A mechanism to minimize errors during non-homologous end joining, bioRxiv, 2019-03-01

SUMMARYEnzymatic processing of DNA underlies all DNA repair, yet inappropriate DNA processing must be avoided. In vertebrates, double-strand breaks are repaired predominantly by non-homologous end-joining (NHEJ), which directly ligates DNA ends. NHEJ has the potential to be highly mutagenic because it employs DNA polymerases, nucleases, and other enzymes that modify incompatible DNA ends to allow their ligation. Using a biochemical system that recapitulates key features of cellular NHEJ, we show that end-processing requires formation of a “short-range synaptic complex” in which DNA ends are closely aligned in a ligation-competent state. Furthermore, single-molecule imaging directly demonstrates that processing occurs within the short-range complex. This confinement of end processing to a ligation-competent complex ensures that DNA ends undergo ligation as soon as they become compatible, thereby minimizing mutagenesis. Our results illustrate how the coordination of enzymatic catalysis with higher-order structural organization of substrate maximizes the fidelity of DNA repair.

biorxiv molecular-biology 0-100-users 2019

A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome, bioRxiv, 2019-03-01

Background Developing insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. By applying the principles of Mendelian randomization, we have undertaken a systematic analysis to evaluate transcriptome-wide associations between gene expression across 48 different tissue types and 395 complex traits. Results Overall, we identified 100,025 gene-trait associations based on conventional genome-wide corrections (P < 5 x 10-08) that also provided evidence of genetic colocalization. These results indicated that genetic variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. We identified many examples of tissue-specific effects, such as genetically-predicted TPO, NR3C2 and SPATA13 expression only associating with thyroid disease in thyroid tissue. Additionally, FBN2 expression was associated with both cardiovascular and lung function traits, but only when analysed in heart and lung tissue respectively. We also demonstrate that conducting phenome-wide evaluations of our results can help flag adverse on-target side effects for therapeutic intervention, as well as propose drug repositioning opportunities. Moreover, we find that exploring the tissue-dependency of associations identified by genome-wide association studies (GWAS) can help elucidate the causal genes and tissues responsible for effects, as well as uncover putative novel associations. Conclusions The atlas of tissue-dependent associations we have constructed should prove extremely valuable to future studies investigating the genetic determinants of complex disease. The follow-up analyses we have performed in this study are merely a guide for future research. Conducting similar evaluations can be undertaken systematically at httpmrcieu.mrsoftware.orgTissue_MR_atlas.

biorxiv genetics 100-200-users 2019

A unicellular relative of animals generates an epithelium-like cell layer by actomyosin-dependent cellularization, bioRxiv, 2019-03-01

In animals, cellularization of a coenocyte is a specialized form of cytokinesis that results in the formation of a polarized epithelium during early embryonic development. It is characterized by coordinated assembly of an actomyosin network, which drives inward membrane invaginations. However, whether coordinated cellularization driven by membrane invagination exists outside animals is not known. To that end, we investigate cellularization in the ichthyosporean Sphaeroforma arctica, a close unicellular relative of animals. We show that the process of cellularization involves coordinated inward plasma membrane invaginations dependent on an actomyosin network, and reveal the temporal order of its assembly. This leads to the formation of a polarized layer of cells resembling an epithelium. We show that this epithelium-like stage is associated with tightly regulated transcriptional activation of genes involved in cell adhesion. Hereby we demonstrate the presence of a self-organized, clonally-generated, polarized layer of cells in a unicellular relative of animals.

biorxiv evolutionary-biology 200-500-users 2019

CTCF confers local nucleosome resiliency after DNA replication and during mitosis, bioRxiv, 2019-03-01

The access of Transcription Factors (TFs) to their cognate DNA binding motifs requires a precise control over nucleosome positioning. This is especially important following DNA replication and during mitosis, both resulting in profound changes in nucleosome organization over TF binding regions. Using mouse Embryonic Stem (ES) cells, we show that the TF CTCF displaces nucleosomes from its binding site and locally organizes large and phased nucleosomal arrays, not only in interphase steady-state but also immediately after replication and during mitosis. While regions bound by other TFs, such as Oct4 and Sox2, display major rearrangement, the post-replication and mitotic nucleosome organization activity of CTCF is not likely to be unique Esrrb binding regions are also characterized by persistent nucleosome positioning. Therefore, we propose that selected TFs, such as CTCF and Esrrb, govern the inheritance of nucleosome positioning at gene regulatory regions throughout the ES cell-cycle.

biorxiv genomics 0-100-users 2019

Deep learning in bioinformatics introduction, application, and perspective in big data era, bioRxiv, 2019-03-01

Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics. With the advances of the big data era in biology, it is foreseeable that deep learning will become increasingly important in the field and will be incorporated in vast majorities of analysis pipelines. In this review, we provide both the exoteric introduction of deep learning, and concrete examples and implementations of its representative applications in bioinformatics. We start from the recent achievements of deep learning in the bioinformatics field, pointing out the problems which are suitable to use deep learning. After that, we introduce deep learning in an easy-to-understand fashion, from shallow neural networks to legendary convolutional neural networks, legendary recurrent neural networks, graph neural networks, generative adversarial networks, variational autoencoder, and the most recent state-of-the-art architectures. After that, we provide eight examples, covering five bioinformatics research directions and all the four kinds of data type, with the implementation written in Tensorflow and Keras. Finally, we discuss the common issues, such as overfitting and interpretability, that users will encounter when adopting deep learning methods and provide corresponding suggestions. The implementations are freely available at httpsgithub.comlykaust15Deep_learning_examples .

biorxiv bioinformatics 100-200-users 2019

High-density spatial transcriptomics arrays for in situ tissue profiling, bioRxiv, 2019-03-01

AbstractTissue function relies on the precise spatial organization of cells characterized by distinct molecular profiles. Single-cell RNA-Seq captures molecular profiles but not spatial organization. Conversely, spatial profiling assays to date have lacked global transcriptome information, throughput or single-cell resolution. Here, we develop High-Density Spatial Transcriptomics (HDST), a method for RNA-Seq at high spatial resolution. Spatially barcoded reverse transcription oligonucleotides are coupled to beads that are randomly deposited into tightly packed individual microsized wells on a slide. The position of each bead is decoded with sequential hybridization using complementary oligonucleotides providing a unique bead-specific spatial address. We then capture, and spatially in situ barcode, RNA from the histological tissue sections placed on the HDST array. HDST recovers hundreds of thousands of transcript-coupled spatial barcodes per experiment at 2 μm resolution. We demonstrate HDST in the mouse brain, use it to resolve spatial expression patterns and cell types, and show how to combine it with histological stains to relate expression patterns to tissue architecture and anatomy. HDST opens the way to spatial analysis of tissues at high resolution.

biorxiv genomics 0-100-users 2019