the same pipeline in cohort 2 and analyzed with monogenic WGS panels, increasing the diagnostic yield to 8%. Next, cohort 3 was analyzed by both CMA and WGS. The WGS data was processed for large (>10kb) SVs genome-wide and for exonic SVs and SNVs in a panel of 887 genes linked to intellectual disability as well as genes matched to patient-specific Human Phenotype Ontology (HPO) phenotypes. This yielded a total of 25 pathogenic variants (SNVs or SVs), of which 12 were detected by CMA as well. We also applied short tandem repeat (STR) expansion detection and discovered one pathologic expansion in ATXN7. Finally, a case of Prader-Willi syndrome with uniparental disomy (UPD) was validated in the WGS data. Important positional information was obtained in all cohorts. Remarkably, 7% of the analyzed cases harbored complex structural variants, as exemplified by a ring chromosome and two duplications found to be an insertional translocation and part of a cryptic unbalanced translocation, respectively.

Conclusion: The overall diagnostic rate of 27% was more than doubled compared to clinical microarray (12%). Using WGS, we detected a wide range of SVs with high accuracy. Since the WGS data also allowed for analysis of SNVs, UPD, and STRs, it represents a powerful comprehensive genetic test in a clinical diagnostic laboratory setting.

Background A comprehensive understanding of the pre-existing genetic variation in genes associated with antibiotic resistance in the Mycobacterium tuberculosis complex (MTBC) is needed to accurately interpret whole-genome sequencing data for genotypic drug susceptibility testing (DST). Methods We investigated mutations in 92 genes implicated in resistance to 21 anti-tuberculosis drugs using the genomes of 405 phylogenetically diverse MTBC strains. The role of phylogenetically informative mutations was assessed by routine phenotypic DST data for the first-line drugs isoniazid, rifampicin, ethambutol, and pyrazinamide from a separate collection of over 7000 clinical strains. Selected mutations/strains were further investigated by minimum inhibitory concentration (MIC) testing. Results Out of 547 phylogenetically informative mutations identified, 138 were classified as not correlating with resistance to first-line drugs. MIC testing did not reveal a discernible impact of a Rv1979c deletion shared by M. africanum lineage 5 strains on resistance to clofazimine. Finally, we found molecular evidence that some MTBC subgroups may be hyper-susceptible to bedaquiline and clofazimine by different loss-of-function mutations affecting a drug efflux pump subunit (MmpL5). Conclusions Our findings underline that the genetic diversity in MTBC has to be studied more systematically to inform the design of clinical trials and to define sound epidemiologic cut-off values (ECOFFs) for new and repurposed anti-tuberculosis drugs. In that regard, our comprehensive variant catalogue provides a solid basis for the interpretation of mutations in genotypic as well as in phenotypic DST assays.

Nearly 40 years ago, DNA was sequenced for the first time. Since then, DNA sequencing has undergone continuous development, passing through three generations of sequencing technology. We are now entering the beginning of a new phase of genomic analysis in which massively parallel sequencing is performed directly in the cell. Two methods have recently been described for in situ RNA sequencing, one targeted and one untargeted, that rely on ligation chemistry. This fourth generation of sequencing technology opens up prospects for transcriptomic analysis, biomarker validation, diagnosis and patient stratification for cancer treatment.

Background

Public trust is central to the collection of genomic and health data and the sustainability of genomic research. To merit trust, those involved in collecting and sharing data need to demonstrate they are trustworthy. However, it is unclear what measures are most likely to demonstrate this.

Methods

We analyse the ‘Your DNA, Your Say’ online survey of public perspectives on genomic data sharing including responses from 36,268 individuals across 22 low-, middle- and high-income countries, gathered in 15 languages. We examine how participants perceived the relative value of measures to demonstrate the trustworthiness of those using donated DNA and/or medical information. We examine between-country variation and present a consolidated ranking of measures.

