Bidirectional Mendelian randomization to explore the causal relationships between body mass index and polycystic ovary syndrome

STUDY QUESTION

What are the causal relationships between polycystic ovary syndrome (PCOS) and body mass index (BMI)?

SUMMARY ANSWER

Bidirectional Mendelian randomization analyses suggest that increased BMI is causal for PCOS while the reverse is not the case.

WHAT IS KNOWN ALREADY

The contribution of obesity to the pathogenesis of PCOS is controversial. To date, published genetic studies addressing this question have generated conflicting results and have not utilized the full extent of known single nucleotide polymorphisms associated with body mass index (BMI).

STUDY DESIGN, SIZE, DURATION

This cross-sectional Mendelian randomization (MR) and genetic association study was conducted in 750 individuals of European origin and with PCOS and 1567 BMI-matched controls.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Cases and controls were matched for BMI as well as for distribution of weight categories (normal weight, overweight, obese). Two-sample MR using inverse variance weighting (IVW) was conducted using a 92-SNP instrument variable for BMI with PCOS as the outcome, followed by two-sample MR with a 16-SNP instrument variable for PCOS with BMI as the outcome. Sensitivity analyses included MR-Egger and maximum likelihood methods. Secondary analyses assessed associations of genetic risk scores and individual SNPs with PCOS, BMI and quantitative androgen-related and glucose homeostasis-related traits.

MAIN RESULTS AND THE ROLE OF CHANCE

Each standard deviation genetically higher BMI was associated with a 4.89 (95% CI 1.46-16.32) higher odds of PCOS. Conversely, genetic risk of PCOS did not influence BMI. Sensitivity analyses yielded directionally consistent results. The genetic risk score of 92 BMI SNPs was associated with the diagnosis of PCOS (OR 1.043, 95% CI 1.009-1.078, P = 0.012). Of the 92 BMI risk variants evaluated, none were associated individually with PCOS after considering multiple testing. The association of FTO SNP rs1421085 with BMI was stronger in women with PCOS (β = 0.071, P = 0.0006) than in controls (β = 0.046, P = 0.065).

LIMITATIONS, REASONS FOR CAUTION

The current sample size, while providing good power for MR and genetic risk score analyses, had limited power to demonstrate association of individual SNPs with PCOS. Cases and controls were not matched for age; however, this was mitigated by adjusting analyses for age. Dietary and lifestyle data, which could have been used to explore the greater association of the FTO SNP with BMI in women with PCOS, was not available.

WIDER IMPLICATIONS OF THE FINDINGS

Increasing BMI appears to be causal for PCOS but having PCOS does not appear to affect BMI. This study used the most comprehensive set of SNPs for BMI currently available. Prior studies using fewer SNPs had yielded conflicting results and may have been confounded because cases and controls were not matched for weight categories. The current results highlight the potential utility of weight management in the prevention and treatment of PCOS.

STUDY FUNDING/COMPETING INTEREST(S)

National Institutes of Health Grants R01-HD29364 and K24-HD01346 (to R.A.), Grant R01-DK79888 (to M.O.G.), Grant U54-HD034449 (to R.S.L.), Grant U19-HL069757 (to R.M.K.). The funders had no influence on the data collection, analyses or conclusions of the study. No conflict of interests to declare.

TRIAL REGISTRATION NUMBER

N/A.

A collaborative filtering-based approach to biomedical knowledge discovery

Motivation

The increase in publication rates makes it challenging for an individual researcher to stay abreast of all relevant research in order to find novel research hypotheses. Literature-based discovery methods make use of knowledge graphs built using text mining and can infer future associations between biomedical concepts that will likely occur in new publications. These predictions are a valuable resource for researchers to explore a research topic. Current methods for prediction are based on the local structure of the knowledge graph. A method that uses global knowledge from across the knowledge graph needs to be developed in order to make knowledge discovery a frequently used tool by researchers.

Results

We propose an approach based on the singular value decomposition (SVD) that is able to combine data from across the knowledge graph through a reduced representation. Using cooccurrence data extracted from published literature, we show that SVD performs better than the leading methods for scoring discoveries. We also show the diminishing predictive power of knowledge discovery as we compare our predictions with real associations that appear further into the future. Finally, we examine the strengths and weaknesses of the SVD approach against another well-performing system using several predicted associations.

