Systematic identification of barriers to human iPSC generation.

Reprogramming of somatic cells to induced pluripotent stem cells (iPSCs) holds enormous promise for regenerative medicine. To elucidate endogenous barriers limiting this process, we systematically dissected human cellular reprogramming by combining a genome-wide RNAi screen, innovative computational methods, extensive single-hit validation, and mechanistic investigation of relevant pathways and networks. We identify reprogramming barriers, including genes involved in transcription, chromatin regulation, ubiquitination, dephosphorylation, vesicular transport, and cell adhesion. Specific a disintegrin and metalloproteinase (ADAM) proteins inhibit reprogramming, and the disintegrin domain of ADAM29 is necessary and sufficient for this function. Clathrin-mediated endocytosis can be targeted with small molecules and opposes reprogramming by positively regulating TGF-ß signaling. Genetic interaction studies of endocytosis or ubiquitination reveal that barrier pathways can act in linear, parallel, or feedforward loop architectures to antagonize reprogramming. These results provide a global view of barriers to human cellular reprogramming.

Measuring DNA mechanics on the genome scale.

Mechanical deformations of DNA such as bending are ubiquitous and have been implicated in diverse cellular functions1. However, the lack of high-throughput tools to measure the mechanical properties of DNA has limited our understanding of how DNA mechanics influence chromatin transactions across the genome. Here we develop 'loop-seq'-a high-throughput assay to measure the propensity for DNA looping-and determine the intrinsic cyclizabilities of 270,806 50-base-pair DNA fragments that span Saccharomyces cerevisiae chromosome V, other genomic regions, and random sequences. We found sequence-encoded regions of unusually low bendability within nucleosome-depleted regions upstream of transcription start sites (TSSs). Low bendability of linker DNA inhibits nucleosome sliding into the linker by the chromatin remodeller INO80, which explains how INO80 can define nucleosome-depleted regions in the absence of other factors2. Chromosome-wide, nucleosomes were characterized by high DNA bendability near dyads and low bendability near linkers. This contrast increases for deeper gene-body nucleosomes but disappears after random substitution of synonymous codons, which suggests that the evolution of codon choice has been influenced by DNA mechanics around gene-body nucleosomes. Furthermore, we show that local DNA mechanics affect transcription through TSS-proximal nucleosomes. Overall, this genome-scale map of DNA mechanics indicates a 'mechanical code' with broad functional implications.

Lineage-specific transcriptional regulation of DICER by MITF in melanocytes.

DICER is a central regulator of microRNA maturation. However, little is known about mechanisms regulating its expression in development or disease. While profiling miRNA expression in differentiating melanocytes, two populations were observed: some upregulated at the pre-miRNA stage, and others upregulated as mature miRNAs (with stable pre-miRNA levels). Conversion of pre-miRNAs to fully processed miRNAs appeared to be dependent upon stimulation of DICER expression--an event found to occur via direct transcriptional targeting of DICER by the melanocyte master transcriptional regulator MITF. MITF binds and activates a conserved regulatory element upstream of DICER's transcriptional start site upon melanocyte differentiation. Targeted KO of DICER is lethal to melanocytes, at least partly via DICER-dependent processing of the pre-miRNA-17 approximately 92 cluster thus targeting BIM, a known proapoptotic regulator of melanocyte survival. These observations highlight a central mechanism underlying lineage-specific miRNA regulation which could exist for other cell types during development.

Leveraging epigenomes and three-dimensional genome organization for interpreting regulatory variation.

Understanding the impact of regulatory variants on complex phenotypes is a significant challenge because the genes and pathways that are targeted by such variants and the cell type context in which regulatory variants operate are typically unknown. Cell-type-specific long-range regulatory interactions that occur between a distal regulatory sequence and a gene offer a powerful framework for examining the impact of regulatory variants on complex phenotypes. However, high-resolution maps of such long-range interactions are available only for a handful of cell types. Furthermore, identifying specific gene subnetworks or pathways that are targeted by a set of variants is a significant challenge. We have developed L-HiC-Reg, a Random Forests regression method to predict high-resolution contact counts in new cell types, and a network-based framework to identify candidate cell-type-specific gene networks targeted by a set of variants from a genome-wide association study (GWAS). We applied our approach to predict interactions in 55 Roadmap Epigenomics Mapping Consortium cell types, which we used to interpret regulatory single nucleotide polymorphisms (SNPs) in the NHGRI-EBI GWAS catalogue. Using our approach, we performed an in-depth characterization of fifteen different phenotypes including schizophrenia, coronary artery disease (CAD) and Crohn's disease. We found differentially wired subnetworks consisting of known as well as novel gene targets of regulatory SNPs. Taken together, our compendium of interactions and the associated network-based analysis pipeline leverages long-range regulatory interactions to examine the context-specific impact of regulatory variation in complex phenotypes.

