ReadOut: structure-based calculation of direct and indirect readout energies and specificities for protein–DNA recognition

Protein–DNA interactions play a central role in regulatory processes at the genetic level. DNA-binding proteins recognize their targets by direct base–amino acid interactions and indirect conformational energy contribution from DNA deformations and elasticity. Knowledge-based approach based on the statistical analysis of protein–DNA complex structures has been successfully used to calculate interaction energies and specificities of direct and indirect readouts in protein–DNA recognition. Here, we have implemented the method as a webserver, which calculates direct and indirect readout energies and Z-scores, as a measure of specificity, using atomic coordinates of protein–DNA complexes. This server is freely available at . The only input to this webserver is the Protein Data Bank (PDB) style coordinate data of atoms or the PDB code itself. The server returns total energy Z-scores, which estimate the degree of sequence specificity of the protein–DNA complex. This webserver is expected to be useful for estimating interaction energy and DNA conformation energy, and relative contributions to the specificity from direct and indirect readout. It may also be useful for checking the quality of protein–DNA complex structures, and for engineering proteins and target DNAs

Modular architecture of protein structures and allosteric communications: potential implications for signaling proteins and regulatory linkages

A new method for studying signal transmission between functional sites by decomposing protein structures into modules demonstrates that protein domains consist of modules interconnected by residues that mediate signaling through the shortest pathways

A Global Transcriptome Analysis Reveals Molecular Hallmarks of Neural Stem Cell Death, Survival, and Differentiation in Response to Partial FGF-2 and EGF Deprivation

Neurosphere cell culture is a commonly used model to study the properties and potential applications of neural stem cells (NSCs). However, standard protocols to culture NSCs have yet to be established, and the mechanisms underlying NSC survival and maintenance of their undifferentiated state, in response to the growth factors FGF-2 and EGF are not fully understood. Using cultures of embryonic and adult olfactory bulb stem cells (eOBSCs and aOBSCs), we analyzed the consequences of FGF-2 and EGF addition at different intervals on proliferation, cell cycle progression, cell death and differentiation, as well as on global gene expression. As opposed to cultures supplemented daily, addition of FGF-2 and EGF every 4 days significantly reduced the neurosphere volume and the total number of cells in the spheres, mainly due to increased cell death. Moreover, partial FGF-2 and EGF deprivation produced an increase in OBSC differentiation during the proliferative phase. These changes were more evident in aOBSC than eOBSC cultures. Remarkably, these effects were accompanied by a significant upregulation in the expression of endogenous Fgf-2 and genes involved in cell death and survival (Cryab), lipid catabolic processes (Pla2g7), cell adhesion (Dscaml1), cell differentiation (Dscaml1, Gpr17, S100b, Ndrg2) and signal transduction (Gpr17, Ndrg2). These findings support that a daily supply of FGF-2 and EGF is critical to maintain the viability and the undifferentiated state of NSCs in culture, and they reveal novel molecular hallmarks of NSC death, survival and the initiation of differentiation. © 2013 Nieto-Estévez et al.Peer Reviewe

Triku: a feature selection method based on nearest neighbors for single-cell data

Abstract BACKGROUND: Feature selection is a relevant step in the analysis of single-cell RNA sequencing datasets. Most of the current feature selection methods are based on general univariate descriptors of the data such as the dispersion or the percentage of zeros. Despite the use of correction methods, the generality of these feature selection methods biases the genes selected towards highly expressed genes, instead of the genes defining the cell populations of the dataset. RESULTS: Triku is a feature selection method that favors genes defining the main cell populations. It does so by selecting genes expressed by groups of cells that are close in the k-nearest neighbor graph. The expression of these genes is higher than the expected expression if the k-cells were chosen at random. Triku efficiently recovers cell populations present in artificial and biological benchmarking datasets, based on adjusted Rand index, normalized mutual information, supervised classification, and silhouette coefficient measurements. Additionally, gene sets selected by triku are more likely to be related to relevant Gene Ontology terms and contain fewer ribosomal and mitochondrial genes. CONCLUSION: Triku is developed in Python 3 and is available at https://github.com/alexmascension/triku.This work was supported by grants from Instituto de Salud Carlos III (AC17/00012 and PI19/01621), cofunded by the Euro- pean Union (European Regional Development Fund/European Sci- ence Foundation, Investing in your future) and the 4D-HEALING project (ERA-Net program EracoSysMed, JTC-2 2017); Diputación Foral de Gipuzkoa, and the Department of Economic Devel- opment and Infrastructures of the Basque Government (KK- 2019/00006, KK-2019/00093); European Union FET project Cir- cular Vision (H2020-FETOPEN, Project 899417), Ministry of Sci- ence and Innovation of Spain; and PID2020-119715GB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe. A.M.A. was supported by a Basque Govern- ment Postgraduate Diploma fellowship (PRE_2020_2_0081), and O.I.S. was supported by a Postgraduate Diploma fellowship from la Caixa Foundation (identification document 100010434; code LCF/BQ/IN18/11660065)