Availability and implementation

All code and results files for this analysis can be accessed at https://github.com/jakelever/knowledgediscovery.

Contact

sjones@bcgsc.ca.

Supplementary information

Supplementary data are available at Bioinformatics online.

Abstract 4340: Integrating whole genome and transcriptome analysis to inform treatment decisions in the metastatic cancer clinical setting

Genomic analysis is an established clinical tool for positioning patients to specific treatment rationales. However, given the vast range of genomic complexity that is observed between tumors the extent to which genomic characterization must be applied in order to generate a true picture of the actionable and informative genomic events in an individual's tumour is still a matter for intense debate. Historically, cost and the complexity of the bioinformatics analysis needed has limited the adoption of comprehensive genomic characterization. However with the emerging clinical significance of genomic signatures, mutation burden and immunogenomic profiling, information that can best be recovered from comprehensive genomic analyses, a better understanding of the utility of extensive genomic characterization of tumours in a clinical setting is needed. For the past 6 years, the Personalized Oncogenomics Project (POG) at the BC Cancer Agency has used paired tumour/normal Whole Genome and Transcriptome Analysis (WGTA) to comprehensively characterize tumour samples from patients with incurable metastatic cancer to inform treatment planning for each patient within a clinically relevant timeframe. To date tumours from over 700 patients have been genomically profiled using this approach. These data represent a cohort of tumours from the post-treatment metastatic setting that have been comprehensively characterized at a whole genome level augmented by comprehensive clinical annotation, including information about the primary diagnosis, progression of the disease and treatment history, as well as follow-up information including the response of the tumour to genome analysis-informed therapy. We will discuss findings from this powerful genomic cohort focusing on aspects that are particularly well represented by using a WGTA approach including; mutation signatures of biological and clinical significance, integration of expression data to complement genomic findings and highlighting the discovery of novel NRG1 gene fusions as a demonstration of how using a comprehensive multi-omic approach for genomic analysis to infer the oncogenic impact of novel genomic events can impact patient care.

Citation Format: Martin R. Jones, Yaoqing Shen, Erin Pleasance, Elisa Majounie, Laura Williamson, Eric Zhao, Eric Chuah, Karen L. Mungall, Andrew J. Mungall, Richard A. Moore, Yussanne Ma, Stephen Yip, Howard Lim, Daniel Renouf, Steven J. Jones, Janesssa Jaskin, Marco A. Marra. Integrating whole genome and transcriptome analysis to inform treatment decisions in the metastatic cancer clinical setting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4340.

Successful targeting of the NRG1 pathway indicates novel treatment strategy for metastatic cancer

Background

NRG1 fusion-positive lung cancers have emerged as potentially actionable events in lung cancer, but clinical support is currently limited and no evidence of efficacy of this approach in cancers beyond lung has been shown.

Patients and methods

Here, we describe two patients with advanced cancers refractory to standard therapies. Patient 1 had lung adenocarcinoma and patient 2 cholangiocarcinoma. Whole-genome and transcriptome sequencing were carried out for these cases with select findings validated by fluorescence in situ hybridization.

Results

Both tumors were found to be positive for NRG1 gene fusions. In patient 1, an SDC4-NRG1 gene fusion was detected, similar gene fusions having been described in lung cancers previously. In patient 2, a novel ATP1B1-NRG1 gene fusion was detected. Cholangiocarcinoma is not a disease type in which NRG1 fusions had been described previously. Integrative genome analysis was used to assess the potential functional significance of the detected genomic events including the gene fusions, prioritizing therapeutic strategies targeting the HER-family of growth factor receptors. Both patients were treated with the pan HER-family kinase inhibitor afatinib and both displayed significant and durable response to treatment. Upon progression sites of disease were sequenced. The lack of obvious genomic events to describe the disease progression indicated that broad transcriptomic or epigenetic mechanisms could be attributed to the lack of prolonged response to afatinib.

Conclusion

These observations lend further support to the use of pan HER-tyrosine kinase inhibitors for the treatment of NRG1 fusion-positive in both cancers of lung and hepatocellular origin and indicate more broadly that cancers found to be NRG1 fusion-positive may benefit from such a clinical approach regardless of their site of origin.

