Comparative genomic analysis of embryonic, lineage-converted and stem cell-derived motor neurons.

Advances in stem cell science allow the production of different cell types in vitro either through the recapitulation of developmental processes, often termed 'directed differentiation', or the forced expression of lineage-specific transcription factors. Although cells produced by both approaches are increasingly used in translational applications, their quantitative similarity to their primary counterparts remains largely unresolved. To investigate the similarity between in vitro-derived and primary cell types, we harvested and purified mouse spinal motor neurons and compared them with motor neurons produced by transcription factor-mediated lineage conversion of fibroblasts or directed differentiation of pluripotent stem cells. To enable unbiased analysis of these motor neuron types and their cells of origin, we then subjected them to whole transcriptome and DNA methylome analysis by RNA sequencing (RNA-seq) and reduced representation bisulfite sequencing (RRBS). Despite major differences in methodology, lineage conversion and directed differentiation both produce cells that closely approximate the primary motor neuron state. However, we identify differences in Fas signaling, the Hox code and synaptic gene expression between lineage-converted and directed differentiation motor neurons that affect their utility in translational studies.

Acetylation of p53 stimulates miRNA processing and determines cell survival following genotoxic stress.

It is widely accepted that different forms of stress activate a common target, p53, yet different outcomes are triggered in a stress-specific manner. For example, activation of p53 by genotoxic agents, such as camptothecin (CPT), triggers apoptosis, while non-genotoxic activation of p53 by Nutlin-3 (Nut3) leads to cell-cycle arrest without significant apoptosis. Such stimulus-specific responses are attributed to differential transcriptional activation of various promoters by p53. In this study, we demonstrate that CPT, but not Nut3, induces miR-203, which downregulates anti-apoptotic bcl-w and promotes cell death in a p53-dependent manner. We find that acetylation of K120 in the DNA-binding domain of p53 augments its association with the Drosha microprocessor and promotes nuclear primary miRNA processing. Knockdown of human orthologue of Males absent On the First (hMOF), the acetyltransferase that targets K120 in p53, abolishes induction of miR-203 and cell death mediated by CPT. Thus, this study reveals that p53 acetylation at K120 plays a critical role in the regulation of the Drosha microprocessor and that post-transcriptional regulation of gene expression by p53 via miRNAs plays a role in determining stress-specific cellular outcomes.

How to make spinal motor neurons.

All muscle movements, including breathing, walking, and fine motor skills rely on the function of the spinal motor neuron to transmit signals from the brain to individual muscle groups. Loss of spinal motor neuron function underlies several neurological disorders for which treatment has been hampered by the inability to obtain sufficient quantities of primary motor neurons to perform mechanistic studies or drug screens. Progress towards overcoming this challenge has been achieved through the synthesis of developmental biology paradigms and advances in stem cell and reprogramming technology, which allow the production of motor neurons in vitro. In this Primer, we discuss how the logic of spinal motor neuron development has been applied to allow generation of motor neurons either from pluripotent stem cells by directed differentiation and transcriptional programming, or from somatic cells by direct lineage conversion. Finally, we discuss methods to evaluate the molecular and functional properties of motor neurons generated through each of these techniques.

NCI Cancer Research Data Commons: Cloud-Based Analytic Resources.

The NCI's Cloud Resources (CR) are the analytical components of the Cancer Research Data Commons (CRDC) ecosystem. This review describes how the three CRs (Broad Institute FireCloud, Institute for Systems Biology Cancer Gateway in the Cloud, and Seven Bridges Cancer Genomics Cloud) provide access and availability to large, cloud-hosted, multimodal cancer datasets, as well as offer tools and workspaces for performing data analysis where the data resides, without download or storage. In addition, users can upload their own data and tools into their workspaces, allowing researchers to create custom analysis workflows and integrate CRDC-hosted data with their own. See related articles by Brady et al., p. 1384, Wang et al., p. 1388, and Kim et al., p. 1404.

NCI Cancer Research Data Commons: Lessons Learned and Future State.

