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Background Advances in whole genome profiling have revolutionized the cancer research field, but at the same time have raised new bioinformatics challenges. For next generation sequencing (NGS), these include data storage, computational costs, sequence processing and alignment, delineating appropriate statistical measures, and data visualization. Currently there is a lack of workflows for efficient analysis of large, MethylCap-seq datasets containing multiple sample groups. Methods The NGS application MethylCap-seq involves the in vitro capture of methylated DNA and subsequent analysis of enriched fragments by massively parallel sequencing. The workflow we describe performs MethylCap-seq experimental Quality Control (QC), sequence file processing and alignment, differential methylation analysis of multiple biological groups, hierarchical clustering, assessment of genome-wide methylation patterns, and preparation of files for data visualization. Results Here, we present a scalable, flexible workflow for MethylCap-seq QC, secondary data analysis, tertiary analysis of multiple experimental groups, and data visualization. We demonstrate the experimental QC procedure with results from a large ovarian cancer study dataset and propose parameters which can identify problematic experiments. Promoter methylation profiling and hierarchical clustering analyses are demonstrated for four groups of acute myeloid leukemia (AML) patients. We propose a Global Methylation Indicator (GMI) function to assess genome-wide changes in methylation patterns between experimental groups. We also show how the workflow facilitates data visualization in a web browser with the application Anno-J. Conclusions This workflow and its suite of features will assist biologists in conducting methylation profiling projects and facilitate meaningful biological interpretation.

Background DNA methylation is an important epigenetic mark and dysregulation of DNA methylation is associated with many diseases including cancer. Advances in next-generation sequencing now allow unbiased methylome profiling of entire patient cohorts, greatly facilitating biomarker discovery and presenting new opportunities to understand the biological mechanisms by which changes in methylation contribute to disease. Enrichment-based sequencing assays such as MethylCap-seq are a cost effective solution for genome-wide determination of methylation status, but the technical reliability of methylation reconstruction from raw sequencing data has not been well characterized. Methods We analyze three MethylCap-seq data sets and perform two different analyses to assess data quality. First, we investigate how data quality is affected by excluding samples that do not meet quality control cutoff requirements. Second, we consider the effect of additional reads on enrichment score, saturation, and coverage. Lastly, we verify a method for the determination of the global amount of methylation from MethylCap-seq data by comparing to a spiked-in control DNA of known methylation status. Results We show that rejection of samples based on our quality control parameters leads to a significant improvement of methylation calling. Additional reads beyond ~13 million unique aligned reads improved coverage, modestly improved saturation, and did not impact enrichment score. Lastly, we find that a global methylation indicator calculated from MethylCap-seq data correlates well with the global methylation level of a sample as obtained from a spike-in DNA of known methylation level. Conclusions We show that with appropriate quality control MethylCap-seq is a reliable tool, suitable for cohorts of hundreds of patients, that provides reproducible methylation information on a feature by feature basis as well as information about the global level of methylation.

Interactions between protein and DNA are essential for cellular function. The incremental process of developing global approaches to study chromatin began with the in vitro characterization of chromatin structural components and modifications of the versatile chromatin immunoprecipitation (ChIP) assay, capable of analyzing protein-DNA interactions in vivo. Among the emerging global approaches are ChIP cloning, ChIP display, differential chromatin scanning, ChIP-chip, DamID chromatin profiling, and chromatin array. These methods have been used to assess transcription-factor binding and (or) histone modification. This review describes these global methods and illustrates their potential in answering biological questions.Key words: ChIP, transcription factor binding, histone modification, ChIP display, differential chromatin scanning, ChIP-chip, DamID chromatin profiling, chromatin array.

