Numerical simulation of universal morphogenesis of fluid interface deformations driven by radiation pressure.

We report on numerical simulation of fluid interface deformations induced by either acoustic or optical radiation pressure. This is done by solving simultaneously the scalar wave propagation equation and the two-phase flow equations using the boundary element method. Using dimensional analysis, we show that interface deformation morphogenesis is universal, i.e., depends on the same dimensionless parameters in acoustics and electromagnetics. We numerically investigate a few selected phenomena-in particular the shape of large deformations and the slenderness transition and its hysteresis-and compare with existing and novel experimental observations. Qualitative agreement between the numerical simulations and experiments is found when the mutual interaction between wave propagation and wave-induced deformations is taken into account. Our results demonstrate the leading role of the radiation pressure in morphogenesis of fluid interface deformations and the importance of the propagation-deformation interplay.

Family history assessment significantly enhances delivery of precision medicine in the genomics era.

BACKGROUND: Family history has traditionally been an essential part of clinical care to assess health risks. However, declining sequencing costs have precipitated a shift towards genomics-first approaches in population screening programs rendering the value of family history unknown. We evaluated the utility of incorporating family history information for genomic sequencing selection. METHODS: To ascertain the relationship between family histories on such population-level initiatives, we analysed whole genome sequences of 1750 research participants with no known pre-existing conditions, of which half received comprehensive family history assessment of up to four generations, focusing on 95 cancer genes. RESULTS: Amongst the 1750 participants, 866 (49.5%) had high-quality standardised family history available. Within this group, 73 (8.4%) participants had an increased family history risk of cancer (increased FH risk cohort) and 1 in 7 participants (n = 10/73) carried a clinically actionable variant inferring a sixfold increase compared with 1 in 47 participants (n = 17/793) assessed at average family history cancer risk (average FH risk cohort) (p = 0.00001) and a sevenfold increase compared to 1 in 52 participants (n = 17/884) where family history was not available (FH not available cohort) (p = 0.00001). The enrichment was further pronounced (up to 18-fold) when assessing only the 25 cancer genes in the American College of Medical Genetics (ACMG) Secondary Findings (SF) genes. Furthermore, 63 (7.3%) participants had an increased family history cancer risk in the absence of an apparent clinically actionable variant. CONCLUSIONS: These findings demonstrate that the collection and analysis of comprehensive family history and genomic data are complementary and in combination can prioritise individuals for genomic analysis. Thus, family history remains a critical component of health risk assessment, providing important actionable data when implementing genomics screening programs. TRIAL REGISTRATION: ClinicalTrials.gov NCT02791152 . Retrospectively registered on May 31, 2016.

Comprehensive Characterization of Transcriptional Activity during Influenza A Virus Infection Reveals Biases in Cap-Snatching of Host RNA Sequences.

Macrophages in the lung detect and respond to influenza A virus (IAV), determining the nature of the immune response. Using terminal-depth cap analysis of gene expression (CAGE), we quantified transcriptional activity of both host and pathogen over a 24-h time course of IAV infection in primary human monocyte-derived macrophages (MDMs). This method allowed us to observe heterogenous host sequences incorporated into IAV mRNA, "snatched" 5' RNA caps, and corresponding RNA sequences from host RNAs. In order to determine whether cap-snatching is random or exhibits a bias, we systematically compared host sequences incorporated into viral mRNA ("snatched") against a complete survey of all background host RNA in the same cells, at the same time. Using a computational strategy designed to eliminate sources of bias due to read length, sequencing depth, and multimapping, we were able to quantify overrepresentation of host RNA features among the sequences that were snatched by IAV. We demonstrate biased snatching of numerous host RNAs, particularly small nuclear RNAs (snRNAs), and avoidance of host transcripts encoding host ribosomal proteins, which are required by IAV for replication. We then used a systems approach to describe the transcriptional landscape of the host response to IAV, observing many new features, including a failure of IAV-treated MDMs to induce feedback inhibitors of inflammation, seen in response to other treatments.IMPORTANCE Infection with influenza A virus (IAV) infection is responsible for an estimated 500,000 deaths and up to 5 million cases of severe respiratory illness each year. In this study, we looked at human primary immune cells (macrophages) infected with IAV. Our method allows us to look at both the host and the virus in parallel. We used these data to explore a process known as "cap-snatching," where IAV snatches a short nucleotide sequence from capped host RNA. This process was believed to be random. We demonstrate biased snatching of numerous host RNAs, including those associated with snRNA transcription, and avoidance of host transcripts encoding host ribosomal proteins, which are required by IAV for replication. We then describe the transcriptional landscape of the host response to IAV, observing new features, including a failure of IAV-treated MDMs to induce feedback inhibitors of inflammation, seen in response to other treatments.

