Meaningful exchange of information is a fundamental challenge in collaborative biomedical research. To help address this, the authors developed the Life Sciences Domain Analysis Model (LS DAM), an information model that provides a framework for communication among domain experts and technical teams developing information systems to support biomedical research. The LS DAM is harmonized with the Biomedical Research Integrated Domain Group (BRIDG) model of protocol-driven clinical research. Together, these models can facilitate data exchange for translational research.
Materials and methods
The content of the LS DAM was driven by analysis of life sciences and translational research scenarios and the concepts in the model are derived from existing information models, reference models and data exchange formats. The model is represented in the Unified Modeling Language and uses ISO 21090 data types.
The LS DAM v2.2.1 is comprised of 130 classes and covers several core areas including Experiment, Molecular Biology, Molecular Databases and Specimen. Nearly half of these classes originate from the BRIDG model, emphasizing the semantic harmonization between these models. Validation of the LS DAM against independently derived information models, research scenarios and reference databases supports its general applicability to represent life sciences research.
The LS DAM provides unambiguous definitions for concepts required to describe life sciences research. The processes established to achieve consensus among domain experts will be applied in future iterations and may be broadly applicable to other standardization efforts.
The LS DAM provides common semantics for life sciences research. Through harmonization with BRIDG, it promotes interoperability in translational science.
Semantics; knowledge representation (computer); interoperability; life sciences; information model; knowledge bases; knowledge representations; data models; clinical; OMICS; genomics; cancer genomics
Genomic profiling has identified a subtype of high-risk B-progenitor acute lymphoblastic leukemia (B-ALL) with alteration of IKZF1, a gene expression profile similar to BCR-ABL1-positive ALL and poor outcome (Ph-like ALL). The genetic alterations that activate kinase signaling in Ph-like ALL are poorly understood. We performed transcriptome and whole genome sequencing on 15 cases of Ph-like ALL, and identified rearrangements involving ABL1, JAK2, PDGFRB, CRLF2 and EPOR, activating mutations of IL7R and FLT3, and deletion of SH2B3, which encodes the JAK2 negative regulator LNK. Importantly, several of these alterations induce transformation that is attenuated with tyrosine kinase inhibitors, suggesting the treatment outcome of these patients may be improved with targeted therapy.
Little is known about which attributes the patients need when they wish to maximise their capability to partner safely in healthcare. We aimed to identify these attributes from the perspective of key opinion leaders.
Delphi study involving indirect group interaction through a structured two-round survey.
International electronic survey.
11 (65%) of the 17 invited internationally recognised experts on patient safety completed the study.
50 patient attributes were rated by the Delphi panel for their ability to contribute maximally to safe health care.
The panellists agreed that 13 attributes are important for patients who want to maximise the role of safe partners. These domains relate to: autonomy, awareness, conscientiousness, knowledge, rationality, responsiveness and vigilance; for example, important attributes of autonomy include the ability to speak up, freedom to act and ability to act independently. Spanning seven domains, the attributes emphasise intellectual attributes and, to a lesser extent, moral attributes.
Whereas current safety discourses emphasise attributes of professionals, this study identified the patient attributes which key opinion leaders believe can maximise the capability of patients to partner safely in healthcare. Further research is needed that asks patients about the attributes they believe are most important.
Non-alcoholic fatty liver disease (NAFLD) is a common liver disease; the histological spectrum of which ranges from steatosis to steatohepatitis. Nonalcoholic steatohepatitis (NASH) often leads to cirrhosis and development of hepatocellular carcinoma. To better understand pathogenesis of NAFLD, we performed the pathway of distinction analysis (PoDA) on a genome-wide association study dataset of 250 non-Hispanic white female adult patients with NAFLD, who were enrolled in the NASH Clinical Research Network (CRN) Database Study, to investigate whether biologic process variation measured through genomic variation of genes within these pathways was related to the development of steatohepatitis or cirrhosis. Pathways such as Recycling of eIF2:GDP, biosynthesis of steroids, Terpenoid biosynthesis and Cholesterol biosynthesis were found to be significantly associated with NASH. SNP variants in Terpenoid synthesis, Cholesterol biosynthesis and biosynthesis of steroids were associated with lobular inflammation and cytologic ballooning while those in Terpenoid synthesis were also associated with fibrosis and cirrhosis. These were also related to the NAFLD activity score (NAS) which is derived from the histological severity of steatosis, inflammation and ballooning degeneration. Eukaryotic protein translation and recycling of eIF2:GDP related SNP variants were associated with ballooning, steatohepatitis and cirrhosis. Il2 signaling events mediated by PI3K, Mitotic metaphase/anaphase transition, and Prostanoid ligand receptors were also significantly associated with cirrhosis. Taken together, the results provide evidence for additional ways, beyond the effects of single SNPs, by which genetic factors might contribute to the susceptibility to develop a particular phenotype of NAFLD and then progress to cirrhosis. Further studies are warranted to explain potential important genetic roles of these biological processes in NAFLD.
