Mutations affecting components of the SWI/SNF complex cause Coffin-Siris syndrome

By exome sequencing, we found de novo SMARCB1 mutations in two of five individuals with typical Coffin-Siris syndrome (CSS), a rare autosomal dominant anomaly syndrome. As SMARCB1 encodes a subunit of the SWItch/Sucrose NonFermenting (SWI/SNF) complex, we screened 15 other genes encoding subunits of this complex in 23 individuals with CSS. Twenty affected individuals (87%) each had a germline mutation in one of six SWI/SNF subunit genes, including SMARCB1, SMARCA4, SMARCA2, SMARCE1, ARID1A and ARID1B.     

Multivariate Time Series Model with Hierarchical Structure for Over‐Dispersed Discrete Outcomes

In this paper, we propose a multivariate time series model for over‐dispersed discrete data to explore the market structure based on sales count dynamics. We first discuss the microstructure to show that over‐dispersion is inherent in the modeling of market structure based on sales count data. The model is built on the likelihood function induced by decomposing sales count response variables according to products' competitiveness and conditioning on their sum of variables, and it augments them to higher levels by using the Poisson–multinomial relationship in a hierarchical way, represented as a tree structure for the market definition. State space priors are applied to the structured likelihood to develop dynamic generalized linear models for discrete outcomes. For the over‐dispersion problem, gamma compound Poisson variables for product sales counts and Dirichlet compound multinomial variables for their shares are connected in a hierarchical fashion. Instead of the density function of compound distributions, we propose a data augmentation approach for more efficient posterior computations in terms of the generated augmented variables, particularly for generating forecasts and predictive density. We present the empirical application using weekly product sales time series in a store to compare the proposed models accommodating over‐dispersion with alternative no over‐dispersed models by several model selection criteria, including in‐sample fit, out‐of‐sample forecasting errors and information criterion. The empirical results show that the proposed modeling works well for the over‐dispersed models based on compound Poisson variables and they provide improved results compared with models with no consideration of over‐dispersion. Copyright © 2014 John Wiley & Sons, Ltd.     

Robustness-epistasis link shapes the fitness landscape of a randomly drifting protein

The distribution of fitness effects of protein mutations is still unknown. Of particular interest is whether accumulating deleterious mutations interact, and how the resulting epistatic effects shape the protein's fitness landscape. Here we apply a model system in which bacterial fitness correlates with the enzymatic activity of TEM-1 beta-lactamase (antibiotic degradation). Subjecting TEM-1 to random mutational drift and purifying selection (to purge deleterious mutations) produced changes in its fitness landscape indicative of negative epistasis; that is, the combined deleterious effects of mutations were, on average, larger than expected from the multiplication of their individual effects. As observed in computational systems, negative epistasis was tightly associated with higher tolerance to mutations (robustness). Thus, under a low selection pressure, a large fraction of mutations was initially tolerated (high robustness), but as mutations accumulated, their fitness toll increased, resulting in the observed negative epistasis. These findings, supported by FoldX stability computations of the mutational effects, prompt a new model in which the mutational robustness (or neutrality) observed in proteins, and other biological systems, is due primarily to a stability margin, or threshold, that buffers the deleterious physico-chemical effects of mutations on fitness. Threshold robustness is inherently epistatic-once the stability threshold is exhausted, the deleterious effects of mutations become fully pronounced, thereby making proteins far less robust than generally assumed.     

Germline KRAS and BRAF mutations in cardio-facio-cutaneous syndrome

Cardio-facio-cutaneous (CFC) syndrome is characterized by a distinctive facial appearance, heart defects and mental retardation. It phenotypically overlaps with Noonan and Costello syndrome, which are caused by mutations in PTPN11 and HRAS, respectively. In 43 individuals with CFC, we identified two heterozygous KRAS mutations in three individuals and eight BRAF mutations in 16 individuals, suggesting that dysregulation of the RAS-RAF-ERK pathway is a common molecular basis for the three related disorders.     

