The single-cell opioid responses in the context of HIV (SCORCH) consortium.

Substance use disorders (SUD) and drug addiction are major threats to public health, impacting not only the millions of individuals struggling with SUD, but also surrounding families and communities. One of the seminal challenges in treating and studying addiction in human populations is the high prevalence of co-morbid conditions, including an increased risk of contracting a human immunodeficiency virus (HIV) infection. Of the ~15 million people who inject drugs globally, 17% are persons with HIV. Conversely, HIV is a risk factor for SUD because chronic pain syndromes, often encountered in persons with HIV, can lead to an increased use of opioid pain medications that in turn can increase the risk for opioid addiction. We hypothesize that SUD and HIV exert shared effects on brain cell types, including adaptations related to neuroplasticity, neurodegeneration, and neuroinflammation. Basic research is needed to refine our understanding of these affected cell types and adaptations. Studying the effects of SUD in the context of HIV at the single-cell level represents a compelling strategy to understand the reciprocal interactions among both conditions, made feasible by the availability of large, extensively-phenotyped human brain tissue collections that have been amassed by the Neuro-HIV research community. In addition, sophisticated animal models that have been developed for both conditions provide a means to precisely evaluate specific exposures and stages of disease. We propose that single-cell genomics is a uniquely powerful technology to characterize the effects of SUD and HIV in the brain, integrating data from human cohorts and animal models. We have formed the Single-Cell Opioid Responses in the Context of HIV (SCORCH) consortium to carry out this strategy.

The Neuroscience Multi-Omic Archive: a BRAIN Initiative resource for single-cell transcriptomic and epigenomic data from the mammalian brain.

Scalable technologies to sequence the transcriptomes and epigenomes of single cells are transforming our understanding of cell types and cell states. The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative Cell Census Network (BICCN) is applying these technologies at unprecedented scale to map the cell types in the mammalian brain. In an effort to increase data FAIRness (Findable, Accessible, Interoperable, Reusable), the NIH has established repositories to make data generated by the BICCN and related BRAIN Initiative projects accessible to the broader research community. Here, we describe the Neuroscience Multi-Omic Archive (NeMO Archive; nemoarchive.org), which serves as the primary repository for genomics data from the BRAIN Initiative. Working closely with other BRAIN Initiative researchers, we have organized these data into a continually expanding, curated repository, which contains transcriptomic and epigenomic data from over 50 million brain cells, including single-cell genomic data from all of the major regions of the adult and prenatal human and mouse brains, as well as substantial single-cell genomic data from non-human primates. We make available several tools for accessing these data, including a searchable web portal, a cloud-computing interface for large-scale data processing (implemented on Terra, terra.bio), and a visualization and analysis platform, NeMO Analytics (nemoanalytics.org).

Mutagenesis of human genomes by endogenous mobile elements on a population scale.

Several large-scale Illumina whole-genome sequencing (WGS) and whole-exome sequencing (WES) projects have emerged recently that have provided exceptional opportunities to discover mobile element insertions (MEIs) and study the impact of these MEIs on human genomes. However, these projects also have presented major challenges with respect to the scalability and computational costs associated with performing MEI discovery on tens or even hundreds of thousands of samples. To meet these challenges, we have developed a more efficient and scalable version of our mobile element locator tool (MELT) called CloudMELT. We then used MELT and CloudMELT to perform MEI discovery in 57,919 human genomes and exomes, leading to the discovery of 104,350 nonredundant MEIs. We leveraged this collection (1) to examine potentially active L1 source elements that drive the mobilization of new Alu, L1, and SVA MEIs in humans; (2) to examine the population distributions and subfamilies of these MEIs; and (3) to examine the mutagenesis of GENCODE genes, ENCODE-annotated features, and disease genes by these MEIs. Our study provides new insights on the L1 source elements that drive MEI mutagenesis and brings forth a better understanding of how this mutagenesis impacts human genomes.

Reprogramming Chromosome Ends by Functional Histone Acetylation.

Cancers harness embryonic programs to evade aging and promote survival. Normally, sequences at chromosome ends called telomeres shorten with cell division, serving as a countdown clock to limit cell replication. Therefore, a crucial aspect of cancerous transformation is avoiding replicative aging by activation of telomere repair programs. Mouse embryonic stem cells (mESCs) activate a transient expression of the gene Zscan4, which correlates with chromatin de-condensation and telomere extension. Head and neck squamous cell carcinoma (HNSCC) cancers reactivate ZSCAN4, which in turn regulates the phenotype of cancer stem cells (CSCs). Our study reveals a new role for human ZSCAN4 in facilitating functional histone H3 acetylation at telomere chromatin. Next-generation sequencing indicates ZSCAN4 enrichment at telomere chromatin. These changes correlate with ZSCAN4-induced histone H3 acetylation and telomere elongation, while CRISPR/Cas9 knockout of ZSCAN4 leads to reduced H3 acetylation and telomere shortening. Our study elucidates the intricate involvement of ZSCAN4 and its significant contribution to telomere chromatin remodeling. These findings suggest that ZSCAN4 induction serves as a novel link between 'stemness' and telomere maintenance. Targeting ZSCAN4 may offer new therapeutic approaches to effectively limit or enhance the replicative lifespan of stem cells and cancer cells.

CD34 defines melanocyte stem cell subpopulations with distinct regenerative properties.