Ovarian cancer remains a significant public health burden, with the highest mortality rate of all the gynecological cancers. This is attributable to the late stage at which the majority of ovarian cancers are diagnosed, coupled with the low and variable response of advanced tumors to standard chemotherapies. To date, clinically useful predictors of treatment response remain lacking. Identifying the genetic determinants of ovarian cancer survival and treatment response is crucial to the development of prognostic biomarkers and personalized therapies that may improve outcomes for the late-stage patients who comprise the majority of cases.
To identify constitutional genetic variations contributing to ovarian cancer mortality, we systematically investigated associations between germline polymorphisms and ovarian cancer survival using data from The Cancer Genome Atlas Project (TCGA). Using stage-stratified Cox proportional hazards regression, we examined 650,000 SNP loci for association with survival. We additionally examined whether the association of significant SNPs with survival was modified by somatic alterations.
Germline polymorphisms at rs4934282 (AGAP11/C10orf116) and rs1857623 (DNAH14) were associated with stage-adjusted survival ( = 1.12e-07 and 1.80e-07, FDR = 1.2e-04 and 2.4e-04, respectively). A third SNP, rs4869 (C10orf116), was additionally identified as significant in the exome sequencing data; it is in near-perfect LD with rs4934282. The associations with survival remained significant when somatic alterations.
Discovery analysis of TCGA data reveals germline genetic variations that may play a role in ovarian cancer survival even among late-stage cases. The significant loci are located near genes previously reported as having a possible relationship to platinum and taxol response. Because the variant alleles at the significant loci are common (frequencies for rs4934282 A/C alleles = 0.54/0.46, respectively; rs1857623 A/G alleles = 0.55/0.45, respectively) and germline variants can be assayed noninvasively, our findings provide potential targets for further exploration as prognostic biomarkers and individualized therapies.
TAF7, a component of the TFIID complex that nucleates the assembly of transcription preinitiation complexes, also independently interacts with and regulates the enzymatic activities of other transcription factors, including P-TEFb, TFIIH, and CIITA, ensuring an orderly progression in transcription initiation. Since not all TAFs are required in terminally differentiated cells, we examined the essentiality of TAF7 in cells at different developmental stages in vivo. Germ line disruption of the TAF7 gene is embryonic lethal between 3.5 and 5.5 days postcoitus. Mouse embryonic fibroblasts with TAF7 deleted cease transcription globally and stop proliferating. In contrast, whereas TAF7 is essential for the differentiation and proliferation of immature thymocytes, it is not required for subsequent, proliferation-independent differentiation of lineage committed thymocytes or for their egress into the periphery. TAF7 deletion in peripheral CD4 T cells affects only a small number of transcripts. However, T cells with TAF7 deleted are not able to undergo activation and expansion in response to antigenic stimuli. These findings suggest that TAF7 is essential for proliferation but not for proliferation-independent differentiation.
As a style of information processing, intuition involves implicit perceptual and cognitive processes that can be quickly and automatically executed without conscious mental will, such that people know more than they can describe. Patient intuition can influence patient and clinician decision-making and behavior. However, physicians may not always see patient intuition as credible or important, and its management in the clinical setting is poorly understood. This paper takes a step toward suggesting conditions under which patient intuition should be taken seriously. These conditions relate to the credibility or accuracy of the intuitive beliefs held by the patient, and their significance to the patient. Credibility may be increased when the intuitions of patients (1) reflect their individualized knowledge, (2) can complement the common absence of scientific evidence in managing health problems, and (3) can quickly and effectively process key information in complex cognitive tasks. Even intuitions that lack credibility can be subjectively rational and meaningful to patients, and help to shape the decisions they and clinicians make.
intuition; decision making; patients
Summary: Bambino is a variant detector and graphical alignment viewer for next-generation sequencing data in the SAM/BAM format, which is capable of pooling data from multiple source files. The variant detector takes advantage of SAM-specific annotations, and produces detailed output suitable for genotyping and identification of somatic mutations. The assembly viewer can display reads in the context of either a user-provided or automatically generated reference sequence, retrieve genome annotation features from a UCSC genome annotation database, display histograms of non-reference allele frequencies, and predict protein-coding changes caused by SNPs.
