The transcription factor SRF (serum response factor) recruits two families of coactivators, the MRTFs (myocardin-related transcription factors) and the TCFs (ternary complex factors), to couple gene ...transcription to growth factor signaling. Here we investigated the role of the SRF network in the immediate transcriptional response of fibroblasts to serum stimulation. SRF recruited its cofactors in a gene-specific manner, and virtually all MRTF binding was directed by SRF. Much of SRF DNA binding was serum-inducible, reflecting a requirement for MRTF-SRF complex formation in nucleosome displacement. We identified 960 serum-responsive SRF target genes, which were mostly MRTF-controlled, as assessed by MRTF chromatin immunoprecipitation (ChIP) combined with deep sequencing (ChIP-seq) and/or sensitivity to MRTF-linked signals. MRTF activation facilitates RNA polymerase II (Pol II) recruitment or promoter escape according to gene context. MRTF targets encode regulators of the cytoskeleton, transcription, and cell growth, underpinning the role of SRF in cytoskeletal dynamics and mechanosensing. Finally, we show that specific activation of either MRTFs or TCFs can reset the circadian clock.
BACKGROUND AND AIMS: The Canterbury Plains of the South Island, New Zealand are being converted to intensive dairy farming; native vegetation now occupies < 0.5 % of the area. Reintroducing native ...species into nutrient-rich systems could provide economic, environmental and ecological benefits. However, native species are adapted to low nitrogen (N) environments. We aimed to determine the growth and N-uptake response of selected native species to elevated soil N loadings and elucidate the effect of these plants on the N speciation in soil. METHODS: Plant growth, N-uptake, and N speciation in rhizosphere soil of selected native species and Lolium perenne (ryegrass, as reference) were measured in greenhouse and field trials. RESULTS: At restoration sites, several native species had similar foliar N concentrations to ryegrass. Deciduous (and N-fixing) species had highest concentrations. There was significant inter-species variation in soil mineral N concentrations in native plant rhizospheres, differing substantially to the ryegrass root-zone. Pot trials revealed that native species tolerated high N-loadings, although there was a negligible growth response. Among the native plants, monocot species assimilated most N. However, total N assimilation by ryegrass would exceed native species at field productivity rates. CONCLUSIONS: Selected native plant species could contribute to the sustainable management of N in intensive agricultural landscapes.
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BFBNIB, DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NMLJ, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The ERK-regulated ternary complex factors (TCFs) act with the transcription factor serum response factor (SRF) to activate mitogen-induced transcription. However, the extent of their involvement in ...the immediate-early transcriptional response, and their wider functional significance, has remained unclear. We show that, in MEFs, TCF inactivation significantly inhibits over 60% of TPA-inducible gene transcription and impairs cell proliferation. Using integrated SRF ChIP-seq and Hi-C data, we identified over 700 TCF-dependent SRF direct target genes involved in signaling, transcription, and proliferation. These also include a significant number of cytoskeletal gene targets for the Rho-regulated myocardin-related transcription factor (MRTF) SRF cofactor family. The TCFs act as general antagonists of MRTF-dependent SRF target gene expression, competing directly with the MRTFs for access to SRF. As a result, TCF-deficient MEFs exhibit hypercontractile and pro-invasive behavior. Thus, competition between TCFs and MRTFs for SRF determines the balance between antagonistic proliferative and contractile programs of gene expression.
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•Integrated ChIP-seq Hi-C analysis identifies over 700 TCF-dependent SRF target genes•Over 60% of TPA-inducible gene transcription is TCF-dependent•TCF-dependent transcription potentiates cell proliferation•TCF/MRTF competition for SRF determines contractility and pro-invasive behavior
Two cofactor families, the TCFs and the MRTFs, compete for binding to SRF, a key regulator of the mitogen-responsive transcription. TCFs not only control proliferative gene expression programs but also regulate access of the MRTFs to SRF, thereby regulating cell contractility and pro-invasive behavior.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In peripheral tissues circadian gene expression can be driven either by local oscillators or by cyclic systemic cues controlled by the master clock in the brain’s suprachiasmatic nucleus. In the ...latter case, systemic signals can activate immediate early transcription factors (IETFs) and thereby control rhythmic transcription. In order to identify IETFs induced by diurnal blood-borne signals, we developed an unbiased experimental strategy, dubbed Synthetic TAndem Repeat PROMoter (STAR-PROM) screening. This technique relies on the observation that most transcription factor binding sites exist at a relatively high frequency in random DNA sequences. Using STAR-PROM we identified serum response factor (SRF) as an IETF responding to oscillating signaling proteins present in human and rodent sera. Our data suggest that in mouse liver SRF is regulated via dramatic diurnal changes of actin dynamics, leading to the rhythmic translocation of the SRF coactivator Myocardin-related transcription factor-B (MRTF-B) into the nucleus.