Results

Providing transparent information about who will benefit from data access was the most important measure to increase trust, endorsed by more than 50% of participants across 20 of 22 countries. It was followed by the option to withdraw data and transparency about who is using data and why. Variation was found for the importance of measures, notably information about sanctions for misuse of data—endorsed by 5% in India but almost 60% in Japan. A clustering analysis suggests alignment between some countries in the assessment of specific measures, such as the UK and Canada, Spain and Mexico and Portugal and Brazil. China and Russia are less closely aligned with other countries in terms of the value of the measures presented.

Conclusions

Our findings highlight the importance of transparency about data use and about the goals and potential benefits associated with data sharing, including to whom such benefits accrue. They show that members of the public value knowing what benefits accrue from the use of data. The study highlights the importance of locally sensitive measures to increase trust as genomic data sharing continues globally.

In the accompanying study, Nimmo and colleagues estimated the dates of emergence of mutations in mmpR5 (Rv0678), the most important resistance gene to the anti-tuberculosis drug bedaquiline, in over 3500 geographically diverse Mycobacterium tuberculosis genomes. This provided important insights to improve the design and analysis of clinical trials, as well as the World Health Organization catalogue of resistance mutations, the global reference for interpreting genotypic antimicrobial susceptibility testing results.

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 x 10(-6)) and AC058822.1 (P = 1.47 x 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 x 10(-5)), demonstrating the importance of diversifying study cohorts.

Because of the changes in demographic structure, the prevalence of Alzheimer's disease is expected to rise dramatically over the next decades. The progression of this degenerative and terminal disease is gradual, with the subclinical stage of illness believed to span several decades. Despite this, no therapy to prevent or cure Alzheimer's disease is currently available. Early disease detection is still important for delaying the onset of the disease with pharmacological treatment and/or lifestyle changes, assessing the efficacy of potential therapeutic agents, or monitoring disease progression more closely using medical imaging. Sensitive cerebrospinal-fluid-derived marker candidates exist, but given the invasiveness of sample collection their use in routine diagnostics may be limited. The pathogenesis of Alzheimer's disease is complex and poorly understood. There is thus a strong case for integrating information across multiple physiological levels, from molecular profiling (metabolomics, lipidomics, proteomics and transcriptomics) and brain imaging to cognitive assessments. To facilitate the integration of heterogeneous data, such as molecular and image data, sophisticated statistical approaches are needed to segment the image data and study their dependencies on molecular changes in the same individuals. Molecular profiling, combined with biophysical modeling of molecular assemblies associated with the disease, offer an opportunity to link the molecular pathway changes with cell- and tissue-level physiology and structure. Given that data acquired at different levels can carry complementary information about early Alzheimer's disease pathology, it is expected that their integration will improve early detection as well as our understanding of the disease.

Background: Several theories have been proposed to conceptualize the pathological processes inherent to schizophrenia. The 'prostaglandin deficiency' hypothesis postulates that defective enzyme systems converting essential fatty acids to prostaglandins lead to diminished levels of prostaglandins, which in turn affect synaptic transmission.

Methods: Here we sought to determine the lipidomic profiles associated with schizophrenia in twin pairs discordant for schizophrenia as well as unaffected twin pairs. The study included serum samples from 19 twin pairs discordant for schizophrenia (mean age 51 +/- 10 years; 7 monozygotic pairs; 13 female pairs) and 34 age and gender matched healthy twins as controls. Neurocognitive assessment data and gray matter density measurements taken from high-resolution magnetic resonance images were also obtained. A lipidomics platform using ultra performance liquid chromatography coupled to time-of-flight mass spectrometry was applied for the analysis of serum samples.

Results: In comparison to their healthy co-twins, the patients had elevated triglycerides and were more insulin resistant. They had diminished lysophosphatidylcholine levels, which associated with decreased cognitive speed.

Conclusions: Our findings may be of pathophysiological relevance since lysophosphatidylcholines, byproducts of phospholipase A2-catalyzed phospholipid hydrolysis, are preferred carriers of polyunsaturated fatty acids across the blood-brain barrier. Furthermore, diminishment of lysophosphatidylcholines suggests that subjects at risk of schizophrenia may be more susceptible to infections. Their association with cognitive speed supports the view that altered neurotransmission in schizophrenia may be in part mediated by reactive lipids such as prostaglandins.

