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Abstract Hormonal contraception exposes women to synthetic progesterone receptor (PR) agonists, progestins, and transiently increases breast cancer risk. How progesterone and progestins affect the breast epithelium is poorly understood because we lack adequate models to study this. We hypothesized that individual progestins differentially affect breast epithelial cell proliferation and hence breast cancer risk. Using mouse mammary tissue ex vivo, we show that testosterone‐related progestins induce the PR target and mediator of PR signaling‐induced cell proliferation receptor activator of NF‐κB ligand (Rankl), whereas progestins with anti‐androgenic properties in reporter assays do not. We develop intraductal xenografts of human breast epithelial cells from 36 women, show they remain hormone‐responsive and that progesterone and the androgenic progestins, desogestrel, gestodene, and levonorgestrel, promote proliferation but the anti‐androgenic, chlormadinone, and cyproterone acetate, do not. Prolonged exposure to androgenic progestins elicits hyperproliferation with cytologic changes. Androgen receptor inhibition interferes with PR agonist‐ and levonorgestrel‐induced RANKL expression and reduces levonorgestrel‐driven cell proliferation. Thus, different progestins have distinct biological activities in the breast epithelium to be considered for more informed choices in hormonal contraception.

Abstract Invasive lobular carcinoma (ILC) is the most frequent special histological subtype of breast cancer, typically characterized by loss of E‐cadherin. It has clinical features distinct from other estrogen receptor‐positive (ER+) breast cancers but the molecular mechanisms underlying its characteristic biology are poorly understood because we lack experimental models to study them. Here, we recapitulate the human disease, including its metastatic pattern, by grafting ILC‐derived breast cancer cell lines, SUM‐44 PE and MDA‐MB‐134‐VI cells, into the mouse milk ducts. Using patient‐derived intraductal xenografts from lobular and non‐lobular ER+ HER2− tumors to compare global gene expression, we identify extracellular matrix modulation as a lobular carcinoma cell‐intrinsic trait. Analysis of TCGA patient datasets shows matrisome signature is enriched in lobular carcinomas with overexpression of elastin, collagens, and the collagen modifying enzyme LOXL1. Treatment with the pan LOX inhibitor BAPN and silencing of LOXL1 expression decrease tumor growth, invasion, and metastasis by disrupting ECM structure resulting in decreased ER signaling. We conclude that LOXL1 inhibition is a promising therapeutic strategy for ILC.

Abstract Background Positional weight matrix (PWM) is a de facto standard model to describe transcription factor (TF) DNA binding specificities. PWMs inferred from in vivo or in vitro data are stored in many databases and used in a plethora of biological applications. This calls for comprehensive benchmarking of public PWM models with large experimental reference sets. Results Here we report results from all-against-all benchmarking of PWM models for DNA binding sites of human TFs on a large compilation of in vitro (HT-SELEX, PBM) and in vivo (ChIP-seq) binding data. We observe that the best performing PWM for a given TF often belongs to another TF, usually from the same family. Occasionally, binding specificity is correlated with the structural class of the DNA binding domain, indicated by good cross-family performance measures. Benchmarking-based selection of family-representative motifs is more effective than motif clustering-based approaches. Overall, there is good agreement between in vitro and in vivo performance measures. However, for some in vivo experiments, the best performing PWM is assigned to an unrelated TF, indicating a binding mode involving protein-protein cooperativity. Conclusions In an all-against-all setting, we compute more than 18 million performance measure values for different PWM-experiment combinations and offer these results as a public resource to the research community. The benchmarking protocols are provided via a web interface and as docker images. The methods and results from this study may help others make better use of public TF specificity models, as well as public TF binding data sets.

How hormonal signaling in the mammary epithelium controls the surrounding extracellular matrix is unclear. Here, the authors show that a secreted protease, Adamts18, induced by upstream estrogen-progesterone activated Wnt4 in myoepithelial cells, remodels the basement membrane and contributes to mammary epithelial stemness.

