Sustained adherence to a Mediterranean diet and physical activity on all-cause mortality in the Melbourne Collaborative Cohort Study: application of the g-formula.

BACKGROUND: Adherence to a traditional Mediterranean diet has been associated with lower mortality and cardiovascular disease risk. The relative importance of diet compared to other lifestyle factors and effects of dietary patterns over time remains unknown. METHODS: We used the parametric G-formula to account for time-dependent confounding, in order to assess the relative importance of diet compared to other lifestyle factors and effects of dietary patterns over time. We included healthy Melbourne Collaborative Cohort Study participants attending a visit during 1995-1999. Questionnaires assessed diet and physical activity at each of three study waves. Deaths were identified by linkage to national registries. We estimated mortality risk over approximately 14 years (1995-2011). RESULTS: Of 22,213 participants, 2163 (9.7%) died during 13.6 years median follow-up. Sustained high physical activity and adherence to a Mediterranean-style diet resulted in an estimated reduction in all-cause mortality of 1.82 per 100 people (95% confidence interval (CI): 0.03, 3.6). The population attributable fraction was 13% (95% CI: 4, 23%) for sustained high physical activity, 7% (95% CI: - 3, 17%) for sustained adherence to a Mediterranean-style diet and 18% (95% CI: 0, 36%) for their combination. CONCLUSIONS: A small reduction in mortality may be achieved by sustained elevated physical activity levels in healthy middle-aged adults, but there may be comparatively little gain from increasing adherence to a Mediterranean-style diet

Expression of the C-terminal flanking peptide of human progastrin in human gastroduodenal mucosa, G-cell hyperplasia and islet cell tumours producing gastrin

Three antisera to the C-terminally extended form of gastrin or the C-terminal flanking peptide of progastrin were used in an attempt to investigate the post-translational processing of progastrin at the cellular level by light and electron microscopical immunocytochemistry.In the normal human gastric antrum, the G-cell secretory granules were found to contain both gastrin and the C-terminal progastrin determinants (progastrin 87-93, 87-95 and 93-101). Immunostaining of serial sections at the light microscopical level revealed that duodenal gastrin-containing cells also express the C-terminal progastrin determinants, as well as gastrin-34. In foetal tissue, cells containing C-terminal gastrin and the C-flanking peptide of progastrin were first seen at 8 weeks of gestation, in the duodenum. They were not found in the stomach until the 11th week. In hyperplastic G-cells and in gastrin-producing tumour cells, the level of C-terminal peptide immunoreactivity was variable and often lower than that seen in normal antrum and only minimal immunoreactivity could be detected using electron immunocytochemistry. This was interpreted as representing altered post-translational processing of progastrin in modified G-cells.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/26782/1/0000338.pd

Regulated on Activation, Normal T cell Expressed and Secreted (RANTES) drives the resolution of allergic asthma

RANTES is implicated in allergic asthma and in T cell-dependent clearance of infection. RANTES receptor family comprises CCR1, CCR3, and CCR5, which are G-protein-coupled receptors consisting of seven transmembrane helices. Infections with respiratory viruses like Rhinovirus cause induction of RANTES production by epithelial cells. Here, we studied the role of RANTES in the peripheral blood mononuclear cells in cohorts of children with and without asthma and validated and extended this study to the airways of adults with and without asthma. We further translated these studies to a murine model of asthma induced by house dust mite allergen in wild-type RANTES and CCR5-deficient mice. Here we show an unpredicted therapeutic role of RANTES in the resolution of allergen-induced asthma by orchestrating the transition of effector GATA-3+CD4+ T cells into immune-regulatory-type T cells and inflammatory eosinophils into resident eosinophils as well as increased IL-10 production in the lung

mTORC1-mediated translational elongation limits intestinal tumour initiation and growth.