Rationale: Mean platelet volume (MPV) and platelet count (PLT) are platelet measures that have been linked to cardiovascular disease (CVD) and mortality risk. Identifying protein biomarkers for these measures may yield insights into CVD mechanisms. Objective: We aimed to identify causal protein biomarkers for MPV and PLT among 71 CVD-related plasma proteins measured in Framingham Heart Study (FHS) participants. Methods and Results: We conducted integrative analyses of genetic variants associated with PLT and MPV with protein quantitative trait locus (pQTL) variants associated with plasma proteins followed by Mendelian randomization (MR) to infer causal relations of proteins for PLT/MPV, and tested protein-PLT/MPV association in FHS participants. Utilizing induced pluripotent stem cell (iPSC)-derived megakaryocyte (MK) clones that produce functional platelets, we conducted RNA-sequencing and analyzed transcriptome-wide differences between low- and high-platelet producing clones. We then performed small interfering RNA (siRNA) gene knockdown experiments targeting genes encoding proteins with putatively causal platelet effects in MK clones to examine effects on platelet production. Protein-trait association analyses were conducted for MPV (n = 4,348) and PLT (n = 4,272). Eleven proteins were associated with MPV and 31 with PLT. MR identified four putatively causal proteins for MPV and four for PLT. Glycoprotein V (GP5), granulin (GRN), and melanoma cell adhesion molecule (MCAM) were associated with PLT in both protein-trait and MR analyses. Myeloperoxidase (MPO) showed significant association with MPV in both analyses. MK RNA-sequencing analysis results were directionally concordant with observed and MR-inferred associations for GP5, GRN, and MCAM. In siRNA gene knockdown experiments, silencing GP5, GRN, and MPO decreased platelet counts. Conclusions: By integrating population genomics data, epidemiological data, and iPSC-derived MK experiments, we identified four proteins that are causally linked to platelet counts. These proteins and genes may be further explored for their utility in increasing platelet production in bioreactors for transfusion medicine purposes as well as their roles in the pathogenesis of CVD via a platelet/blood coagulation-based mechanism.

Exaggerated platelet aggregation at the site of vascular injury is the underlying pathophysiology of thrombotic diseases. Here, we conduct the largest whole genome sequencing (WGS) effort to uncover the genetic determinants of platelet aggregation. Leveraging 3,855 NHLBI Trans-Omics for Precision Medicine (TOPMed) individuals deeply phenotyped for platelet aggregation, we identify 18 loci using single-variant approaches. This includes the novel RGS18 locus encoding a myeloerythroid lineage-specific regulator of G-protein signaling that co-localizes with eQTL signatures for RGS18 expression in platelets. A gene-based approach focusing on deleterious coding variants identifies the SVEP1 gene, previously shown to be associated with coronary artery disease, as a novel determinant of platelet aggregation. Finally, in an integrative approach leveraging epigenetic data on megakaryocytes, we find strong association between rare variants mapping to a super enhancer region for PEAR1. This is a novel finding implicating the importance of rare variants with regulatory potential in a previously documented GWAS-identified locus.

The Janus tyrosine kinase (JAK) family of non-receptor tyrosine kinases includes four isoforms (JAK1, JAK2, JAK3, and TYK2) and is responsible for signal transduction downstream of diverse cytokine receptors. JAK inhibitors have emerged as important therapies for immun(onc)ological disorders, but their use is limited by undesirable side effects presumed to arise from poor isoform selectivity, a common challenge for inhibitors targeting the ATP-binding pocket of kinases. Here we describe the chemical proteomic discovery of a druggable allosteric cysteine present in the non-catalytic pseudokinase domain of JAK1 (C817) and TYK2 (C838), but absent from JAK2 or JAK3. Electrophilic compounds selectively engaging this site block JAK1-dependent trans-phosphorylation and cytokine signaling, while appearing to act largely as ‘silent’ ligands for TYK2. Importantly, the allosteric JAK1 inhibitors do not impair JAK2-dependent cytokine signaling and are inactive in cells expressing a C817A JAK1 mutant. Our findings thus reveal an allosteric approach for inhibiting JAK1 with unprecedented isoform selectivity.Chemical proteomics identified covalent ligands targeting an isoform-restricted allosteric cysteine in JAK1. The compounds inhibit JAK1-dependent signaling in immune cells with unprecedented selectivity.

The fibroblast growth factor (FGF)-FGF receptor (FGFR) signaling system plays critical roles in a variety of normal developmental and physiological processes. It is also well documented that dysregulation of FGF-FGFR signaling may have important roles in tumor development and progression. The FGFR4-FGF19 signaling axis has been implicated in the development of hepatocellular carcinomas (HCCs) in mice, and potentially in humans. In this study, we demonstrate that FGFR4 is required for hepatocarcinogenesis; the progeny of FGF19 transgenic mice, which have previously been shown to develop HCCs, bred with FGFR4 knockout mice fail to develop liver tumors. To further test the importance of FGFR4 in HCC, we developed a blocking anti-FGFR4 monoclonal antibody (LD1). LD1 inhibited: 1) FGF1 and FGF19 binding to FGFR4, 2) FGFR4-mediated signaling, colony formation, and proliferation in vitro, and 3) tumor growth in a preclinical model of liver cancer in vivo. Finally, we show that FGFR4 expression is elevated in several types of cancer, including liver cancer, as compared to normal tissues. These findings suggest a modulatory role for FGFR4 in the development and progression of hepatocellular carcinoma and that FGFR4 may be an important and novel therapeutic target in treating this disease.

