Inference of differential gene regulatory networks using boosted differential trees.

Summary: Diseases can be caused by molecular perturbations that induce specific changes in regulatory interactions and their coordinated expression, also referred to as network rewiring. However, the detection of complex changes in regulatory connections remains a challenging task and would benefit from the development of novel nonparametric approaches. We develop a new ensemble method called BoostDiff (boosted differential regression trees) to infer a differential network discriminating between two conditions. BoostDiff builds an adaptively boosted (AdaBoost) ensemble of differential trees with respect to a target condition. To build the differential trees, we propose differential variance improvement as a novel splitting criterion. Variable importance measures derived from the resulting models are used to reflect changes in gene expression predictability and to build the output differential networks. BoostDiff outperforms existing differential network methods on simulated data evaluated in four different complexity settings. We then demonstrate the power of our approach when applied to real transcriptomics data in COVID-19, Crohn's disease, breast cancer, prostate adenocarcinoma, and stress response in Bacillus subtilis. BoostDiff identifies context-specific networks that are enriched with genes of known disease-relevant pathways and complements standard differential expression analyses. Availability and implementation: BoostDiff is available at https://github.com/scibiome/boostdiff_inference.

On the limits of 16S rRNA gene-based metagenome prediction and functional profiling.

Molecular profiling techniques such as metagenomics, metatranscriptomics or metabolomics offer important insights into the functional diversity of the microbiome. In contrast, 16S rRNA gene sequencing, a widespread and cost-effective technique to measure microbial diversity, only allows for indirect estimation of microbial function. To mitigate this, tools such as PICRUSt2, Tax4Fun2, PanFP and MetGEM infer functional profiles from 16S rRNA gene sequencing data using different algorithms. Prior studies have cast doubts on the quality of these predictions, motivating us to systematically evaluate these tools using matched 16S rRNA gene sequencing, metagenomic datasets, and simulated data. Our contribution is threefold: (i) using simulated data, we investigate if technical biases could explain the discordance between inferred and expected results; (ii) considering human cohorts for type two diabetes, colorectal cancer and obesity, we test if health-related differential abundance measures of functional categories are concordant between 16S rRNA gene-inferred and metagenome-derived profiles and; (iii) since 16S rRNA gene copy number is an important confounder in functional profiles inference, we investigate if a customised copy number normalisation with the rrnDB database could improve the results. Our results show that 16S rRNA gene-based functional inference tools generally do not have the necessary sensitivity to delineate health-related functional changes in the microbiome and should thus be used with care. Furthermore, we outline important differences in the individual tools tested and offer recommendations for tool selection.

A 19-color single-tube full spectrum flow cytometry assay for the detection of measurable residual disease in acute myeloid leukemia.

Multiparameter flow cytometry (MFC) has emerged as a standard method for quantifying measurable residual disease (MRD) in acute myeloid leukemia. However, the limited number of available channels on conventional flow cytometers requires the division of a diagnostic sample into several tubes, restricting the number of cells and the complexity of immunophenotypes that can be analyzed. Full spectrum flow cytometers overcome this limitation by enabling the simultaneous use of up to 40 fluorescent markers. Here, we used this approach to develop a good laboratory practice-conform single-tube 19-color MRD detection assay that complies with recommendations of the European LeukemiaNet Flow-MRD Working Party. We based our assay on clinically-validated antibody clones and evaluated its performance on an IVD-certified full spectrum flow cytometer. We measured MRD and normal bone marrow samples and compared the MRD data to a widely used reference MRD-MFC panel generating highly concordant results. Using our newly developed single-tube panel, we established reference values in healthy bone marrow for 28 consensus leukemia-associated immunophenotypes and introduced a semi-automated dimensionality-reduction, clustering and cell type identification approach that aids the unbiased detection of aberrant cells. In summary, we provide a comprehensive full spectrum MRD-MFC workflow with the potential for rapid implementation for routine diagnostics due to reduced cell requirements and ease of data analysis with increased reproducibility in comparison to conventional FlowMRD routines.

Alternative splicing impacts microRNA regulation within coding regions.