Inactivation of APC is a strongly predisposing event in the development of colorectal cancer, prompting the search for vulnerabilities specific to cells that have lost APC function. Signalling through the mTOR pathway is known to be required for epithelial cell proliferation and tumour growth, and the current paradigm suggests that a critical function of mTOR activity is to upregulate translational initiation through phosphorylation of 4EBP1 (refs 6, 7). This model predicts that the mTOR inhibitor rapamycin, which does not efficiently inhibit 4EBP1 (ref. 8), would be ineffective in limiting cancer progression in APC-deficient lesions. Here we show in mice that mTOR complex 1 (mTORC1) activity is absolutely required for the proliferation of Apc-deficient (but not wild-type) enterocytes, revealing an unexpected opportunity for therapeutic intervention. Although APC-deficient cells show the expected increases in protein synthesis, our study reveals that it is translation elongation, and not initiation, which is the rate-limiting component. Mechanistically, mTORC1-mediated inhibition of eEF2 kinase is required for the proliferation of APC-deficient cells. Importantly, treatment of established APC-deficient adenomas with rapamycin (which can target eEF2 through the mTORC1-S6K-eEF2K axis) causes tumour cells to undergo growth arrest and differentiation. Taken together, our data suggest that inhibition of translation elongation using existing, clinically approved drugs, such as the rapalogs, would provide clear therapeutic benefit for patients at high risk of developing colorectal cancer

Serological response and breakthrough infection after COVID-19 vaccination in patients with cirrhosis and post-liver transplant

BACKGROUND: Vaccine hesitancy and lack of access remain major issues in disseminating COVID-19 vaccination to liver patients globally. Factors predicting poor response to vaccination and risk of breakthrough infection are important data to target booster vaccine programs. The primary aim of the current study was to measure humoral responses to 2 doses of COVID-19 vaccine. Secondary aims included the determination of factors predicting breakthrough infection. METHODS: COVID-19 vaccination and Biomarkers in cirrhosis And post-Liver Transplantation is a prospective, multicenter, observational case-control study. Participants were recruited at 4-10 weeks following first and second vaccine doses in cirrhosis [n = 325; 94% messenger RNA (mRNA) and 6% viral vaccine], autoimmune liver disease (AILD) (n = 120; 77% mRNA and 23% viral vaccine), post-liver transplant (LT) (n = 146; 96% mRNA and 3% viral vaccine), and healthy controls (n = 51; 72% mRNA, 24% viral and 4% heterologous combination). Serological end points were measured, and data regarding breakthrough SARS-CoV-2 infection were collected. RESULTS: After adjusting by age, sex, and time of sample collection, anti-Spike IgG levels were the lowest in post-LT patients compared to cirrhosis (p < 0.0001), AILD (p < 0.0001), and control (p = 0.002). Factors predicting reduced responses included older age, Child-Turcotte-Pugh B/C, and elevated IL-6 in cirrhosis; non-mRNA vaccine in AILD; and coronary artery disease, use of mycophenolate and dysregulated B-call activating factor, and lymphotoxin-α levels in LT. Incident infection occurred in 6.6%, 10.6%, 7.4%, and 15.6% of cirrhosis, AILD, post-LT, and control, respectively. The only independent factor predicting infection in cirrhosis was low albumin level. CONCLUSIONS: LT patients present the lowest response to the SARS-CoV-2 vaccine. In cirrhosis, the reduced response is associated with older age, stage of liver disease and systemic inflammation, and breakthrough infection with low albumin level

Integrated genomic characterization of oesophageal carcinoma

Oesophageal cancers are prominent worldwide; however, there are few targeted therapies and survival rates for these cancers remain dismal. Here we performed a comprehensive molecular analysis of 164 carcinomas of the oesophagus derived from Western and Eastern populations. Beyond known histopathological and epidemiologic distinctions, molecular features differentiated oesophageal squamous cell carcinomas from oesophageal adenocarcinomas. Oesophageal squamous cell carcinomas resembled squamous carcinomas of other organs more than they did oesophageal adenocarcinomas. Our analyses identified three molecular subclasses of oesophageal squamous cell carcinomas, but none showed evidence for an aetiological role of human papillomavirus. Squamous cell carcinomas showed frequent genomic amplifications of CCND1 and SOX2 and/or TP63, whereas ERBB2, VEGFA and GATA4 and GATA6 were more commonly amplified in adenocarcinomas. Oesophageal adenocarcinomas strongly resembled the chromosomally unstable variant of gastric adenocarcinoma, suggesting that these cancers could be considered a single disease entity. However, some molecular features, including DNA hypermethylation, occurred disproportionally in oesophageal adenocarcinomas. These data provide a framework to facilitate more rational categorization of these tumours and a foundation for new therapies