BACKGROUND: Persons with schizophrenia and other psychotic disorders have a high prevalence of obesity, impaired glucose tolerance, and lipid abnormalities, particularly hypertriglyceridemia and low high-density lipoprotein. More detailed molecular information on the metabolic abnormalities may reveal clues about the pathophysiology of these changes, as well as about disease specificity.

METHODS: We applied comprehensive metabolomics in serum samples from a general population-based study in Finland. The study included all persons with DSM-IV primary psychotic disorder (schizophrenia, n = 45; other non-affective psychosis (ONAP), n = 57; affective psychosis, n = 37) and controls matched by age, sex, and region of residence. Two analytical platforms for metabolomics were applied to all serum samples: a global lipidomics platform based on ultra-performance liquid chromatography coupled to mass spectrometry, which covers molecular lipids such as phospholipids and neutral lipids; and a platform for small polar metabolites based on two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS).

RESULTS: Compared with their matched controls, persons with schizophrenia had significantly higher metabolite levels in six lipid clusters containing mainly saturated triglycerides, and in two small-molecule clusters containing, among other metabolites, (1) branched chain amino acids, phenylalanine and tyrosine, and (2) proline, glutamic, lactic and pyruvic acids. Among these, serum glutamic acid was elevated in all psychoses (P = 0.0020) compared to controls, while proline upregulation (P = 0.000023) was specific to schizophrenia. After adjusting for medication and metabolic comorbidity in linear mixed models, schizophrenia remained independently associated with higher levels in seven of these eight clusters (P < 0.05 in each cluster). The metabolic abnormalities were less pronounced in persons with ONAP or affective psychosis.

CONCLUSIONS: Our findings suggest that specific metabolic abnormalities related to glucoregulatory processes and proline metabolism are specifically associated with schizophrenia and reflect two different disease-related pathways. Metabolomics, which is sensitive to both genetic and environmental variation, may become a powerful tool in psychiatric research to investigate disease susceptibility, clinical course, and treatment response.

Background Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.Methods Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.Results scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment.Conclusions We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).

Background: Cancer patients often show no or only modest benefit from a given therapy. This major problem in oncology is generally attributed to the lack of specific predictive biomarkers, yet a global measure of cancer cell activity may support a comprehensive mechanistic understanding of therapy efficacy. We reasoned that network analysis of omic data could help to achieve this goal. Methods: A measure of "cancer network activity" (CNA) was implemented based on a previously defined network feature of communicability. The network nodes and edges corresponded to human proteins and experimentally identified interactions, respectively. The edges were weighted proportionally to the expression of the genes encoding for the corresponding proteins and relative to the number of direct interactors. The gene expression data corresponded to the basal conditions of 595 human cancer cell lines. Therapeutic responses corresponded to the impairment of cell viability measured by the half maximal inhibitory concentration (IC50) of 130 drugs approved or under clinical development. Gene ontology, signaling pathway, and transcription factor-binding annotations were taken from public repositories. Predicted synergies were assessed by determining the viability of four breast cancer cell lines and by applying two different analytical methods. Results: The effects of drug classes were associated with CNAs formed by different cell lines. CNAs also differentiate target families and effector pathways. Proteins that occupy a central position in the network largely contribute to CNA. Known key cancer-associated biological processes, signaling pathways, and master regulators also contribute to CNA. Moreover, the major cancer drivers frequently mediate CNA and therapeutic differences. Cell-based assays centered on these differences and using uncorrelated drug effects reveals novel synergistic combinations for the treatment of breast cancer dependent on PI3K-mTOR signaling. Conclusions: Cancer therapeutic responses can be predicted on the basis of a systems-level analysis of molecular interactions and gene expression. Fundamental cancer processes, pathways, and drivers contribute to this feature, which can also be exploited to predict precise synergistic drug combinations.