MicroRNAs (miRNAs) are small non-coding RNA molecules that bind to target sites in different gene regions and regulate post-transcriptional gene expression. Approximately 95% of human multi-exon genes can be spliced alternatively, which enables the production of functionally diverse transcripts and proteins from a single gene. Through alternative splicing, transcripts might lose the exon with the miRNA target site and become unresponsive to miRNA regulation. To check this hypothesis, we studied the role of miRNA target sites in both coding and non-coding regions using six cancer data sets from The Cancer Genome Atlas (TCGA) and Parkinson's disease data from PPMI. First, we predicted miRNA target sites on mRNAs from their sequence using TarPmiR. To check whether alternative splicing interferes with this regulation, we trained linear regression models to predict miRNA expression from transcript expression. Using nested models, we compared the predictive power of transcripts with miRNA target sites in the coding regions to that of transcripts without target sites. Models containing transcripts with target sites perform significantly better. We conclude that alternative splicing does interfere with miRNA regulation by skipping exons with miRNA target sites within the coding region.

Independent Tissue-Based Biomarkers in Endometrioid Endometrial Cancer: Tumor Budding in Microsatellite Instability and WHO Grading in Copy-Number-Low Patients.

The molecular characterization of endometrial endometrioid adenocarcinomas has provided major advances in its prognostic stratification. However, risk assessment of microsatellite instability (MSI) and copy-number (CN)-low cases remains a challenge. Thus, we aimed to identify tissue-based morphologic biomarkers that might help in the prognostic stratification of these cases. Histomorphologic parameters (WHO grading, tumor budding (TB), tumor-stroma ratio (as a quantitative description of stromal desmoplasia), tumor-infiltrating lymphocytes (TIL), "microcystic, elongated, fragmented" (MELF) pattern) were analyzed in resection specimens of the TCGA-UCEC cohort (n = 228). For each quantitative parameter, a two-tiered system was developed utilizing systematically determined cutoffs. Associations with survival outcomes were calculated in univariate and multivariate analysis and validated in two independent cohorts. In MSI tumors, only TB remained an independent prognostic factor. TB (≥3 buds/high-power field) was associated with inferior outcomes and with lymph node metastases. The prognostic significance of TB was confirmed in two validation cohorts. For CN-low tumors, established grading defined by the WHO was independently prognostic with inferior outcomes for high-grade tumors. The evaluation of TB might help in identifying MSI-patients with unfavorable prognosis who, e.g., could benefit from lymphadenectomy. WHO-based grading facilitates independent prognostic stratification of CN-low endometrioid adenocarcinomas. Therefore, we propose the utilization of TB and WHO-based grading, two tissue-based and easy-to-assess biomarkers, in MSI/CN-low endometrial carcinomas for improved clinical management.

The limits of molecular signatures for pancreatic ductal adenocarcinoma subtyping.

Molecular signatures have been suggested as biomarkers to classify pancreatic ductal adenocarcinoma (PDAC) into two, three, four or five subtypes. Since the robustness of existing signatures is controversial, we performed a systematic evaluation of four established signatures for PDAC stratification across nine publicly available datasets. Clustering revealed inconsistency of subtypes across independent datasets and in some cases a different number of PDAC subgroups than in the original study, casting doubt on the actual number of existing subtypes. Next, we built sixteen classification models to investigate the ability of the signatures for tumor subtype prediction. The overall classification performance ranged from ∼35% to ∼90% accuracy, suggesting instability of the signatures. Notably, permuted subtypes and random gene sets achieved very similar performance. Cellular decomposition and functional pathway enrichment analysis revealed strong tissue-specificity of the predicted classes. Our study highlights severe limitations and inconsistencies that can be attributed to technical biases in sample preparation and tumor purity, suggesting that PDAC molecular signatures do not generalize across datasets. How stromal heterogeneity and immune compartment interplay in the diverging development of PDAC is still unclear. Therefore, a more mechanistic or a cross-platform multi-omic approach seems necessary to extract more robust and clinically exploitable insights.

Tumour cell budding and spread through air spaces in squamous cell carcinoma of the lung - Determination and validation of optimal prognostic cut-offs.