Super-Enhancers and Broad H3K4me3 Domains Form Complex Gene Regulatory Circuits Involving Chromatin Interactions.

Stretched histone regions, such as super-enhancers and broad H3K4me3 domains, are associated with maintenance of cell identity and cancer. We connected super-enhancers and broad H3K4me3 domains in the K562 chronic myelogenous leukemia cell line as well as the MCF-7 breast cancer cell line with chromatin interactions. Super-enhancers and broad H3K4me3 domains showed higher association with chromatin interactions than their typical counterparts. Interestingly, we identified a subset of super-enhancers that overlap with broad H3K4me3 domains and show high association with cancer-associated genes including tumor suppressor genes. Besides cell lines, we could observe chromatin interactions by a Chromosome Conformation Capture (3C)-based method, in primary human samples. Several chromatin interactions involving super-enhancers and broad H3K4me3 domains are constitutive and can be found in both cancer and normal samples. Taken together, these results reveal a new layer of complexity in gene regulation by super-enhancers and broad H3K4me3 domains.

A resource of ribosomal RNA-depleted RNA-Seq data from different normal adult and fetal human tissues.

Gene expression is the most fundamental level at which the genotype leads to the phenotype of the organism. Enabled by ultra-high-throughput next-generation DNA sequencing, RNA-Seq involves shotgun sequencing of fragmented RNA transcripts by next-generation sequencing followed by in silico assembly, and is rapidly becoming the most popular method for gene expression analysis. Poly[A]+ RNA-Seq analyses of normal human adult tissue samples such as Illumina's Human BodyMap 2.0 Project and the RNA-Seq atlas have provided a useful global resource and framework for comparisons with diseased tissues such as cancer. However, these analyses have failed to provide information on poly[A]-RNA, which is abundant in our cells. The most recent advances in RNA-Seq analyses use ribosomal RNA-depletion to provide information on both poly[A]+ and poly[A]-RNA. In this paper, we describe the use of Illumina's HiSeq 2000 to generate high quality rRNA-depleted RNA-Seq datasets from human fetal and adult tissues. The datasets reported here will be useful in understanding the different expression profiles in different tissues.

CAGE-defined promoter regions of the genes implicated in Rett Syndrome.

BACKGROUND: Mutations in three functionally diverse genes cause Rett Syndrome. Although the functions of Forkhead box G1 (FOXG1), Methyl CpG binding protein 2 (MECP2) and Cyclin-dependent kinase-like 5 (CDKL5) have been studied individually, not much is known about their relation to each other with respect to expression levels and regulatory regions. Here we analyzed data from hundreds of mouse and human samples included in the FANTOM5 project, to identify transcript initiation sites, expression levels, expression correlations and regulatory regions of the three genes. RESULTS: Our investigations reveal the predominantly used transcription start sites (TSSs) for each gene including novel transcription start sites for FOXG1. We show that FOXG1 expression is poorly correlated with the expression of MECP2 and CDKL5. We identify promoter shapes for each TSS, the predicted location of enhancers for each gene and the common transcription factors likely to regulate the three genes. Our data imply Polycomb Repressive Complex 2 (PRC2) mediated silencing of Foxg1 in cerebellum. CONCLUSIONS: Our analyses provide a comprehensive picture of the regulatory regions of the three genes involved in Rett Syndrome.