Background We report the findings from 4437 individuals (3219 patients and 1218 relatives) who have been analyzed by whole genome sequencing (WGS) at the Genomic Medicine Center Karolinska-Rare Diseases (GMCK-RD) since mid-2015. GMCK-RD represents a long-term collaborative initiative between Karolinska University Hospital and Science for Life Laboratory to establish advanced, genomics-based diagnostics in the Stockholm healthcare setting. Methods Our analysis covers detection and interpretation of SNVs, INDELs, uniparental disomy, CNVs, balanced structural variants, and short tandem repeat expansions. Visualization of results for clinical interpretation is carried out in Scout-a custom-developed decision support system. Results from both singleton (84%) and trio/family (16%) analyses are reported. Variant interpretation is done by 15 expert teams at the hospital involving staff from three clinics. For patients with complex phenotypes, data is shared between the teams. Results Overall, 40% of the patients received a molecular diagnosis ranging from 19 to 54% for specific disease groups. There was heterogeneity regarding causative genes (n = 754) with some of the most common ones being COL2A1 (n = 12; skeletal dysplasia), SCN1A (n = 8; epilepsy), and TNFRSF13B (n = 4; inborn errors of immunity). Some causative variants were recurrent, including previously known founder mutations, some novel mutations, and recurrent de novo mutations. Overall, GMCK-RD has resulted in a large number of patients receiving specific molecular diagnoses. Furthermore, negative cases have been included in research studies that have resulted in the discovery of 17 published, novel disease-causing genes. To facilitate the discovery of new disease genes, GMCK-RD has joined international data sharing initiatives, including ClinVar, UDNI, Beacon, and MatchMaker Exchange. Conclusions Clinical WGS at GMCK-RD has provided molecular diagnoses to over 1200 individuals with a broad range of rare diseases. Consolidation and spread of this clinical-academic partnership will enable large-scale national collaboration.

Background: The proteome is a major source of therapeutic targets. We conducted a proteome-wide Mendelian randomization (MR) study to identify candidate protein markers and therapeutic targets for colorectal cancer (CRC).

Methods: Protein quantitative trait loci (pQTLs) were derived from seven published genome-wide association studies (GWASs) on plasma proteome, and summary-level data were extracted for 4853 circulating protein markers. Genetic associations with CRC were obtained from a large-scale GWAS meta-analysis (16,871 cases and 26,328 controls), the FinnGen cohort (4957 cases and 304,197 controls), and the UK Biobank (9276 cases and 477,069 controls). Colocalization and summary-data-based MR (SMR) analyses were performed sequentially to verify the causal role of candidate proteins. Single cell-type expression analysis, protein-protein interaction (PPI), and druggability evaluation were further conducted to detect the specific cell type with enrichment expression and prioritize potential therapeutic targets.

Results: Collectively, genetically predicted levels of 13 proteins were associated with CRC risk. Elevated levels of two proteins (GREM1, CHRDL2) and decreased levels of 11 proteins were associated with an increased risk of CRC, among which four (GREM1, CLSTN3, CSF2RA, CD86) were prioritized with the most convincing evidence. These protein-coding genes are mainly expressed in tissue stem cells, epithelial cells, and monocytes in colon tumor tissue. Two interactive pairs of proteins (GREM1 and CHRDL2; MMP2 and TIMP2) were identified to be involved in osteoclast differentiation and tumorigenesis pathways; four proteins (POLR2F, CSF2RA, CD86, MMP2) have been targeted for drug development on autoimmune diseases and other cancers, with the potentials of being repurposed as therapeutic targets for CRC.

Conclusions: This study identified several protein biomarkers to be associated with CRC risk and provided new insights into the etiology and promising targets for the development of screening biomarkers and therapeutic drugs for CRC.

