BACKGROUND: Berberine and Bifidobacterium have been reported to improve glucose tolerance in people with hyperglycemia or other metabolic disorders. This study aimed to assess the hypoglycemic effect and the regulation of the gut microbiota caused by berberine and Bifidobacterium and the possible additive benefits of their combination. METHODS: This was an 18-week, multi-center, randomized, double-blind, parallel-controlled study of patients newly diagnosed with hyperglycemia. After a 2-week run-in period, 300 participants were randomly assigned to the following four groups for 16 weeks of treatment: berberine (Be), Bifidobacterium (Bi), berberine and Bifidobacterium (BB), and placebo group. The primary efficacy endpoint was the absolute value of fasting plasma glucose (FPG) compared with baseline after 16 weeks of treatment. RESULTS: Between October 2015 and April 2018, a total of 297 participants were included in the primary analysis. Significant reductions of FPG were observed in the Be and BB groups compared with the placebo group, with a least square (LS) mean difference of - 0.50, 95% CI [- 0.85, - 0.15] mmol/L, and - 0.55, 95% CI [- 0.91, - 0.20] mmol/L, respectively. The Be and BB groups also showed significant reductions in 2-h postprandial plasma glucose. A pronounced decrease in HbA1c occurred in the BB group compared to the placebo group. Moreover, compared with the Bi and placebo groups, the Be and BB groups had more changes in the gut microbiota from the baseline. CONCLUSIONS: Berberine could regulate the structure and function of the human gut microbiota, and Bifidobacterium has the potential to enhance the hypoglycemic effect of berberine. These findings provide new insights into the hypoglycemic potential of berberine and Bifidobacterium. TRIAL REGISTRATION: ClinicalTrials.gov , NCT03330184. Retrospectively registered on 18 October 2017.

Author information: (1)Department of Medical Genetics, University of British Columbia and Children's & Women's Hospital, Vancouver, BC, V6H3N1, Canada. indhu.babu@bcchr.ca. (2)Department of Medical and Molecular Genetics, King's College London, Strand, London, WC2R 2LS, UK. indhu.babu@bcchr.ca. (3)Department of Medical Genetics, University of British Columbia and Children's & Women's Hospital, Vancouver, BC, V6H3N1, Canada. (4)Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, BC, V5Z4S6, Canada. (5)Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, V6T1Z4, Canada. (6)Illumina Inc., San Diego, CA, 92121, USA.

BACKGROUND: Cancer evolution depends on epigenetic and genetic diversity. Historically, in multiple myeloma (MM), subclonal diversity and tumor evolution have been investigated mostly from a genetic perspective. METHODS: Here, we performed an analysis of 42 MM samples from 21 patients by using enhanced reduced representation bisulfite sequencing (eRRBS). We combined several metrics of epigenetic heterogeneity to analyze DNA methylation heterogeneity in MM patients. RESULTS: We show that MM is characterized by the continuous accumulation of stochastic methylation at the promoters of development-related genes. High combinatorial entropy change is associated with poor outcomes in our pilot study and depends predominantly on partially methylated domains (PMDs). These PMDs, which represent the major source of inter- and intrapatient DNA methylation heterogeneity in MM, are linked to other key epigenetic aberrations, such as CpG island (CGI)/transcription start site (TSS) hypermethylation and H3K27me3 redistribution as well as 3D organization alterations. In addition, transcriptome analysis revealed that intratumor methylation heterogeneity was associated with low-level expression and high variability. CONCLUSIONS: We propose that disrupted DNA methylation in MM is responsible for high epigenetic and transcriptomic instability allowing tumor cells to adapt to environmental changes by tapping into a pool of evolutionary trajectories.