Transcriptional profiling of the human fibrillin/LTBP gene family, key regulators of mesenchymal cell functions.

The fibrillins and latent transforming growth factor binding proteins (LTBPs) form a superfamily of extracellular matrix (ECM) proteins characterized by the presence of a unique domain, the 8-cysteine transforming growth factor beta (TGFß) binding domain. These proteins are involved in the structure of the extracellular matrix and controlling the bioavailability of TGFß family members. Genes encoding these proteins show differential expression in mesenchymal cell types which synthesize the extracellular matrix. We have investigated the promoter regions of the seven gene family members using the FANTOM5 CAGE database for human. While the protein and nucleotide sequences show considerable sequence similarity, the promoter regions were quite diverse. Most genes had a single predominant transcription start site region but LTBP1 and LTBP4 had two regions initiating different transcripts. Most of the family members were expressed in a range of mesenchymal and other cell types, often associated with use of alternative promoters or transcription start sites within a promoter in different cell types. FBN3 was the lowest expressed gene, and was found only in embryonic and fetal tissues. The different promoters for one gene were more similar to each other in expression than to promoters of the other family members. Notably expression of all 22 LTBP2 promoters was tightly correlated and quite distinct from all other family members. We located candidate enhancer regions likely to be involved in expression of the genes. Each gene was associated with a unique subset of transcription factors across multiple promoters although several motifs including MAZ, SP1, GTF2I and KLF4 showed overrepresentation across the gene family. FBN1 and FBN2, which had similar expression patterns, were regulated by different transcription factors. This study highlights the role of alternative transcription start sites in regulating the tissue specificity of closely related genes and suggests that this important class of extracellular matrix proteins is subject to subtle regulatory variations that explain the differential roles of members of this gene family.

The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line.

Using deep sequencing (deepCAGE), the FANTOM4 study measured the genome-wide dynamics of transcription-start-site usage in the human monocytic cell line THP-1 throughout a time course of growth arrest and differentiation. Modeling the expression dynamics in terms of predicted cis-regulatory sites, we identified the key transcription regulators, their time-dependent activities and target genes. Systematic siRNA knockdown of 52 transcription factors confirmed the roles of individual factors in the regulatory network. Our results indicate that cellular states are constrained by complex networks involving both positive and negative regulatory interactions among substantial numbers of transcription factors and that no single transcription factor is both necessary and sufficient to drive the differentiation process.

Network modeling links breast cancer susceptibility and centrosome dysfunction.

Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network modeling strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or 'omic') data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer-associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network modeling strategy should be useful for the discovery of additional cancer-associated genes.

Systematic interactome mapping and genetic perturbation analysis of a C. elegans TGF-beta signaling network.

To initiate a system-level analysis of C. elegans DAF-7/TGF-beta signaling, we combined interactome mapping with single and double genetic perturbations. Yeast two-hybrid (Y2H) screens starting with known DAF-7/TGF-beta pathway components defined a network of 71 interactions among 59 proteins. Coaffinity purification (co-AP) assays in mammalian cells confirmed the overall quality of this network. Systematic perturbations of the network using RNAi, both in wild-type and daf-7/TGF-beta pathway mutant animals, identified nine DAF-7/TGF-beta signaling modifiers, seven of which are conserved in humans. We show that one of these has functional homology to human SNO/SKI oncoproteins and that mutations at the corresponding genetic locus daf-5 confer defects in DAF-7/TGF-beta signaling. Our results reveal substantial molecular complexity in DAF-7/TGF-beta signal transduction. Integrating interactome maps with systematic genetic perturbations may be useful for developing a systems biology approach to this and other signaling modules.