Clinical trial information

Personalized Oncogenomics (POG) Program of British Columbia: Utilization of Genomic Analysis to Better Understand Tumour Heterogeneity and Evolution (NCT02155621).

Comparative RNA-Sequencing Analysis Benefits a Pediatric Patient With Relapsed Cancer

Clinical detection of sequence and structural variants in known cancer genes points to viable treatment options for a minority of children with cancer.¹ To increase the number of children who benefit from genomic profiling, gene expression information must be considered alongside mutations.^2,3 Although high expression has been used to nominate drug targets for pediatric cancers,^4,5 its utility has not been evaluated in a systematic way.⁶ We describe a child with a rare sarcoma that was profiled with whole-genome and RNA sequencing (RNA-Seq) techniques. Although the tumor did not harbor DNA mutations targetable by available therapies, incorporation of gene expression information derived from RNA-Seq analysis led to a therapy that produced a significant clinical response. We use this case to describe a framework for inclusion of gene expression into the clinical genomic evaluation of pediatric tumors.

Personalized oncogenomic analysis of metastatic adenoid cystic carcinoma: using whole-genome sequencing to inform clinical decision-making

Metastatic adenoid cystic carcinomas (ACCs) can cause significant morbidity and mortality. Because of their slow growth and relative rarity, there is limited evidence for systemic therapy regimens. Recently, molecular profiling studies have begun to reveal the genetic landscape of these poorly understood cancers, and new treatment possibilities are beginning to emerge. The objective is to use whole-genome and transcriptome sequencing and analysis to better understand the genetic alterations underlying the pathology of metastatic and rare ACCs and determine potentially actionable therapeutic targets. We report five cases of metastatic ACC, not originating in the salivary glands, in patients enrolled in the Personalized Oncogenomics (POG) Program at the BC Cancer Agency. Genomic workup included whole-genome and transcriptome sequencing, detailed analysis of tumor alterations, and integration with existing knowledge of drug–target combinations to identify potential therapeutic targets. Analysis reveals low mutational burden in these five ACC cases, and mutation signatures that are commonly observed in multiple cancer types. Notably, the only recurrent structural aberration identified was the well-described MYB-NFIB fusion that was present in four of five cases, and one case exhibited a closely related MYBL1-NFIB fusion. Recurrent mutations were also identified in BAP1 and BCOR, with additional mutations in individual samples affecting NOTCH1 and the epigenetic regulators ARID2, SMARCA2, and SMARCB1. Copy changes were rare, and they included amplification of MYC and homozygous loss of CDKN2A in individual samples. Genomic analysis revealed therapeutic targets in all five cases and served to inform a therapeutic choice in three of the cases to date.

Whole genome and whole transcriptome genomic profiling of a metastatic eccrine porocarcinoma

Eccrine porocarcinomas (EPs) are rare malignant tumours of the intraepidermic sweat gland duct and most often arise from benign eccrine poromas. Some recurrent somatic genomic events have been identified in these malignancies, but very little is known about the complexity of their molecular pathophysiology. We describe the whole genome and whole transcriptome genomic profiling of a metastatic EP in a 66-year-old male patient with a previous history of localized porocarcinoma of the scalp. Whole genome and whole transcriptome genomic profiling was performed on the metastatic EP. Whole genome sequencing was performed on blood-derived DNA in order to allow a comparison between germline and somatic events. We found somatic copy losses of several tumour suppressor genes including APC, PTEN and CDKN2A, CDKN2B and CDKN1A. We identified a somatic hemizygous CDKN2A pathogenic splice site variant. De novo transcriptome assembly revealed abnormal splicing of CDKN2A p14ARF and p16INK4a. Elevated expression of oncogenes EGFR and NOTCH1 was noted and no somatic mutations were found in these genes. Wnt pathway somatic alterations were also observed. In conclusion, our results suggest that the molecular pathophysiology of malignant EP features high complexity and subtle interactions of multiple key genes. Cell cycle dysregulation and CDKN2A loss of function was found to be a new potential driver in EP tumourigenesis. Moreover, the combination of somatic copy number variants and abnormal gene expression perhaps partly related to epigenetic mechanisms, all likely contribute to the development of this rare malignancy in our patient.