Update of bioRxiv. 2021 Apr 06;:

BACKGROUND: Multidrug-resistant Mycobacterium tuberculosis (Mtb) is a significant global public health threat. Genotypic resistance prediction from Mtb DNA sequences offers an alternative to laboratory-based drug-susceptibility testing. User-friendly and accurate resistance prediction tools are needed to enable public health and clinical practitioners to rapidly diagnose resistance and inform treatment regimens. RESULTS: We present Translational Genomics platform for Tuberculosis (GenTB), a free and open web-based application to predict antibiotic resistance from next-generation sequence data. The user can choose between two potential predictors, a Random Forest (RF) classifier and a Wide and Deep Neural Network (WDNN) to predict phenotypic resistance to 13 and 10 anti-tuberculosis drugs, respectively. We benchmark GenTB's predictive performance along with leading TB resistance prediction tools (Mykrobe and TB-Profiler) using a ground truth dataset of 20,408 isolates with laboratory-based drug susceptibility data. All four tools reliably predicted resistance to first-line tuberculosis drugs but had varying performance for second-line drugs. The mean sensitivities for GenTB-RF and GenTB-WDNN across the nine shared drugs were 77.6% (95% CI 76.6-78.5%) and 75.4% (95% CI 74.5-76.4%), respectively, and marginally higher than the sensitivities of TB-Profiler at 74.4% (95% CI 73.4-75.3%) and Mykrobe at 71.9% (95% CI 70.9-72.9%). The higher sensitivities were at an expense of ≤ 1.5% lower specificity: Mykrobe 97.6% (95% CI 97.5-97.7%), TB-Profiler 96.9% (95% CI 96.7 to 97.0%), GenTB-WDNN 96.2% (95% CI 96.0 to 96.4%), and GenTB-RF 96.1% (95% CI 96.0 to 96.3%). Averaged across the four tools, genotypic resistance sensitivity was 11% and 9% lower for isoniazid and rifampicin respectively, on isolates sequenced at low depth (< 10× across 95% of the genome) emphasizing the need to quality control input sequence data before prediction. We discuss differences between tools in reporting results to the user including variants underlying the resistance calls and any novel or indeterminate variants CONCLUSIONS: GenTB is an easy-to-use online tool to rapidly and accurately predict resistance to anti-tuberculosis drugs. GenTB can be accessed online at https://gentb.hms.harvard.edu , and the source code is available at https://github.com/farhat-lab/gentb-site .

BACKGROUND: Exercise training is well established as the most effective way to enhance muscle performance and muscle building. The composition of skeletal muscle fiber type affects systemic energy expenditures, and perturbations in metabolic homeostasis contribute to the onset of obesity and other metabolic dysfunctions. Long noncoding RNAs (lncRNAs) have been demonstrated to play critical roles in diverse cellular processes and diseases, including human cancers; however, the functional importance of lncRNAs in muscle performance, energy balance, and obesity remains elusive. We previously reported that the lncRNA H19 regulates the poly-ubiquitination and protein stability of dystrophin (DMD) in muscular dystrophy. METHODS: Here, we identified mouse/human H19-interacting proteins using mouse/human skeletal muscle tissues and liquid chromatography-mass spectrometry (LC-MS). Human induced pluripotent stem-derived skeletal muscle cells (iPSC-SkMC) from a healthy donor and Becker Muscular Dystrophy (BMD) patients were utilized to study DMD post-translational modifications and associated proteins. We identified a gain-of-function (GOF) mutant of H19 and characterized the effects on myoblast differentiation and fusion to myotubes using iPSCs. We then conjugated H19 RNA gain-of-function oligonucleotides (Rgof) with the skeletal muscle enrichment peptide agrin (referred to as AGR-H19-Rgof) and evaluated AGR-H19-Rgof's effects on skeletal muscle performance using wild-type (WT) C57BL/6 J mice and its anti-obesity effects using high-fat diet (HFD)- and leptin deficiency-induced obese mouse models. RESULTS: We demonstrated that both human and mouse H19 associated with DMD and that the H19 GOF exhibited enhanced interaction with DMD compared to WT H19. DMD was found to associate with serine/threonine-protein kinase MRCK alpha (MRCKα) and α-synuclein (SNCA) in iPSC-SkMC derived from BMD patients. Inhibition of MRCKα and SNCA-mediated phosphorylation of DMD antagonized the interaction between H19 and DMD. These signaling events led to improved skeletal muscle cell differentiation and myotube fusion. The administration of AGR-H19-Rgof improved the muscle mass, muscle performance, and base metabolic rate of WT mice. Furthermore, mice treated with AGR-H19-Rgof exhibited resistance to HFD- or leptin deficiency-induced obesity. CONCLUSIONS: Our study suggested the functional importance of the H19 GOF mutant in enhancing muscle performance and anti-obesity effects.