Metazoan core promoters, which direct the initiation of transcription by RNA polymerase II (Pol II), may contain short sequence motifs termed core promoter elements/motifs (e.g. the TATA box, initiator (Inr) and downstream core promoter element (DPE)), which recruit Pol II via the general transcription machinery. The DPE was discovered and extensively characterized in Drosophila, where it is strictly dependent on both the presence of an Inr and the precise spacing from it. Since the Drosophila DPE is recognized by the human transcription machinery, it is most likely that some human promoters contain a downstream element that is similar, though not necessarily identical, to the Drosophila DPE. However, only a couple of human promoters were shown to contain a functional DPE, and attempts to computationally detect human DPE-containing promoters have mostly been unsuccessful. Using a newly-designed motif discovery strategy based on Expectation-Maximization probabilistic partitioning algorithms, we discovered preferred downstream positions (PDP) in human promoters that resemble the Drosophila DPE. Available chromatin accessibility footprints revealed that Drosophila and human Inr+DPE promoter classes are not only highly structured, but also similar to each other, particularly in the proximal downstream region. Clustering of the corresponding sequence motifs using a neighbor-joining algorithm strongly suggests that canonical Inr+DPE promoters could be common to metazoan species. Using reporter assays we demonstrate the contribution of the identified downstream positions to the function of multiple human promoters. Furthermore, we show that alteration of the spacing between the Inr and PDP by two nucleotides results in reduced promoter activity, suggesting a spacing dependency of the newly discovered human PDP on the Inr. Taken together, our strategy identified novel functional downstream positions within human core promoters, supporting the existence of DPE-like motifs in human promoters.

Attenuator barriers, in contrast to conventional safety nets, tend to smoothly guide impacting rocks instead of absorbing large amounts of strain energy arresting them. It has been shown that the rock’s rotation plays an important role in the bearing capacity of these systems. Although experimental tests have to be conducted to gain a detailed insight into the behavior of both the structures and the rock itself, these tests are usually costly, time-consuming, and offer limited generalizability to other structure/environment combinations. Thus, in order to support the engineer’s design decision, reinforce test results and confidently predict barrier performance beyond experimental configurations this work describes an appropriate numerical modeling and simulation method of this coupled problem. For this purpose, the Discrete Element Method (DEM) and the Finite Element Method (FEM) are coupled in an open-source multi-physics code. In order to flexibly model rocks of any shape, sphere clusters are used which employ simple and efficient contact algorithms despite arbitrarily complicated shapes. A general summary of the FEM formulation is presented as well as detailed derivations of finite elements particularly pertinent to rockfall simulations. The presented modeling and coupling method is validated against experimental testing conducted by the company Geobrugg. Good agreement is achieved between the simulated and experimental results, demonstrating the successful practical application of the proposed method.

This article presents a staggered approach to couple the interaction of very flexible tension structures with large deformations, described with the finite element method (FEM), and objects undergoing large, complex, and arbitrary motions discretized with particle methods, in this case the discrete element method (DEM). The quantitative solution quality and convergence rate of this partitioned approach is highly time step dependent. Thus, the strong coupling approach is presented here, where the convergence is achieved in an iterative manner within each time step. This approach helps increase the time step size significantly, decreases the overall computational costs, and improves the numerical stability. Moreover, the proposed algorithm enables the application of two independent, standalone codes for simulating DEM and structural FEM as blackbox solvers. Systematic evaluations of the newly proposed iterative coupling scheme with respect to accuracy, robustness, and efficiency as well as cross comparisons between strong and weak FEM-DEM coupling approaches are performed. Additionally, the approach is validated against the rest position of an impacting object, and further examples with objects impacting highly flexible protection structures are presented. Here, the protection nets are described with nonlinear structural finite elements and the impacting objects as DEM elements. To allow the interested reader to independently reproduce the results, detailed code and algorithm descriptions are included in the appendix.

