Peripheral blood transcriptomic profiling of molecular mechanisms commonly regulated by binge drinking and placebo effects.

Molecular responses to alcohol consumption are dynamic, context-dependent, and arise from a complex interplay of biological and external factors. While many have studied genetic risk associated with drinking patterns, comprehensive studies identifying dynamic responses to pharmacologic and psychological/placebo effects underlying binge drinking are lacking. We investigated transcriptome-wide response to binge, medium, and placebo alcohol consumption by 17 healthy heavy social drinkers enrolled in a controlled, in-house, longitudinal study of up to 12 days. Using RNA-seq, we identified 251 and 13 differentially expressed genes (DEGs) in response to binge drinking and placebo, respectively. Eleven protein-coding DEGs had very large effect sizes in response to binge drinking (Cohen's d > 1). Furthermore, binge dose significantly impacted the Cytokine-cytokine receptor interaction pathway (KEGG: hsa04060) across all experimental sequences. Placebo also impacted hsa04060, but only when administered following regular alcohol drinking sessions. Similarly, medium-dose and placebo commonly impacted KEGG pathways of Systemic lupus erythematosus, Neutrophil extracellular trap formation, and Alcoholism based on the sequence of drinking sessions. These findings together indicate the "dose-extending effects" of placebo at a molecular level. Furthermore, besides supporting alcohol dose-specific molecular changes, results suggest that the placebo effects may induce molecular responses within the same pathways regulated by alcohol.

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.

in silico transcriptome dissection of neocortical excitatory neurogenesis via joint matrix decomposition and transfer learning.

The rising quality and amount of multi-omic data across biomedical science demands that we build innovative solutions to harness their collective discovery potential. From publicly available repositories, we have assembled and curated a compendium of gene-level transcriptomic data focused on mammalian excitatory neurogenesis in the neocortex. This collection is open for exploration by both computational and cell biologists at nemoanalytics.org, and this report forms a demonstration of its utility. Applying our novel structured joint decomposition approach to mouse, macaque and human data from the collection, we define transcriptome dynamics that are conserved across mammalian excitatory neurogenesis and which map onto the genetics of human brain structure and disease. Leveraging additional data within NeMO Analytics via projection methods, we chart the dynamics of these fundamental molecular elements of neurogenesis across developmental time and space and into postnatal life. Reversing the direction of our investigation, we use transcriptomic data from laminar-specific dissection of adult human neocortex to define molecular signatures specific to excitatory neuronal cell types resident in individual layers of the mature neocortex, and trace their emergence across development. We show that while many lineage defining transcription factors are most highly expressed at early fetal ages, the laminar neuronal identities which they drive take years to decades to reach full maturity. Finally, we interrogated data from stem-cell derived cerebral organoid systems demonstrating that many fundamental elements of in vivo development are recapitulated with high-fidelity in vitro, while specific transcriptomic programs in neuronal maturation are absent. We propose these analyses as specific applications of the general approach of combining joint decomposition with large curated collections of analysis-ready multi-omics data matrices focused on particular cell and disease contexts. Importantly, these open environments are accessible to, and must be fueled with emerging data by, cell biologists with and without coding expertise.

Peripheral blood transcriptomic profiling indicates molecular mechanisms commonly regulated by binge-drinking and placebo-effects.

Molecular changes associated with alcohol consumption arise from complex interactions between pharmacological effects of alcohol, psychological/placebo context surrounding drinking, and other environmental and biological factors. The goal of this study was to tease apart molecular mechanisms regulated by pharmacological effects of alcohol - particularly at binge-drinking, from underlying placebo effects. Transcriptome-wide RNA-seq analyses were performed on peripheral blood samples collected from healthy heavy social drinkers (N=16) enrolled in a 12-day randomized, double-blind, cross-over human laboratory trial testing three alcohol doses: Placebo, moderate (0.05g/kg (men), 0.04g/kg (women)), and binge (1g/kg (men), 0.9g/kg (women)), administered in three 4-day experiments, separated by minimum of 7-day washout periods. Effects of beverage doses on the normalized gene expression counts were analyzed within each experiment compared to its own baseline using paired-t-tests. Differential expression of genes (DEGs) across experimental sequences in which each beverage dose was administered, as well as responsiveness to regular alcohol compared to placebo (i.e., pharmacological effects), were analyzed using generalized linear mixed-effects models. The 10% False discovery rate-adjusted DEGs varied across experimental sequences in response to all three beverage doses. We identified and validated 22 protein coding DEGs potentially responsive to pharmacological effects of binge and medium doses, of which 11 were selectively responsive to binge dose. Binge-dose significantly impacted the Cytokine-cytokine receptor interaction pathway (KEGG: hsa04060) across all experimental-sequences that it was administered in, and during dose-extending placebo. Medium dose and placebo impacted pathways hsa05322, hsa04613, and hsa05034, in the first two and last experimental sequences, respectively. In summary, our findings add novel, and confirm previously reported data supporting dose-dependent effects of alcohol on molecular mechanisms and suggest that the placebo effects may induce molecular responses within the same pathways regulated by alcohol. Innovative study designs are required to validate molecular correlates of placebo effects underlying drinking.