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► STAR-PROM screening reveals daily rhythms in SRF activity ► SRF activity is regulated by diurnal blood-borne signals via the actin-MRTF pathway ► In liver, actin dynamics and MRTF shuttling display dramatic diurnal changes ► Per2 is a MRTF-SRF target gene
A method developed in this study, STAR-PROM, identifies the transcription factor SRF as an immediate early responder to systemic circadian cues. These cues modulate SRF activity via changes in actin dynamics, which in turn drive circadian oscillations in the cytoplasmic-nuclear shuttling of the SRF coactivator MRTF.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The carboxy-terminal domain (CTD) of RNA polymerase (Pol) II is composed of a repetition of YSPTSPS heptads and functions as a loading platform for protein complexes that regulate transcription, ...splicing, and maturation of RNAs. Here, we studied mammalian CTD mutants to analyze the function of tyrosine1 residues in the transcription cycle. Mutation of 3/4 of the tyrosine residues (YFFF mutant) resulted in a massive read-through transcription phenotype in the antisense direction of promoters as well as in the 3′ direction several hundred kilobases downstream of genes. The YFFF mutant shows reduced Pol II at promoter-proximal pause sites, a loss of interaction with the Mediator and Integrator complexes, and impaired recruitment of these complexes to chromatin. Consistent with these observations, Pol II loading at enhancers and maturation of snRNAs are altered in the YFFF context genome-wide. We conclude that tyrosine1 residues of the CTD control termination of transcription by Pol II.
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•Mammalian Pol II CTD Tyrosine1 is required for transcription termination control•Tyrosine1 mutations result in massive read-through transcription at gene ends•Tyrosine1 mutations impair Pol II association with Mediator and Integrator•Tyrosine1 are involved in the maturation and/or stability of non-polyadenylated RNAs
Transcription of eukaryotic genes requires an efficient termination to avoid pervasive transcript synthesis. Here, Shah and Maqbool et al. show that tyrosine residues of RNA polymerase II CTD are essential for termination and recruitment of the Mediator and Integrator complexes. A massive read-through phenotype is observed when these residues are mutated.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The exploration of heath data by clustering algorithms allows to better describe the populations of interest by seeking the sub-profiles that compose it. This therefore reinforces medical knowledge, ...whether it is about a disease or a targeted population in real life. Nevertheless, contrary to the so-called conventional biostatistical methods where numerous guidelines exist, the standardization of data science approaches in clinical research remains a little discussed subject. This results in a significant variability in the execution of data science projects, whether in terms of algorithms used, reliability and credibility of the designed approach. Taking the path of parsimonious and judicious choice of both algorithms and implementations at each stage, this article proposes Qluster, a practical workflow for performing clustering tasks. Indeed, this workflow makes a compromise between (1) genericity of applications (e.g. usable on small or big data, on continuous, categorical or mixed variables, on database of high-dimensionality or not), (2) ease of implementation (need for few packages, few algorithms, few parameters, ...), and (3) robustness (e.g. use of proven algorithms and robust packages, evaluation of the stability of clusters, management of noise and multicollinearity). This workflow can be easily automated and/or routinely applied on a wide range of clustering projects. It can be useful both for data scientists with little experience in the field to make data clustering easier and more robust, and for more experienced data scientists who are looking for a straightforward and reliable solution to routinely perform preliminary data mining. A synthesis of the literature on data clustering as well as the scientific rationale supporting the proposed workflow is also provided. Finally, a detailed application of the workflow on a concrete use case is provided, along with a practical discussion for data scientists. An implementation on the Dataiku platform is available upon request to the authors.
The transcription initiation and elongation steps of protein-coding genes usually rely on unrelated protein complexes. However, the TFIIS elongation factor is implicated in both processes. We found ...that, in the absence of the Med31 Mediator subunit, yeast cells required the TFIIS polymerase II (Pol II)-binding domain but not its RNA cleavage stimulatory activity that is associated with its elongation function. We also found that the TFIIS Pol II-interacting domain was needed for the full recruitment of Pol II to several promoters in the absence of Med31. This work demonstrated that, in addition to its thoroughly characterized role in transcription elongation, TFIIS is implicated through its Pol II-binding domain in the formation or stabilization of the transcription initiation complex in vivo.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
Addressing the heterogeneity of both the outcome of a disease and the treatment response to an intervention is a mandatory pathway for regulatory approval of medicines. In randomized clinical trials ...(RCTs), confirmatory subgroup analyses focus on the assessment of drugs in predefined subgroups, while exploratory ones allow a posteriori the identification of subsets of patients who respond differently. Within the latter area, subgroup discovery (SD) data mining approach is widely used-particularly in precision medicine-to evaluate treatment effect across different groups of patients from various data sources (be it from clinical trials or real-world data). However, both the limited consideration by standard SD algorithms of recommended criteria to define credible subgroups and the lack of statistical power of the findings after correcting for multiple testing hinder the generation of hypothesis and their acceptance by healthcare authorities and practitioners. In this paper, we present the Q-Finder algorithm that aims to generate statistically credible subgroups to answer clinical questions, such as finding drivers of natural disease progression or treatment response. It combines an exhaustive search with a cascade of filters based on metrics assessing key credibility criteria, including relative risk reduction assessment, adjustment on confounding factors, individual feature's contribution to the subgroup's effect, interaction tests for assessing between-subgroup treatment effect interactions and tests adjustment (multiple testing). This allows Q-Finder to directly target and assess subgroups on recommended credibility criteria. The top-k credible subgroups are then selected, while accounting for subgroups' diversity and, possibly, clinical relevance. Those subgroups are tested on independent data to assess their consistency across databases, while preserving statistical power by limiting the number of tests. To illustrate this algorithm, we applied it on the database of the International Diabetes Management Practice Study (IDMPS) to better understand the drivers of improved glycemic control and rate of episodes of hypoglycemia in type 2 diabetics patients. We compared Q-Finder with state-of-the-art approaches from both Subgroup Identification and Knowledge Discovery in Databases literature. The results demonstrate its ability to identify and support a short list of highly credible and diverse data-driven subgroups for both prognostic and predictive tasks.