Differential DNA Methylation of THOR and hTAPAS in the Regulation of hTERT and the Diagnosis of Cancer

BACKGROUND Although DNA methylation in the gene promoters usually represses gene expression, the TERT hypermethylated oncological region (THOR) located 5' of the hTERT gene is hypermethylated when hTERT is expressed in diverse cancer types, including urothelial cancer (UC). METHODS Comprehensive MeDIP and DNA methylation array analyses complemented by the technically independent method of bisulfite genomic sequencing were applied on pathologically reviewed and classified urothelial carcinoma specimens and healthy urothelial tissue samples to reveal the methylation status of THOR in detail. RESULTS The detailed DNA methylation profiles reveal the exact positions of differentially methylated CpG dinucleotides within THOR in urothelial cancer and provide evidence ofa diverging role of methylation of these CpGs in the regulation of hTERT. In particular, our data suggest a regulating mechanism in which THOR methylation acts on hTERT expression through epigenetic silencing of the lncRNA hTERT antisense promoter-associated (hTAPAS), which represses hTERT. CONCLUSIONS These findings precisely define the most differentially methylated CpGs of THOR in early urothelial cancer, enabling optimal design of Methylation-Specific PCR (MSPCR) primers to reliably probe these methylation differences for diagnostic and prognostic purposes. In addition, this strategy presents a prime example that is also applicable to many other malignancies. Finally, the first evidence for the underlying epigenetic mechanism regulating hTERT expression through the methylation status of THOR is provided

GeromiRs Are Downregulated in the Tumor Microenvironment during Colon Cancer Colonization of the Liver in a Murine Metastasis Model

Cancer is a phenomenon broadly related to ageing in various ways such as cell cycle deregulation, metabolic defects or telomerases dysfunction as principal processes. Although the tumor cell is the main actor in cancer progression, it is not the only element of the disease. Cells and the matrix surrounding the tumor, called the tumor microenvironment (TME), play key roles in cancer progression. Phenotypic changes of the TME are indispensable for disease progression and a few of these transformations are produced by epigenetic changes including miRNA dysregulation. In this study, we found that a specific group of miRNAs in the liver TME produced by colon cancer called geromiRs, which are miRNAs related to the ageing process, are significantly downregulated. The three principal cell types involved in the liver TME, namely, liver sinusoidal endothelial cells, hepatic stellate (Ito) cells and Kupffer cells, were isolated from a murine hepatic metastasis model, and the miRNA and gene expression profiles were studied. From the 115 geromiRs and their associated hallmarks of aging, which we compiled from the literature, 75 were represented in the used microarrays, 26 out of them were downregulated in the TME cells during colon cancer colonization of the liver, and none of them were upregulated. The histone modification hallmark of the downregulated geromiRs is significantly enriched with the geromiRs miR-15a, miR-16, miR-26a, miR-29a, miR-29b and miR-29c. We built a network of all of the geromiRs downregulated in the TME cells and their gene targets from the MirTarBase database, and we analyzed the expression of these geromiR gene targets in the TME. We found that Cercam and Spsb4, identified as prognostic markers in a few cancer types, are associated with downregulated geromiRs and are upregulated in the TME cells.This work was supported by grants from Instituto de Salud Carlos III (AC17/00012), cofounded by the European Union projects (European Regional Development Fund/European Science Foundation, Investing in your future), (ERA-Net program EracoSysMed, JTC-2 2017) and (H2020-FETOPEN, Circular Vision, Project 899417); Diputación Foral de Gipuzkoa and the Department of Economic Development and Infrastructures of the Basque Government (DFG109/20) and the Department of Economic Development and Infrastructures of the Basque Government (DFG109/Grants Health Department of the Basque Government (Spain), RIS3 call, Exp. No. 2020333039 and 2020333001. 20)

Epiblast Stem Cell Subpopulations Represent Mouse Embryos of Distinct Pregastrulation Stages

SummaryEmbryonic stem cells (ESCs) comprise at least two populations of cells with divergent states of pluripotency. Here, we show that epiblast stem cells (EpiSCs) also comprise two distinct cell populations that can be distinguished by the expression of a specific Oct4-GFP marker. These two subpopulations, Oct4-GFP positive and negative EpiSCs, are capable of converting into each other in vitro. Oct4-GFP positive and negative EpiSCs are distinct from ESCs with respect to global gene expression pattern, epigenetic profile, and Oct4 enhancer utilization. Oct4-GFP negative cells share features with cells of the late mouse epiblast and cannot form chimeras. However, Oct4-GFP positive EpiSCs, which only represent a minor EpiSC fraction, resemble cells of the early epiblast and can readily contribute to chimeras. Our findings suggest that the rare ability of EpiSCs to contribute to chimeras is due to the presence of the minor EpiSC fraction representing the early epiblast