Genome-wide Analyses Identify KIF5A as a Novel ALS Gene

To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.Peer reviewe

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Kernel density estimation: advances and applications

The availability of an accurate estimator of conditional densities is very important in part due to the high use and potential use of conditional densities in econometrics. It provides a wide range of properties, such as mean, dispersion, tail behavior and asymmetry in the examined data. Hence it allows the researcher to investigate a wider range of hypotheses than would be the case for the regression model and its many variations. The use of kernel estimation provides a convenient mathematical framework without the need to assume a particular parametric form of the examined data distribution. For the kernel density estimator, the selected bandwidth (the tuner parameter) is the most influential factor on estimator accuracy. Therefore, to increase the utility of the conditional kernel density estimators a variety of appropriate bandwidth selection methods is needed. Moreover, the flexibility of the kernel estimator has great potential in hypothesis testing because it does not require assuming a particular parametric distribution under the null and alternative hypotheses. The purpose of this thesis is to suggest two new bandwidth selection methods for the conditional density estimator, targeted at two different types of users. Another goal is to develop a model clarification procedure that is versatile enough to be applicable to test different types of models and different types of changes. Finally, we aim to broaden the model clarification procedure to examining functional models. The first contribution of this thesis is the suggested implementation of the Markov chain Monte Carlo (MCMC) estimation algorithm for optimal bandwidth selection (Zhang,King & Hyndman 2006) for the conditional density estimator. In addition, we propose a generalization to the Kullback-Leibler information and to the mean squared error criterion and apply them to assessing the accuracy of conditional density estimators. We conduct a comparison of the various conditional density estimators based on several bandwidth selection methods. Our numerical study shows that when the data has two modes or there is a correlation among the conditional covariates, the least square cross-validation for direct conditional density estimation (Hall, Racine & Li 2004) appears to be the preferred method. This, however, comes at very high computational cost, particularly for large data sets. The MCMC approach provides a density estimator that is much faster and only slightly less accurate, which makes it preferable in these situations. When the data is distributed with only one mode, the conditional normal reference rule bandwidth selection method (Bashtannyk & Hyndman 2001, Hyndman, Bashtannyk & Grunwald 1996) leads to the most accurate conditional density estimator and enjoys a low computational cost. The other examined bandwidth selection methods include the normal reference rule (Scott 1992), the plug-in bandwidth selector (Duong & Hazelton 2003) and the smooth cross-validation selector (Duong & Hazelton 2005a). In order to simplify the application of the conditional density kernel estimator, we derive a reference rule for bandwidth selection. In contrast to the usual simple assumption of normally or uniformly distributed data, we assume that the distribution of y given x and the distribution of x are both skew t (with includes the normal, the skew normal and the Student's t distributions as special cases). Moreover, we allow distribution parameters to change as linear functions of the conditional x values. This flexible framework allows us to capture the variations in the skewness and in the kurtosis of the conditional density, as well as the change in its location and scale, as functions of the conditioning variables. We illustrate the improvement in the conditional density estimator accuracy when we choose the bandwidths under the skew t distribution assumption instead of the normality assumption(Bashtannyk & Hyndman 2001, Hyndman et al. 1996) on simulated data. The next contribution of this work is the development of a method for the analysis of the model in use, and the examination of whether or not the model's predictive ability is still good enough. The proposed prediction capability testing procedure is based on a nonparametric density estimation of potential realizations from the examined model. An important property of this procedure is that it can provide guidance after a relatively low number of new realizations. The procedure's ability to recognize a change in the `reality' is demonstrated through AR(1) and linear models. We find that the procedure has correct empirical size and high power to recognize the changes in the data generating process after 10 to 15 new observations, depending on the type and the extent of the change. Finally, we propose a pattern characteristics testing procedure for validating the predictive abilities of a functional model. With the growing interest in functional data analysis in the last several decades and with the expansion of the functional modeling to a diverse range of scientific disciplines, a procedure that clarifies the validity of the functional model is a vital tool. Our approach involves generation of many potential paths from the examined model and summarizing their characterizing dynamics using a density of the scores resulting from a functional principal component decomposition. Two sets of simulation experiments are presented to illustrate the size and power of the procedure. An example, testing the fertility rates forecasting method suggested by Hyndman & Ullah (2007), shows the application of the procedure to Australian fertility rates in years 1921 - 2002