Spectral clustering of single-cell multi-omics data on multilayer graphs.

MOTIVATION: Single-cell sequencing technologies that simultaneously generate multimodal cellular profiles present opportunities for improved understanding of cell heterogeneity in tissues. How the multimodal information can be integrated to obtain a common cell type identification, however, poses a computational challenge. Multilayer graphs provide a natural representation of multi-omic single-cell sequencing datasets, and finding cell clusters may be understood as a multilayer graph partition problem. RESULTS: We introduce two spectral algorithms on multilayer graphs, spectral clustering on multilayer graphs and the weighted locally linear (WLL) method, to cluster cells in multi-omic single-cell sequencing datasets. We connect these algorithms through a unifying mathematical framework that represents each layer using a Hamiltonian operator and a mixture of its eigenstates to integrate the multiple graph layers, demonstrating in the process that the WLL method is a rigorous multilayer spectral graph theoretic reformulation of the popular Seurat weighted nearest neighbor (WNN) algorithm. Implementing our algorithms and applying them to a CITE-seq dataset of cord blood mononuclear cells yields results similar to the Seurat WNN analysis. Our work thus extends spectral methods to multimodal single-cell data analysis. AVAILABILITY AND IMPLEMENTATION: The code used in this study can be found at https://github.com/jssong-lab/sc-spectrum. All public data used in the article are accurately cited and described in Materials and Methods and in Supplementary Information. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform.

We present Knowledge Engine for Genomics (KnowEnG), a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis, and expression signature analysis. The system specializes in "knowledge-guided" data mining and machine learning algorithms, in which user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge bases and encoded in a massive "Knowledge Network." KnowEnG adheres to "FAIR" principles (findable, accessible, interoperable, and reuseable): its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution, and are interoperable with other computing platforms. The analysis tools are made available through multiple access modes, including a web portal with specialized visualization modules. We demonstrate the KnowEnG system's potential value in democratization of advanced tools for the modern genomics era through several case studies that use its tools to recreate and expand upon the published analysis of cancer data sets.

Inhibition of mTOR induces a paused pluripotent state.

Cultured pluripotent stem cells are a cornerstone of regenerative medicine owing to their ability to give rise to all cell types of the body. Although pluripotent stem cells can be propagated indefinitely in vitro, pluripotency is paradoxically a transient state in vivo, lasting 2-3 days around the time of blastocyst implantation. The exception to this rule is embryonic diapause, a reversible state of suspended development triggered by unfavourable conditions. Diapause is a physiological reproductive strategy widely employed across the animal kingdom, including in mammals, but its regulation remains poorly understood. Here we report that the partial inhibition of mechanistic target of rapamycin (mTOR), a major nutrient sensor and promoter of growth, induces reversible pausing of mouse blastocyst development and allows their prolonged culture ex vivo. Paused blastocysts remain pluripotent and competent-able to give rise to embryonic stem (ES) cells and live, fertile mice. We show that both naturally diapaused blastocysts in vivo and paused blastocysts ex vivo display pronounced reductions in mTOR activity, translation, histone modifications associated with gene activity and transcription. Pausing can be induced directly in cultured ES cells and sustained for weeks without appreciable cell death or deviations from cell cycle distributions. We show that paused ES cells display a remarkable global suppression of transcription, maintain a gene expression signature of diapaused blastocysts and remain pluripotent. These results uncover a new pluripotent stem cell state corresponding to the epiblast of the diapaused blastocyst and indicate that mTOR regulates developmental timing at the peri-implantation stage. Our findings have implications in the fields of assisted reproduction, regenerative medicine, cancer, metabolic disorders and ageing.

Epigenetic engineering of yeast reveals dynamic molecular adaptation to methylation stress and genetic modulators of specific DNMT3 family members.

