Sex- and age-dependent association of SLC11A1 polymorphisms with tuberculosis in Chinese: a case control study

BACKGROUND: Host genetic factors are important determinants in tuberculosis (TB). The SLC11A1 (or NRAMP1) gene has been studied extensively for genetic association with TB, but with inconsistent findings. In addition, no study has yet looked into the effect of sex and age on the relationship between SLC11A1 polymorphisms and TB. METHODS: A case-control study was conducted. In total, 278 pulmonary TB patients and 282 sex- and age-matched controls without TB were recruited. All subjects were ethnic Chinese. On the basis of linkage disequilibrium pattern, three genetic markers from SLC11A1 and one from the nearby IL8RB locus were selected and examined for association with TB susceptibility. These markers were genotyped using single strand conformation polymorphism analysis or fragment analysis of amplified products. RESULTS: Statistically significant differences in allele (P = 0.0165, OR = 1.51) and genotype (P = 0.0163, OR = 1.59) frequencies of the linked markers SLC6a/b (classically called D543N and 3'UTR) of the SLC11A1 locus were found between patients and controls. With stratification by sex, positive associations were identified in the female group for both allele (P = 0.0049, OR = 2.54) and genotype (P = 0.0075, OR = 2.74) frequencies. With stratification by age, positive associations were demonstrated in the young age group (age ≤65 years) for both allele (P = 0.0047, OR = 2.52) and genotype (P = 0.0031, OR = 2.92) frequencies. All positive findings remained significant even after correction for multiple comparisons. No significant differences were noted in either the male group or the older age group. No significant differences were found for the other markers (one SLC11A1 marker and one IL8RB marker) either. CONCLUSION: This study confirmed the association between SLC11A1 and TB susceptibility and demonstrated for the first time that the association was restricted to females and the young age group

Fine Scale Analysis of Crossover and Non-Crossover and Detection of Recombination Sequence Motifs in the Honeybee (Apis mellifera)

BACKGROUND: Meiotic exchanges are non-uniformly distributed across the genome of most studied organisms. This uneven distribution suggests that recombination is initiated by specific signals and/or regulations. Some of these signals were recently identified in humans and mice. However, it is unclear whether or not sequence signals are also involved in chromosomal recombination of insects. METHODOLOGY: We analyzed recombination frequencies in the honeybee, in which genome sequencing provided a large amount of SNPs spread over the entire set of chromosomes. As the genome sequences were obtained from a pool of haploid males, which were the progeny of a single queen, an oocyte method (study of recombination on haploid males that develop from unfertilized eggs and hence are the direct reflect of female gametes haplotypes) was developed to detect recombined pairs of SNP sites. Sequences were further compared between recombinant and non-recombinant fragments to detect recombination-specific motifs. CONCLUSIONS: Recombination events between adjacent SNP sites were detected at an average distance of 92 bp and revealed the existence of high rates of recombination events. This study also shows the presence of conversion without crossover (i. e. non-crossover) events, the number of which largely outnumbers that of crossover events. Furthermore the comparison of sequences that have undergone recombination with sequences that have not, led to the discovery of sequence motifs (CGCA, GCCGC, CCGCA), which may correspond to recombination signals

Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015

SummaryBackground The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors—the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6–58·8) of global deaths and 41·2% (39·8–42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Funding Bill & Melinda Gates Foundation