PURPOSE: Prognostic stratification of patients with squamous cell carcinomas of the lung (SCC-L) is challenging. Therefore, we investigated several histomorphological parameters (tumour cell budding (TCB), spread through air spaces (STAS), tumour-stroma-ratio, immune cell infiltration) which could potentially serve as prognostic parameters in SCC-L. We aimed to systematically determine optimal cut-off-values and assess the prognostic capability of these patterns. We furthermore assessed interobserver variability (IOV) for prognostically significant patterns TCB and STAS. EXPERIMENTAL DESIGN: The Cancer Genome Atlas (TCGA) study cohort consisted of 335 patients with SCC-L. Histomorphological parameters analysed comprised TCB, minimal cell nest size (MCNS), STAS, stroma content and immune cell infiltration. The most significant cut-off-values were determined and univariate and multivariate survival outcomes were estimated. The identified cut-off-points were validated in an independent SCC-L cohort (n = 346 patients). Two experienced pathologists probed IOV in the validation cohort. RESULTS: In the TCGA study cohort, TCB, STAS and immune cell infiltration were identified as significant prognostic parameters. TCB-high tumours, a high number of STAS foci, extensive STAS for distance of STAS in alveoli and a low immune cell infiltration remained as independent prognostic factors in multivariate Cox proportional hazard analyses for overall survival (OS). The significance of TCB, number of STAS foci and distance of STAS in alveoli for OS could be validated in the validation cohort. IOV reached a Kappa ≥ 0.89 for prognostic parameters. CONCLUSIONS: We determined optimal cut-offs and identified TCB and STAS (number of STAS foci, distance of STAS in alveoli) as independent and uncorrelated prognostic factors for patients with SCC-L. The significance was validated in a large independent cohort. IOV was almost perfect for prognostic parameters. We propose the application of TCB- and STAS-based grading in SCC-L as prognostic morphological classifiers.

Carrying asymptomatic gallstones is not associated with changes in intestinal microbiota composition and diversity but cholecystectomy with significant dysbiosis.

Gallstone disease affects up to twenty percent of the population in western countries and is a significant contributor to morbidity and health care expenditure. Intestinal microbiota have variously been implicated as either contributing to gallstone formation or to be affected by cholecystectomy. We conducted a large-scale investigation on 404 gallstone carriers, 580 individuals post-cholecystectomy and 984 healthy controls with similar distributions of age, sex, body mass index, smoking habits, and food-frequency-score. All 1968 subjects were recruited from the population-based Study-of-Health-in-Pomerania (SHIP), which includes transabdominal gallbladder ultrasound. Fecal microbiota profiles were determined by 16S rRNA gene sequencing. No significant differences in microbiota composition were detected between gallstone carriers and controls. Individuals post-cholecystectomy exhibited reduced microbiota diversity, a decrease in the potentially beneficial genus Faecalibacterium and an increase in the opportunistic pathogen Escherichia/Shigella. The absence of an association between the gut microbiota and the presence of gallbladder stones suggests that there is no intestinal microbial risk profile increasing the likelihood of gallstone formation. Cholecystectomy, on the other hand, is associated with distinct microbiota changes that have previously been implicated in unfavorable health effects and may not only contribute to gastrointestinal infection but also to the increased colon cancer risk of cholecystectomized patients.

BiCoN: network-constrained biclustering of patients and omics data.

MOTIVATION: Unsupervised learning approaches are frequently used to stratify patients into clinically relevant subgroups and to identify biomarkers such as disease-associated genes. However, clustering and biclustering techniques are oblivious to the functional relationship of genes and are thus not ideally suited to pinpoint molecular mechanisms along with patient subgroups. RESULTS: We developed the network-constrained biclustering approach Biclustering Constrained by Networks (BiCoN) which (i) restricts biclusters to functionally related genes connected in molecular interaction networks and (ii) maximizes the difference in gene expression between two subgroups of patients. This allows BiCoN to simultaneously pinpoint molecular mechanisms responsible for the patient grouping. Network-constrained clustering of genes makes BiCoN more robust to noise and batch effects than typical clustering and biclustering methods. BiCoN can faithfully reproduce known disease subtypes as well as novel, clinically relevant patient subgroups, as we could demonstrate using breast and lung cancer datasets. In summary, BiCoN is a novel systems medicine tool that combines several heuristic optimization strategies for robust disease mechanism extraction. BiCoN is well-documented and freely available as a python package or a web interface. AVAILABILITY AND IMPLEMENTATION: PyPI package: https://pypi.org/project/bicon. WEB INTERFACE: https://exbio.wzw.tum.de/bicon. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Long-term instability of the intestinal microbiome is associated with metabolic liver disease, low microbiota diversity, diabetes mellitus and impaired exocrine pancreatic function.

