Oct4-induced pluripotency in adult neural stem cells.

The four transcription factors Oct4, Sox2, Klf4, and c-Myc can induce pluripotency in mouse and human fibroblasts. We previously described direct reprogramming of adult mouse neural stem cells (NSCs) by Oct4 and either Klf4 or c-Myc. NSCs endogenously express Sox2, c-Myc, and Klf4 as well as several intermediate reprogramming markers. Here we report that exogenous expression of the germline-specific transcription factor Oct4 is sufficient to generate pluripotent stem cells from adult mouse NSCs. These one-factor induced pluripotent stem cells (1F iPS) are similar to embryonic stem cells in vitro and in vivo. Not only can these cells can be efficiently differentiated into NSCs, cardiomyocytes, and germ cells in vitro, but they are also capable of teratoma formation and germline transmission in vivo. Our results demonstrate that Oct4 is required and sufficient to directly reprogram NSCs to pluripotency.

A graphical model approach visualizes regulatory relationships between genome-wide transcription factor binding profiles.

Integrated analysis of multiple genome-wide transcription factor (TF)-binding profiles will be vital to advance our understanding of the global impact of TF binding. However, existing methods for measuring similarity in large numbers of chromatin immunoprecipitation assays with sequencing (ChIP-seq), such as correlation, mutual information or enrichment analysis, are limited in their ability to display functionally relevant TF relationships. In this study, we propose the use of graphical models to determine conditional independence between TFs and showed that network visualization provides a promising alternative to distinguish 'direct' versus 'indirect' TF interactions. We applied four algorithms to measure 'direct' dependence to a compendium of 367 mouse haematopoietic TF ChIP-seq samples and obtained a consensus network known as a 'TF association network' where edges in the network corresponded to likely causal pairwise relationships between TFs. The 'TF association network' illustrates the role of TFs in developmental pathways, is reminiscent of combinatorial TF regulation, corresponds to known protein-protein interactions and indicates substantial TF-binding reorganization in leukemic cell types. With the rapid increase in TF ChIP-Seq data sets, the approach presented here will be a powerful tool to study transcriptional programmes across a wide range of biological systems.

CODEX: a next-generation sequencing experiment database for the haematopoietic and embryonic stem cell communities.

CODEX (http://codex.stemcells.cam.ac.uk/) is a user-friendly database for the direct access and interrogation of publicly available next-generation sequencing (NGS) data, specifically aimed at experimental biologists. In an era of multi-centre genomic dataset generation, CODEX provides a single database where these samples are collected, uniformly processed and vetted. The main drive of CODEX is to provide the wider scientific community with instant access to high-quality NGS data, which, irrespective of the publishing laboratory, is directly comparable. CODEX allows users to immediately visualize or download processed datasets, or compare user-generated data against the database's cumulative knowledge-base. CODEX contains four types of NGS experiments: transcription factor chromatin immunoprecipitation coupled to high-throughput sequencing (ChIP-Seq), histone modification ChIP-Seq, DNase-Seq and RNA-Seq. These are largely encompassed within two specialized repositories, HAEMCODE and ESCODE, which are focused on haematopoiesis and embryonic stem cell samples, respectively. To date, CODEX contains over 1000 samples, including 221 unique TFs and 93 unique cell types. CODEX therefore provides one of the most complete resources of publicly available NGS data for the direct interrogation of transcriptional programmes that regulate cellular identity and fate in the context of mammalian development, homeostasis and disease.

Constrained transcription factor spacing is prevalent and important for transcriptional control of mouse blood cells.