To detect functional somatic mutations in tumor samples, whole-exome sequencing (WES) is often used for its reliability and relative low cost. RNA-seq, while generally used to measure gene expression, can potentially also be used for identification of somatic mutations. However there has been little systematic evaluation of the utility of RNA-seq for identifying somatic mutations. Here, we develop and evaluate a pipeline for processing RNA-seq data from glioblastoma multiforme (GBM) tumors in order to identify somatic mutations. The pipeline entails the use of the STAR aligner 2-pass procedure jointly with MuTect2 from genome analysis toolkit (GATK) to detect somatic variants. Variants identified from RNA-seq data were evaluated by comparison against the COSMIC and dbSNP databases, and also compared to somatic variants identified by exome sequencing. We also estimated the putative functional impact of coding variants in the most frequently mutated genes in GBM. Interestingly, variants identified by RNA-seq alone showed better representation of GBM-related mutations cataloged by COSMIC. RNA-seq-only data substantially outperformed the ability of WES to reveal potentially new somatic mutations in known GBM-related pathways, and allowed us to build a high-quality set of somatic mutations common to exome and RNA-seq calls. Using RNA-seq data in parallel with WES data to detect somatic mutations in cancer genomes can thus broaden the scope of discoveries and lend additional support to somatic variants identified by exome sequencing alone.

Regulation of mRNA stability by RNA-protein interactions contributes significantly to quantitative aspects of gene expression. We have identified potential mRNA targets of the AU-rich element binding protein AUF1. Myc-tagged AUF1 p42 was induced in mouse NIH/3T3 cells and RNA-protein complexes isolated using anti-myc tag antibody beads. Bound mRNAs were analyzed with Affymetrix microarrays. We have identified 508 potential target mRNAs that were at least 3-fold enriched compared to control cells without myc-AUF1. 22.3% of the enriched mRNAs had an AU-rich cluster in the ARED Organism database, against 16.3% of non-enriched control mRNAs. The enrichment towards AU-rich elements was also visible by AREScore with an average value of 5.2 in the enriched mRNAs versus 4.2 in the control group. Yet, numerous mRNAs were enriched without a high ARE score. The enrichment of tetrameric and pentameric sequences suggests a broad AUF1 p42-binding spectrum at short U-rich sequences flanked by A or G. Still, some enriched mRNAs were highly unstable, as those of TNFSF11 (known as RANKL), KLF10, HES1, CCNT2, SMAD6, and BCL6. We have mapped some of the instability determinants. HES1 mRNA appeared to have a coding region determinant. Detailed analysis of the RANKL and BCL6 3'UTR revealed for both that full instability required two elements, which are conserved in evolution. In RANKL mRNA both elements are AU-rich and separated by 30 bases, while in BCL6 mRNA one is AU-rich and 60 bases from a non AU-rich element that potentially forms a stem-loop structure.

The recruitment of RNA-Pol-II to the transcription start site (TSS) is an important step in gene regulation in all organisms. Core promoter elements (CPE) are conserved sequence motifs that guide Pol-II to the TSS by interacting with specific transcription factors (TFs). However, only a minority of animal promoters contains CPEs. It is still unknown how Pol-II selects the TSS in their absence. Here we present a comparative analysis of promoters' sequence composition and chromatin architecture in five eukaryotic model organisms, which shows the presence of common and unique DNA-encoded features used to organize chromatin. Analysis of Pol-II initiation patterns uncovers that, in the absence of certain CPEs, there is a strong correlation between the spread of initiation and the intensity of the 10 bp periodic signal in the nearest downstream nucleosome. Moreover, promoters' primary and secondary initiation sites show a characteristic 10 bp periodicity in the absence of CPEs. We also show that DNA natural variants in the region immediately downstream the TSS are able to affect both the nucleosome-DNA affinity and Pol-II initiation pattern. These findings support the notion that, in addition to CPEs mediated selection, sequence-induced nucleosome positioning could be a common and conserved mechanism of TSS selection in animals.

The advent of high-throughput technologies such as ChIP-seq has made possible the study of histone modifications. A problem of particular interest is the identification of regions of the genome where different cell types from the same organism exhibit different patterns of histone enrichment. This problem turns out to be surprisingly difficult, even in simple pairwise comparisons, because of the significant level of noise in ChIP-seq data. In this paper we propose a two-stage statistical method, called ChIPnorm, to normalize ChIP-seq data, and to find differential regions in the genome, given two libraries of histone modifications of different cell types. We show that the ChIPnorm method removes most of the noise and bias in the data and outperforms other normalization methods. We correlate the histone marks with gene expression data and confirm that histone modifications H3K27me3 and H3K4me3 act as respectively a repressor and an activator of genes. Compared to what was previously reported in the literature, we find that a substantially higher fraction of bivalent marks in ES cells for H3K27me3 and H3K4me3 move into a K27-only state. We find that most of the promoter regions in protein-coding genes have differential histone-modification sites. The software for this work can be downloaded from http://lcbb.epfl.ch/software.html.