OBJECTIVE: The intestinal microbiome affects the prevalence and pathophysiology of a variety of diseases ranging from inflammation to cancer. A reduced taxonomic or functional diversity of the microbiome was often observed in association with poorer health outcomes or disease in general. Conversely, factors or manifest diseases that determine the long-term stability or instability of the microbiome are largely unknown. We aimed to identify disease-relevant phenotypes associated with faecal microbiota (in-)stability. DESIGN: A total of 2564 paired faecal samples from 1282 participants of the population-based Study of Health in Pomerania (SHIP) were collected at a 5-year (median) interval and microbiota profiles determined by 16S rRNA gene sequencing. The changes in faecal microbiota over time were associated with highly standardised and comprehensive phenotypic data to determine factors related to microbiota (in-)stability. RESULTS: The overall microbiome landscape remained remarkably stable over time. The greatest microbiome instability was associated with factors contributing to metabolic syndrome such as fatty liver disease and diabetes mellitus. These, in turn, were associated with an increase in facultative pathogens such as Enterobacteriaceae or Escherichia/Shigella. Greatest stability of the microbiome was determined by higher initial alpha diversity, female sex, high household income and preserved exocrine pancreatic function. Participants who newly developed fatty liver disease or diabetes during the 5-year follow-up already displayed significant microbiota changes at study entry when the diseases were absent. CONCLUSION: This study identifies distinct components of metabolic liver disease to be associated with instability of the intestinal microbiome, increased abundance of facultative pathogens and thus greater susceptibility toward dysbiosis-associated diseases.

The Gut Microbiome in Patients With Chronic Pancreatitis Is Characterized by Significant Dysbiosis and Overgrowth by Opportunistic Pathogens.

INTRODUCTION: Exocrine pancreatic function is a critical host factor in determining the intestinal microbiota composition. Diseases affecting the exocrine pancreas could therefore influence the gut microbiome. We investigated the changes in gut microbiota of patients with chronic pancreatitis (CP). METHODS: Patients with clinical and imaging evidence of CP (n = 51) were prospectively recruited and compared with twice the number of nonpancreatic disease controls matched for distribution in age, sex, body mass index, smoking, diabetes mellitus, and exocrine pancreatic function (stool elastase). From stool samples of these 153 subjects, DNA was extracted, and intestinal microbiota composition was determined by bacterial 16S ribosomal RNA gene sequencing. RESULTS: Patients with CP exhibited severely reduced microbial diversity (Shannon diversity index and Simpson diversity number, P < 0.001) with an increased abundance of facultative pathogenic organisms (P < 0.001) such as Enterococcus (q < 0.001), Streptococcus (q < 0.001), and Escherichia.Shigella (q = 0.002). The CP-associated changes were independent of exocrine pancreatic insufficiency. Short-chain fatty acid producers, considered protective for epithelia such as Faecalibacterium (q < 0.001), showed reduced abundance in patients with CP. Of 4 additional patients with CP previously treated with antibiotics (ceftriaxone and metronidazole), 3 patients were characterized by distinct Enterococcus overgrowth. DISCUSSION: CP is associated with marked gut microbiota dysbiosis, greatly reduced diversity, and increased abundance of opportunistic pathogens, specifically those previously isolated from infected pancreatic necrosis. Taxa with a potentially beneficial role in intestinal barrier function are depleted. These changes can increase the probability of complications from pancreatitis such as infected fluid collections or small intestinal bacterial overgrowth (see Graphical Abstract, Supplementary Digital Content 1, http://links.lww.com/CTG/A383).

Association of proteome and metabolome signatures with severity in patients with community-acquired pneumonia.