Combinatorial transcription factor (TF) binding is essential for cell-type-specific gene regulation. However, much remains to be learned about the mechanisms of TF interactions, including to what extent constrained spacing and orientation of interacting TFs are critical for regulatory element activity. To examine the relative prevalence of the 'enhanceosome' versus the 'TF collective' model of combinatorial TF binding, a comprehensive analysis of TF binding site sequences in large scale datasets is necessary. We developed a motif-pair discovery pipeline to identify motif co-occurrences with preferential distance(s) between motifs in TF-bound regions. Utilizing a compendium of 289 mouse haematopoietic TF ChIP-seq datasets, we demonstrate that haematopoietic-related motif-pairs commonly occur with highly conserved constrained spacing and orientation between motifs. Furthermore, motif clustering revealed specific associations for both heterotypic and homotypic motif-pairs with particular haematopoietic cell types. We also showed that disrupting the spacing between motif-pairs significantly affects transcriptional activity in a well-known motif-pair-E-box and GATA, and in two previously unknown motif-pairs with constrained spacing-Ets and Homeobox as well as Ets and E-box. In this study, we provide evidence for widespread sequence-specific TF pair interaction with DNA that conforms to the 'enhanceosome' model, and furthermore identify associations between specific haematopoietic cell-types and motif-pairs.

TRES predicts transcription control in embryonic stem cells.

SUMMARY: Unraveling transcriptional circuits controlling embryonic stem cell maintenance and fate has great potential for improving our understanding of normal development as well as disease. To facilitate this, we have developed a novel web tool called 'TRES' that predicts the likely upstream regulators for a given gene list. This is achieved by integrating transcription factor (TF) binding events from 187 ChIP-sequencing and ChIP-on-chip datasets in murine and human embryonic stem (ES) cells with over 1000 mammalian TF sequence motifs. Using 114 TF perturbation gene sets, as well as 115 co-expression clusters in ES cells, we validate the utility of this approach. AVAILABILITY AND IMPLEMENTATION: TRES is freely available at http://www.tres.roslin.ed.ac.uk. CONTACT: Anagha.Joshi@roslin.ed.ac.uk or bg200@cam.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Type 2 diabetes risk alleles demonstrate extreme directional differentiation among human populations, compared to other diseases.

Many disease-susceptible SNPs exhibit significant disparity in ancestral and derived allele frequencies across worldwide populations. While previous studies have examined population differentiation of alleles at specific SNPs, global ethnic patterns of ensembles of disease risk alleles across human diseases are unexamined. To examine these patterns, we manually curated ethnic disease association data from 5,065 papers on human genetic studies representing 1,495 diseases, recording the precise risk alleles and their measured population frequencies and estimated effect sizes. We systematically compared the population frequencies of cross-ethnic risk alleles for each disease across 1,397 individuals from 11 HapMap populations, 1,064 individuals from 53 HGDP populations, and 49 individuals with whole-genome sequences from 10 populations. Type 2 diabetes (T2D) demonstrated extreme directional differentiation of risk allele frequencies across human populations, compared with null distributions of European-frequency matched control genomic alleles and risk alleles for other diseases. Most T2D risk alleles share a consistent pattern of decreasing frequencies along human migration into East Asia. Furthermore, we show that these patterns contribute to disparities in predicted genetic risk across 1,397 HapMap individuals, T2D genetic risk being consistently higher for individuals in the African populations and lower in the Asian populations, irrespective of the ethnicity considered in the initial discovery of risk alleles. We observed a similar pattern in the distribution of T2D Genetic Risk Scores, which are associated with an increased risk of developing diabetes in the Diabetes Prevention Program cohort, for the same individuals. This disparity may be attributable to the promotion of energy storage and usage appropriate to environments and inconsistent energy intake. Our results indicate that the differential frequencies of T2D risk alleles may contribute to the observed disparity in T2D incidence rates across ethnic populations.

Pluripotent stem cells induced from adult neural stem cells by reprogramming with two factors.