Background For the majority of rare clinical missense variants, pathogenicity status cannot currently be classified. Classical homocystinuria, characterized by elevated homocysteine in plasma and urine, is caused by variants in the cystathionine beta-synthase (CBS) gene, most of which are rare. With early detection, existing therapies are highly effective. Methods Damaging CBS variants can be detected based on their failure to restore growth in yeast cells lacking the yeast ortholog CYS4. This assay has only been applied reactively, after first observing a variant in patients. Using saturation codon-mutagenesis, en masse growth selection, and sequencing, we generated a comprehensive, proactive map of CBS missense variant function. Results Our CBS variant effect map far exceeds the performance of computational predictors of disease variants. Map scores correlated strongly with both disease severity (Spearman's rho = 0.9) and human clinical response to vitamin B-6 (rho = 0.93). Conclusions We demonstrate that highly multiplexed cell-based assays can yield proactive maps of variant function and patient response to therapy, even for rare variants not previously seen in the clinic.

BACKGROUND: Antimicrobial-resistant (AMR) Neisseria gonorrhoeae is an urgent threat to public health, as strains resistant to at least one of the two last-line antibiotics used in empiric therapy of gonorrhoea, ceftriaxone and azithromycin, have spread internationally. Whole genome sequencing (WGS) data can be used to identify new AMR clones and transmission networks and inform the development of point-of-care tests for antimicrobial susceptibility, novel antimicrobials and vaccines. Community-driven tools that provide an easy access to and analysis of genomic and epidemiological data is the way forward for public health surveillance.

METHODS: Here we present a public health-focussed scheme for genomic epidemiology of N. gonorrhoeae at Pathogenwatch ( https://pathogen.watch/ngonorrhoeae ). An international advisory group of experts in epidemiology, public health, genetics and genomics of N. gonorrhoeae was convened to inform on the utility of current and future analytics in the platform. We implement backwards compatibility with MLST, NG-MAST and NG-STAR typing schemes as well as an exhaustive library of genetic AMR determinants linked to a genotypic prediction of resistance to eight antibiotics. A collection of over 12,000 N. gonorrhoeae genome sequences from public archives has been quality-checked, assembled and made public together with available metadata for contextualization.

RESULTS: AMR prediction from genome data revealed specificity values over 99% for azithromycin, ciprofloxacin and ceftriaxone and sensitivity values around 99% for benzylpenicillin and tetracycline. A case study using the Pathogenwatch collection of N. gonorrhoeae public genomes showed the global expansion of an azithromycin-resistant lineage carrying a mosaic mtr over at least the last 10 years, emphasising the power of Pathogenwatch to explore and evaluate genomic epidemiology questions of public health concern.

CONCLUSIONS: The N. gonorrhoeae scheme in Pathogenwatch provides customised bioinformatic pipelines guided by expert opinion that can be adapted to public health agencies and departments with little expertise in bioinformatics and lower-resourced settings with internet connection but limited computational infrastructure. The advisory group will assess and identify ongoing public health needs in the field of gonorrhoea, particularly regarding gonococcal AMR, in order to further enhance utility with modified or new analytic methods.

Following the publication of the complete human genomic sequence, the post-genomic era is driven by the need to extract useful information from genomic data. Genomics, transcriptomics, proteomics, metabolomics, epidemiological data and microbial data provide different angles to our understanding of gene-environment interactions and the determinants of disease and health. Our goal and our challenge are to integrate these very different types of data and perspectives of disease into a global model suitable for dissecting the mechanisms of disease and for predicting novel therapeutic strategies. This review aims to highlight the need for and problems with complex data integration, and proposes a framework for data integration. While there are many obstacles to overcome, biological models based upon multiple datasets will probably become the basis that drives future biomedical research.

Background: Skeletal muscle is one of the primary tissues involved in the development of type 2 diabetes (T2D). The close association between obesity and T2D makes it difficult to isolate specific effects attributed to the disease alone. Therefore, here we set out to identify and characterize intrinsic properties of myocytes, associated independently with T2D or obesity. Methods: We generated and analyzed RNA-seq data from primary differentiated myotubes from 24 human subjects, using a factorial design (healthy/T2D and non-obese/obese), to determine the influence of each specific factor on genome-wide transcription. This setup enabled us to identify intrinsic properties, originating from muscle precursor cells and retained in the corresponding myocytes. Bioinformatic and statistical methods, including differential expression analysis, gene-set analysis, and metabolic network analysis, were used to characterize the different myocytes. Results: We found that the transcriptional program associated with obesity alone was strikingly similar to that induced specifically by T2D. We identified a candidate epigenetic mechanism, H3K27me3 histone methylation, mediating these transcriptional signatures. T2D and obesity were independently associated with dysregulated myogenesis, down-regulated muscle function, and up-regulation of inflammation and extracellular matrix components. Metabolic network analysis identified that in T2D but not obesity a specific metabolite subnetwork involved in sphingolipid metabolism was transcriptionally regulated. Conclusions: Our findings identify inherent characteristics in myocytes, as a memory of the in vivo phenotype, without the influence from a diabetic or obese extracellular environment, highlighting their importance in the development of T2D.