Matrix and analysis metadata standards (MAMS) to facilitate harmonization and reproducibility of single-cell data.

A large number of genomic and imaging datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While much effort has been devoted to capturing information related to biospecimen information and experimental procedures, the metadata standards that describe data matrices and the analysis workflows that produced them are relatively lacking. Detailed metadata schema related to data analysis are needed to facilitate sharing and interoperability across groups and to promote data provenance for reproducibility. To address this need, we developed the Matrix and Analysis Metadata Standards (MAMS) to serve as a resource for data coordinating centers and tool developers. We first curated several simple and complex "use cases" to characterize the types of feature-observation matrices (FOMs), annotations, and analysis metadata produced in different workflows. Based on these use cases, metadata fields were defined to describe the data contained within each matrix including those related to processing, modality, and subsets. Suggested terms were created for the majority of fields to aid in harmonization of metadata terms across groups. Additional provenance metadata fields were also defined to describe the software and workflows that produced each FOM. Finally, we developed a simple list-like schema that can be used to store MAMS information and implemented in multiple formats. Overall, MAMS can be used as a guide to harmonize analysis-related metadata which will ultimately facilitate integration of datasets across tools and consortia. MAMS specifications, use cases, and examples can be found at https://github.com/single-cell-mams/mams/.

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).

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.

The evolution of synaptic and cognitive capacity: Insights from the nervous system transcriptome of Aplysia.

The gastropod mollusk Aplysia is an important model for cellular and molecular neurobiological studies, particularly for investigations of molecular mechanisms of learning and memory. We developed an optimized assembly pipeline to generate an improved Aplysia nervous system transcriptome. This improved transcriptome enabled us to explore the evolution of cognitive capacity at the molecular level. Were there evolutionary expansions of neuronal genes between this relatively simple gastropod Aplysia (20,000 neurons) and Octopus (500 million neurons), the invertebrate with the most elaborate neuronal circuitry and greatest behavioral complexity? Are the tremendous advances in cognitive power in vertebrates explained by expansion of the synaptic proteome that resulted from multiple rounds of whole genome duplication in this clade? Overall, the complement of genes linked to neuronal function is similar between Octopus and Aplysia. As expected, a number of synaptic scaffold proteins have more isoforms in humans than in Aplysia or Octopus. However, several scaffold families present in mollusks and other protostomes are absent in vertebrates, including the Fifes, Lev10s, SOLs, and a NETO family. Thus, whereas vertebrates have more scaffold isoforms from select families, invertebrates have additional scaffold protein families not found in vertebrates. This analysis provides insights into the evolution of the synaptic proteome. Both synaptic proteins and synaptic plasticity evolved gradually, yet the last deuterostome-protostome common ancestor already possessed an elaborate suite of genes associated with synaptic function, and critical for synaptic plasticity.

Advancing discovery in hearing research via biologist-friendly access to multi-omic data.

High-throughput cell type-specific multi-omic analyses have advanced our understanding of inner ear biology in an unprecedented way. The full benefit of these data, however, is reached from their re-use. Successful re-use of data requires identifying the natural users and ensuring proper data democratization and federation for their seamless and meaningful access. Here we discuss universal challenges in access and re-use of multi-omic data, possible solutions, and introduce the gEAR (the gene Expression Analysis Resource, umgear.org)-a tool for multi-omic data visualization, sharing and access for the ear field.

Translatome changes in acute myeloid leukemia cells post exposure to pegcrisantaspase and venetoclax.

The clinical outcomes of patients with acute myeloid leukemia (AML) treated with available therapy remain unsatisfactory. We recently reported that the BCL-2 inhibitor venetoclax synergizes with pegcrisantaspase (Ven-PegC) and exhibits remarkable in vivo efficacy in a preclinical model of AML with complex karyotype. The Ven-PegC combination blocks synthesis of proteins in AML cells by inhibiting cap-dependent translation of mRNA. To further explore the impact of Ven-PegC on protein translation, we used polysome profiling and high-throughput RNA sequencing to characterize Ven-PegC-dependent changes to the translatome. Here we report that the translation of five mRNAs, including two microRNAs, one rRNA, and two mitochondrial genes, was altered after exposure to all three treatments (Ven, PegC, and Ven-PegC). We focused our translatome validation studies on six additional genes related to translational efficiency that were modified by Ven-PegC. Notably, Ven-PegC treatment increased the RNA translation and protein levels of Tribbles homologue 3 (TRIB3), eukaryotic translation initiation factor 3 subunit C (eIF3C), doublesex and mab-3-related transcription factor 1 (DMRT1), and salt-inducible kinase 1 (SIK1). We validated the observed changes in gene/protein expression in vitro and confirmed our cell line-based studies in the bone marrow of an AML patient-derived xenograft model after Ven-PegC treatment. These results support examining alterations in the translatome post chemotherapy to offer insight into the drug's mechanism of action and to inform future therapeutic decisions.

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.

Comparative cellular analysis of motor cortex in human, marmoset and mouse.

The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.

A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex.

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1-3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas-containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities-is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis.