Non-canonical inflammasome activation targets caspase-11

Caspase-1 activation by inflammasome scaffolds comprised of intracellular nucleotide-binding oligomerization domain (NOD)-like receptors (NLRs) and the adaptor ASC is believed to be essential for production of the pro-inflammatory cytokines interleukin (IL)-1β and IL-18 during the innate immune response. Here we show, with C57BL/6 Casp11 gene-targeted mice, that caspase-11 (also known as caspase-4) is critical for caspase-1 activation and IL-1β production in macrophages infected with Escherichia coli, Citrobacter rodentium or Vibrio cholerae. Strain 129 mice, like Casp11(-/-) mice, exhibited defects in IL-1β production and harboured a mutation in the Casp11 locus that attenuated caspase-11 expression. This finding is important because published targeting of the Casp1 gene was done using strain 129 embryonic stem cells. Casp1 and Casp11 are too close in the genome to be segregated by recombination; consequently, the published Casp1(-/-) mice lack both caspase-11 and caspase-1. Interestingly, Casp11(-/-) macrophages secreted IL-1β normally in response to ATP and monosodium urate, indicating that caspase-11 is engaged by a non-canonical inflammasome. Casp1(-/-)Casp11(129mt/129mt) macrophages expressing caspase-11 from a C57BL/6 bacterial artificial chromosome transgene failed to secrete IL-1β regardless of stimulus, confirming an essential role for caspase-1 in IL-1β production. Caspase-11 rather than caspase-1, however, was required for non-canonical inflammasome-triggered macrophage cell death, indicating that caspase-11 orchestrates both caspase-1-dependent and -independent outputs. Caspase-1 activation by non-canonical stimuli required NLRP3 and ASC, but caspase-11 processing and cell death did not, implying that there is a distinct activator of caspase-11. Lastly, loss of caspase-11 rather than caspase-1 protected mice from a lethal dose of lipopolysaccharide. These data highlight a unique pro-inflammatory role for caspase-11 in the innate immune response to clinically significant bacterial infections.     

长江流域3个群体草鱼mtDNA D-loop区段的PCR-RFLP分析

对采自长江流域湖北省监利县、江西省九江市以及江苏省扬州市邗江区3个群体的141尾草鱼线粒体DNA的D-loop区段进行PCR扩增,使用12种核酸内切限制酶酶切.结果显示,扩增出的140尾试验鱼的mtDNA D-loop区段长度约为1.6 kbp,监利群体中的1尾约为1.8 kbp,个体间存在长度变异现象;6种限制酶具有酶切位点,共检测到两种单倍型,其中监利群体中有长度变异的1尾为一种单倍型,另外140尾为另一种单倍型.依据单倍型频率的组成情况计算出九江和邗江两群体内的单倍型多样性指数、核苷酸多样性指数均为0,监利群体内的分别为0.046 6、0.001 80;监利群体与邗江(或九江)群体间的Rogers遗传距离为0.040 3;x2检验结果显示3个群体间的遗传差异不显著(P＞0.05).这些结果表明草鱼mtDNA D-loop区段的遗传多样性很低.     

Measuring large‐scale market responses and forecasting aggregated sales: Regression for sparse high‐dimensional data

In this article, we propose a regression model for sparse high‐dimensional data from aggregated store‐level sales data. The modeling procedure includes two sub‐models of topic model and hierarchical factor regressions. These are applied in sequence to accommodate high dimensionality and sparseness and facilitate managerial interpretation. First, the topic model is applied to aggregated data to decompose the daily aggregated sales volume of a product into sub‐sales for several topics by allocating each unit sale (“word” in text analysis) in a day (“document”) into a topic based on joint‐purchase information. This stage reduces the dimensionality of data inside topics because the topic distribution is nonuniform and product sales are mostly allocated into smaller numbers of topics. Next, the market response regression model for the topic is estimated from information about items in the same topic. The hierarchical factor regression model we introduce, based on canonical correlation analysis for original high‐dimensional sample spaces, further reduces the dimensionality within topics. Feature selection is then performed on the basis of the credible interval of the parameters' posterior density. Empirical results show that (i) our model allows managerial implications from topic‐wise market responses according to the particular context, and (ii) it performs better than do conventional category regressions in both in‐sample and out‐of‐sample forecasts.