Cytosine methylation is a ubiquitous modification in mammalian DNA generated and maintained by several DNA methyltransferases (DNMTs) with partially overlapping functions and genomic targets. To systematically dissect the factors specifying each DNMT's activity, we engineered combinatorial knock-in of human DNMT genes in Komagataella phaffii, a yeast species lacking endogenous DNA methylation. Time-course expression measurements captured dynamic network-level adaptation of cells to DNMT3B1-induced DNA methylation stress and showed that coordinately modulating the availability of S-adenosyl methionine (SAM), the essential metabolite for DNMT-catalyzed methylation, is an evolutionarily conserved epigenetic stress response, also implicated in several human diseases. Convolutional neural networks trained on genome-wide CpG-methylation data learned distinct sequence preferences of DNMT3 family members. A simulated annealing interpretation method resolved these preferences into individual flanking nucleotides and periodic poly(A) tracts that rotationally position highly methylated cytosines relative to phased nucleosomes. Furthermore, the nucleosome repeat length defined the spatial unit of methylation spreading. Gene methylation patterns were similar to those in mammals, and hypo- and hypermethylation were predictive of increased and decreased transcription relative to control, respectively, in the absence of mammalian readers of DNA methylation. Introducing controlled epigenetic perturbations in yeast thus enabled characterization of fundamental genomic features directing specific DNMT3 proteins.

SequencEnG: an interactive knowledge base of sequencing techniques.

SUMMARY: Next-generation sequencing (NGS) techniques are revolutionizing biomedical research by providing powerful methods for generating genomic and epigenomic profiles. The rapid progress is posing an acute challenge to students and researchers to stay acquainted with the numerous available methods. We have developed an interactive online educational resource called Sequencing Techniques Engine for Genomics (SequencEnG) to provide a tree-structured knowledge base of 66 different sequencing techniques and step-by-step NGS data analysis pipelines comparing popular tools. SequencEnG is designed to facilitate barrier-free learning of current NGS techniques and provides a user-friendly interface for searching through experimental and analysis methods. AVAILABILITY AND IMPLEMENTATION: SequencEnG is part of the project Knowledge Engine for Genomics (KnowEnG) and is freely available at http://education.knoweng.org/sequenceng/.

NF45 and NF90/NF110 coordinately regulate ESC pluripotency and differentiation.

While years of investigation have elucidated many aspects of embryonic stem cell (ESC) regulation, the contributions of post-transcriptional and translational mechanisms to the pluripotency network remain largely unexplored. In particular, little is known in ESCs about the function of RNA binding proteins (RBPs), the protein agents of post-transcriptional regulation. We performed an unbiased RNAi screen of RBPs in an ESC differentiation assay and identified two related genes, NF45 (Ilf2) and NF90/NF110 (Ilf3), whose knockdown promoted differentiation to an epiblast-like state. Characterization of NF45 KO, NF90 + NF110 KO, and NF110 KO ESCs showed that loss of NF45 or NF90 + NF110 impaired ESC proliferation and led to dysregulated differentiation down embryonic lineages. Additionally, we found that NF45 and NF90/NF110 physically interact and influence the expression of each other at different levels of regulation. Globally across the transcriptome, NF45 KO ESCs and NF90 + NF110 KO ESCs show similar expression changes. Moreover, NF90 + NF110 RNA immunoprecipitation (RIP)-seq in ESCs suggested that NF90/NF110 directly regulate proliferation, differentiation, and RNA-processing genes. Our data support a model in which NF45, NF90, and NF110 operate in feedback loops that enable them, through both overlapping and independent targets, to help balance the push and pull of pluripotency and differentiation cues.

Chd1 is essential for the high transcriptional output and rapid growth of the mouse epiblast.

The pluripotent mammalian epiblast undergoes unusually fast cell proliferation. This rapid growth is expected to generate a high transcriptional demand, but the underlying mechanisms remain unknown. We show here that the chromatin remodeler Chd1 is required for transcriptional output and development of the mouse epiblast. Chd1(-/-) embryos exhibit proliferation defects and increased apoptosis, are smaller than controls by E5.5 and fail to grow, to become patterned or to gastrulate. Removal of p53 allows progression of Chd1(-/-) mutants only to E7.0-8.0, highlighting the crucial requirement for Chd1 during early post-implantation development. Chd1(-/-) embryonic stem cells (ESCs) have a self-renewal defect and a genome-wide reduction in transcriptional output at both known mRNAs and intergenic transcripts. These transcriptional defects were only uncovered when cell number-normalized approaches were used, and correlate with a lower engagement of RNAP II with transcribed genes in Chd1(-/-) ESCs. We further show that Chd1 directly binds to ribosomal DNA, and that both Chd1(-/-) epiblast cells in vivo and ESCs in vitro express significantly lower levels of ribosomal RNA. In agreement with these findings, mutant cells in vivo and in vitro exhibit smaller and more elongated nucleoli. Thus, the RNA output by both Pol I and II is reduced in Chd1(-/-) cells. Our data indicate that Chd1 promotes a globally elevated transcriptional output required to sustain the distinctly rapid growth of the mouse epiblast.