Assessment of Capture and Amplicon-Based Approaches for the Development of a Targeted Next-Generation Sequencing Pipeline to Personalize Lymphoma Management

Targeted next-generation sequencing panels are increasingly used to assess the value of gene mutations for clinical diagnostic purposes. For assay development, amplicon-based methods have been preferentially used on the basis of short preparation time and small DNA input amounts. However, capture sequencing has emerged as an alternative approach because of high testing accuracy. We compared capture hybridization and amplicon sequencing approaches using fresh-frozen and formalin-fixed, paraffin-embedded tumor samples from eight lymphoma patients. Next, we developed a targeted sequencing pipeline using a 32-gene panel for accurate detection of actionable mutations in formalin-fixed, paraffin-embedded tumor samples of the most common lymphocytic malignancies: chronic lymphocytic leukemia, diffuse large B-cell lymphoma, and follicular lymphoma. We show that hybrid capture is superior to amplicon sequencing by providing deep more uniform coverage and yielding higher sensitivity for variant calling. Sanger sequencing of 588 variants identified specificity limits of thresholds for mutation calling, and orthogonal validation on 66 cases indicated 93% concordance with whole-genome sequencing. The developed pipeline and assay identified at least one actionable mutation in 91% of tumors from 219 lymphoma patients and revealed subtype-specific mutation patterns and frequencies consistent with the literature. This pipeline is an accurate and sensitive method for identifying actionable gene mutations in routinely acquired biopsy materials, suggesting further assessment of capture-based assays in the context of personalized lymphoma management.

Abstract A184: Clinical application of whole genome and transcriptome sequencing in cancer care

Aim: To investigate feasibility and utility of whole genome and RNA sequencing in cancer care. The use of comprehensive personal genomic information to guide cancer treatment decisions has gained momentum in recent years. Here we present the Personalized Oncogenomics (POG) project launched at British Columbia Cancer Agency in 2012. This project uses paired tumor/normal whole genome and transcriptome sequencing to characterize a patient's tumor and inform treatment options within a clinically relevant time frame. In the past 5 years, around 600 patients with metastatic cancers (including 49 pediatric cases) have been sequenced and analyzed, representing over 50 cancer types. The breadth and depth of data in POG enable the detection of multiple types of alterations, from simple mutations, indels, and copy number changes, to more complex alterations such as gene fusion, mutation signatures, and homologous recombination deficiency score. Incorporation of gene expression data through transcriptome sequencing informs on the impact of observed genomic alterations, and provides information regarding specific diagnosis via expression comparison to other cancer subtypes. All genomic and transcriptomic variants are integrated to build a personalized tumor-specific molecular profile, identify actionable items supported by an in-house knowledgebase and publically available molecular oncology, and characterize each individual tumor by intensive pathway analysis and literature search. Such multidimensional data also impose challenges in interpretation and communication. We developed a pipeline to translate complex genomic data into clinically actionable and hypothetical recommendations for the treatment of individual patients. The pipeline produces two reports: a panel-like report that contains known SNVs and fusions with therapeutic relevance from a collection of more than 4000 events, and a second comprehensive and manually curated report to fully characterize the tumor using the whole genome and transcriptome datasets. Genomic data are presented and discussed at a multidisciplinary molecular tumor board consisting of medical oncologists, pathologists, bioinformaticians, geneticists, and biologists. This approach has enabled clinicians to make informed clinical decisions based on the genomic data integrated with other clinical features, as well as to form new treatment-related hypothesis. POG has shown that use of both whole genome and transcriptome sequencing allows identification of therapeutic targets in a significant proportion of patients. Almost 80% cases were found to have one or more actionable alterations (100% in pediatric cases), and almost one-third of these are defined only using RNA data. Based on molecular tumor board discussion, patients are directed to clinical trials, positioned to genomically informed standard-of-care options, or treated with off-label drugs. With demonstrated effectiveness, the integrative approach developed by POG not only provides molecular insight and treatment options into individual tumors, but also provides a rich resource of molecular data with matched clinical information that will aid our understanding of tumor biology and therapy response mechanisms to inform treatment strategies in the future.