More than ever, scientific progress in cancer research hinges on our ability to combine datasets and extract meaningful interpretations to better understand diseases and ultimately inform the development of better treatments and diagnostic tools. To enable the successful sharing and use of big data, the NCI developed the Cancer Research Data Commons (CRDC), providing access to a large, comprehensive, and expanding collection of cancer data. The CRDC is a cloud-based data science infrastructure that eliminates the need for researchers to download and store large-scale datasets by allowing them to perform analysis where data reside. Over the past 10 years, the CRDC has made significant progress in providing access to data and tools along with training and outreach to support the cancer research community. In this review, we provide an overview of the history and the impact of the CRDC to date, lessons learned, and future plans to further promote data sharing, accessibility, interoperability, and reuse. See related articles by Brady et al., p. 1384, Wang et al., p. 1388, and Pot et al., p. 1396.

Automated Identification of Germline de novo Mutations in Family Trios: A Consensus-Based Informatic Approach.

Accurate identification of germline de novo variants (DNVs) remains a challenging problem despite rapid advances in sequencing technologies as well as methods for the analysis of the data they generate, with putative solutions often involving ad hoc filters and visual inspection of identified variants. Here, we present a purely informatic method for the identification of DNVs by analyzing short-read genome sequencing data from proband-parent trios. Our method evaluates variant calls generated by three genome sequence analysis pipelines utilizing different algorithms-GATK HaplotypeCaller, DeepTrio and Velsera GRAF-exploring the assumption that a requirement of consensus can serve as an effective filter for high-quality DNVs. We assessed the efficacy of our method by testing DNVs identified using a previously established, highly accurate classification procedure that partially relied on manual inspection and used Sanger sequencing to validate a DNV subset comprising less confident calls. The results show that our method is highly precise and that applying a force-calling procedure to putative variants further removes false-positive calls, increasing precision of the workflow to 99.6%. Our method also identified novel DNVs, 87% of which were validated, indicating it offers a higher recall rate without compromising accuracy. We have implemented this method as an automated bioinformatics workflow suitable for large-scale analyses without need for manual intervention.

A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis.

Patient-derived xenografts (PDX) model human intra- and intertumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histologic imaging via hematoxylin and eosin (H&E) staining is routinely performed on PDX samples, which could be harnessed for computational analysis. Prior studies of large clinical H&E image repositories have shown that deep learning analysis can identify intercellular and morphologic signals correlated with disease phenotype and therapeutic response. In this study, we developed an extensive, pan-cancer repository of >1,000 PDX and paired parental tumor H&E images. These images, curated from the PDX Development and Trial Centers Research Network Consortium, had a range of associated genomic and transcriptomic data, clinical metadata, pathologic assessments of cell composition, and, in several cases, detailed pathologic annotations of neoplastic, stromal, and necrotic regions. The amenability of these images to deep learning was highlighted through three applications: (i) development of a classifier for neoplastic, stromal, and necrotic regions; (ii) development of a predictor of xenograft-transplant lymphoproliferative disorder; and (iii) application of a published predictor of microsatellite instability. Together, this PDX Development and Trial Centers Research Network image repository provides a valuable resource for controlled digital pathology analysis, both for the evaluation of technical issues and for the development of computational image-based methods that make clinical predictions based on PDX treatment studies. Significance: A pan-cancer repository of >1,000 patient-derived xenograft hematoxylin and eosin-stained images will facilitate cancer biology investigations through histopathologic analysis and contributes important model system data that expand existing human histology repositories.

Inhibition of microRNA-302 (miR-302) by bone morphogenetic protein 4 (BMP4) facilitates the BMP signaling pathway.