Comprehensive molecular profiling of UV-induced metastatic melanoma in Nme1/Nme2-deficient mice reveals novel markers of survival in human patients.

Hepatocyte growth factor-overexpressing mice that harbor a deletion of the Ink4a/p16 locus (HP mice) form melanomas with low metastatic potential in response to UV irradiation. Here we report that these tumors become highly metastatic following hemizygous deletion of the Nme1 and Nme2 metastasis suppressor genes (HPN mice). Whole-genome sequencing of melanomas from HPN mice revealed a striking increase in lung metastatic activity that is associated with missense mutations in eight signature genes (Arhgap35, Atp8b4, Brca1, Ift172, Kif21b, Nckap5, Pcdha2, and Zfp869). RNA-seq analysis of transcriptomes from HP and HPN primary melanomas identified a 32-gene signature (HPN lung metastasis signature) for which decreased expression is strongly associated with lung metastatic potential. Analysis of transcriptome data from The Cancer Genome Atlas revealed expression profiles of these genes that predict improved survival of patients with cutaneous or uveal melanoma. Silencing of three representative HPN lung metastasis signature genes (ARRDC3, NYNRIN, RND3) in human melanoma cells resulted in increased invasive activity, consistent with roles for these genes as mediators of the metastasis suppressor function of NME1 and NME2. In conclusion, our studies have identified a family of genes that mediate suppression of melanoma lung metastasis, and which may serve as prognostic markers and/or therapeutic targets for clinical management of metastatic melanoma.

Best practices on the differential expression analysis of multi-species RNA-seq.

Advances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

FADU: a Quantification Tool for Prokaryotic Transcriptomic Analyses.

Quantification tools for RNA sequencing (RNA-Seq) analyses are often designed and tested using human transcriptomics data sets, in which full-length transcript sequences are well annotated. For prokaryotic transcriptomics experiments, full-length transcript sequences are seldom known, and coding sequences must instead be used for quantification steps in RNA-Seq analyses. However, operons confound accurate quantification of coding sequences since a single transcript does not necessarily equate to a single gene. Here, we introduce FADU (Feature Aggregate Depth Utility), a quantification tool designed specifically for prokaryotic RNA-Seq analyses. FADU assigns partial count values proportional to the length of the fragment overlapping the target feature. To assess the ability of FADU to quantify genes in prokaryotic transcriptomics analyses, we compared its performance to those of eXpress, featureCounts, HTSeq, kallisto, and Salmon across three paired-end read data sets of (i) Ehrlichia chaffeensis, (ii) Escherichia coli, and (iii) the Wolbachia endosymbiont wBm. Across each of the three data sets, we find that FADU can more accurately quantify operonic genes by deriving proportional counts for multigene fragments within operons. FADU is available at https://github.com/IGS/FADUIMPORTANCE Most currently available quantification tools for transcriptomics analyses have been designed for human data sets, in which full-length transcript sequences, including the untranslated regions, are well annotated. In most prokaryotic systems, full-length transcript sequences have yet to be characterized, leading to prokaryotic transcriptomics analyses being performed based on only the coding sequences. In contrast to eukaryotes, prokaryotes contain polycistronic transcripts, and when genes are quantified based on coding sequences instead of transcript sequences, this leads to an increased abundance of improperly assigned ambiguous multigene fragments, specifically those mapping to multiple genes in operons. Here, we describe FADU, a quantification tool for prokaryotic RNA-Seq analyses designed to assign proportional counts with the purpose of better quantifying operonic genes while minimizing the pitfalls associated with improperly assigning fragment counts from ambiguous transcripts.

Venetoclax and pegcrisantaspase for complex karyotype acute myeloid leukemia.

Complex karyotype acute myeloid leukemia (CK-AML) has a dismal outcome with current treatments, underscoring the need for new therapies. Here, we report synergistic anti-leukemic activity of the BCL-2 inhibitor venetoclax (Ven) and the asparaginase formulation Pegylated Crisantaspase (PegC) in CK-AML in vitro and in vivo. Ven-PegC combination inhibited growth of multiple AML cell lines and patient-derived primary CK-AML cells in vitro. In vivo, Ven-PegC showed potent reduction of leukemia burden and improved survival, compared with each agent alone, in a primary patient-derived CK-AML xenograft. Superiority of Ven-PegC, compared to single drugs, and, importantly, the clinically utilized Ven-azacitidine combination, was also demonstrated in vivo in CK-AML. We hypothesized that PegC-mediated plasma glutamine depletion inhibits 4EBP1 phosphorylation, decreases the expression of proteins such as MCL-1, whose translation is cap dependent, synergizing with the BCL-2 inhibitor Ven. Ven-PegC treatment decreased cellular MCL-1 protein levels in vitro by enhancing eIF4E-4EBP1 interaction on the cap-binding complex via glutamine depletion. In vivo, Ven-PegC treatment completely depleted plasma glutamine and asparagine and inhibited mRNA translation and cellular protein synthesis. Since this novel mechanistically-rationalized regimen combines two drugs already in use in acute leukemia treatment, we plan a clinical trial of the Ven-PegC combination in relapsed/refractory CK-AML.