Reprogramming of somatic cells is a valuable tool to understand the mechanisms of regaining pluripotency and further opens up the possibility of generating patient-specific pluripotent stem cells. Reprogramming of mouse and human somatic cells into pluripotent stem cells, designated as induced pluripotent stem (iPS) cells, has been possible with the expression of the transcription factor quartet Oct4 (also known as Pou5f1), Sox2, c-Myc and Klf4 (refs 1-11). Considering that ectopic expression of c-Myc causes tumorigenicity in offspring and that retroviruses themselves can cause insertional mutagenesis, the generation of iPS cells with a minimal number of factors may hasten the clinical application of this approach. Here we show that adult mouse neural stem cells express higher endogenous levels of Sox2 and c-Myc than embryonic stem cells, and that exogenous Oct4 together with either Klf4 or c-Myc is sufficient to generate iPS cells from neural stem cells. These two-factor iPS cells are similar to embryonic stem cells at the molecular level, contribute to development of the germ line, and form chimaeras. We propose that, in inducing pluripotency, the number of reprogramming factors can be reduced when using somatic cells that endogenously express appropriate levels of complementing factors.

Folding the human proteome using BioNeMo: A fused dataset of structural models for machine learning purposes.

Human proteins are crucial players in both health and disease. Understanding their molecular landscape is a central topic in biological research. Here, we present an extensive dataset of predicted protein structures for 42,042 distinct human proteins, including splicing variants, derived from the UniProt reference proteome UP000005640. To ensure high quality and comparability, the dataset was generated by combining state-of-the-art modeling-tools AlphaFold 2, OpenFold, and ESMFold, provided within NVIDIA's BioNeMo platform, as well as homology modeling using Innophore's CavitomiX platform. Our dataset is offered in both unedited and edited formats for diverse research requirements. The unedited version contains structures as generated by the different prediction methods, whereas the edited version contains refinements, including a dataset of structures without low prediction-confidence regions and structures in complex with predicted ligands based on homologs in the PDB. We are confident that this dataset represents the most comprehensive collection of human protein structures available today, facilitating diverse applications such as structure-based drug design and the prediction of protein function and interactions.

Systematic identification of DNA variants associated with ultraviolet radiation using a novel Geographic-Wide Association Study (GeoWAS).

BACKGROUND: Long-term environmental variables are widely understood to play important roles in DNA variation. Previously, clinical studies examining the impacts of these variables on the human genome were localized to a single country, and used preselected DNA variants. Furthermore, clinical studies or surveys are either not available or difficult to carry out for developing countries. A systematic approach utilizing bioinformatics to identify associations among environmental variables, genetic variation, and diseases across various geographical locations is needed but has been lacking. METHODS: Using a novel Geographic-Wide Association Study (GeoWAS) methodology, we identified Single Nucleotide Polymorphisms (SNPs) in the Human Genome Diversity Project (HGDP) with population allele frequencies associated geographical ultraviolet radiation exposure, and then assessed the diseases known to be assigned with these SNPs. RESULTS: 2,857 radiation SNPs were identified from over 650,000 SNPs in 52 indigenous populations across the world. Using a quantitative disease-SNP database curated from 5,065 human genetic papers, we identified disease associations with those radiation SNPs. The correlation of the rs16891982 SNP in the SLC45A2 gene with melanoma was used as a case study for analysis of disease risk, and the results were consistent with the incidence and mortality rates of melanoma in published scientific literature. Finally, by analyzing the ontology of genes in which the radiation SNPs were significantly enriched, potential associations between SNPs and neurological disorders such as Alzheimer's disease were hypothesized. CONCLUSION: A systematic approach using GeoWAS has enabled us to identify DNA variation associated with ultraviolet radiation and their connections to diseases such as skin cancers. Our analyses have led to a better understating at the genetic level of why certain diseases are more predominant in specific geographical locations, due to the interactions between environmental variables such as ultraviolet radiation and the population types in those regions. The hypotheses proposed in GeoWAS can lead to future testing and interdisciplinary research.

Integrative approach to pain genetics identifies pain sensitivity loci across diseases.

