Informing Building Strategies to Reduce Infectious Aerosol Transmission Risk by Integrating DNA Aerosol Tracers with Quantitative Microbial Risk Assessment.

Using aerosol-based tracers to estimate risk of infectious aerosol transmission aids in the design of buildings with adequate protection against aerosol transmissible pathogens, such as SARS-CoV-2 and influenza. We propose a method for scaling a SARS-CoV-2 bulk aerosol quantitative microbial risk assessment (QMRA) model for impulse emissions, coughing or sneezing, with aerosolized synthetic DNA tracer concentration measurements. With point-of-emission ratios describing relationships between tracer and respiratory aerosol emission characteristics (i.e., volume and RNA or DNA concentrations) and accounting for aerosolized pathogen loss of infectivity over time, we scale the inhaled pathogen dose and risk of infection with time-integrated tracer concentrations measured with a filter sampler. This tracer-scaled QMRA model is evaluated through scenario testing, comparing the impact of ventilation, occupancy, masking, and layering interventions on infection risk. We apply the tracer-scaled QMRA model to measurement data from an ambulatory care room to estimate the risk reduction resulting from HEPA air cleaner operation. Using DNA tracer measurements to scale a bulk aerosol QMRA model is a relatively simple method of estimating risk in buildings and can be applied to understand the impact of risk mitigation efforts.

Crunch: integrated processing and modeling of ChIP-seq data in terms of regulatory motifs.

Although ChIP-seq has become a routine experimental approach for quantitatively characterizing the genome-wide binding of transcription factors (TFs), computational analysis procedures remain far from standardized, making it difficult to compare ChIP-seq results across experiments. In addition, although genome-wide binding patterns must ultimately be determined by local constellations of DNA-binding sites, current analysis is typically limited to identifying enriched motifs in ChIP-seq peaks. Here we present Crunch, a completely automated computational method that performs all ChIP-seq analysis from quality control through read mapping and peak detecting and that integrates comprehensive modeling of the ChIP signal in terms of known and novel binding motifs, quantifying the contribution of each motif and annotating which combinations of motifs explain each binding peak. By applying Crunch to 128 data sets from the ENCODE Project, we show that Crunch outperforms current peak finders and find that TFs naturally separate into "solitary TFs," for which a single motif explains the ChIP-peaks, and "cobinding TFs," for which multiple motifs co-occur within peaks. Moreover, for most data sets, the motifs that Crunch identified de novo outperform known motifs, and both the set of cobinding motifs and the top motif of solitary TFs are consistent across experiments and cell lines. Crunch is implemented as a web server, enabling standardized analysis of any collection of ChIP-seq data sets by simply uploading raw sequencing data. Results are provided both in a graphical web interface and as downloadable files.

Luteipulveratus halotolerans sp. nov., an actinobacterium (Dermacoccaceae) from forest soil.

The taxonomic position of an actinobacterium strain, C296001T, isolated from a soil sample collected in Sarawak, Malaysia, was established using a polyphasic approach. Phylogenetically, strain C296001T was closely associated with the genus Luteipulveratus and formed a distinct monophyletic clade with the only described species, Luteipulveratus mongoliensis NBRC 105296T. The 16S rRNA gene sequence similarity between strain C296001T and L. mongoliensis was 98.7 %. DNA-DNA hybridization results showed that the relatedness of strain C296001T to L. mongoliensis was only 21.5 %. The DNA G+C content of strain C296001T was 71.7 mol%. Using a PacBio RS II system, whole genome sequences for strains C296001T and NBRC 105296T were obtained. The genome sizes of 4.5 Mbp and 5.4 Mbp determined were similar to those of other members of the family Dermacoccaceae. The cell-wall peptidoglycan contained lysine, alanine, aspartic acid, glutamic acid and serine, representing the peptidoglycan type A4α l-Lys-l-Ser-d-Asp. The major menaquinones were MK-8(H4), MK-8 and MK-8(H2). Phosphatidylglycerol, phosphatidylinositol, diphosphatidylglycerol and phosphoglycolipid were the polar lipids, while the whole-cell sugars were glucose, fucose and lesser amounts of ribose and galactose. The major fatty acids were iso-C16 : 0, anteiso-C17 : 0, iso-C16 : 1 H, anteiso-C17 : 1ω9c, iso-C18 : 0 and 10-methyl C17 : 0. Chemotaxonomic analyses showed that C296001T had typical characteristics of members of the genus Luteipulveratus, with the main differences occurring in phenotypic characteristics. On the basis of the phenotypic and chemotaxonomic evidence, it is proposed that strain C296001T be classified as a representative of a novel species in the genus Luteipulveratus, for which the name Luteipulveratus halotolerans sp. nov. is recommended. The type strain is C296001T ( = ATCC TSD-4T = JCM 30660T).