Background: The mechanisms by which genetic variants, such as single nucleotide polymorphisms (SNPs), identified in genome-wide association studies act to influence body mass remain unknown for most of these SNPs, which continue to puzzle the scientific community. Recent evidence points to the epigenetic and chromatin states of the genome as having important roles. Methods: We genotyped 355 healthy young individuals for 52 known obesity-associated SNPs and obtained DNA methylation levels in their blood using the Illumina 450 K BeadChip. Associations between alleles and methylation at proximal cytosine residues were tested using a linear model adjusted for age, sex, weight category, and a proxy for blood cell type counts. For replication in other tissues, we used two open-access datasets (skin fibroblasts, n = 62; four brain regions, n = 121-133) and an additional dataset in subcutaneous and visceral fat (n = 149). Results: We found that alleles at 28 of these obesity-associated SNPs associate with methylation levels at 107 proximal CpG sites. Out of 107 CpG sites, 38 are located in gene promoters, including genes strongly implicated in obesity (MIR148A, BDNF, PTPMT1, NR1H3, MGAT1, SCGB3A1, HOXC12, PMAIP1, PSIP1, RPS10-NUDT3, RPS10, SKOR1, MAP2K5, SIX5, AGRN, IMMP1L, ELP4, ITIH4, SEMA3G, POMC, ADCY3, SSPN, LGR4, TUFM, MIR4721, SULT1A1, SULT1A2, APOBR, CLN3, SPNS1, SH2B1, ATXN2L, and IL27). Interestingly, the associated SNPs are in known eQTLs for some of these genes. We also found that the 107 CpGs are enriched in enhancers in peripheral blood mononuclear cells. Finally, our results indicate that some of these associations are not blood-specific as we successfully replicated four associations in skin fibroblasts. Conclusions: Our results strongly suggest that many obesity-associated SNPs are associated with proximal gene regulation, which was reflected by association of obesity risk allele genotypes with differential DNA methylation. This study highlights the importance of DNA methylation and other chromatin marks as a way to understand the molecular basis of genetic variants associated with human diseases and traits.

A report on the 9th Annual Cold Spring Harbor/Wellcome Trust meeting 'Pharmacogenomics and Personalized Medicine', Hinxton, Cambridge, UK, 29 September to 2 October 2011.

Background: Although polygenic risk score (PRS) has emerged as a promising tool for predicting cancer risk from genome-wide association studies (GWAS), the individual-level accuracy of lung cancer PRS and the extent to which its impact on subsequent clinical applications remains largely unexplored.

Methods: Lung cancer PRSs and confidence/credible interval (CI) were constructed using two statistical approaches for each individual: (1) the weighted sum of 16 GWAS-derived significant SNP loci and the CI through the bootstrapping method (PRS-16-CV) and (2) LDpred2 and the CI through posteriors sampling (PRS-Bayes), among 17,166 lung cancer cases and 12,894 controls with European ancestry from the International Lung Cancer Consortium. Individuals were classified into different genetic risk subgroups based on the relationship between their own PRS mean/PRS CI and the population level threshold.