Citation Format: Yaoqing Shen, Martin R. Jones, Erin Pleasance, Melika Bonakdar, Carolyn Ch'ng, Caralyn Reisle, Laura Williamson, Elisa Majounie, Greg Taylor, Simon Chan, Brandon Pierce, Wei Zhang, Amir Muhammadzadeh, Eric Y. Zhao, Dustin Bleile, Karen Mungall, Nina Thiessen, Eric Chuah, Tina Wong, Richard Corbett, Yussanne Ma, Richard A. Moore, Andrew J. Mungall, Yongjun Zhao, Stephen Yip, Anna F. Lee, Rod Rassekh, Rebecca Deyell, Howard Lim, Daniel Renouf, Robyn Roscoe, Steven J.M Jones, Janessa Laskin, Marco A. Marra. Clinical application of whole genome and transcriptome sequencing in cancer care [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr A184.

High performance computation of landscape genomic models including local indicators of spatial association

With the increasing availability of both molecular and topo-climatic data, the main challenges facing landscape genomics - that is the combination of landscape ecology with population genomics - include processing large numbers of models and distinguishing between selection and demographic processes (e.g. population structure). Several methods address the latter, either by estimating a null model of population history or by simultaneously inferring environmental and demographic effects. Here we present samβada, an approach designed to study signatures of local adaptation, with special emphasis on high performance computing of large-scale genetic and environmental data sets. samβada identifies candidate loci using genotype-environment associations while also incorporating multivariate analyses to assess the effect of many environmental predictor variables. This enables the inclusion of explanatory variables representing population structure into the models to lower the occurrences of spurious genotype-environment associations. In addition, samβada calculates local indicators of spatial association for candidate loci to provide information on whether similar genotypes tend to cluster in space, which constitutes a useful indication of the possible kinship between individuals. To test the usefulness of this approach, we carried out a simulation study and analysed a data set from Ugandan cattle to detect signatures of local adaptation with samβada, bayenv, lfmm and an F_ST outlier method (FDIST approach in arlequin) and compare their results. samβada - an open source software for Windows, Linux and Mac OS X available at http://lasig.epfl.ch/sambada - outperforms other approaches and better suits whole-genome sequence data processing.

Abstract 2473: Breast cancer whole genomes link homologous recombination deficiency (HRD) with therapeutic outcomes

Background: Homologous recombination deficiency (HRD) is common in cancer - germline BRCA1 & BRCA2 mutations account for 5-10% of breast cancers and confer 85% lifetime risk. HRD cancers exhibit genomic instability and sensitivity to platinum-based therapy and PARP inhibitors. While not all causes of HRD are known, recent sequencing efforts have revealed genome-wide somatic mutation signatures that characterize the HRD genomic instability phenotype, also known as “BRCA-ness”. This provides a promising new assay to predict sensitivity to platinum-based therapy. Here, we integrate two whole-genome sequencing metrics to assess their association with therapeutic outcomes in a breast cancer cohort.

Methods: Whole-genome sequencing of 47 breast cancer tumors (100x coverage) and matched normals (60x) was performed on an Illumina HiSeq. Alignment, assembly, SNV calling, and loss of heterozygosity (LOH) detection were performed with BWA, ABySS, Strelka, and APOLLOH respectively. SNV signatures were deciphered by non-negative matrix factorization with Monte Carlo resampling. An HRD score comprised of LOH, telomeric allelic imbalance (TAI), and large scale transition (LST) counts was computed. Clinical endpoints were obtained by retrospective review of treatment and imaging reports. Analysis is ongoing in an independent validation cohort of 62 sequenced cases.

Results: The HRD-linked SNV signature was significantly associated with radiographic clinical response (CR) to platinum-based therapy (p=0.015). Logistic regression demonstrated a 59% improved odds of CR to platinum-based therapy per 1000 somatic SNVs attributed to HRD (odds ratio 1.16-2.50). Tumors carried up to 10,246 such SNVs and all patients with CR were among the top quartile. The LOH-TAI-LST score was correlated with SNV signature (r=0.6, p=7×10-6) and associated with CR (p=0.025). Notably, elevated HRD signatures associated with CR were identified in tumors with wild-type BRCA1/BRCA2 or variants of unknown significance. Tumors with above median HRD signatures were associated with a 69-day longer time to treatment failure and an 18% daily decreased probability of treatment failure per 1000 HRD-attributed SNVs (hazard ratio 0.71-0.95, p = 0.007).