BACKGROUND: Metagenome sampling bias for geographical location and lifestyle is partially responsible for the incomplete catalog of reference genomes of gut microbial species. Thus, genome assembly from currently under-represented populations may effectively expand the reference gut microbiome and improve taxonomic and functional profiling. METHODS: We assembled genomes using public whole-metagenomic shotgun sequencing (WMS) data for 110 and 645 fecal samples from India and Japan, respectively. In addition, we assembled genomes from newly generated WMS data for 90 fecal samples collected from Korea. Expecting genome assembly for low-abundance species may require a much deeper sequencing than that usually employed, so we performed ultra-deep WMS (> 30 Gbp or > 100 million read pairs) for the fecal samples from Korea. We consequently assembled 29,082 prokaryotic genomes from 845 fecal metagenomes for the three under-represented Asian countries and combined them with the Unified Human Gastrointestinal Genome (UHGG) to generate an expanded catalog, the Human Reference Gut Microbiome (HRGM). RESULTS: HRGM contains 232,098 non-redundant genomes for 5414 representative prokaryotic species including 780 that are novel, > 103 million unique proteins, and > 274 million single-nucleotide variants. This is an over 10% increase from the UHGG. The new 780 species were enriched for the Bacteroidaceae family, including species associated with high-fiber and seaweed-rich diets. Single-nucleotide variant density was positively associated with the speciation rate of gut commensals. We found that ultra-deep sequencing facilitated the assembly of genomes for low-abundance taxa, and deep sequencing (e.g., > 20 million read pairs) may be needed for the profiling of low-abundance taxa. Importantly, the HRGM significantly improved the taxonomic and functional classification of sequencing reads from fecal samples. Finally, analysis of human self-antigen homologs on the HRGM species genomes suggested that bacterial taxa with high cross-reactivity potential may contribute more to the pathogenesis of gut microbiome-associated diseases than those with low cross-reactivity potential by promoting inflammatory condition. CONCLUSIONS: By including gut metagenomes from previously under-represented Asian countries, Korea, India, and Japan, we developed a substantially expanded microbiome catalog, HRGM. Information of the microbial genomes and coding genes is publicly available ( www.mbiomenet.org/HRGM/ ). HRGM will facilitate the identification and functional analysis of disease-associated gut microbiota.

Author information: (1)Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Harvard Medical School, 221 Longwood Avenue, Boston, MA, 02115, USA. bcade@bwh.harvard.edu. (2)Division of Sleep Medicine, Harvard Medical School, Boston, MA, 02115, USA. bcade@bwh.harvard.edu. (3)Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, USA. bcade@bwh.harvard.edu. (4)Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Harvard Medical School, 221 Longwood Avenue, Boston, MA, 02115, USA. (5)Division of Sleep Medicine, Harvard Medical School, Boston, MA, 02115, USA. (6)Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, 02142, USA. (7)Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA. (8)Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. (9)Center for Precision Health, School of Public Health and School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. (10)Computational Medicine Core, Center for Lung Biology, UW Medicine Sleep Center, Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle, WA, 98195, USA. (11)VA Boston Healthcare System, Boston, MA, 02132, USA. (12)The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA. (13)Center for Genomic Medicine and Department of Anesthesia, Pain, and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA. (14)Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA. (15)Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA. (16)Department of Data Science, University of Mississippi Medical Center, Jackson, MS, 29216, USA. (17)Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA. (18)Division of Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA. (19)California Pacific Medical Center Research Institute, San Francisco, CA, 94107, USA. (20)Department of Pulmonary Physiology and Sleep Medicine, Sir Charles Gairdner Hospital, Perth, Western Australia, 6009, Australia. (21)Sleep Health Service, Respiratory and Sleep Services, Southern Adelaide Local Health Network, Adelaide, South Australia, Australia. (22)Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia, Australia. (23)School of Public Health, University of Adelaide, Adelaide, South Australia, 5000, Australia. (24)Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA. (25)Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA. (26)Department of Medicine, University of Mississippi Medical Center, Jackson, MS, 39216, USA. (27)Jackson Heart Study, Jackson, MS, 39216, USA. (28)Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA. (29)Framingham Heart Study, Framingham, MA, 01702, USA. (30)Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, 10461, USA. (31)Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA. (32)Northwest Genomics Center, Seattle, WA, 98105, USA. (33)Department of Epidemiology and Carolina Center of Genome Sciences, University of North Carolina, Chapel Hill, NC, 27514, USA. (34)Cardiovascular Health Study, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA, 98101, USA. (35)Kaiser Permanente Washington Health Research Institute, Seattle, WA, 98101, USA. (36)Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA. (37)Department of Pathology, University of Vermont, Colchester, VT, 05405, USA. (38)Sections of Preventive Medicine and Epidemiology and Cardiology, Department of Medicine, Boston University School of Medicine, Boston, MA, 02118, USA. (39)Department of Epidemiology, Boston University School of Public Health, Boston, MA, 02118, USA. (40)Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, 39216, USA. (41)Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA.