Identifying human genes relevant for the processing of pain requires difficult-to-conduct and expensive large-scale clinical trials. Here, we examine a novel integrative paradigm for data-driven discovery of pain gene candidates, taking advantage of the vast amount of existing disease-related clinical literature and gene expression microarray data stored in large international repositories. First, thousands of diseases were ranked according to a disease-specific pain index (DSPI), derived from Medical Subject Heading (MESH) annotations in MEDLINE. Second, gene expression profiles of 121 of these human diseases were obtained from public sources. Third, genes with expression variation significantly correlated with DSPI across diseases were selected as candidate pain genes. Finally, selected candidate pain genes were genotyped in an independent human cohort and prospectively evaluated for significant association between variants and measures of pain sensitivity. The strongest signal was with rs4512126 (5q32, ABLIM3, P = 1.3×10⁻¹°) for the sensitivity to cold pressor pain in males, but not in females. Significant associations were also observed with rs12548828, rs7826700 and rs1075791 on 8q22.2 within NCALD (P = 1.7×10⁻4, 1.8×10⁻4, and 2.2×10⁻4 respectively). Our results demonstrate the utility of a novel paradigm that integrates publicly available disease-specific gene expression data with clinical data curated from MEDLINE to facilitate the discovery of pain-relevant genes. This data-derived list of pain gene candidates enables additional focused and efficient biological studies validating additional candidates.

Neural induction intermediates exhibit distinct roles of Fgf signaling.

Formation of the neural plate is an intricate process in early mammalian embryonic development mediated by cells of the inner cell mass and involving a series of steps, including development of the epiblast. Here, we report on the creation of an embryonic stem (ES) cell-based system to isolate and identify neural induction intermediates with characteristics of epiblast cells and neural plate. We demonstrate that neural commitment requires prior differentiation of ES cells into epiblast cells that are indistinguishable from those derived from natural embryos. We also demonstrate that epiblast cells can be isolated and cultured as epiblast stem cell lines. Fgf signaling is shown to be required for the differentiation of ES cells into these epiblast cells. Fgf2, widely used for maintenance of both human ES cells and epiblast stem cells, inhibits formation of early neural cells by epiblast intermediates in a dose-dependent manner and is sufficient to promote transient self-renewal of epiblast stem cells. In contrast, Fgf8, the endogenous embryonic neural inducer, fails to promote epiblast self-renewal, but rather promotes more homogenous neural induction with transient self-renewal of early neural cells. Removal of Fgf signaling entirely from epiblast cells promotes rapid neural induction and subsequent neurogenesis. We conclude that Fgf signaling plays different roles during the differentiation of ES cells, with an initial requirement in epiblast formation and a subsequent role in self-renewal. Fgf2 and Fgf8 thus stimulate self-renewal in different cell types.

Comparison of automated and human assignment of MeSH terms on publicly-available molecular datasets.

Publicly available molecular datasets can be used for independent verification or investigative repurposing, but depends on the presence, consistency and quality of descriptive annotations. Annotation and indexing of molecular datasets using well-defined controlled vocabularies or ontologies enables accurate and systematic data discovery, yet the majority of molecular datasets available through public data repositories lack such annotations. A number of automated annotation methods have been developed; however few systematic evaluations of the quality of annotations supplied by application of these methods have been performed using annotations from standing public data repositories. Here, we compared manually-assigned Medical Subject Heading (MeSH) annotations associated with experiments by data submitters in the PRoteomics IDEntification (PRIDE) proteomics data repository to automated MeSH annotations derived through the National Center for Biomedical Ontology Annotator and National Library of Medicine MetaMap programs. These programs were applied to free-text annotations for experiments in PRIDE. As many submitted datasets were referenced in publications, we used the manually curated MeSH annotations of those linked publications in MEDLINE as "gold standard". Annotator and MetaMap exhibited recall performance 3-fold greater than that of the manual annotations. We connected PRIDE experiments in a network topology according to shared MeSH annotations and found 373 distinct clusters, many of which were found to be biologically coherent by network analysis. The results of this study suggest that both Annotator and MetaMap are capable of annotating public molecular datasets with a quality comparable, and often exceeding, that of the actual data submitters, highlighting a continuous need to improve and apply automated methods to molecular datasets in public data repositories to maximize their value and utility.