A combined OMICS screening approach of human plasma and serum was used to characterize protein and metabolome signatures displaying association to severity of Community-acquired pneumonia (CAP). 240 serum and BD P100 EDTA plasma samples from patients diagnosed with CAP, collected during the day of enrolment to the hospital, were analyzed by a metabolomic and proteomic approach, respectively. Disease severity of CAP patients was stratified using the Sequential Organ Failure Assessment (SOFA) score. Quantitative proteome and metabolome data, derived by LC-MS/MS, were associated to SOFA and specific parameters of SOFA using linear regression models adjusted for age, BMI, sex, smoking and technical variables. Both proteome and metabolome profiling revealed remarkable strong changes in plasma and serum composition in relation to severity of CAP. Proteins and metabolites displaying SOFA associated levels are involved in immune response, particularly in processes of lipid metabolism. Proteins, which show an association to SOFA score, are involved in acute phase response, coagulation, complement activation and inflammation. Many of these metabolites and proteins displayed not only associations to SOFA, but also to parameters of SOFA score, which likely reflect the strong influence of lung-, liver-, kidney- and heart-dysfunction on the metabolome and proteome patterns. SIGNIFICANCE: Community-acquired pneumonia is the most frequent infection disease with high morbidity and mortality. So far, only few studies focused on the identification of proteins or metabolites associated to severity of CAP, often based on smaller sample sets. A screening for new diagnostic markers requires extensive sample collections in combination with high quality clinical data. To characterize the proteomic and metabolomics pattern associated to severity of CAP we performed a combined metabolomics and proteomic approach of serum and plasma sample from a multi-center clinical study focused on patients with CAP, requiring hospitalization. The results of this association study of omics data to the SOFA score enable not only an interpretation of changes in molecular patterns with severity of CAP but also an assignment of altered molecules to dysfunctions of respiratory, renal, coagulation, cardiovascular systems as well as liver.

Helicobacter pylori infection associates with fecal microbiota composition and diversity.

Helicobacter (H.) pylori is the most important cause for peptic ulcer disease and a risk factor for gastric carcinoma. How colonization with H. pylori affects the intestinal microbiota composition in humans is unknown. We investigated the association of H. pylori infection with intestinal microbiota composition in the population-based cohort Study-of-Health-in-Pomerania (SHIP)-TREND. Anti-H. pylori serology and H. pylori stool antigen tests were used to determine the H. pylori infection status. The fecal microbiota composition of 212 H. pylori positive subjects and 212 matched negative control individuals was assessed using 16S rRNA gene sequencing. H. pylori infection was found to be significantly associated with fecal microbiota alterations and a general increase in fecal microbial diversity. In infected individuals, the H. pylori stool antigen load determined a larger portion of the microbial variation than age or sex. The highest H. pylori stool antigen loads were associated with a putatively harmful microbiota composition. This study demonstrates profound alterations in human fecal microbiota of H. pylori infected individuals. While the increased microbiota diversity associated with H. pylori infection as well as changes in abundance of specific genera could be considered to be beneficial, others may be associated with adverse health effects, reflecting the complex relationship between H. pylori and its human host.

Impaired Exocrine Pancreatic Function Associates With Changes in Intestinal Microbiota Composition and Diversity.

BACKGROUND & AIMS: Changes in intestinal microbiome composition are associated with inflammatory, metabolic, and malignant disorders. We studied how exocrine pancreatic function affects intestinal microbiota. METHODS: We performed 16S ribosomal RNA gene sequencing analysis of stool samples from 1795 volunteers from the population-based Study of Health in Pomerania who had no history of pancreatic disease. We also measured fecal pancreatic elastase by enzyme-linked immunosorbent assay and performed quantitative imaging of secretin-stimulated pancreatic fluid secretion. Associations of exocrine pancreatic function with microbial diversity or individual genera were calculated by permutational analysis of variance or linear regression, respectively. RESULTS: Differences in pancreatic elastase levels associated with significantly (P < .0001) greater changes in microbiota diversity than with participant age, body mass index, sex, smoking, alcohol consumption, or dietary factors. Significant changes in the abundance of 30 taxa, such as an increase in Prevotella (q < .0001) and a decrease of Bacteroides (q < .0001), indicated a shift from a type-1 to a type-2 enterotype. Changes in pancreatic fluid secretion alone were also associated with changes in microbial diversity (P = .0002), although to a lesser degree. CONCLUSIONS: In an analysis of fecal samples from 1795 volunteers, pancreatic acinar cell, rather than duct cell, function is presently the single most significant host factor to be associated with changes in intestinal microbiota composition.

MicroRNA analysis of gastroenteropancreatic neuroendocrine tumors and metastases.

The incidence of neuroendocrine neoplasias (NEN) continues to increase. Since the primary tumor cannot be diagnosed in some cases of metastatic disease, new biomarkers are clearly needed to find the most probable site of origin. Tissue samples from 79 patients were analyzed and microRNA profiles were generated from a total of 76 primary tumors, 31 lymph node and 14 solid organ metastases. NEN metastases were associated with elevated levels of miR-30a-5p, miR-210, miR-339-3p, miR-345 and miR-660. Three microRNAs showed a strong correlation between proliferation index and metastatic disease in general (miR-150, miR-21 and miR-660). Further, each anatomic location (primary or metastatic) had one or more site-specific microRNAs more highly expressed in these tissues. Comparison between primary tumors and metastases revealed an overlap only in pancreatic (miR-127) and ileal tumors (let-7g, miR-200a and miR-331). This thorough analysis of gastroenteropancreatic neuroendocrine tumors demonstrates site-specific microRNA profiles, correlation with proliferation indices as well as corresponding nodal and distant metastases. Using microRNA profiling might improve NEN diagnostics by linking metastases to a most probable site of origin.