Krüppel-associated box domain-zinc finger proteins (KRAB-ZFPs) are tetrapod-specific transcriptional repressors encoded in the hundreds by the human genome. In order to explore their as yet ill-defined impact on gene expression, we developed an ectopic repressor assay, allowing the study of KRAB-mediated transcriptional regulation at hundreds of different transcriptional units. By targeting a drug-controllable KRAB-containing repressor to gene-trapping lentiviral vectors, we demonstrate that KRAB and its corepressor KAP1 can silence promoters located several tens of kilobases (kb) away from their DNA binding sites, with an efficiency which is generally higher for promoters located within 15 kb or less. Silenced promoters exhibit a loss of histone H3-acetylation, an increase in H3 lysine 9 trimethylation (H3K9me3), and a drop in RNA Pol II recruitment, consistent with a block of transcriptional initiation following the establishment of silencing marks. Furthermore, we reveal that KRAB-mediated repression is established by the long-range spreading of H3K9me3 and heterochromatin protein 1 beta (HP1beta) between the repressor binding site and the promoter. We confirm the biological relevance of this phenomenon by documenting KAP1-dependent transcriptional repression at an endogenous KRAB-ZFP gene cluster, where KAP1 binds to the 3' end of genes and mediates propagation of H3K9me3 and HP1beta towards their 5' end. Together, our data support a model in which KRAB/KAP1 recruitment induces long-range repression through the spread of heterochromatin. This finding not only suggests auto-regulatory mechanisms in the control of KRAB-ZFP gene clusters, but also provides important cues for interpreting future genome-wide DNA binding data of KRAB-ZFPs and KAP1.

Chromatin immunoprecipitation combined with massively parallel sequencing methods (ChIP-seq) is becoming the standard approach to study interactions of transcription factors (TF) with genomic sequences. At the example of public STAT1 ChIP-seq data sets, we present novel approaches for the interpretation of ChIP-seq data.We compare recently developed approaches to determine STAT1 binding sites from ChIP-seq data. Assessing the content of the established consensus sequence for STAT1 binding sites, we find that the usage of "negative control" ChIP-seq data fails to provide substantial advantages. We derive a single refined probabilistic model of STAT1 binding sequences from these ChIP-seq data. Contrary to previous claims, we find no evidence that STAT1 binds to multiple distinct motifs upon interferon-gamma stimulation in vivo. While a large majority of genomic sites with high ChIP-seq signal is associated with a nucleotide sequence resembling a STAT1 binding site, only a very small subset of the over 5 million potential STAT1 binding sites in the human genome is covered by ChIP-seq data. Furthermore a surprisingly large fraction of the ChIP-seq signal (5%) is absorbed by a small family of repetitive sequences (MER41). The observation of the binding of activated STAT1 protein to a specific repetitive element bolsters similar reports concerning p53 and other TFs, and strengthens the notion of an involvement of repeats in gene regulation. Incidentally MER41 are specific to primates, consequently, regulatory mechanisms in the IFN-STAT pathway might fundamentally differ between primates and rodents. On a methodological aspect, the presence of large numbers of nearly identical binding sites in repetitive sequences may lead to wrong conclusions about intrinsic binding preferences of TF as illustrated by the spacing analysis STAT1 tandem motifs. Therefore, ChIP-seq data should be analyzed independently within repetitive and non-repetitive sequences.