Availability: Bambino is written in platform-independent Java and available from https://cgwb.nci.nih.gov/goldenPath/bamview/documentation/index.html, along with documentation and example data. Bambino may be launched online via Java Web Start or downloaded and run locally.
Relapsed acute lymphoblastic leukaemia (ALL) is a leading cause of death due to disease in young people, but the biologic determinants of treatment failure remain poorly understood. Recent genome-wide profiling of structural DNA alterations in ALL have identified multiple submicroscopic somatic mutations targeting key cellular pathways1,2, and have demonstrated substantial evolution in genetic alterations from diagnosis to relapse3. However, detailed analysis of sequence mutations in ALL has not been performed. To identify novel mutations in relapsed ALL, we resequenced 300 genes in matched diagnosis and relapse samples from 23 patients with ALL. This identified 52 somatic non-synonymous mutations in 32 genes, many of which were novel, including the transcriptional coactivators CREBBP and NCOR1, the transcription factors ERG, SPI1, TCF4 and TCF7L2, components of the Ras signalling pathway, histone genes, genes involved in histone modification (CREBBP and CTCF), and genes previously shown to be targets of recurring DNA copy number alteration in ALL. Analysis of an extended cohort of 71 diagnosis-relapse cases and 270 acute leukaemia cases that did not relapse found that 18.3% of relapse cases had sequence or deletion mutations of CREBBP, which encodes the transcriptional coactivator and histone acetyltransferase (HAT) CREB-binding protein (CBP)4. The mutations were either present at diagnosis or acquired at relapse, and resulted in truncated alleles or deleterious substitutions in conserved residues of the HAT domain. Functionally, the mutations impaired histone acetylation and transcriptional regulation of CREBBP targets, including glucocorticoid responsive genes. Several mutations acquired at relapse were detected in subclones at diagnosis, suggesting that the mutations may confer resistance to therapy. These results extend the landscape of genetic alterations in leukaemia, and identify mutations targeting transcriptional and epigenetic regulation as a mechanism of resistance in ALL.
Genome-wide association studies (GWAS) have become increasingly common due to advances in technology and have permitted the identification of differences in single nucleotide polymorphism (SNP) alleles that are associated with diseases. However, while typical GWAS analysis techniques treat markers individually, complex diseases (cancers, diabetes, and Alzheimers, amongst others) are unlikely to have a single causative gene. Thus, there is a pressing need for multi–SNP analysis methods that can reveal system-level differences in cases and controls. Here, we present a novel multi–SNP GWAS analysis method called Pathways of Distinction Analysis (PoDA). The method uses GWAS data and known pathway–gene and gene–SNP associations to identify pathways that permit, ideally, the distinction of cases from controls. The technique is based upon the hypothesis that, if a pathway is related to disease risk, cases will appear more similar to other cases than to controls (or vice versa) for the SNPs associated with that pathway. By systematically applying the method to all pathways of potential interest, we can identify those for which the hypothesis holds true, i.e., pathways containing SNPs for which the samples exhibit greater within-class similarity than across classes. Importantly, PoDA improves on existing single–SNP and SNP–set enrichment analyses, in that it does not require the SNPs in a pathway to exhibit independent main effects. This permits PoDA to reveal pathways in which epistatic interactions drive risk. In this paper, we detail the PoDA method and apply it to two GWAS: one of breast cancer and the other of liver cancer. The results obtained strongly suggest that there exist pathway-wide genomic differences that contribute to disease susceptibility. PoDA thus provides an analytical tool that is complementary to existing techniques and has the power to enrich our understanding of disease genomics at the systems-level.
