Identification of a Shared Genetic Susceptibility Locus for Coronary Heart Disease and Periodontitis

Recent studies indicate a mutual epidemiological relationship between coronary heart disease (CHD) and periodontitis. Both diseases are associated with similar risk factors and are characterized by a chronic inflammatory process. In a candidate-gene association study, we identify an association of a genetic susceptibility locus shared by both diseases. We confirm the known association of two neighboring linkage disequilibrium regions on human chromosome 9p21.3 with CHD and show the additional strong association of these loci with the risk of aggressive periodontitis. For the lead SNP of the main associated linkage disequilibrium region, rs1333048, the odds ratio of the autosomal-recessive mode of inheritance is 1.99 (95% confidence interval 1.33–2.94; P = 6.9×10−4) for generalized aggressive periodontitis, and 1.72 (1.06–2.76; P = 2.6×10−2) for localized aggressive periodontitis. The two associated linkage disequilibrium regions map to the sequence of the large antisense noncoding RNA ANRIL, which partly overlaps regulatory and coding sequences of CDKN2A/CDKN2B. A closely located diabetes-associated variant was independent of the CHD and periodontitis risk haplotypes. Our study demonstrates that CHD and periodontitis are genetically related by at least one susceptibility locus, which is possibly involved in ANRIL activity and independent of diabetes associated risk variants within this region. Elucidation of the interplay of ANRIL transcript variants and their involvement in increased susceptibility to the interactive diseases CHD and periodontitis promises new insight into the underlying shared pathogenic mechanisms of these complex common diseases

Estimating global injuries morbidity and mortality: methods and data used in the Global Burden of Disease 2017 study

BACKGROUND: While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria. METHODS: In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced. RESULTS: GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes. CONCLUSIONS: GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future

Clinical classification of cancer cachexia:phenotypic correlates in human skeletal muscle

Aim – To relate muscle phenotype to a range of current diagnostic criteria for cancer cachexia Methods – 41 patients with resectable upper gastrointestinal (GI) or pancreatic cancer underwent characterisation for cachexia based on weight-loss (WL) and / or low muscularity (LM). Four diagnostic criteria were used >5%WL, >10% WL, LM, and LM + >2%WL. Patients underwent biopsy of the rectus muscle. Analysis included immunohistochemistry for fibre size and type, protein and nucleic acid concentration, and Western blots for markers of autophagy, SMAD signalling, and inflammation. Results – Compared with non-cachectic cancer patients, if patients were classified by LM or LM + >2%WL, mean muscle fibre diameter was significantly reduced (p = 0.02 and p = 0.001) repectively. No difference in fibre diameter was observed if patients were classified with WL alone. Regardless of classification, there was no difference in fibre number or proportion of fibre type across all myosin heavy chain isoforms. Mean muscle protein content was reduced and the ratio of RNA/DNA decreased if patients were classified by either >5% WL or LM + >2%WL. Compared with non-cachectic patients, when patients were classified according to >5% WL, SMAD3 protein levels were increased (p=0.022) and with >10% WL, beclin (p = 0.05) and ATG5 (p = 0.01) protein levels were also increased. There were no differences in pNFkB or pSTAT3 levels across any of the groups. Conclusions – Whereas fibre type is not targeted selectively, muscle fibre size, biochemical composition and pathway phenotype can vary according to whether the criteria for cachexia include both a measure of low muscularity and weight loss

Intimate adhesion of Neisseria meningitidis to human epithelial cells is under the control of the crgA gene, a novel LysR-type transcriptional regulator

PilC1, a pilus-associated protein in Neisseria menin– gitidis, is a key element in initial meningococcal adhesion to target cells. A promoter element (CREN, contact regulatory element of Neisseria) is responsible for the transient induction of this gene upon cell contact. crgA (contact-regulated gene A) encodes a transcriptional regulator whose expression is also induced upon cell contact from a promoter region similar to the CREN of pilC1. CrgA shows significant sequence homologies to LysR-type transcriptional regulators. Its inactivation in meningococci provokes a dramatic reduction in bacterial adhesion to epithelial cells. Moreover, this mutant is unable to undergo intimate adhesion to epithelial cells or to provoke effacing of microvilli on infected cells. Purified CrgA is able to bind to pilC1 and crgA promoters, and CrgA seems to repress the expression of pilC1 and crgA. Our results support a dynamic model of bacteria–cell interaction involving a network of regulators acting in cascade. CrgA could be an intermediate regulator in such a network