Transcriptome-Wide Analysis Identifies Novel Associations With Blood Pressure.

Hypertension represents a major cardiovascular risk factor. The pathophysiology of increased blood pressure (BP) is not yet completely understood. Transcriptome profiling offers possibilities to uncover genetics effects on BP. Based on 2 populations including 2549 individuals, a meta-analyses of monocytic transcriptome-wide profiles were performed to identify transcripts associated with BP. Replication was performed in 2 independent studies of whole-blood transcriptome data including 1990 individuals. For identified candidate genes, a direct link between long-term changes in BP and gene expression over time and by treatment with BP-lowering therapy was assessed. The predictive value of protein levels encoded by candidate genes for subsequent cardiovascular disease was investigated. Eight transcripts (CRIP1, MYADM, TIPARP, TSC22D3, CEBPA, F12, LMNA, and TPPP3) were identified jointly accounting for up to 13% (95% confidence interval, 8.7-16.2) of BP variability. Changes in CRIP1, MYADM, TIPARP, LMNA, TSC22D3, CEBPA, and TPPP3 expression associated with BP changes-among these, CRIP1 gene expression was additionally correlated to measures of cardiac hypertrophy. Assessment of circulating CRIP1 (cystein-rich protein 1) levels as biomarkers showed a strong association with increased risk for incident stroke (hazard ratio, 1.06; 95% confidence interval, 1.03-1.09; P=5.0×10-5). Our comprehensive analysis of global gene expression highlights 8 novel transcripts significantly associated with BP, providing a link between gene expression and BP. Translational approaches further established evidence for the potential use of CRIP1 as emerging disease-related biomarker.

Genome-wide association study of caffeine metabolites provides new insights to caffeine metabolism and dietary caffeine-consumption behavior.

Caffeine is the most widely consumed psychoactive substance in the world and presents with wide interindividual variation in metabolism. This variation may modify potential adverse or beneficial effects of caffeine on health. We conducted a genome-wide association study (GWAS) of plasma caffeine, paraxanthine, theophylline, theobromine and paraxanthine/caffeine ratio among up to 9,876 individuals of European ancestry from six population-based studies. A single SNP at 6p23 (near CD83) and several SNPs at 7p21 (near AHR), 15q24 (near CYP1A2) and 19q13.2 (near CYP2A6) met GW-significance (P < 5 × 10-8) and were associated with one or more metabolites. Variants at 7p21 and 15q24 associated with higher plasma caffeine and lower plasma paraxanthine/caffeine (slow caffeine metabolism) were previously associated with lower coffee and caffeine consumption behavior in GWAS. Variants at 19q13.2 associated with higher plasma paraxanthine/caffeine (slow paraxanthine metabolism) were also associated with lower coffee consumption in the UK Biobank (n = 94 343, P < 1.0 × 10-6). Variants at 2p24 (in GCKR), 4q22 (in ABCG2) and 7q11.23 (near POR) that were previously associated with coffee consumption in GWAS were nominally associated with plasma caffeine or its metabolites. Taken together, we have identified genetic factors contributing to variation in caffeine metabolism and confirm an important modulating role of systemic caffeine levels in dietary caffeine consumption behavior. Moreover, candidate genes identified encode proteins with important clinical functions that extend beyond caffeine metabolism.

Transcriptome Alterations In X-Irradiated Human Gingiva Fibroblasts.