Gold nanoparticles of diameter 1.4 nm trigger necrosis by oxidative stress and mitochondrial damage.

Gold nanoparticles (AuNPs) are generally considered nontoxic, similar to bulk gold, which is inert and biocompatible. AuNPs of diameter 1.4 nm capped with triphenylphosphine monosulfonate (TPPMS), Au1.4MS, are much more cytotoxic than 15-nm nanoparticles (Au15MS) of similar chemical composition. Here, major cell-death pathways are studied and it is determined that the cytotoxicity is caused by oxidative stress. Indicators of oxidative stress, reactive oxygen species (ROS), mitochondrial potential and integrity, and mitochondrial substrate reduction are all compromised. Genome-wide expression profiling using DNA gene arrays indicates robust upregulation of stress-related genes after 6 and 12 h of incubation with a 2 x IC50 concentration of Au1.4MS but not with Au15MS nanoparticles. The caspase inhibitor Z-VAD-fmk does not rescue the cells, which suggests that necrosis, not apoptosis, is the predominant pathway at this concentration. Pretreatment of the nanoparticles with reducing agents/antioxidants N-acetylcysteine, glutathione, and TPPMS reduces the toxicity of Au1.4MS. AuNPs of similar size but capped with glutathione (Au1.1GSH) likewise do not induce oxidative stress. Besides the size dependency of AuNP toxicity, ligand chemistry is a critical parameter determining the degree of cytotoxicity. AuNP exposure most likely causes oxidative stress that is amplified by mitochondrial damage. Au1.4MS nanoparticle cytotoxicity is associated with oxidative stress, endogenous ROS production, and depletion of the intracellular antioxidant pool.

Pluripotency associated genes are reactivated by chromatin-modifying agents in neurosphere cells.

Chromatin architecture in stem cells determines the pattern of gene expression and thereby cell identity and fate. The chromatin-modifying agents trichostatin A (TSA) and 5-Aza-2'-deoxycytidine (AzaC) affect histone acetylation and DNA methylation, respectively, and thereby influence chromatin structure and gene expression. In our previous work, we demonstrated that TSA/AzaC treatment of neurosphere cells induces hematopoietic activity in vivo that is long-term, multilineage, and transplantable. Here, we have analyzed the TSA/AzaC-induced changes in gene expression by global gene expression profiling. TSA/AzaC caused both up- and downregulation of genes, without increasing the total number of expressed genes. Chromosome analysis showed no hot spot of TSA/AzaC impact on a particular chromosome or chromosomal region. Hierarchical cluster analysis revealed common gene expression patterns among neurosphere cells treated with TSA/AzaC, embryonic stem (ES) cells, and hematopoietic stem cells. Furthermore, our analysis identified several stem cell genes and pluripotency-associated genes that are induced by TSA/AzaC in neurosphere cells, including Cd34, Cd133, Oct4, Nanog, Klf4, Bex1, and the Dppa family members Dppa2, 3, 4, and 5. Sox2 and c-Myc are constitutively expressed in neurosphere cells. We propose a model in which TSA/AzaC, by removal of epigenetic inhibition, induces the reactivation of several stem cell and pluripotency-associated genes, and their coordinate expression enlarges the differentiation potential of somatic precursor cells.

The transcription factor repertoire of Flt3+ hematopoietic stem cells.

Hematopoietic stem cells maintain the development of all mature blood cells throughout life due to their sustained self-renewal capacity and multilineage differentiation potential. During development into specific cell lineages, the options of stem cells and multipotent progenitor cells become increasingly restricted concomitant with a successive decline in self-renewal potential. Here we describe an Flt3+CD11b+ multipotent progenitor that can be amplified in vitro with a specific combination of cytokines to yield homogeneous populations in high cell numbers. By employing gene expression profiling with DNA microarrays, we studied the transcription factor repertoire of Flt3+CD11b+ progenitors and related it to the transcription factor repertoire of hematopoietic stem cells and embryonic stem cells. We report here on overlapping and nonoverlapping expression patterns of transcription factors in these cells and thus provide novel insights into the dynamic networks of transcriptional regulators in embryonic and adult stem cells. Additionally, the results obtained open the perspective for elucidating lineage and 'stemness' determinants in hematopoiesis.

