Mutation update and genotype-phenotype correlations of novel and previously described mutations in TPM2 and TPM3 causing congenital myopathies

Mutations affecting skeletal muscle isoforms of the tropomyosin genes may cause nemaline myopathy, cap myopathy, core-rod myopathy, congenital fiber-type disproportion, distal arthrogryposes, and Escobar syndrome. We correlate the clinical picture of these diseases with novel (19) and previously reported (31) mutations of the TPM2 and TPM3 genes. Included are altogether 93 families: 53 with TPM2 mutations and 40 with TPM3 mutations. Thirty distinct pathogenic variants of TPM2 and 20 of TPM3 have been published or listed in the Leiden Open Variant Database (http://www.dmd.nl/). Most are heterozygous changes associated with autosomal-dominant disease. Patients with TPM2 mutations tended to present with milder symptoms than those with TPM3 mutations, DA being present only in the TPM2 group. Previous studies have shown that five of the mutations in TPM2 and one in TPM3 cause increased Ca2+ sensitivity resulting in a hypercontractile molecular phenotype. Patients with hypercontractile phenotype more often had contractures of the limb joints (18/19) and jaw (6/19) than those with nonhypercontractile ones (2/22 and 1/22), whereas patients with the non-hypercontractile molecular phenotype more often (19/22) had axial contractures than the hypercontractile group (7/19). Our in silico predictions show that most mutations affect tropomyosin–actin association or tropomyosin head-to-tail binding

Cross-infection of virulent Dichelobacter nodosus between sheep and co-grazing cattle

AbstractDichelobacter nodosus is the main aetiological agent of ovine footrot and the bacterium has also been associated with interdigital dermatitis is cattle. The aim of this study was to investigate possible cross-infection of virulent D. nodosus between sheep and co-grazing cattle. Five farms, where sheep previously diagnosed with virulent D. nodosus were co-grazing with cattle for different periods of time, were included. The study sample consisted of 200 cows and 50 sheep. All cows were examined for the presence of interdigital dermatitis, and ten ewes, preferably with symptoms of footrot, had the footrot scores recorded. On each farm, the same ten ewes and ten cows were chosen for bacterial analyses. Swabs were analysed for D. nodosus by PCR and culturing. D. nodosus isolates were virulence-tested and assigned to serogroups by fimA variant determination. Biopsies were evaluated histopathologically and analysed by fluorescent in situ hybridization for D. nodosus, Treponema spp. and Fusobacterium necrophorum. D. nodosus defined as virulent by the gelatin gel test were isolated from 16 sheep from four farms and from five cows from two of the same farms. All five cows had interdigital dermatitis. Two of the cows stayed infected for at least eight months. By pulsed-field gel electrophoresis (PFGE), the isolates from the five cows were found to be genetically indistinguishable or closely related to isolates from sheep from the same farm. This indicates that cross-infection between sheep and cows have occurred

Differential Producibility Analysis (DPA) of Transcriptomic Data with Metabolic Networks: Deconstructing the Metabolic Response of M. tuberculosis

A general paucity of knowledge about the metabolic state of Mycobacterium tuberculosis within the host environment is a major factor impeding development of novel drugs against tuberculosis. Current experimental methods do not allow direct determination of the global metabolic state of a bacterial pathogen in vivo, but the transcriptional activity of all encoded genes has been investigated in numerous microarray studies. We describe a novel algorithm, Differential Producibility Analysis (DPA) that uses a metabolic network to extract metabolic signals from transcriptome data. The method utilizes Flux Balance Analysis (FBA) to identify the set of genes that affect the ability to produce each metabolite in the network. Subsequently, Rank Product Analysis is used to identify those metabolites predicted to be most affected by a transcriptional signal. We first apply DPA to investigate the metabolic response of E. coli to both anaerobic growth and inactivation of the FNR global regulator. DPA successfully extracts metabolic signals that correspond to experimental data and provides novel metabolic insights. We next apply DPA to investigate the metabolic response of M. tuberculosis to the macrophage environment, human sputum and a range of in vitro environmental perturbations. The analysis revealed a previously unrecognized feature of the response of M. tuberculosis to the macrophage environment: a down-regulation of genes influencing metabolites in central metabolism and concomitant up-regulation of genes that influence synthesis of cell wall components and virulence factors. DPA suggests that a significant feature of the response of the tubercle bacillus to the intracellular environment is a channeling of resources towards remodeling of its cell envelope, possibly in preparation for attack by host defenses. DPA may be used to unravel the mechanisms of virulence and persistence of M. tuberculosis and other pathogens and may have general application for extracting metabolic signals from other “-omics” data

Thinking about and working with archives and records: a personal reflection on theory and practice

Looking back over a career that has lasted 40 years (so far) the author reflects on developments in his own thinking and the influences involved. Not least amongst these are: the British public records tradition which predominated at UCL when he studied there; the American historical manuscripts tradition which was in the process of aligning with strands of postmodernism when he held visiting fellowships in the USA; the reconfiguration of Records Management in sub-Saharan Africa in response to public sector reform in which he was involved as an advisor; and the experience of teaching postgraduate students in Britain and overseas. The author’s publications have appeared in a wide range of journals and as monographs, some of them published overseas. Here he draws together the common strands that connect them. Finally he argues that hermeneutic techniques and the concept of fiduciarity deserve to be given serious consideration in debates about archive and records theory

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability