TTF1 control of LncRNA synthesis delineates a tumor suppressor pathway directly regulating the ribosomal RNA genes.

The tumor suppressor p14/19ARF regulates ribosomal RNA (rRNA) synthesis by controlling the nucleolar localization of Transcription Termination Factor 1 (TTF1). However, the role played by TTF1 in regulating the rRNA genes and in potentially controlling growth has remained unclear. We now show that TTF1 expression regulates cell growth by determining the cellular complement of ribosomes. Unexpectedly, it achieves this by acting as a "roadblock" to synthesis of the noncoding LncRNA and pRNA that we show are generated from the "Spacer Promoter" duplications present upstream of the 47S pre-rRNA promoter on the mouse and human ribosomal RNA genes. Unexpectedly, the endogenous generation of these noncoding RNAs does not induce CpG methylation or gene silencing. Rather, it acts in cis to suppress 47S preinitiation complex formation and hence de novo pre-rRNA synthesis by a mechanism reminiscent of promoter interference or occlusion. Taken together, our data delineate a pathway from p19ARF to cell growth suppression via the regulation of ribosome biogenesis by noncoding RNAs and validate a key cellular growth law in mammalian cells.

Glucocorticoid stimulation induces regionalized gene responses within topologically associating domains.

Transcription-factor binding to cis-regulatory regions regulates the gene expression program of a cell, but occupancy is often a poor predictor of the gene response. Here, we show that glucocorticoid stimulation led to the reorganization of transcriptional coregulators MED1 and BRD4 within topologically associating domains (TADs), resulting in active or repressive gene environments. Indeed, we observed a bias toward the activation or repression of a TAD when their activities were defined by the number of regions gaining and losing MED1 and BRD4 following dexamethasone (Dex) stimulation. Variations in Dex-responsive genes at the RNA levels were consistent with the redistribution of MED1 and BRD4 at the associated cis-regulatory regions. Interestingly, Dex-responsive genes without the differential recruitment of MED1 and BRD4 or binding by the glucocorticoid receptor were found within TADs, which gained or lost MED1 and BRD4, suggesting a role of the surrounding environment in gene regulation. However, the amplitude of the response of Dex-regulated genes was higher when the differential recruitment of the glucocorticoid receptor and transcriptional coregulators was observed, reaffirming the role of transcription factor-driven gene regulation and attributing a lesser role to the TAD environment. These results support a model where a signal-induced transcription factor induces a regionalized effect throughout the TAD, redefining the notion of direct and indirect effects of transcription factors on target genes.

IRSOM2: a web server for predicting bifunctional RNAs.

Recent advances have shown that some biologically active non-coding RNAs (ncRNAs) are actually translated into polypeptides that have a physiological function as well. This paradigm shift requires adapted computational methods to predict this new class of 'bifunctional RNAs'. Previously, we developed IRSOM, an open-source algorithm to classify non-coding and coding RNAs. Here, we use the binary statistical model of IRSOM as a ternary classifier, called IRSOM2, to identify bifunctional RNAs as a rejection of the two other classes. We present its easy-to-use web interface, which allows users to perform predictions on large datasets of RNA sequences in a short time, to re-train the model with their own data, and to visualize and analyze the classification results thanks to the implementation of self-organizing maps (SOM). We also propose a new benchmark of experimentally validated RNAs that play both protein-coding and non-coding roles, in different organisms. Thus, IRSOM2 showed promising performance in detecting these bifunctional transcripts among ncRNAs of different types, such as circRNAs and lncRNAs (in particular those of shorter lengths). The web server is freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr.

Subversion of infiltrating prostate macrophages to a mixed immunosuppressive tumor-associated macrophage phenotype.

Tumor-associated macrophages (TAMs) support tumor progression within the tumor microenvironment (TME). Many questions remain as to the origin, development, and function of TAMs within the prostate TME. Evaluation of TAMs in prostate cancer (PCa) patients identified the immunosuppressive TAM marker CD163 in adjacent normal epithelium as an independent predictor of metastases or PCa death. Flow cytometry analyses identified prostate TAMs as frequently expressing both proinflammatory M1 (CCR7+) and immunosuppressive M2 (CD163+) markers. In vitro, we demonstrate PCa cells similarly subvert human M1 macrophages toward a mixed M1/M2 macrophage phenotype favoring tumor growth. Further the cytokine milieu-induced transition between immunosuppressive M2 to proinflammatory M1 (M2→M1) macrophages is abrogated by the presence of PCa cells. RNA sequencing suggests alterations in chemokine expression in prostate TAMs due to the presence of PCa cells. Together, our results suggest that prostate TAMs originate from inflammatory infiltrating macrophages, which are then reprogrammed mainly by PCa cells, but also the cytokine milieu. A better understanding of this subversion of macrophages within the prostate may lead to novel treatment strategies.

Yeasts isolated from a lotic continental environment in Brazil show potential to produce amylase, cellulase and protease.

Yeasts have wide applicability in the industrial field, as in the production of enzymes used in biocatalysts. Biocatalysts are more efficient when compared to chemical catalysts, with emphasis on hydrolytic enzymes, such as amylase, cellulase and protease. Here we focused on prospecting yeasts, with a high capacity to synthesize hydrolytic enzymes, from a continental lotic ecosystem environment in Brazil. 75 yeasts were grown in Yeast Extract-Peptone-Dextrose (YPD) medium supplemented with antibacterial and their capacity for enzymatic production was tested in specific media. Accordingly, 64 yeasts showed enzyme production capacity. From those, six showed good enzyme indexes, 3 for amylase, 2 for cellulase and 1 for protease. All showed at least one hydrolytic enzyme activity for the tested enzymes (amylase, cellulase and protease), which suggested that the yeasts are metabolically active. By sequencing the 26S gene, we identified Naganishia diffluens and Apiotrichum mycotoxinivorans as the species with highest enzyme production activities. Those species showed potential for application as biological catalysts in the biotechnological scope, collaborating in a sustainable way for the development of industrial products.

Modulating HSF1 levels impacts expression of the estrogen receptor α and antiestrogen response.

Master transcription factors control the transcriptional program and are essential to maintain cellular functions. Among them, steroid nuclear receptors, such as the estrogen receptor α (ERα), are central to the etiology of hormone-dependent cancers which are accordingly treated with corresponding endocrine therapies. However, resistance invariably arises. Here, we show that high levels of the stress response master regulator, the heat shock factor 1 (HSF1), are associated with antiestrogen resistance in breast cancer cells. Indeed, overexpression of HSF1 leads to ERα degradation, decreased expression of ERα-activated genes, and antiestrogen resistance. Furthermore, we demonstrate that reducing HSF1 levels reinstates expression of the ERα and restores response to antiestrogens. Last, our results establish a proof of concept that inhibition of HSF1, in combination with antiestrogens, is a valid strategy to tackle resistant breast cancers. Taken together, we are proposing a mechanism where high HSF1 levels interfere with the ERα-dependent transcriptional program leading to endocrine resistance in breast cancer.

Proximity-dependent Mapping of the Androgen Receptor Identifies Kruppel-like Factor 4 as a Functional Partner.

Prostate cancer (PCa) is the most frequently diagnosed cancer in men and the third cause of cancer mortality. PCa initiation and growth are driven by the androgen receptor (AR). The AR is activated by androgens such as testosterone and controls prostatic cell proliferation and survival. Here, we report an AR signaling network generated using BioID proximity labeling proteomics in androgen-dependent LAPC4 cells. We identified 31 AR-associated proteins in nonstimulated cells. Strikingly, the AR signaling network increased to 182 and 200 proteins, upon 24 h or 72 h of androgenic stimulation, respectively, for a total of 267 nonredundant AR-associated candidates. Among the latter group, we identified 213 proteins that were not previously reported in databases. Many of these new AR-associated proteins are involved in DNA metabolism, RNA processing, and RNA polymerase II transcription. Moreover, we identified 44 transcription factors, including the Kru¨ppel-like factor 4 (KLF4), which were found interacting in androgen-stimulated cells. Interestingly, KLF4 repressed the well-characterized AR-dependent transcription of the KLK3 (PSA) gene; AR and KLF4 also colocalized genome-wide. Taken together, our data report an expanded high-confidence proximity network for AR, which will be instrumental to further dissect the molecular mechanisms underlying androgen signaling in PCa cells.

miRNAFold: a web server for fast miRNA precursor prediction in genomes.

Computational methods are required for prediction of non-coding RNAs (ncRNAs), which are involved in many biological processes, especially at post-transcriptional level. Among these ncRNAs, miRNAs have been largely studied and biologists need efficient and fast tools for their identification. In particular, ab initio methods are usually required when predicting novel miRNAs. Here we present a web server dedicated for miRNA precursors identification at a large scale in genomes. It is based on an algorithm called miRNAFold that allows predicting miRNA hairpin structures quickly with high sensitivity. miRNAFold is implemented as a web server with an intuitive and user-friendly interface, as well as a standalone version. The web server is freely available at: http://EvryRNA.ibisc.univ-evry.fr/miRNAFold.