The Immune Response to Melanoma Is Limited by Thymic Selection of Self-Antigens

The expression of melanoma-associated antigens (MAA) being limited to normal melanocytes and melanomas, MAAs are ideal targets for immunotherapy and melanoma vaccines. As MAAs are derived from self, immune responses to these may be limited by thymic tolerance. The extent to which self-tolerance prevents efficient immune responses to MAAs remains unknown. The autoimmune regulator (AIRE) controls the expression of tissue-specific self-antigens in thymic epithelial cells (TECs). The level of antigens expressed in the TECs determines the fate of auto-reactive thymocytes. Deficiency in AIRE leads in both humans (APECED patients) and mice to enlarged autoreactive immune repertoires. Here we show increased IgG levels to melanoma cells in APECED patients correlating with autoimmune skin features. Similarly, the enlarged T cell repertoire in AIRE−/− mice enables them to mount anti-MAA and anti-melanoma responses as shown by increased anti-melanoma antibodies, and enhanced CD4+ and MAA-specific CD8+ T cell responses after melanoma challenge. We show that thymic expression of gp100 is under the control of AIRE, leading to increased gp100-specific CD8+ T cell frequencies in AIRE−/− mice. TRP-2 (tyrosinase-related protein), on the other hand, is absent from TECs and consequently TRP-2 specific CD8+ T cells were found in both AIRE−/− and AIRE+/+ mice. This study emphasizes the importance of investigating thymic expression of self-antigens prior to their inclusion in vaccination and immunotherapy strategies

From Acting What’s next to Speeding Trap: Co-Evolutionary Dynamics of an Emerging Technology-Leader

JEL Classifications: O33, O53, L63[[abstract]]How does technological innovation emerge and evolve? We approach such an inquiry by synthesizing the perspectives of dynamic capabilities and co-evolutionary dynamics to portray organizational routines and multi-phase strategic renewals of an emerging technology-leader. To untangle the emergence of technological innovation, we conducted a longitudinal case study on the first and the largest dedicated semiconductor foundry, TSMC, located in the emerging economy of Taiwan. The firm-case of TSMC illustrates two unique co-evolutionary paths, that is, transforming from industry-latecomer to technology-leader and from process innovation to product innovation. We found multi-motor co-evolutionary dynamics between TSMC and the semiconductor industry, where its co-evolutionary mechanism of managed selection in its creating phase of mature process-innovation (1987-1998) has migrated to hierarchical renewal in its extending phase of advanced process-innovation (1999-2001), and then to holistic renewal in its modifying phase of product-innovation (2002-2007). During such paths, our research discovered a unique type of organizational routines, acting what’s next because TSMC has proactively searched for potential problems sooner than its competitors. However, such routines, although driving technological innovation, also lead to a unique type of success-trap, that is, speeding trap. When an emerging technology-leader fundamentally changes the industrial structures to over-specs, the growth driven by technology speeding may trap such a leader in a loop of over-exploration.[[sponsorship]]The authors are grateful to the research grant from the National Science Council (NSC) in Taiwan. The earlier manuscript of this paper was presented at the 2009 Annual Meeting of Academy of International Business (AIB) in San Diego, USA.[[notice]]補正完畢[[journaltype]]國外[[ispeerreviewed]]Y[[booktype]]紙本[[booktype]]電子版[[countrycodes]]CA

Accurate Prediction of Secreted Substrates and Identification of a Conserved Putative Secretion Signal for Type III Secretion Systems

The type III secretion system is an essential component for virulence in many Gram-negative bacteria. Though components of the secretion system apparatus are conserved, its substrates—effector proteins—are not. We have used a novel computational approach to confidently identify new secreted effectors by integrating protein sequence-based features, including evolutionary measures such as the pattern of homologs in a range of other organisms, G+C content, amino acid composition, and the N-terminal 30 residues of the protein sequence. The method was trained on known effectors from the plant pathogen Pseudomonas syringae and validated on a set of effectors from the animal pathogen Salmonella enterica serovar Typhimurium (S. Typhimurium) after eliminating effectors with detectable sequence similarity. We show that this approach can predict known secreted effectors with high specificity and sensitivity. Furthermore, by considering a large set of effectors from multiple organisms, we computationally identify a common putative secretion signal in the N-terminal 20 residues of secreted effectors. This signal can be used to discriminate 46 out of 68 total known effectors from both organisms, suggesting that it is a real, shared signal applicable to many type III secreted effectors. We use the method to make novel predictions of secreted effectors in S. Typhimurium, some of which have been experimentally validated. We also apply the method to predict secreted effectors in the genetically intractable human pathogen Chlamydia trachomatis, identifying the majority of known secreted proteins in addition to providing a number of novel predictions. This approach provides a new way to identify secreted effectors in a broad range of pathogenic bacteria for further experimental characterization and provides insight into the nature of the type III secretion signal