We present a novel method for multi–SNP analysis of genome-wide association studies. The method is motivated by the intuition that, if a set of SNPs is associated with disease, cases and controls will exhibit more within-group similarity than across-group similarity for the SNPs in the set of interest. Our method, Pathways of Distinction Analysis (PoDA), uses GWAS data and known pathway–gene and gene–SNP associations to identify pathways that permit the distinction of cases from controls. By systematically applying the method to all pathways of potential interest, we can identify pathways containing SNPs for which the cases and controls are distinguished and infer those pathways' role in disease. We detail the PoDA method and describe its results in breast and liver cancer GWAS data, demonstrating its utility as a method for systems-level analysis of GWAS data.
The PathOlogist is a new tool designed to transform large sets of gene expression data into quantitative descriptors of pathway-level behavior. The tool aims to provide a robust alternative to the search for single-gene-to-phenotype associations by accounting for the complexity of molecular interactions.
Molecular abundance data is used to calculate two metrics - 'activity' and 'consistency' - for each pathway in a set of more than 500 canonical molecular pathways (source: Pathway Interaction Database, http://pid.nci.nih.gov). The tool then allows a detailed exploration of these metrics through integrated visualization of pathway components and structure, hierarchical clustering of pathways and samples, and statistical analyses designed to detect associations between pathway behavior and clinical features.
The PathOlogist provides a straightforward means to identify the functional processes, rather than individual molecules, that are altered in disease. The statistical power and biologic significance of this approach are made easily accessible to laboratory researchers and informatics analysts alike. Here we show as an example, how the PathOlogist can be used to establish pathway signatures that robustly differentiate breast cancer cell lines based on response to treatment.
BioPAX (Biological Pathway Exchange) is a standard language to represent biological pathways at the molecular and cellular level. Its major use is to facilitate the exchange of pathway data (http://www.biopax.org). Pathway data captures our understanding of biological processes, but its rapid growth necessitates development of databases and computational tools to aid interpretation. However, the current fragmentation of pathway information across many databases with incompatible formats presents barriers to its effective use. BioPAX solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. BioPAX was created through a community process. Through BioPAX, millions of interactions organized into thousands of pathways across many organisms, from a growing number of sources, are available. Thus, large amounts of pathway data are available in a computable form to support visualization, analysis and biological discovery.
pathway data integration; pathway database; standard exchange format; ontology; information system
Translational research projects target a wide variety of diseases, test many different kinds of biomedical hypotheses, and employ a large assortment of experimental methodologies. Diverse data, complex execution environments, and demanding security and reliability requirements make the implementation of these projects extremely challenging and require novel e-Science technologies.
High resolution, system-wide characterizations have demonstrated the capacity to identify genomic regions that undergo genomic aberrations. Such research efforts often aim at associating these regions with disease etiology and outcome. Identifying the corresponding biologic processes that are responsible for disease and its outcome remains challenging. Using novel analytic methods that utilize the structure of biologic networks, we are able to identify the specific networks that are highly significantly, nonrandomly altered by regions of copy number amplification observed in a systems-wide analysis. We demonstrate this method in breast cancer, where the state of a subset of the pathways identified through these regions is shown to be highly associated with disease survival and recurrence.
Tamoxifen was approved for breast cancer risk reduction in high-risk women based on the National Surgical Adjuvant Breast and Bowel Project's Breast Cancer Prevention Trial (P-1:BCPT), which showed 50% fewer breast cancers with tamoxifen versus placebo, supporting tamoxifen's efficacy in preventing breast cancer. Poor metabolizing CYP2D6 variants are currently the subject of intensive scrutiny regarding their impact on clinical outcomes in the adjuvant setting. Our study extends to variants in a wider spectrum of tamoxifen-metabolizing genes and applies to the prevention setting.
Our case-only study, nested within P-1:BCPT, explored associations of polymorphisms in estrogen/tamoxifen-metabolizing genes with responsiveness to preventive tamoxifen. Thirty-nine candidate polymorphisms in 17 candidate genes were genotyped in 249 P-1:BCPT cases.