The signaling pathway mediated by BMPs plays an essential role during development as well as the maintenance of homeostasis in adult. Aberrant activation or inactivation of BMP signaling can lead to developmental defects and various human disorders. To fine-tune its activity, BMP signaling is regulated both positively and negatively by extrinsic and intrinsic regulatory factors that modulate binding of ligand to the receptors, and the activity of receptors and their dedicated signal transducers, the Smad proteins. Upon BMP binding to the receptor complex, Smad proteins translocate to the nucleus and modulate gene expression transcriptionally by directly associating with the promoter region of target genes, or post-transcriptionally through modulation of microRNA (miRNA) synthesis. In this study, we demonstrate that BMP signaling down-regulates transcription of the miRNA-302∼367 gene cluster. We show that the type II BMP receptor (BMPRII) is a novel target of miR-302. Upon overexpression, miR-302 targets a partially complementary sequence localized in the 3'-untranslated region (UTR) of BMPRII transcripts and leads to destabilization of the transcripts and down-regulation of BMP signaling. We propose that the negative regulatory loop of BMP4-miR-302-BMPRII is a potential mechanism for the maintenance and fine-tuning of the BMP signaling pathway in various systems.

Bone morphogenetic protein signaling in vascular disease: anti-inflammatory action through myocardin-related transcription factor A.

Pulmonary artery hypertension (PAH) patients exhibit elevated levels of inflammatory cytokines and infiltration of inflammatory cells in the lung. Concurrently, mutations of bmpr2, the gene encoding the type II receptor of bone morphogenetic proteins (BMP), are found in ∼75% of patients with familial PAH, but a possible nexus between increased inflammation and diminished BMP signaling has hitherto remained elusive. We previously showed that BMP4 triggers nuclear localization of the Myocardin-related transcription factor A (MRTF-A) in human pulmonary artery smooth muscle cells (PASMC), resulting in the induction of contractile proteins. Here we report the BMPR2-dependent repression of a set of inflammatory mediators in response to BMP4 stimulation of PASMC. Forced expression of MRTF-A precisely emulates the anti-inflammatory effect of BMP4, while MRTF-A depletion precludes BMP4-mediated cytokine inhibition. BMP4 and MRTF-A block signaling through NF-κB, the keystone of most pathways leading to inflammatory responses, at the level of chromatin recruitment and promoter activation. Moreover, MRTF-A physically interacts with RelA/p65, the NF-κB subunit endowed with a transcription activation domain. Interestingly, the MRTF-A-NF-κB interaction is mutually antagonistic: stimulation of NF-κB signaling by TNFα, as well as p65 overexpression, hinders MRTF-A activity and the expression of contractile genes. Thus, a molecular inhibitory pathway linking BMP4 signaling, activation of MRTF-A, and inhibition of NF-κB provides insights into the etiology of PAH and a potential focus of therapeutic intervention.

Bone morphogenetic protein 4 promotes vascular smooth muscle contractility by activating microRNA-21 (miR-21), which down-regulates expression of family of dedicator of cytokinesis (DOCK) proteins.

The bone morphogenetic protein 4 (BMP4) signaling pathway plays a critical role in the promotion and maintenance of the contractile phenotype in vascular smooth muscle cell (vSMC). Misexpression or inactivating mutations of the BMP receptor gene can lead to dedifferentiation of vSMC characterized by increased migration and proliferation that is linked to vascular proliferative disorders. Previously we demonstrated that vSMCs increase microRNA-21 (miR-21) biogenesis upon BMP4 treatment, which induces contractile gene expression by targeting programmed cell death 4 (PDCD4). To identify novel targets of miR-21 that are critical for induction of the contractile phenotype by BMP4, biotinylated miR-21 was expressed in vSMCs followed by an affinity purification of mRNAs associated with miR-21. Nearly all members of the dedicator of cytokinesis (DOCK) 180-related protein superfamily were identified as targets of miR-21. Down-regulation of DOCK4, -5, and -7 by miR-21 inhibited cell migration and promoted cytoskeletal organization by modulating an activity of small GTPase. Thus, this study uncovers a regulatory mechanism of the vSMC phenotype by the BMP4-miR-21 axis through DOCK family proteins.

Building a collaborative cloud platform to accelerate heart, lung, blood, and sleep research.