Discussion: We found that HRD mutation signatures are associated with clinical response and longer time to treatment failure with platinum-based therapy. While similar benefits were observed in patients with somatic bi-allelic loss of BRCA1/BRCA2, such cases are less common (8% of our cohort) compared to those with elevated HRD signature. Thus, mutation signature methods may identify patients who stand to benefit from platinum-based therapy missed by BRCA screening alone.

Citation Format: Eric Y. Zhao, Yaoqing Shen, Erin Pleasance, Katayoon Kasaian, Martin R. Jones, Carolyn Ch'ng, Caralyn Reisle, Peter Eirew, Karen Mungall, Nina Thiessen, Yussanne Ma, Alexandra Fok, Andrew J. Mungall, Yongjun Zhao, Richard Moore, Diego Villa, Tamara Shenkier, Caroline Lohrisch, Stephen Chia, Stephen Yip, Karen Gelmon, Howard Lim, Sophie Sun, Kasmintan A. Schrader, Sean Young, Aly Karsan, Robyn Roscoe, Janessa Laskin, Marco A. Marra, Steven J. Jones. Breast cancer whole genomes link homologous recombination deficiency (HRD) with therapeutic outcomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2473. doi:10.1158/1538-7445.AM2017-2473

How close are we to complete annotation of metabolomes?

The metabolome describes the full complement of the tens to hundreds of thousands of low molecular weight metabolites present within a biological system. Identification of the metabolome is critical for discovering the maximum amount of biochemical knowledge from metabolomics datasets. Yet no exhaustive experimental characterisation of any organismal metabolome has been reported to date, dramatically contrasting with the genome sequencing of thousands of plants, animals and microbes. Here, we review the status of metabolome annotation and describe advances in the analytical methodologies being applied. In part through new international coordination, we conclude that we are now entering a new era of metabolome annotation.

High performance computation of landscape genomic models including local indicators of spatial association

With the increasing availability of both molecular and topo-climatic data, the main challenges facing landscape genomics - that is the combination of landscape ecology with population genomics - include processing large numbers of models and distinguishing between selection and demographic processes (e.g. population structure). Several methods address the latter, either by estimating a null model of population history or by simultaneously inferring environmental and demographic effects. Here we present samβada, an approach designed to study signatures of local adaptation, with special emphasis on high performance computing of large-scale genetic and environmental data sets. samβada identifies candidate loci using genotype-environment associations while also incorporating multivariate analyses to assess the effect of many environmental predictor variables. This enables the inclusion of explanatory variables representing population structure into the models to lower the occurrences of spurious genotype-environment associations. In addition, samβada calculates local indicators of spatial association for candidate loci to provide information on whether similar genotypes tend to cluster in space, which constitutes a useful indication of the possible kinship between individuals. To test the usefulness of this approach, we carried out a simulation study and analysed a data set from Ugandan cattle to detect signatures of local adaptation with samβada, bayenv, lfmm and an F_ST outlier method (FDIST approach in arlequin) and compare their results. samβada - an open source software for Windows, Linux and Mac OS X available at http://lasig.epfl.ch/sambada - outperforms other approaches and better suits whole-genome sequence data processing.

Muscle spindle alterations precede onset of sensorimotor deficits in Charcot-Marie-Tooth type 2E

Charcot-Marie-Tooth (CMT) is the most common inherited peripheral neuropathy, affecting approximately 2.8 million people. The CMT leads to distal neuropathy that is characterized by reduced motor nerve conduction velocity, ataxia, muscle atrophy and sensory loss. We generated a mouse model of CMT type 2E (CMT2E) expressing human neurofilament light E396K (hNF-L^E396K ), which develops decreased motor nerve conduction velocity, ataxia and muscle atrophy by 4 months of age. Symptomatic hNF-L^E396K mice developed phenotypes that were consistent with proprioceptive sensory defects as well as reduced sensitivity to mechanical stimulation, while thermal sensitivity and auditory brainstem responses were unaltered. Progression from presymptomatic to symptomatic included a 50% loss of large diameter sensory axons within the fifth lumbar dorsal root of hNF-L^E396K mice. Owing to proprioceptive deficits and loss of large diameter sensory axons, we analyzed muscle spindle morphology in presymptomatic and symptomatic hNF-L^E396K and hNF-L control mice. Muscle spindle cross-sectional area and volume were reduced in all hNF-L^E396K mice analyzed, suggesting that alterations in muscle spindle morphology occurred prior to the onset of typical CMT pathology. These data suggested that CMT2E pathology initiated in the muscle spindles altering the proprioceptive sensory system. Early sensory pathology in CMT2E could provide a unifying hypothesis for the convergence of pathology observed in CMT.