Results: Considerable variances in PRS point estimates at the individual level were observed for both methods, with an average standard deviation (s.d.) of 0.12 for PRS-16-CV and a much larger s.d. of 0.88 for PRS-Bayes. Using PRS-16-CV, only 25.0% of individuals with PRS point estimates in the lowest decile of PRS and 16.8% in the highest decile have their entire 95% CI fully contained in the lowest and highest decile, respectively, while PRS-Bayes was unable to find any eligible individuals. Only 19% of the individuals were concordantly identified as having high genetic risk (> 90th percentile) using the two PRS estimators. An increased relative risk of lung cancer comparing the highest PRS percentile to the lowest was observed when taking the CI into account (OR = 2.73, 95% CI: 2.12–3.50, P-value = 4.13 × 10⁻¹⁵) compared to using PRS-16-CV mean (OR = 2.23, 95% CI: 1.99–2.49, P-value = 5.70 × 10⁻⁴⁶). Improved risk prediction performance with higher AUC was consistently observed in individuals identified by PRS-16-CV CI, and the best performance was achieved by incorporating age, gender, and detailed smoking pack-years (AUC: 0.73, 95% CI = 0.72–0.74). Conclusions: Lung cancer PRS estimates using different methods have modest correlations at the individual level, highlighting the importance of considering individual-level uncertainty when evaluating the practical utility of PRS.

Editorial summaryGenomic medicine has considerable potential to provide novel diagnostic and therapeutic solutions for patients who have molecularly complex diseases and who are not responding to existing therapies. To bridge the gap between genomic medicine and clinical practice, integration of various data types, resources, and joint international initiatives will be required.

Background The human plasma proteome is important for many biological processes and targets for diagnostics and therapy. It is therefore of great interest to understand the interplay of genetic and environmental factors to determine the specific protein levels in individuals and to gain a deeper insight of the importance of genetic architecture related to the individual variability of plasma levels of proteins during adult life. Methods We have combined whole-genome sequencing, multiplex plasma protein profiling, and extensive clinical phenotyping in a longitudinal 2-year wellness study of 101 healthy individuals with repeated sampling. Analyses of genetic and non-genetic associations related to the variability of blood levels of proteins in these individuals were performed. Results The analyses showed that each individual has a unique protein profile, and we report on the intra-individual as well as inter-individual variation for 794 plasma proteins. A genome-wide association study (GWAS) using 7.3 million genetic variants identified by whole-genome sequencing revealed 144 independent variants across 107 proteins that showed strong association (P < 6 x 10(-11)) between genetics and the inter-individual variability on protein levels. Many proteins not reported before were identified (67 out of 107) with individual plasma level affected by genetics. Our longitudinal analysis further demonstrates that these levels are stable during the 2-year study period. The variability of protein profiles as a consequence of environmental factors was also analyzed with focus on the effects of weight loss and infections. Conclusions We show that the adult blood levels of many proteins are determined at birth by genetics, which is important for efforts aimed to understand the relationship between plasma proteome profiles and human biology and disease.

Background The human plasma proteome is important for many biological processes and targets for diagnostics and therapy. It is therefore of great interest to understand the interplay of genetic and environmental factors to determine the specific protein levels in individuals and to gain a deeper insight of the importance of genetic architecture related to the individual variability of plasma levels of proteins during adult life. Methods We have combined whole-genome sequencing, multiplex plasma protein profiling, and extensive clinical phenotyping in a longitudinal 2-year wellness study of 101 healthy individuals with repeated sampling. Analyses of genetic and non-genetic associations related to the variability of blood levels of proteins in these individuals were performed. Results The analyses showed that each individual has a unique protein profile, and we report on the intra-individual as well as inter-individual variation for 794 plasma proteins. A genome-wide association study (GWAS) using 7.3 million genetic variants identified by whole-genome sequencing revealed 144 independent variants across 107 proteins that showed strong association (P < 6 x 10(-11)) between genetics and the inter-individual variability on protein levels. Many proteins not reported before were identified (67 out of 107) with individual plasma level affected by genetics. Our longitudinal analysis further demonstrates that these levels are stable during the 2-year study period. The variability of protein profiles as a consequence of environmental factors was also analyzed with focus on the effects of weight loss and infections. Conclusions We show that the adult blood levels of many proteins are determined at birth by genetics, which is important for efforts aimed to understand the relationship between plasma proteome profiles and human biology and disease.