TeachEnG: a Teaching Engine for Genomics.

MOTIVATION: Bioinformatics is a rapidly growing field that has emerged from the synergy of computer science, statistics and biology. Given the interdisciplinary nature of bioinformatics, many students from diverse fields struggle with grasping bioinformatic concepts only from classroom lectures. Interactive tools for helping students reinforce their learning would be thus desirable. Here, we present an interactive online educational tool called TeachEnG (acronym for Teaching Engine for Genomics) for reinforcing key concepts in sequence alignment and phylogenetic tree reconstruction. Our instructional games allow students to align sequences by hand, fill out the dynamic programming matrix in the Needleman-Wunsch global sequence alignment algorithm, and reconstruct phylogenetic trees via the maximum parsimony, Unweighted Pair Group Method with Arithmetic mean (UPGMA) and Neighbor-Joining algorithms. With an easily accessible interface and instant visual feedback, TeachEnG will help promote active learning in bioinformatics. AVAILABILITY AND IMPLEMENTATION: TeachEnG is freely available at http://teacheng.illinois.edu. The source code is available from https://github.com/KnowEnG/TeachEnG under the Artistic License 2.0. It is written in JavaScript and compatible with Firefox, Safari, Chrome and Microsoft Edge. CONTACT: songj@illinois.edu.

A unified computational framework for modeling genome-wide nucleosome landscape.

Nucleosomes form the fundamental building blocks of eukaryotic chromatin, and previous attempts to understand the principles governing their genome-wide distribution have spurred much interest and debate in biology. In particular, the precise role of DNA sequence in shaping local chromatin structure has been controversial. This paper rigorously quantifies the contribution of hitherto-debated sequence features-including G+C content, 10.5 bp periodicity, and poly(dA:dT) tracts-to three distinct aspects of genome-wide nucleosome landscape: occupancy, translational positioning and rotational positioning. Our computational framework simultaneously learns nucleosome number and nucleosome-positioning energy from genome-wide nucleosome maps. In contrast to other previous studies, our model can predict both in vitro and in vivo nucleosome maps in Saccharomyces cerevisiae. We find that although G+C content is the primary determinant of MNase-derived nucleosome occupancy, MNase digestion biases may substantially influence this GC dependence. By contrast, poly(dA:dT) tracts are seen to deter nucleosome formation, regardless of the experimental method used. We further show that the 10.5 bp nucleotide periodicity facilitates rotational but not translational positioning. Applying our method to in vivo nucleosome maps demonstrates that, for a subset of genes, the regularly-spaced nucleosome arrays observed around transcription start sites can be partially recapitulated by DNA sequence alone. Finally, in vivo nucleosome occupancy derived from MNase-seq experiments around transcription termination sites can be mostly explained by the genomic sequence. Implications of these results and potential extensions of the proposed computational framework are discussed.

Maximum entropy methods for extracting the learned features of deep neural networks.

New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

Negative regulation of tumor suppressor p53 by microRNA miR-504.

Tumor suppressor p53 plays a central role in tumor prevention. p53 protein levels and activity are under a tight and complex regulation in cells to maintain the proper function of p53. MicroRNAs play a key role in the regulation of gene expression. Here we report the regulation of p53 through miR-504. miR-504 acts as a negative regulator of human p53 through its direct binding to two sites in the p53 3' untranslated region. Overexpression of miR-504 decreases p53 protein levels and functions in cells, including p53 transcriptional activity, p53-mediated apoptosis, and cell-cycle arrest in response to stress, and furthermore promotes tumorigenecity of cells in vivo. These results demonstrate the direct negative regulation of p53 by miR-504 as a mechanism for p53 regulation in cells, which highlights the importance of microRNAs in tumorigenesis.

Intronic miR-211 assumes the tumor suppressive function of its host gene in melanoma.

When it escapes early detection, malignant melanoma becomes a highly lethal and treatment-refractory cancer. Melastatin is greatly downregulated in metastatic melanomas and is widely believed to function as a melanoma tumor suppressor. Here we report that tumor suppressive activity is not mediated by melastatin but instead by a microRNA (miR-211) hosted within an intron of melastatin. Increasing expression of miR-211 but not melastatin reduced migration and invasion of malignant and highly invasive human melanomas characterized by low levels of melastatin and miR-211. An unbiased network analysis of melanoma-expressed genes filtered for their roles in metastasis identified three central node genes: IGF2R, TGFBR2, and NFAT5. Expression of these genes was reduced by miR-211, and knockdown of each gene phenocopied the effects of increased miR-211 on melanoma invasiveness. These data implicate miR-211 as a suppressor of melanoma invasion whose expression is silenced or selected against via suppression of the entire melastatin locus during human melanoma progression.