Research increasingly relies on interrogating large-scale data resources. The NIH National Heart, Lung, and Blood Institute developed the NHLBI BioData CatalystⓇ (BDC), a community-driven ecosystem where researchers, including bench and clinical scientists, statisticians, and algorithm developers, find, access, share, store, and compute on large-scale datasets. This ecosystem provides secure, cloud-based workspaces, user authentication and authorization, search, tools and workflows, applications, and new innovative features to address community needs, including exploratory data analysis, genomic and imaging tools, tools for reproducibility, and improved interoperability with other NIH data science platforms. BDC offers straightforward access to large-scale datasets and computational resources that support precision medicine for heart, lung, blood, and sleep conditions, leveraging separately developed and managed platforms to maximize flexibility based on researcher needs, expertise, and backgrounds. Through the NHLBI BioData Catalyst Fellows Program, BDC facilitates scientific discoveries and technological advances. BDC also facilitated accelerated research on the coronavirus disease-2019 (COVID-19) pandemic.

down-regulation of Kruppel-like factor-4 (KLF4) by microRNA-143/145 is critical for modulation of vascular smooth muscle cell phenotype by transforming growth factor-beta and bone morphogenetic protein 4.

In the postnatal vasculature, fully differentiated and quiescent vascular smooth muscle cells (VSMCs) in a "contractile" phenotype are required for the normal regulation of vascular tone. The transforming growth factor-ß (TGF-ß) superfamily of growth factors (TGF-ßs and bone morphogenetic proteins (BMPs)) are potent inducers of contractile phenotype and mediate (i) induction of contractile genes, and (ii) inhibition of VSMC growth and migration. Transcription of contractile genes is positively regulated by a regulatory DNA element called a CArG box. The CArG box is activated by the binding of serum response factor and its coactivators, myocardin (Myocd) or Myocd-related transcription factors (MRTFs). Krüppel-like factor-4 (KLF4) is known to inhibit activation of the CArG box. However, the potential role of KLF4 in the contractile activities of TGF-ß or BMP has not been explored. Here, we demonstrate that TGF-ß and BMP4 rapidly down-regulate KLF4 through induction of microRNA-143 (miR-143) and miR-145, which leads to a reduction of KLF4 transcripts and decreased KLF4 protein expression. Inhibition of miR-145 prevents down-regulation of KLF4 and activation of contractile genes by TGF-ß or BMP4, suggesting that modulation of KLF4 is a prerequisite for induction of contractile genes by TGF-ß and BMP4. Interestingly, both TGF-ß and BMP4 activate transcription of the miR-143/145 gene cluster through the CArG box, however, TGF-ß mediates this effect through induction of Myocd expression, whereas BMP4 utilizes nuclear translocation of MRTF-A. Thus, this study sheds light on both the similarities and the differences of TGF-ß and BMP4 signaling in the regulation of KLF4 and contractile genes.

Micromanaging vascular smooth muscle cell differentiation and phenotypic modulation.

The phenotype of vascular smooth muscle cells (VSMCs) is dynamically regulated in response to various stimuli. In a cellular process known as phenotype switching, VSMCs alternate between a contractile and synthetic phenotype state. Deregulation of phenotype switching is associated with vascular disorders such as atherosclerosis, restenosis after angioplasty, and pulmonary hypertension. An important role for microRNAs (miRNAs) in VSMC development and phenotype switching has recently been uncovered. Individual miRNAs are involved in promoting both contractile and synthetic VSMC phenotype. In this review, we summarize recent advances in the understanding of miRNA function in the regulation of VSMC phenotype regulation.

Pan-African genome demonstrates how population-specific genome graphs improve high-throughput sequencing data analysis.