Melanocyte stem cells (McSCs) are the undifferentiated melanocytic cells of the mammalian hair follicle (HF) responsible for recurrent generation of a large number of differentiated melanocytes during each HF cycle. HF McSCs reside in both the CD34+ bulge/lower permanent portion (LPP) and the CD34- secondary hair germ (SHG) regions of the HF during telogen. Using Dct-H2BGFP mice, we separate bulge/LPP and SHG McSCs using FACS with GFP and anti-CD34 to show that these two subsets of McSCs are functionally distinct. Genome-wide expression profiling results support the distinct nature of these populations, with CD34- McSCs exhibiting higher expression of melanocyte differentiation genes and with CD34+ McSCs demonstrating a profile more consistent with a neural crest stem cell. In culture and in vivo, CD34- McSCs regenerate pigmentation more efficiently whereas CD34+ McSCs selectively exhibit the ability to myelinate neurons. CD34+ McSCs, and their counterparts in human skin, may be useful for myelinating neurons in vivo, leading to new therapeutic opportunities for demyelinating diseases and traumatic nerve injury.

Identification of a gene expression signature associated with the metastasis suppressor function of NME1: prognostic value in human melanoma.

Although NME1 is well known for its ability to suppress metastasis of melanoma, the molecular mechanisms underlying this activity are not completely understood. Herein, we utilized a bioinformatics approach to systematically identify genes whose expression is correlated with the metastasis suppressor function of NME1. This was accomplished through a search for genes that were regulated by NME1, but not by NME1 variants lacking metastasis suppressor activity. This approach identified a number of novel genes, such as ALDOC, CXCL11, LRP1b, and XAGE1 as well as known targets such as NETO2, which were collectively designated as an NME1-Regulated Metastasis Suppressor Signature (MSS). The MSS was associated with prolonged overall survival in a large cohort of melanoma patients in The Cancer Genome Atlas (TCGA). The median overall survival of melanoma patients with elevated expression of the MSS genes was >5.6 years longer compared with that of patients with lower expression of the MSS genes. These data demonstrate that NMEl represents a powerful tool for identifying genes whose expression is associated with metastasis and survival of melanoma patients, suggesting their potential applications as prognostic markers and therapeutic targets in advanced forms of this lethal cancer.

Natural genetic variation caused by small insertions and deletions in the human genome.

Human genetic variation is expected to play a central role in personalized medicine. Yet only a fraction of the natural genetic variation that is harbored by humans has been discovered to date. Here we report almost 2 million small insertions and deletions (INDELs) that range from 1 bp to 10,000 bp in length in the genomes of 79 diverse humans. These variants include 819,363 small INDELs that map to human genes. Small INDELs frequently were found in the coding exons of these genes, and several lines of evidence indicate that such variation is a major determinant of human biological diversity. Microarray-based genotyping experiments revealed several interesting observations regarding the population genetics of small INDEL variation. For example, we found that many of our INDELs had high levels of linkage disequilibrium (LD) with both HapMap SNPs and with high-scoring SNPs from genome-wide association studies. Overall, our study indicates that small INDEL variation is likely to be a key factor underlying inherited traits and diseases in humans.

Making Common Fund data more findable: catalyzing a data ecosystem.

The Common Fund Data Ecosystem (CFDE) has created a flexible system of data federation that enables researchers to discover datasets from across the US National Institutes of Health Common Fund without requiring that data owners move, reformat, or rehost those data. This system is centered on a catalog that integrates detailed descriptions of biomedical datasets from individual Common Fund Programs' Data Coordination Centers (DCCs) into a uniform metadata model that can then be indexed and searched from a centralized portal. This Crosscut Metadata Model (C2M2) supports the wide variety of data types and metadata terms used by individual DCCs and can readily describe nearly all forms of biomedical research data. We detail its use to ingest and index data from 11 DCCs.

RNA-Seq analysis of isolate- and growth phase-specific differences in the global transcriptomes of enteropathogenic Escherichia coli prototype isolates.

Enteropathogenic Escherichia coli (EPEC) are a leading cause of diarrheal illness among infants in developing countries. E. coli isolates classified as typical EPEC are identified by the presence of the locus of enterocyte effacement (LEE) and the bundle-forming pilus (BFP), and absence of the Shiga-toxin genes, while the atypical EPEC also encode LEE but do not encode BFP or Shiga-toxin. Comparative genomic analyses have demonstrated that EPEC isolates belong to diverse evolutionary lineages and possess lineage- and isolate-specific genomic content. To investigate whether this genomic diversity results in significant differences in global gene expression, we used an RNA sequencing (RNA-Seq) approach to characterize the global transcriptomes of the prototype typical EPEC isolates E2348/69, B171, C581-05, and the prototype atypical EPEC isolate E110019. The global transcriptomes were characterized during laboratory growth in two different media and three different growth phases, as well as during adherence of the EPEC isolates to human cells using in vitro tissue culture assays. Comparison of the global transcriptomes during these conditions was used to identify isolate- and growth phase-specific differences in EPEC gene expression. These analyses resulted in the identification of genes that encode proteins involved in survival and metabolism that were coordinately expressed with virulence factors. These findings demonstrate there are isolate- and growth phase-specific differences in the global transcriptomes of EPEC prototype isolates, and highlight the utility of comparative transcriptomics for identifying additional factors that are directly or indirectly involved in EPEC pathogenesis.