Bacteria of the SAR11 clade constitute up to one half of all microbial cells in the oxygen-rich surface ocean. SAR11 bacteria are also abundant in oxygen minimum zones (OMZs), where oxygen falls below detection and anaerobic microbes have vital roles in converting bioavailable nitrogen to N 2 gas. Anaerobic metabolism has not yet been observed in SAR11, and it remains unknown how these bacteria contribute to OMZ biogeochemical cycling. Here, genomic analysis of single cells from the world’s largest OMZ revealed previously uncharacterized SAR11 lineages with adaptations for life without oxygen, including genes for respiratory nitrate reductases (Nar). SAR11 nar genes were experimentally verified to encode proteins catalysing the nitrite-producing first step of denitrification and constituted ~40% of OMZ nar transcripts, with transcription peaking in the anoxic zone of maximum nitrate reduction activity. These results link SAR11 to pathways of ocean nitrogen loss, redefining the ecological niche of Earth’s most abundant organismal group. Bacteria of the SAR11 clade constitute up to one half of all marine microbes and are thought to require oxygen for growth; here, a subgroup of SAR11 bacteria are shown to thrive in ocean oxygen minimum zones and to encode abundant respiratory nitrate reductases. An anoxic niche for SAR11 bacteria SAR11 bacteria, the most abundant type of microbe in the world's oceans, are thought to require oxygen for growth, yet they are also abundant in waters where oxygen levels are low. Frank Stewart and colleagues show here that a subgroup of SAR11 bacteria that thrives in ocean oxygen minimum zones have adapted to the microaerobic/anaerobic conditions there, and they encode abundant respiratory nitrate reductases that perform the first step in denitrification. These results redefine the ecological niche of Earth's most abundant organismal group and suggest that they are substantial contributors to nitrogen loss in oxygen minimum zones.

Mitochondria take up Ca 2+ through the mitochondrial calcium uniporter complex to regulate energy production, cytosolic Ca 2+ signalling and cell death 1 , 2 . In mammals, the uniporter complex (uniplex) contains four core components: the pore-forming MCU protein, the gatekeepers MICU1 and MICU2, and an auxiliary subunit, EMRE, essential for Ca 2+ transport 3 – 8 . To prevent detrimental Ca 2+ overload, the activity of MCU must be tightly regulated by MICUs, which sense changes in cytosolic Ca 2+ concentrations to switch MCU on and off 9 , 10 . Here we report cryo-electron microscopic structures of the human mitochondrial calcium uniporter holocomplex in inhibited and Ca 2+ -activated states. These structures define the architecture of this multicomponent Ca 2+ -uptake machinery and reveal the gating mechanism by which MICUs control uniporter activity. Our work provides a framework for understanding regulated Ca 2+ uptake in mitochondria, and could suggest ways of modulating uniporter activity to treat diseases related to mitochondrial Ca 2+ overload. Cryo-electron microscopy reveals the structures of the mitochondrial calcium uniporter holocomplex in low- and high-calcium conditions, showing the gating mechanism that underlies uniporter activation in response to intracellular calcium signals.

Applying a new, more sensitive single-cell transcriptomics method to diagnosis, remission and progression samples from patients with chronic myeloid leukemia reveals insight into the heterogeneity of cells that resist treatment with targeted therapy, as well as into the dynamics of disease progression and its effects on nontransformed hematopoietic stem cells. Recent advances in single-cell transcriptomics are ideally placed to unravel intratumoral heterogeneity and selective resistance of cancer stem cell (SC) subpopulations to molecularly targeted cancer therapies. However, current single-cell RNA-sequencing approaches lack the sensitivity required to reliably detect somatic mutations. We developed a method that combines high-sensitivity mutation detection with whole-transcriptome analysis of the same single cell. We applied this technique to analyze more than 2,000 SCs from patients with chronic myeloid leukemia (CML) throughout the disease course, revealing heterogeneity of CML-SCs, including the identification of a subgroup of CML-SCs with a distinct molecular signature that selectively persisted during prolonged therapy. Analysis of nonleukemic SCs from patients with CML also provided new insights into cell-extrinsic disruption of hematopoiesis in CML associated with clinical outcome. Furthermore, we used this single-cell approach to identify a blast-crisis-specific SC population, which was also present in a subclone of CML-SCs during the chronic phase in a patient who subsequently developed blast crisis. This approach, which might be broadly applied to any malignancy, illustrates how single-cell analysis can identify subpopulations of therapy-resistant SCs that are not apparent through cell-population analysis.