BACKGROUND:High-throughput gene expression profiling technologies generating a wealth of data, are increasingly used for characterization of tumor biopsies for clinical trials. By applying machine learning algorithms to such clinically documented data sets, one hopes to improve tumor diagnosis, prognosis, as well as prediction of treatment response. However, the limited number of patients enrolled in a single trial study limits the power of machine learning approaches due to over-fitting. One could partially overcome this limitation by merging data from different studies. Nevertheless, such data sets differ from each other with regard to technical biases, patient selection criteria and follow-up treatment. It is therefore not clear at all whether the advantage of increased sample size outweighs the disadvantage of higher heterogeneity of merged data sets. Here, we present a systematic study to answer this question specifically for breast cancer data sets. We use survival prediction based on Cox regression as an assay to measure the added value of merged data sets. RESULTS:Using time-dependent Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) and hazard ratio as performance measures, we see in overall no significant improvement or deterioration of survival prediction with merged data sets as compared to individual data sets. This apparently was due to the fact that a few genes with strong prognostic power were not available on all microarray platforms and thus were not retained in the merged data sets. Surprisingly, we found that the overall best performance was achieved with a single-gene predictor consisting of CYB5D1. CONCLUSIONS:Merging did not deteriorate performance on average despite (a) The diversity of microarray platforms used. (b) The heterogeneity of patients cohorts. (c) The heterogeneity of breast cancer disease. (d) Substantial variation of time to death or relapse. (e) The reduced number of genes in the merged data sets. Predictors derived from the merged data sets were more robust, consistent and reproducible across microarray platforms. Moreover, merging data sets from different studies helps to better understand the biases of individual studies and can lead to the identification of strong survival factors like CYB5D1 expression.

The class II trans-activator CIITA is a transcriptional co-activator required for the expression of Major Histocompatibility Complex (MHC) genes. Although the latter function is well established, the global target-gene specificity of CIITA had not been defined. We therefore generated a comprehensive list of its target genes by performing genome-wide scans employing four different approaches designed to identify promoters that are occupied by CIITA in two key antigen presenting cells, B cells and dendritic cells. Surprisingly, in addition to MHC genes, only nine new targets were identified and validated by extensive functional and expression analysis. Seven of these genes are known or likely to function in processes contributing to MHC-mediated antigen presentation. The remaining two are of unknown function. CIITA is thus uniquely dedicated for genes implicated in antigen presentation. The finding that CIITA regulates such a highly focused gene expression module sets it apart from all other transcription factors, for which large-scale binding-site mapping has indicated that they exert pleiotropic functions and regulate large numbers of genes.

Searching for matches between large collections of short (14-30 nucleotides) words and sequence databases comprising full genomes or transcriptomes is a common task in biological sequence analysis. We investigated the performance of simple indexing strategies for handling such tasks and developed two programs, fetchGWI and tagger, that index either the database or the query set. Either strategy outperforms megablast for searches with more than 10,000 probes. FetchGWI is shown to be a versatile tool for rapidly searching multiple genomes, whose performance is limited in most cases by the speed of access to the filesystem. We have made publicly available a Web interface for searching the human, mouse, and several other genomes and transcriptomes with oligonucleotide queries.

There are a variety of bacterial defense strategies to survive in a hostile environment. Generation of extracellular polysaccharides has proved to be a simple but effective strategy against the host's innate immune system. A comparative genomics approach led us to identify a new protein family termed Stealth, most likely involved in the synthesis of extracellular polysaccharides. This protein family is characterized by a series of domains conserved across phylogeny from bacteria to eukaryotes. In bacteria, Stealth (previously characterized as SacB, XcbA, or WefC) is encoded by subsets of strains mainly colonizing multicellular organisms, with evidence for a protective effect against the host innate immune defense. More specifically, integrating all the available information about Stealth proteins in bacteria, we propose that Stealth is a D-hexose-1-phosphoryl transferase involved in the synthesis of polysaccharides. In the animal kingdom, Stealth is strongly conserved across evolution from social amoebas to simple and complex multicellular organisms, such as Dictyostelium discoideum, hydra, and human. Based on the occurrence of Stealth in most Eukaryotes and a subset of Prokaryotes together with its potential role in extracellular polysaccharide synthesis, we propose that metazoan Stealth functions to regulate the innate immune system. Moreover, there is good reason to speculate that the acquisition and spread of Stealth could be responsible for future epidemic outbreaks of infectious diseases caused by a large variety of eubacterial pathogens. Our in silico identification of a homologous protein in the human host will help to elucidate the causes of Stealth-dependent virulence. At a more basic level, the characterization of the molecular and cellular function of Stealth proteins may shed light on fundamental mechanisms of innate immune defense against microbial invasion.