CYP2D6_C1111T, individually and within a CYP2D6 haplotype, showed borderline significant association with treatment arm. Path analysis of the entire tamoxifen pathway gene network showed that the tamoxifen pathway model was consistent with the pattern of observed genotype variability within the placebo-arm dataset. However, correlation of variations in genes in the tamoxifen arm differed significantly from the predictions of the tamoxifen pathway model. Strong correlations between allelic variation in the tamoxifen pathway at CYP1A1-CYP3A4, CYP3A4-CYP2C9, and CYP2C9-SULT1A2, in addition to CYP2D6 and its adjacent genes, were seen in the placebo-arm but not the tamoxifen-arm. In conclusion, beyond reinforcing a role for CYP2D6 in tamoxifen response, our pathway analysis strongly suggests that specific combinations of allelic variants in other genes make major contributions to the tamoxifen-resistance phenotype.
Breast cancer; tamoxifen resistance; chemoprevention; pathway analysis; breast cancer risk; genomic polymorphisms
An unhappy patient suggests poor quality care, but Glyn Elwyn and colleagues point out that using measures of satisfaction to assess health providers is not without problems
Purpose: Tamoxifen was approved for breast cancer risk reduction in high-risk women based on the National Surgical Adjuvant Breast and Bowel Project's Breast Cancer Prevention Trial (P-1:BCPT), which showed 50% fewer breast cancers with tamoxifen versus placebo, supporting tamoxifen's efficacy in preventing breast cancer. Poor metabolizing CYP2D6 variants are currently the subject of intensive scrutiny regarding their impact on clinical outcomes in the adjuvant setting. Our study extends to variants in a wider spectrum of tamoxifen-metabolizing genes and applies to the prevention setting. Methods: Our case-only study, nested within P-1:BCPT, explored associations of polymorphisms in estrogen/tamoxifen-metabolizing genes with responsiveness to preventive tamoxifen. Thirty-nine candidate polymorphisms in 17 candidate genes were genotyped in 249 P-1:BCPT cases. Results: CVP2D6_C1111T, individually and within a CYP2D6 haplotype, showed borderline significant association with treatment arm. Path analysis of the entire tamoxifen pathway gene network showed that the tamoxifen pathway model was consistent with the pattern of observed genotype variability within the placebo-arm dataset. However, correlation of variations in genes in the tamoxifen arm differed significantly from the predictions of the tamoxifen pathway model. Strong correlations between allelic variation in the tamoxifen pathway at CYP1A1-CYP3A4, CYP3A4-CYP2C9, and CYP2C9-SULT1A2, in addition to CYP2D6 and its adjacent genes, were seen in the placebo-arm but not the tamoxifen-arm. In conclusion, beyond reinforcing a role for CYP2D6 in tamoxifen response, our pathway analysis strongly suggests that specific combinations of allelic variants in other genes make major contributions to the tamoxifen-resistance phenotype.
Breast cancer; tamoxifen resistance; chemoprevention; pathway analysis; breast cancer risk; genomic
Finding better therapies for the treatment of brain tumors is hampered by the lack of consistently obtained molecular data in a large sample set, and ability to integrate biomedical data from disparate sources enabling translation of therapies from bench to bedside. Hence, a critical factor in the advancement of biomedical research and clinical translation is the ease with which data can be integrated, redistributed and analyzed both within and across functional domains. Novel biomedical informatics infrastructure and tools are essential for developing individualized patient treatment based on the specific genomic signatures in each patient’s tumor. Here we present Rembrandt, Repository of Molecular BRAin Neoplasia DaTa, a cancer clinical genomics database and a web-based data mining and analysis platform aimed at facilitating discovery by connecting the dots between clinical information and genomic characterization data. To date, Rembrandt contains data generated through the Glioma Molecular Diagnostic Initiative from 874 glioma specimens comprising nearly 566 gene expression arrays, 834 copy number arrays and 13,472 clinical phenotype data points. Data can be queried and visualized for a selected gene across all data platforms or for multiple genes in a selected platform. Additionally, gene sets can be limited to clinically important annotations including secreted, kinase, membrane, and known gene-anomaly pairs to facilitate the discovery of novel biomarkers and therapeutic targets. We believe that REMBRANDT represents a prototype of how high throughput genomic and clinical data can be integrated in a way that will allow expeditious and efficient translation of laboratory discoveries to the clinic.
Rembrandt; personalized medicine; translational research; clinical genomics; data integration
A novel method for quantifying the similarity between phenotypes by the use of ontologies can be used to search for candidate genes, pathway members, and human disease models on the basis of phenotypes alone.