Triangle network motifs predict complexes by complementing high-error interactomes with structural information

BackgroundA lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles.ResultsWe find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from Munich Information center for Protein Sequences (MIPS). PPI-SDDI-PPI triangles have ~20 times higher overlap with MIPS complexes than using second-level neighbors in PPINs without SDDIs. The biological interpretation for triangles is that a SDDI causes two proteins to be observed with common interaction partners in high-throughput experiments. The relatively few SDDIs overlapping with PPINs are part of highly connected SDDI components, and are more likely to be detected in experimental studies. We demonstrate the utility of PPI-SDDI-PPI triangles by reconstructing myosin-actin processes in the nucleus, cytoplasm, and cytoskeleton, which were not obvious in the original PPIN. Using other complementary datatypes in place of SDDIs to form triangles, such as PubMed co-occurrences or threading information, results in a similar ability to find protein complexes.ConclusionGiven high-error PPINs with missing information, triangles of mixed datatypes are a promising direction for finding protein complexes. Integrating PPINs with SDDIs improves finding complexes. Structural SDDIs partially explain the high functional similarity of second-level neighbors in PPINs. We estimate that relatively little structural information would be sufficient for finding complexes involving most of the proteins and interactions in a typical PPIN

Azimuthal Charged-Particle Correlations and Possible Local Strong Parity Violation

Parity-odd domains, corresponding to nontrivial topological solutions of the QCD vacuum, might be created during relativistic heavy-ion collisions. These domains are predicted to lead to charge separation of quarks along the system’s orbital momentum axis. We investigate a three-particle azimuthal correlator which is a P even observable, but directly sensitive to the charge separation effect. We report measurements of charged hadrons near center-of-mass rapidity with this observable in Au+Au and Cu+Cu collisions at √sNN=200  GeV using the STAR detector. A signal consistent with several expectations from the theory is detected. We discuss possible contributions from other effects that are not related to parity violation

Mammalian transcriptional hotspots are enriched for tissue specific enhancers near cell type specific highly expressed genes and are predicted to act as transcriptional activator hubs

BACKGROUND: Transcriptional hotspots are defined as genomic regions bound by multiple factors. They have been identified recently as cell type specific enhancers regulating developmentally essential genes in many species such as worm, fly and humans. The in-depth analysis of hotspots across multiple cell types in same species still remains to be explored and can bring new biological insights. RESULTS: We therefore collected 108 transcription-related factor (TF) ChIP sequencing data sets in ten murine cell types and classified the peaks in each cell type in three groups according to binding occupancy as singletons (low-occupancy), combinatorials (mid-occupancy) and hotspots (high-occupancy). The peaks in the three groups clustered largely according to the occupancy, suggesting priming of genomic loci for mid occupancy irrespective of cell type. We then characterized hotspots for diverse structural functional properties. The genes neighbouring hotspots had a small overlap with hotspot genes in other cell types and were highly enriched for cell type specific function. Hotspots were enriched for sequence motifs of key TFs in that cell type and more than 90% of hotspots were occupied by pioneering factors. Though we did not find any sequence signature in the three groups, the H3K4me1 binding profile had bimodal peaks at hotspots, distinguishing hotspots from mono-modal H3K4me1 singletons. In ES cells, differentially expressed genes after perturbation of activators were enriched for hotspot genes suggesting hotspots primarily act as transcriptional activator hubs. Finally, we proposed that ES hotspots might be under control of SetDB1 and not DNMT for silencing. CONCLUSION: Transcriptional hotspots are enriched for tissue specific enhancers near cell type specific highly expressed genes. In ES cells, they are predicted to act as transcriptional activator hubs and might be under SetDB1 control for silencing. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-014-0412-0) contains supplementary material, which is available to authorized users