Update of NUREBASE: nuclear hormone receptor functional genomics.

Nuclear hormone receptors are an abundant class of ligand-activated transcriptional regulators, found in varying numbers in all animals. Based on our experience of managing the official nomenclature of nuclear receptors, we have developed NUREBASE, a database containing protein and DNA sequences, reviewed protein alignments and phylogenies, taxonomy and annotations for all nuclear receptors. New developments in NUREBASE include explicit declaration of alternative transcripts of each gene, and expression data for human and mouse nuclear receptors. The core of NUREBASE is reviewed, and it is completed by NUREBASE_DAILY, automatically updated every 24 h. All information on accessing and installing NUREBASE may be found at http://www. ens-lyon.fr/LBMC/laudet/nurebase/nurebase.html.

DNMT3A Loss Drives Enhancer Hypomethylation in FLT3-ITD-Associated Leukemias.

DNMT3A, the gene encoding the de novo DNA methyltransferase 3A, is among the most frequently mutated genes in hematologic malignancies. However, the mechanisms through which DNMT3A normally suppresses malignancy development are unknown. Here, we show that DNMT3A loss synergizes with the FLT3 internal tandem duplication in a dose-influenced fashion to generate rapid lethal lymphoid or myeloid leukemias similar to their human counterparts. Loss of DNMT3A leads to reduced DNA methylation, predominantly at hematopoietic enhancer regions in both mouse and human samples. Myeloid and lymphoid diseases arise from transformed murine hematopoietic stem cells. Broadly, our findings support a role for DNMT3A as a guardian of the epigenetic state at enhancer regions, critical for inhibition of leukemic transformation.

Data Science Solution to Event Prediction in Outsourced Clinical Trial Models.

Late phase clinical trials are regularly outsourced to a Contract Research Organisation (CRO) while the risk and accountability remain within the sponsor company. Many statistical tasks are delivered by the CRO and later revalidated by the sponsor. Here, we report a technological approach to standardised event prediction. We have built a dynamic web application around an R-package with the aim of delivering reliable event predictions, simplifying communication and increasing trust between the CRO and the in-house statisticians via transparency. Short learning curve, interactivity, reproducibility and data diagnostics are key here. The current implementation is motivated by time-to-event prediction in oncology. We demonstrate a clear benefit of standardisation for both parties. The tool can be used for exploration, communication, sensitivity analysis and generating standard reports. At this point we wish to present this tool and share some of the insights we have gained during the development.

Disease risk factors identified through shared genetic architecture and electronic medical records.

Genome-wide association studies have identified genetic variants for thousands of diseases and traits. We evaluated the relationships between specific risk factors (for example, blood cholesterol level) and diseases on the basis of their shared genetic architecture in a comprehensive human disease-single-nucleotide polymorphism association database (VARIMED), analyzing the findings from 8962 published association studies. Similarity between traits and diseases was statistically evaluated on the basis of their association with shared gene variants. We identified 120 disease-trait pairs that were statistically similar, and of these, we tested and validated five previously unknown disease-trait associations by searching electronic medical records (EMRs) from three independent medical centers for evidence of the trait appearing in patients within 1 year of first diagnosis of the disease. We validated that the mean corpuscular volume is elevated before diagnosis of acute lymphoblastic leukemia; both have associated variants in the gene IKZF1. Platelet count is decreased before diagnosis of alcohol dependence; both are associated with variants in the gene C12orf51. Alkaline phosphatase level is elevated in patients with venous thromboembolism; both share variants in ABO. Similarly, we found that prostate-specific antigen and serum magnesium levels were altered before the diagnosis of lung cancer and gastric cancer, respectively. Disease-trait associations identify traits that could serve as future prognostics, if validated through EMR and subsequent prospective trials.