Graph-based genome reference representations have seen significant development, motivated by the inadequacy of the current human genome reference to represent the diverse genetic information from different human populations and its inability to maintain the same level of accuracy for non-European ancestries. While there have been many efforts to develop computationally efficient graph-based toolkits for NGS read alignment and variant calling, methods to curate genomic variants and subsequently construct genome graphs remain an understudied problem that inevitably determines the effectiveness of the overall bioinformatics pipeline. In this study, we discuss obstacles encountered during graph construction and propose methods for sample selection based on population diversity, graph augmentation with structural variants and resolution of graph reference ambiguity caused by information overload. Moreover, we present the case for iteratively augmenting tailored genome graphs for targeted populations and demonstrate this approach on the whole-genome samples of African ancestry. Our results show that population-specific graphs, as more representative alternatives to linear or generic graph references, can achieve significantly lower read mapping errors and enhanced variant calling sensitivity, in addition to providing the improvements of joint variant calling without the need of computationally intensive post-processing steps.

PDXNet portal: patient-derived Xenograft model, data, workflow and tool discovery.

We created the PDX Network (PDXNet) portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources, to facilitate collaboration among researchers and to make these data easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols and training materials for processing PDX data. Currently, the portal contains PDXNet model information and data resources from 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCI's Patient-Derived Model Repository (PDMR) for public access. These models have 2134 associated sequencing files from 873 samples across 308 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal includes results from freely available, robust, validated and standardized analysis workflows on PDXNet sequencing files and PDMR data (3857 samples from 629 patients across 85 disease types). The PDXNet portal is continuously updated with new data and is of significant utility to the cancer research community as it provides a centralized location for PDXNet resources, which support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials.

Smad-mediated miRNA processing: a critical role for a conserved RNA sequence.

microRNAs (miRNAs) are short, 21-24 nucleotide (nt), non-coding RNAs that post-transcriptionally regulate the expression of messenger RNAs (mRNAs). Through the regulation of their cognate mRNAs, miRNAs control diverse aspects of biology, including development, cellular differentiation, proliferation, metabolism, and death. Thus, miRNAs play a critical role in the determination of normal cellular physiology and misexpression of miRNAs leads to pathological responses. Understanding the mechanisms that control miRNA expression is an important step forward as novel functions of miRNAs continue to be uncovered. In addition to transcriptional regulation, multiple pathways of post-transcriptional modulation of miRNA expression have been uncovered. In this review we discuss the role of the Smads in the regulation of miRNA processing in response to Transforming Growth Factor-ß stimulation.

Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidatesfor targeted treatment.

Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs' recapitulation of human tumors.

Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts.

Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution during PDX engraftment and propagation, affecting the accuracy of PDX modeling of human cancer. Here, we exhaustively analyze copy number alterations (CNAs) in 1,451 PDX and matched patient tumor (PT) samples from 509 PDX models. CNA inferences based on DNA sequencing and microarray data displayed substantially higher resolution and dynamic range than gene expression-based inferences, and they also showed strong CNA conservation from PTs through late-passage PDXs. CNA recurrence analysis of 130 colorectal and breast PT/PDX-early/PDX-late trios confirmed high-resolution CNA retention. We observed no significant enrichment of cancer-related genes in PDX-specific CNAs across models. Moreover, CNA differences between patient and PDX tumors were comparable to variations in multiregion samples within patients. Our study demonstrates the lack of systematic copy number evolution driven by the PDX mouse host.

Building Portable and Reproducible Cancer Informatics Workflows: An RNA Sequencing Case Study.

The Seven Bridges Cancer Genomics Cloud (CGC) is part of the National Cancer Institute Cloud Resource project, which was created to explore the paradigm of co-locating massive datasets with the computational resources to analyze them. The CGC was designed to allow researchers to easily find the data they need and analyze it with robust applications in a scalable and reproducible fashion. To enable this, individual tools are packaged within Docker containers and described by the Common Workflow Language (CWL), an emerging standard for enabling reproducible data analysis. On the CGC, researchers can deploy individual tools and customize massive workflows by chaining together tools. Here, we discuss a case study in which RNA sequencing data is analyzed with different methods and compared on the Seven Bridges CGC. We highlight best practices for designing command line tools, Docker containers, and CWL descriptions to enable massively parallelized and reproducible biomedical computation with cloud resources.