Categorical spectral analysis of periodicity in nucleosomal DNA.

DNA helical twist imposes geometric constraints on the location of histone-DNA interaction sites along nucleosomal DNA. Certain 10.5-bp periodic nucleotides in phase with these geometric constraints have been suggested to facilitate nucleosome positioning. However, the extent of nucleotide periodicity in nucleosomal DNA and its significance in directing nucleosome positioning still remain unclear. We clarify these issues by applying categorical spectral analysis to high-resolution nucleosome maps in two yeast species. We find that only a small fraction of nucleosomal sequences contain significant 10.5-bp periodicity. We further develop a spectral decomposition method to show that the previously observed periodicity in aligned nucleosomal sequences mainly results from proper phasing among nucleosomal sequences, and not from a preponderant occurrence of periodicity within individual sequences. Importantly, we show that this phasing may arise from the histones' proclivity for putting preferred nucleotides at some of the evenly spaced histone-DNA contact points with respect to the dyad axis. We demonstrate that 10.5-bp periodicity, when present, significantly facilitates rotational, but not translational, nucleosome positioning. Finally, although periodicity only moderately affects nucleosome occupancy genome wide, reduced periodicity is an evolutionarily conserved signature of nucleosome-depleted regions around transcription start/termination sites.

Quantitative analysis and prediction of G-quadruplex forming sequences in double-stranded DNA.

G-quadruplex (GQ) is a four-stranded DNA structure that can be formed in guanine-rich sequences. GQ structures have been proposed to regulate diverse biological processes including transcription, replication, translation and telomere maintenance. Recent studies have demonstrated the existence of GQ DNA in live mammalian cells and a significant number of potential GQ forming sequences in the human genome. We present a systematic and quantitative analysis of GQ folding propensity on a large set of 438 GQ forming sequences in double-stranded DNA by integrating fluorescence measurement, single-molecule imaging and computational modeling. We find that short minimum loop length and the thymine base are two main factors that lead to high GQ folding propensity. Linear and Gaussian process regression models further validate that the GQ folding potential can be predicted with high accuracy based on the loop length distribution and the nucleotide content of the loop sequences. Our study provides important new parameters that can inform the evaluation and classification of putative GQ sequences in the human genome.

HiTSelect: a comprehensive tool for high-complexity-pooled screen analysis.

Genetic screens of an unprecedented scale have recently been made possible by the availability of high-complexity libraries of synthetic oligonucleotides designed to mediate either gene knockdown or gene knockout, coupled with next-generation sequencing. However, several sources of random noise and statistical biases complicate the interpretation of the resulting high-throughput data. We developed HiTSelect, a comprehensive analysis pipeline for rigorously selecting screen hits and identifying functionally relevant genes and pathways by addressing off-target effects, controlling for variance in both gene silencing efficiency and sequencing depth of coverage and integrating relevant metadata. We document the superior performance of HiTSelect using data from both genome-wide RNAi and CRISPR/Cas9 screens. HiTSelect is implemented as an open-source package, with a user-friendly interface for data visualization and pathway exploration. Binary executables are available at http://sourceforge.net/projects/hitselect/, and the source code is available at https://github.com/diazlab/HiTSelect.

The Cancer Genome Atlas Analysis Predicts MicroRNA for Targeting Cancer Growth and Vascularization in Glioblastoma.

Using in silico analysis of The Cancer Genome Atlas (TCGA), we identified microRNAs associated with glioblastoma (GBM) survival, and predicted their functions in glioma growth and progression. Inhibition of two "risky" miRNAs, miR-148a and miR-31, in orthotopic xenograft GBM mouse models suppressed tumor growth and thereby prolonged animal survival. Intracranial tumors treated with uncomplexed miR-148a and miR-31 antagomirs exhibited reduced proliferation, stem cell depletion, and normalized tumor vasculature. Growth-promoting functions of these two miRNAs were, in part, mediated by the common target, the factor inhibiting hypoxia-inducible factor 1 (FIH1), and the downstream pathways involving hypoxia-inducible factor HIF1α and Notch signaling. Therefore, miR-31 and miR-148a regulate glioma growth by maintaining tumor stem cells and their niche, and providing the tumor a way to activate angiogenesis even in a normoxic environment. This is the first study that demonstrates intratumoral uptake and growth-inhibiting effects of uncomplexed antagomirs in orthotopic glioma.