ISMARA: automated modeling of genomic signals as a democracy of regulatory motifs.

Accurate reconstruction of the regulatory networks that control gene expression is one of the key current challenges in molecular biology. Although gene expression and chromatin state dynamics are ultimately encoded by constellations of binding sites recognized by regulators such as transcriptions factors (TFs) and microRNAs (miRNAs), our understanding of this regulatory code and its context-dependent read-out remains very limited. Given that there are thousands of potential regulators in mammals, it is not practical to use direct experimentation to identify which of these play a key role for a particular system of interest. We developed a methodology that models gene expression or chromatin modifications in terms of genome-wide predictions of regulatory sites and completely automated it into a web-based tool called ISMARA (Integrated System for Motif Activity Response Analysis). Given only gene expression or chromatin state data across a set of samples as input, ISMARA identifies the key TFs and miRNAs driving expression/chromatin changes and makes detailed predictions regarding their regulatory roles. These include predicted activities of the regulators across the samples, their genome-wide targets, enriched gene categories among the targets, and direct interactions between the regulators. Applying ISMARA to data sets from well-studied systems, we show that it consistently identifies known key regulators ab initio. We also present a number of novel predictions including regulatory interactions in innate immunity, a master regulator of mucociliary differentiation, TFs consistently disregulated in cancer, and TFs that mediate specific chromatin modifications.

Tead2 expression levels control the subcellular distribution of Yap and Taz, zyxin expression and epithelial-mesenchymal transition.

The cellular changes during an epithelial-mesenchymal transition (EMT) largely rely on global changes in gene expression orchestrated by transcription factors. Tead transcription factors and their transcriptional co-activators Yap and Taz have been previously implicated in promoting an EMT; however, their direct transcriptional target genes and their functional role during EMT have remained elusive. We have uncovered a previously unanticipated role of the transcription factor Tead2 during EMT. During EMT in mammary gland epithelial cells and breast cancer cells, levels of Tead2 increase in the nucleus of cells, thereby directing a predominant nuclear localization of its co-factors Yap and Taz via the formation of Tead2-Yap-Taz complexes. Genome-wide chromatin immunoprecipitation and next generation sequencing in combination with gene expression profiling revealed the transcriptional targets of Tead2 during EMT. Among these, zyxin contributes to the migratory and invasive phenotype evoked by Tead2. The results demonstrate that Tead transcription factors are crucial regulators of the cellular distribution of Yap and Taz, and together they control the expression of genes critical for EMT and metastasis.

Sox4 is a master regulator of epithelial-mesenchymal transition by controlling Ezh2 expression and epigenetic reprogramming.

Gene expression profiling has uncovered the transcription factor Sox4 with upregulated activity during TGF-ß-induced epithelial-mesenchymal transition (EMT) in normal and cancerous breast epithelial cells. Sox4 is indispensable for EMT and cell survival in vitro and for primary tumor growth and metastasis in vivo. Among several EMT-relevant genes, Sox4 directly regulates the expression of Ezh2, encoding the Polycomb group histone methyltransferase that trimethylates histone 3 lysine 27 (H3K27me3) for gene repression. Ablation of Ezh2 expression prevents EMT, whereas forced expression of Ezh2 restores EMT in Sox4-deficient cells. Ezh2-mediated H3K27me3 marks associate with key EMT genes, representing an epigenetic EMT signature that predicts patient survival. Our results identify Sox4 as a master regulator of EMT by governing the expression of the epigenetic modifier Ezh2.

Klf4 is a transcriptional regulator of genes critical for EMT, including Jnk1 (Mapk8).

We have identified the zinc-finger transcription factor Kruppel-like factor 4 (Klf4) among the transcription factors that are significantly downregulated in their expression during epithelial-mesenchymal transition (EMT) in mammary epithelial cells and in breast cancer cells. Loss and gain of function experiments demonstrate that the down-regulation of Klf4 expression is required for the induction of EMT in vitro and for metastasis in vivo. In addition, reduced Klf4 expression correlates with shorter disease-free survival of subsets of breast cancer patients. Yet, reduced expression of Klf4 also induces apoptosis in cells undergoing TGFß-induced EMT. Chromatin immunoprecipitation/deep-sequencing in combination with gene expression profiling reveals direct Klf4 target genes, including E-cadherin (Cdh1), N-cadherin (Cdh2), vimentin (Vim), ß-catenin (Ctnnb1), VEGF-A (Vegfa), endothelin-1 (Edn1) and Jnk1 (Mapk8). Thereby, Klf4 acts as a transcriptional activator of epithelial genes and as a repressor of mesenchymal genes. Specifically, increased expression of Jnk1 (Mapk8) upon down-regulation of its transcriptional repressor Klf4 is required for EMT cell migration and for the induction of apoptosis. The data demonstrate a central role of Klf4 in the maintenance of epithelial cell differentiation and the prevention of EMT and metastasis.