Unified Model of Nanosecond Charge Recombination in Closed Reaction Centers from Rhodobacter sphaeroides: Role of Protein Polarization Dynamics

Ongoing questions surround the influence of protein dynamics on rapid processes such as biological electron transfer. Such questions are particularly addressable in light-activated systems. In Rhodobacter sphaeroides reaction centers, charge recombination or back electron transfer from the reduced bacteriopheophytin, HA(-), to the oxidized dimeric bacteriochlorophyll, P(+), may be monitored by both transient absorption spectroscopy and transient fluorescence spectroscopy. Signals measured with both these techniques decay in a similar three-exponential fashion with lifetimes of ∼0.6-0.7, ∼2-4, and ∼10-20 ns, revealing the complex character of this electron transfer reaction. In this study a single kinetic model was developed to connect lifetime and amplitude data from both techniques. The model took into account the possibility that electron transfer from HA(-) to P(+) may occur with transient formation of the state P(+)BA(-). As a result it was possible to model the impact of nanosecond protein relaxation on the free energy levels of both P(+)HA(-) and P(+)BA(-) states relative to that of the singlet excited state of P, P*. Surprisingly, whereas the free energy gap between P* and P(+)HA(-) increased with time in response to protein reorganization, the free energy gap between P* and P(+)BA(-) decreased. This finding may be accounted for by a gradual polarization of the protein environment which stabilizes the state P(+)HA(-) and destabilizes the state P(+)BA(-), favoring productive charge separation over unproductive charge recombination.

Response to angiotensin blockade with irbesartan in a patient with metastatic colorectal cancer

BACKGROUND

A patient suffering from metastatic colorectal cancer, treatment-related toxicity and resistance to standard chemotherapy and radiation was assessed as part of a personalized oncogenomics initiative to derive potential alternative therapeutic strategies.

PATIENTS AND METHODS

Whole-genome and transcriptome sequencing was used to interrogate a metastatic tumor refractory to standard treatments of a patient with mismatch repair-deficient metastatic colorectal cancer.

RESULTS

Integrative genomic analysis indicated overexpression of the AP-1 transcriptional complex suggesting experimental therapeutic rationales, including blockade of the renin-angiotensin system. This led to the repurposing of the angiotensin II receptor antagonist, irbesartan, as an anticancer therapy, resulting in the patient experiencing a dramatic and durable response.

CONCLUSIONS

This case highlights the utility of comprehensive integrative genomic profiling and bioinformatics analysis to provide hypothetical rationales for personalized treatment options.

Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling

INTRODUCTION

The generic metabolomics data processing workflow is constructed with a serial set of processes including peak picking, quality assurance, normalisation, missing value imputation, transformation and scaling. The combination of these processes should present the experimental data in an appropriate structure so to identify the biological changes in a valid and robust manner.

OBJECTIVES

Currently, different researchers apply different data processing methods and no assessment of the permutations applied to UHPLC-MS datasets has been published. Here we wish to define the most appropriate data processing workflow.

METHODS

We assess the influence of normalisation, missing value imputation, transformation and scaling methods on univariate and multivariate analysis of UHPLC-MS datasets acquired for different mammalian samples.

RESULTS

Our studies have shown that once data are filtered, missing values are not correlated with m/z, retention time or response. Following an exhaustive evaluation, we recommend PQN normalisation with no missing value imputation and no transformation or scaling for univariate analysis. For PCA we recommend applying PQN normalisation with Random Forest missing value imputation, glog transformation and no scaling method. For PLS-DA we recommend PQN normalisation, KNN as the missing value imputation method, generalised logarithm transformation and no scaling. These recommendations are based on searching for the biologically important metabolite features independent of their measured abundance.