Scientists and clinicians who study genetic alterations and disease have traditionally described phenotypes in natural language. The considerable variation in these free-text descriptions has posed a hindrance to the important task of identifying candidate genes and models for human diseases and indicates the need for a computationally tractable method to mine data resources for mutant phenotypes. In this study, we tested the hypothesis that ontological annotation of disease phenotypes will facilitate the discovery of new genotype-phenotype relationships within and across species. To describe phenotypes using ontologies, we used an Entity-Quality (EQ) methodology, wherein the affected entity (E) and how it is affected (Q) are recorded using terms from a variety of ontologies. Using this EQ method, we annotated the phenotypes of 11 gene-linked human diseases described in Online Mendelian Inheritance in Man (OMIM). These human annotations were loaded into our Ontology-Based Database (OBD) along with other ontology-based phenotype descriptions of mutants from various model organism databases. Phenotypes recorded with this EQ method can be computationally compared based on the hierarchy of terms in the ontologies and the frequency of annotation. We utilized four similarity metrics to compare phenotypes and developed an ontology of homologous and analogous anatomical structures to compare phenotypes between species. Using these tools, we demonstrate that we can identify, through the similarity of the recorded phenotypes, other alleles of the same gene, other members of a signaling pathway, and orthologous genes and pathway members across species. We conclude that EQ-based annotation of phenotypes, in conjunction with a cross-species ontology, and a variety of similarity metrics can identify biologically meaningful similarities between genes by comparing phenotypes alone. This annotation and search method provides a novel and efficient means to identify gene candidates and animal models of human disease, which may shorten the lengthy path to identification and understanding of the genetic basis of human disease.
Model organisms such as fruit flies, mice, and zebrafish are useful for investigating gene function because they are easy to grow, dissect, and genetically manipulate in the laboratory. By examining mutations in these organisms, one can identify candidate genes that cause disease in humans, and develop models to better understand human disease and gene function. A fundamental roadblock for analysis is, however, the lack of a computational method for describing and comparing phenotypes of mutant animals and of human diseases when the genetic basis is unknown. We describe here a novel method using ontologies to record and quantify the similarity between phenotypes. We tested our method by using the annotated mutant phenotype of one member of the Hedgehog signaling pathway in zebrafish to identify other pathway members with similar recorded phenotypes. We also compared human disease phenotypes to those produced by mutation in model organisms, and show that orthologous and biologically relevant genes can be identified by this method. Given that the genetic basis of human disease is often unknown, this method provides a means for identifying candidate genes, pathway members, and disease models by computationally identifying similar phenotypes within and across species.
Background. Common but seldom published are Parkinson's disease (PD) medication errors involving late, extra, or missed doses. These errors can reduce medication effectiveness and the quality of life of people with PD and their caregivers. Objective. To explore lay perspectives of factors contributing to medication timing errors for PD in hospital and community settings. Design and Methods. This qualitative research purposively sampled individuals with PD, or a proxy of their choice, throughout New Zealand during 2008-2009. Data collection involved 20 semistructured, personal interviews by telephone. A general inductive analysis of the data identified core insights consistent with the study objective. Results. Five themes help to account for possible timing adherence errors by people with PD, their caregivers or professionals. The themes are the abrupt withdrawal of PD medication; wrong, vague or misread instructions; devaluation of the lay role in managing PD medications; deficits in professional knowledge and in caring behavior around PD in formal health care settings; and lay forgetfulness. Conclusions. The results add to the limited published research on medication errors in PD and help to confirm anecdotal experience internationally. They indicate opportunities for professionals and lay people to work together to reduce errors in the timing of medication for PD in hospital and community settings.
The structure of S. aureus MenB, an enzyme in the biosynthetic pathway to vitamin K2, has been determined and compared with the enzyme derived from another important pathogen, M. tuberculosis.