Modeling of epigenome dynamics identifies transcription factors that mediate Polycomb targeting.

Although changes in chromatin are integral to transcriptional reprogramming during cellular differentiation, it is currently unclear how chromatin modifications are targeted to specific loci. To systematically identify transcription factors (TFs) that can direct chromatin changes during cell fate decisions, we model the relationship between genome-wide dynamics of chromatin marks and the local occurrence of computationally predicted TF binding sites. By applying this computational approach to a time course of Polycomb-mediated H3K27me3 marks during neuronal differentiation of murine stem cells, we identify several motifs that likely regulate the dynamics of this chromatin mark. Among these, the sites bound by REST and by the SNAIL family of TFs are predicted to transiently recruit H3K27me3 in neuronal progenitors. We validate these predictions experimentally and show that absence of REST indeed causes loss of H3K27me3 at target promoters in trans, specifically at the neuronal progenitor state. Moreover, using targeted transgenic insertion, we show that promoter fragments containing REST or SNAIL binding sites are sufficient to recruit H3K27me3 in cis, while deletion of these sites results in loss of H3K27me3. These findings illustrate that the occurrence of TF binding sites can determine chromatin dynamics. Local determination of Polycomb activity by REST and SNAIL motifs exemplifies such TF based regulation of chromatin. Furthermore, our results show that key TFs can be identified ab initio through computational modeling of epigenome data sets using a modeling approach that we make readily accessible.

SwissRegulon, a database of genome-wide annotations of regulatory sites: recent updates.

Identification of genomic regulatory elements is essential for understanding the dynamics of cellular processes. This task has been substantially facilitated by the availability of genome sequences for many species and high-throughput data of transcripts and transcription factor (TF) binding. However, rigorous computational methods are necessary to derive accurate genome-wide annotations of regulatory sites from such data. SwissRegulon (http://swissregulon.unibas.ch) is a database containing genome-wide annotations of regulatory motifs, promoters and TF binding sites (TFBSs) in promoter regions across model organisms. Its binding site predictions were obtained with rigorous Bayesian probabilistic methods that operate on orthologous regions from related genomes, and use explicit evolutionary models to assess the evidence of purifying selection on each site. New in the current version of SwissRegulon is a curated collection of 190 mammalian regulatory motifs associated with ∼340 TFs, and TFBS annotations across a curated set of ∼35 000 promoters in both human and mouse. Predictions of TFBSs for Saccharomyces cerevisiae have also been significantly extended and now cover 158 of yeast's ∼180 TFs. All data are accessible through both an easily navigable genome browser with search functions, and as flat files that can be downloaded for further analysis.

MotEvo: integrated Bayesian probabilistic methods for inferring regulatory sites and motifs on multiple alignments of DNA sequences.

MOTIVATION: Probabilistic approaches for inferring transcription factor binding sites (TFBSs) and regulatory motifs from DNA sequences have been developed for over two decades. Previous work has shown that prediction accuracy can be significantly improved by incorporating features such as the competition of multiple transcription factors (TFs) for binding to nearby sites, the tendency of TFBSs for co-regulated TFs to cluster and form cis-regulatory modules and explicit evolutionary modeling of conservation of TFBSs across orthologous sequences. However, currently available tools only incorporate some of these features, and significant methodological hurdles hampered their synthesis into a single consistent probabilistic framework. RESULTS: We present MotEvo, a integrated suite of Bayesian probabilistic methods for the prediction of TFBSs and inference of regulatory motifs from multiple alignments of phylogenetically related DNA sequences, which incorporates all features just mentioned. In addition, MotEvo incorporates a novel model for detecting unknown functional elements that are under evolutionary constraint, and a new robust model for treating gain and loss of TFBSs along a phylogeny. Rigorous benchmarking tests on ChIP-seq datasets show that MotEvo's novel features significantly improve the accuracy of TFBS prediction, motif inference and enhancer prediction. AVAILABILITY: Source code, a user manual and files with several example applications are available at www.swissregulon.unibas.ch.

Exploring the contribution of district nurses to public health.

To date the public health role of district nursing has been less well defined than in other branches of community nursing. A qualitative study examining the public health role of district nurses was undertaken in a West Yorkshire primary care trust. The study explored the range of public health activities currently undertaken by district nurses, the agendas perceived as most conducive to input and the organizational barriers that prevented greater involvement in public health from district nurses. Focus group and individual semi-structured interviews were undertaken with 21 members of the district nursing team and analysed using thematic content analysis. The findings suggest that district nurses were already undertaking a range of public health and health education activities but much of the work was opportunistic. District nurses perceived themselves as well placed to undertake locally based health action because of their profile in the local communities together with local intelligence. However, ineffective referral criteria were considered to be the most prohibitive barrier against making a greater contribution to health improvement.