CONCLUSION

The appropriate choice of normalisation, missing value imputation, transformation and scaling methods differs depending on the data analysis method and the choice of method is essential to maximise the biological derivations from UHPLC-MS datasets.

let-65 is cytoplasmic methionyl tRNA synthetase in C. elegans

Cytoplasmic methionyl tRNA synthetase (MetRS) is one of more than 20 cytoplasmic aminoacyl tRNA synthetase enzymes (ARS). This family of enzymes catalyzes a process fundamental for protein translation. Using a combination of genetic mapping, oligonucleotide array comparative genomic hybridization, and phenotypic correlation, we show that mutations in the essential gene, let-65, reside within the predicted Caenorhabditis elegans homologue of MetRS, which we have named mars-1. We demonstrate that the lethality associated with alleles of let-65 is fully rescued by a transgenic array that spans the mars-1 genomic region. Furthermore, sequence analysis reveals that six let-65 alleles lead to the alteration of highly conserved amino acids.

Frequency-domain optical coherence tomography assessment of human carotid atherosclerosis using saline flush for blood clearance without balloon occlusion

FD-OCT is a new imaging technique that allows unprecedented in vivo microlevel assessment of human carotid plaque morphologic patterns and stent-vessel interactions. Prior reports describing the use of this technique have used balloon occlusion of the target vessel or iodinated contrast media to facilitate imaging. We report, for the first time, in vivo FD-OCT imaging of human carotid arteries without the use of iodinated contrast material or balloon occlusion techniques.

The ortholog of the human proto-oncogene ROS1 is required for epithelial development in C. elegans

The orphan receptor ROS1 is a human proto-oncogene, mutations of which are found in an increasing number of cancers. Little is known about the role of ROS1, however in vertebrates it has been implicated in promoting differentiation programs in specialized epithelial tissues. In this study we show that the C. elegans ortholog of ROS1, the receptor tyrosine kinase ROL-3, has an essential role in orchestrating the morphogenesis and development of specialized epidermal tissues, highlighting a potentially conserved function in coordinating crosstalk between developing epithelial cells. We also provide evidence of a direct relationship between ROL-3, the mucin SRAP-1, and BCC-1, the homolog of mRNA regulating protein Bicaudal-C. This study answers a longstanding question as to the developmental function of ROL-3, identifies three new genes that are expressed and function in the developing epithelium of C. elegans, and introduces the nematode as a potentially powerful model system for investigating the increasingly important, yet poorly understood, human oncogene ROS1. genesis 51:545–561.

Systematic evaluation of validated type 2 diabetes and glycaemic trait loci for association with insulin clearance

AIMS/HYPOTHESIS

Insulin clearance is a highly heritable trait, for which few quantitative trait loci have been discovered. We sought to determine whether validated type 2 diabetes and/or glycaemic trait loci are associated with insulin clearance.

METHODS

Hyperinsulinaemic-euglycaemic clamps were performed in two Hispanic-American family cohorts totalling 1329 participants in 329 families. The Metabochip was used to fine-map about 50 previously identified loci for type 2 diabetes, fasting glucose, fasting insulin, 2 h glucose or HbA1c. This resulted in 17,930 variants, which were tested for association with clamp-derived insulin clearance via meta-analysis of the two cohorts.

RESULTS

In the meta-analysis, 38 variants located within seven loci demonstrated association with insulin clearance (p < 0.001). The top signals for each locus were rs10241087 (DGKB/TMEM195 [TMEM195 also known as AGMO]) (p = 4.4 × 10(-5)); chr1:217605433 (LYPLAL1) (p = 3.25 × 10(-4)); rs2380949 (GLIS3) (p = 3.4 × 10(-4)); rs55903902 (FADS1) (p = 5.6 × 10(-4)); rs849334 (JAZF1) (p = 6.4 × 10(-4)); rs35749 (IGF1) (p = 6.7 × 10(-4)); and rs9460557 (CDKAL1) (p = 6.8 × 10(-4)).

CONCLUSIONS/INTERPRETATION

While the majority of validated loci for type 2 diabetes and related traits do not appear to influence insulin clearance in Hispanics, several of these loci do show evidence of association with this trait. It is therefore possible that these loci could have pleiotropic effects on insulin secretion, insulin sensitivity and insulin clearance.