Serum response factor (SRF) is a ubiquitously expressed transcription factor that binds DNA at CArG (CCA/T6GG) domains in association with myocardin-family proteins (eg, myocardin-related ...transcription factor A MRTFA) or the ternary complex factor family of E26 transformation-specific (ETS) proteins. In primary hematopoietic cells, knockout of either SRF or MRTFA decreases megakaryocyte (Mk) maturation causing thrombocytopenia. The human erythroleukemia (HEL) cell line mimics the effects of MRTFA on Mk maturation, and MRTFA overexpression (MRTFAOE) in HEL cells enhances megakaryopoiesis. To identify the mechanisms underlying these effects, we performed integrated analyses of anti-SRF chromatin immunoprecipitation (ChIP) and RNA-sequencing data from noninduced and phorbol ester (12-O-tetradecanoylphorbol-13-acetate TPA)–induced HEL cells, with and without MRTFAOE. We found that 11% of genes were upregulated with TPA induction, which was enhanced by MRTFAOE, resulting in an upregulation of 25% of genes. MRTFAOE increased binding of SRF to genomic sites and enhanced TPA-induced expression of SRF target genes. The TPA-induced genes are predicted to be regulated by SRF and ETS factors, whereas those upregulated by TPA plus MRTFAOE lack ETS binding motifs, and MRTFAOE skews SRF binding to genomic regions with CArG sites in regions relatively lacking in ETS binding motifs. Finally, ChIP–polymerase chain reaction using HEL cells and primary human CD34+ cell–derived subpopulations confirms that both SRF and MRTFA have increased binding during megakaryopoiesis at upregulated target genes (eg, CORO1A). We show for the first time that MRTFA increases both the genomic association and activity of SRF and upregulates genes that enhance primary human megakaryopoiesis.
•MRTFAOE promotes megakaryocytic maturation.•Both SRF/ternary complex factor and SRF/MRTFA regulatory axes play a role in megakaryopoiesis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
We investigated the relationship among ERK signaling, histone modifications, and transcription factor activity, focusing on the ERK-regulated ternary complex factor family of SRF partner proteins. In ...MEFs, activation of ERK by TPA stimulation induced a common pattern of H3K9acS10ph, H4K16ac, H3K27ac, H3K9acK14ac, and H3K4me3 at hundreds of transcription start site (TSS) regions and remote regulatory sites. The magnitude of the increase in histone modification correlated well with changes in transcription. H3K9acS10ph preceded the other modifications. Most induced changes were TCF dependent, but TCF-independent TSSs exhibited the same hierarchy, indicating that it reflects gene activation per se. Studies with TCF Elk-1 mutants showed that TCF-dependent ERK-induced histone modifications required Elk-1 to be phosphorylated and competent to activate transcription. Analysis of direct TCF-SRF target genes and chromatin modifiers confirmed this and showed that H3S10ph required only Elk-1 phosphorylation. Induction of histone modifications following ERK stimulation is thus directed by transcription factor activation and transcription.
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•TPA-driven ERK activation induces a common pattern of histone modifications at TSSs•Most ERK-induced histone modifications are dependent on TCF family of SRF cofactors•At these TSSs, induced histone modifications require ERK-dependent TCF phosphorylation•TCF-dependent histone modifications require TCF to recruit the transcription machinery
In MEFs, TPA-induced ERK activation induces a common pattern of histone modifications at more than 2,000 TSSs. Modifications at most TSSs are dependent on the TCF family of ERK-regulated SRF cofactors. At these TSSs, induced modifications are dependent both on phosphorylation of TCF and on its ability to recruit the transcriptional machinery.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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