Vitamin K2, or menaquinone, is an essential cofactor for many organisms and the enzymes involved in its biosynthesis are potential antimicrobial drug targets. One of these enzymes, 1,4-dihydroxy-2-naphthoyl-CoA synthase (MenB) from the pathogen Staphylococcus aureus, has been obtained in recombinant form and its quaternary structure has been analyzed in solution. Cubic crystals of the enzyme allowed a low-resolution structure (2.9 Å) to be determined. The asymmetric unit consists of two subunits and a crystallographic threefold axis of symmetry generates a hexamer consistent with size-exclusion chromatography. Analytical ultracentrifugation indicates the presence of six states in solution, monomeric through to hexameric, with the dimer noted as being particularly stable. MenB displays the crotonase-family fold with distinct N- and C-terminal domains and a flexible segment of structure around the active site. The smaller C-terminal domain plays an important role in oligomerization and also in substrate binding. The presence of acetoacetyl-CoA in one of the two active sites present in the asymmetric unit indicates how part of the substrate binds and facilitates comparisons with the structure of Mycobacterium tuberculosis MenB.
crotonase; synthase; vitamin biosynthesis; menaquinone; MenB
Recent publications have described and applied a novel metric that quantifies the
genetic distance of an individual with respect to two population samples, and
have suggested that the metric makes it possible to infer the presence of an
individual of known genotype in a sample for which only the marginal allele
frequencies are known. However, the assumptions, limitations, and utility of
this metric remained incompletely characterized. Here we present empirical tests
of the method using publicly accessible genotypes, as well as analytical
investigations of the method's strengths and limitations. The results
reveal that the null distribution is sensitive to the underlying assumptions,
making it difficult to accurately calibrate thresholds for classifying an
individual as a member of the population samples. As a result, the
false-positive rates obtained in practice are considerably higher than
previously believed. However, despite the metric's inadequacies for
identifying the presence of an individual in a sample, our results suggest
potential avenues for future research on tuning this method to problems of
ancestry inference or disease prediction. By revealing both the strengths and
limitations of the proposed method, we hope to elucidate situations in which
this distance metric may be used in an appropriate manner. We also discuss the
implications of our findings in forensics applications and in the protection of
GWAS participant privacy.
In this report, we evaluate a recently-published method for resolving whether
individuals are present in a complex genomic DNA mixture. Based on the intuition
that an individual will be genetically “closer” to a sample
containing him than to a sample not, the method investigated here uses a
distance metric to quantify the similarity of an individual relative to two
population samples. Although initial applications of this approach showed a
promising false-negative rate, the accuracy of the assumed null distribution
(and hence the true false-positive rate) remained uninvestigated; here, we
explore this question analytically and describe tests of this method to assess
the likelihood that an individual who is not in the mixture is mistakenly
classified as being a member. Our results show that the method has a high
false-positive rate in practice due to its sensitivity to underlying
assumptions, limiting its utility for inferring the presence of an individual in
a population. By revealing both the strengths and limitations of the proposed
method, we elucidate situations in which this distance metric may be used in an
The molecular mechanisms underlying pluripotency and lineage specification from embryonic stem (ES) cells are largely unclear. Differentiation pathways may be determined by the targeted activation of lineage specific genes or by selective silencing of genome regions during differentiation. Here we show that the ES cell genome is transcriptionally globally hyperactive and undergoes global silencing as cells differentiate. Normally silent repeat regions are active in ES cells and tissue-specific genes are sporadically expressed at low levels. Whole genome tiling arrays demonstrate widespread transcription in both coding and non-coding regions in pluripotent ES cells whereas the transcriptional landscape becomes more discrete as differentiation proceeds. The transcriptional hyperactivity in ES cells is accompanied by disproportionate expression of chromatin-remodeling genes and the general transcription machinery, but not histone modifying activities. Interference with several chromatin remodeling activities in ES cells affects their proliferation and differentiation behavior. We propose that global transcriptional activity is a hallmark of pluripotent ES cells that contributes to their plasticity and that lineage specification is strongly driven by reduction of the actively transcribed portion of the genome.
Amid neglect of patients' contribution to error has been a failure to ask whether patients are morally responsible for their errors. This paper aims to help answer this question and so define a worthy response to the errors. Recent work on medical errors has emphasised system deficiencies and discouraged finding people to blame. We scrutinise this approach from an incompatibilist, agent causation position and draw on Hart's taxonomy of four senses of moral responsibility: role responsibility; capacity responsibility; causal responsibility; and liability responsibility. Each sense is shown to contribute to an overall theoretical judgment as to whether patients are morally responsible for their errors (and success in avoiding them). Though how to weight the senses is unclear, patients appear to be morally responsible for the avoidable errors they make, contribute to or can influence.
patient; error; moral responsibility