Ionizing radiation is known to induce genomic lesions, such as DNA double strand breaks, whose repair can lead to mutations that can modulate cellular and organismal fate. Soon after radiation exposure, cells induce transcriptional changes and alterations of cell cycle programs to respond to the received DNA damage. Radiation-induced mutations occur through misrepair in a stochastic manner and increase the risk of developing cancers years after the incident, especially after high dose radiation exposures. Here, the authors analyzed the transcriptomic response of primary human gingival fibroblasts exposed to increasing doses of acute high dose-rate x rays. In the dataset obtained after 0.5 and 5 Gy x-ray exposures and two different repair intervals (0.5 h and 16 h), the authors discovered several radiation-induced fusion transcripts in conjunction with dose-dependent gene expression changes involving a total of 3,383 genes. Principal component analysis of repeated experiments revealed that the duration of the post-exposure repair intervals had a stronger impact than irradiation dose. Subsequent overrepresentation analyses showed a number of KEGG gene sets and WikiPathways, including pathways known to relate to radioresistance in fibroblasts (Wnt, integrin signaling). Moreover, a significant radiation-induced modulation of microRNA targets was detected. The data sets on IR-induced transcriptomic alterations in primary gingival fibroblasts will facilitate genomic comparisons in various genotoxic exposure scenarios.

Platelet-Related Variants Identified by Exomechip Meta-analysis in 157,293 Individuals.

Platelet production, maintenance, and clearance are tightly controlled processes indicative of platelets' important roles in hemostasis and thrombosis. Platelets are common targets for primary and secondary prevention of several conditions. They are monitored clinically by complete blood counts, specifically with measurements of platelet count (PLT) and mean platelet volume (MPV). Identifying genetic effects on PLT and MPV can provide mechanistic insights into platelet biology and their role in disease. Therefore, we formed the Blood Cell Consortium (BCX) to perform a large-scale meta-analysis of Exomechip association results for PLT and MPV in 157,293 and 57,617 individuals, respectively. Using the low-frequency/rare coding variant-enriched Exomechip genotyping array, we sought to identify genetic variants associated with PLT and MPV. In addition to confirming 47 known PLT and 20 known MPV associations, we identified 32 PLT and 18 MPV associations not previously observed in the literature across the allele frequency spectrum, including rare large effect (FCER1A), low-frequency (IQGAP2, MAP1A, LY75), and common (ZMIZ2, SMG6, PEAR1, ARFGAP3/PACSIN2) variants. Several variants associated with PLT/MPV (PEAR1, MRVI1, PTGES3) were also associated with platelet reactivity. In concurrent BCX analyses, there was overlap of platelet-associated variants with red (MAP1A, TMPRSS6, ZMIZ2) and white (PEAR1, ZMIZ2, LY75) blood cell traits, suggesting common regulatory pathways with shared genetic architecture among these hematopoietic lineages. Our large-scale Exomechip analyses identified previously undocumented associations with platelet traits and further indicate that several complex quantitative hematological, lipid, and cardiovascular traits share genetic factors.

Large-Scale Exome-wide Association Analysis Identifies Loci for White Blood Cell Traits and Pleiotropy with Immune-Mediated Diseases.

White blood cells play diverse roles in innate and adaptive immunity. Genetic association analyses of phenotypic variation in circulating white blood cell (WBC) counts from large samples of otherwise healthy individuals can provide insights into genes and biologic pathways involved in production, differentiation, or clearance of particular WBC lineages (myeloid, lymphoid) and also potentially inform the genetic basis of autoimmune, allergic, and blood diseases. We performed an exome array-based meta-analysis of total WBC and subtype counts (neutrophils, monocytes, lymphocytes, basophils, and eosinophils) in a multi-ancestry discovery and replication sample of âˆ¼157,622 individuals from 25 studies. We identified 16 common variants (8 of which were coding variants) associated with one or more WBC traits, the majority of which are pleiotropically associated with autoimmune diseases. Based on functional annotation, these loci included genes encoding surface markers of myeloid, lymphoid, or hematopoietic stem cell differentiation (CD69, CD33, CD87), transcription factors regulating lineage specification during hematopoiesis (ASXL1, IRF8, IKZF1, JMJD1C, ETS2-PSMG1), and molecules involved in neutrophil clearance/apoptosis (C10orf54, LTA), adhesion (TNXB), or centrosome and microtubule structure/function (KIF9, TUBD1). Together with recent reports of somatic ASXL1 mutations among individuals with idiopathic cytopenias or clonal hematopoiesis of undetermined significance, the identification of a common regulatory 3' UTR variant of ASXL1 suggests that both germline and somatic ASXL1 mutations contribute to lower blood counts in otherwise asymptomatic individuals. These association results shed light on genetic mechanisms that regulate circulating WBC counts and suggest a prominent shared genetic architecture with inflammatory and autoimmune diseases.