Scratching the surface – tobacco-induced bacterial biofilms

Individual environmental factors, such as iron, temperature and oxygen, are known to have a profound effect on bacterial phenotype. Therefore, it is surprising so little known is about the influence of chemically complex cigarette smoke on bacterial physiology. Recent evidence has demonstrated that tobacco smoke and components alter the bacterial surface and promote biofilm formation in several important human pathogens, including Staphylococcus aureus, Streptococcus mutans, Klebsiella pneumonia, Porphyromonas gingivalis and Pseudomonas aeruginosa. The mechanisms underlying this phenomenon and the relevance to increased susceptibility to infectious disease in smokers and to treatment are reviewed

Genes contributing to Porphyromonas gingivalis fitness in abscess and epithelial cell colonization environments

Porphyromonas gingivalis is an important cause of serious periodontal diseases, and is emerging as a pathogen in several systemic conditions including some forms of cancer. Initial colonization by P. gingivalis involves interaction with gingival epithelial cells, and the organism can also access host tissues and spread haematogenously. To better understand the mechanisms underlying these properties, we utilized a highly saturated transposon insertion library of P. gingivalis, and assessed the fitness of mutants during epithelial cell colonization and survival in a murine abscess model by high-throughput sequencing (Tn-Seq). Transposon insertions in many genes previously suspected as contributing to virulence showed significant fitness defects in both screening assays. In addition, a number of genes not previously associated with P. gingivalis virulence were identified as important for fitness. We further examined fitness defects of four such genes by generating defined mutations. Genes encoding a carbamoyl phosphate synthetase, a replication-associated recombination protein, a nitrosative stress responsive HcpR transcription regulator, and RNase Z, a zinc phosphodiesterase, showed a fitness phenotype in epithelial cell colonization and in a competitive abscess infection. This study verifies the importance of several well-characterized putative virulence factors of P. gingivalis and identifies novel fitness determinants of the organism

Insights into the accuracy of social scientists' forecasts of societal change

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender–career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists’ forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data

Porphyromonas gingivalis genes conferring fitness in a tobacco-rich environment

Smokers are more likely than non-smokers to harbour Porphyromonas gingivalis, they are more susceptible to destructive periodontal disease and smokers may, ultimately, benefit from tobacco-specific preventive and treatment strategies. A Mariner transposon insertion library for P. gingivalis ATCC 33277 was exploited to define 256 genes as essential for P. gingivalis survival in a tobacco-rich environment. Genes whose products play roles in protein transport and catabolism, nicotinamide processing, protection against oxidative stress, drug resistance and transcriptional regulation have all been identified as essential for CSE survival. Many of these tobacco-essential genes are also requisite for epithelial colonization and abscess formation, suggestive of a core stress-related P. gingivalis genome. Single-gene deletions in several of the TnSeq-implicated genes led to significantly reduced P. gingivalis fitness upon competition with the parent strain, under conditions of cigarette smoke extract-induced stress (1000 ng/ml nicotine equivalents). This study identifies, for the first time, a subset of P. gingivalis genes required for surviving the plethora of insults present in cigarette smoke. Such conditionally essential genes may delineate bacterial persistence strategies and represent novel therapeutic foci for the prevention of P. gingivalis infection and related diseases in smokers and in general

Altered Antigenic Profiling and Infectivity of Porphyromonas gingivalis

BACKGROUND: Cigarette smokers are more susceptible to periodontal diseases and are more likely to be infected with Porphyromonas gingivalis than non-smokers. Furthermore, smoking is known to alter the expression of P. gingivalis surface components and to compromise IgG generation. The aim of this study was to evaluate whether IgG response to P. gingivalis is suppressed in smokers in vivo and whether previously established in vitro tobacco-induced phenotypic P. gingivalis changes would be reflected in vivo. METHODS: We examined the humoral response to several P. gingivalis strains as well as specific tobacco-regulated outer membrane proteins (FimA and RagB) by ELISA in biochemically-validated (salivary cotinine) smokers and non-smokers with chronic (CP, n = 13) or aggressive (AP, n = 20) periodontitis. We also monitored the local and systemic presence of P. gingivalis DNA by PCR. RESULTS: Smoking was associated with decreased total IgG responses against clinical (10512, 5607, and 10208C; all p < 0.05) but not laboratory (ATCC 33277, W83) P. gingivalis strains. Smoking did not influence IgG produced against specific cell surface proteins, although a non-significant pattern towards increased total FimA-specific IgG in CP subjects, but not AP subjects, was observed. Seropositive smokers were more likely to be infected orally and systemically with P. gingivalis (p < 0.001), as determined by 16S RNA analysis. CONCLUSIONS: Smoking alters the humoral response against P. gingivalis, strengthening the evidence that mechanisms of periodontal disease progression in smokers may differ from non-smokers with the same disease classification

Insights into the accuracy of social scientists' forecasts of societal change

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists' forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data. How accurate are social scientists in predicting societal change, and what processes underlie their predictions? Grossmann et al. report the findings of two forecasting tournaments. Social scientists' forecasts were on average no more accurate than those of simple statistical models

Insights into accuracy of social scientists' forecasts of societal change

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. Following provision of historical trend data on the domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N=86 teams/359 forecasts), with an opportunity to update forecasts based on new data six months later (Tournament 2; N=120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that social scientists’ forecasts were on average no more accurate than simple statistical models (historical means, random walk, or linear regressions) or the aggregate forecasts of a sample from the general public (N=802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models, and based predictions on prior data