BACKGROUND: Genetic variants in the voltage-gated sodium channels SCN1A, SCN2A, SCN3A, and SCN8A are leading causes of epilepsy, developmental delay, and autism spectrum disorder. The mRNA splicing patterns of all four genes vary across development in the rodent brain, including mutually exclusive copies of the fifth protein-coding exon detected in the neonate (5N) and adult (5A). A second pair of mutually exclusive exons is reported in SCN8A only (18N and 18A). We aimed to quantify the expression of individual exons in the developing human brain. METHODS: RNA-seq data from 783 human brain samples across development were analyzed to estimate exon-level expression. Developmental changes in exon utilization were validated by assessing intron splicing. Exon expression was also estimated in RNA-seq data from 58 developing mouse neocortical samples. RESULTS: In the mature human neocortex, exon 5A is consistently expressed at least 4-fold higher than exon 5N in all four genes. For SCN2A, SCN3A, and SCN8A, a brain-wide synchronized 5N to 5A transition occurs between 24 post-conceptual weeks (2nd trimester) and 6 years of age. In mice, the equivalent 5N to 5A transition begins at or before embryonic day 15.5. In SCN8A, over 90% of transcripts in the mature human cortex include exon 18A. Early in fetal development, most transcripts include 18N or skip both 18N and 18A, with a transition to 18A inclusion occurring from 13 post-conceptual weeks to 6 months of age. No other protein-coding exons showed comparably dynamic developmental trajectories. CONCLUSIONS: Exon usage in SCN1A, SCN2A, SCN3A, and SCN8A changes dramatically during human brain development. These splice isoforms, which alter the biophysical properties of the encoded channels, may account for some of the observed phenotypic differences across development and between specific variants. Manipulation of the proportion of splicing isoforms at appropriate stages of development may act as a therapeutic strategy for specific mutations or even epilepsy in general.

Erratum in Genome Med. 2021 Aug 30;13(1):139.

BACKGROUND: The enrichment of Gram-negative bacteria of oral origin in the esophageal microbiome has been associated with the development of metaplasia. However, to date, no study has comprehensively assessed the relationships between the esophageal microbiome and the host. METHODS: Here, we examine the esophageal microenvironment in gastro-esophageal reflux disease and metaplasia using multi-omics strategies targeting the microbiome and host transcriptome, followed by targeted culture, comparative genomics, and host-microbial interaction studies of bacterial signatures of interest. RESULTS: Profiling of the host transcriptome from esophageal mucosal biopsies revealed profound changes during metaplasia. Importantly, five biomarkers showed consistent longitudinal changes with disease progression from reflux disease to metaplasia. We showed for the first time that the esophageal microbiome is distinct from the salivary microbiome and the enrichment of Campylobacter species as a consistent signature in disease across two independent cohorts. Shape fitting and matrix correlation identified associations between the microbiome and host transcriptome profiles, with a novel co-exclusion relationship found between Campylobacter and napsin B aspartic peptidase. Targeted culture of Campylobacter species from the same cohort revealed a subset of isolates to have a higher capacity to survive within primary human macrophages. Comparative genomic analyses showed these isolates could be differentiated by specific genomic features, one of which was validated to be associated with intracellular fitness. Screening for these Campylobacter strain-specific signatures in shotgun metagenomics data from another cohort showed an increase in prevalence with disease progression. Comparative transcriptomic analyses of primary esophageal epithelial cells exposed to the Campylobacter isolates revealed expression changes within those infected with strains with high intracellular fitness that could explain the increased likelihood of disease progression. CONCLUSIONS: We provide a comprehensive assessment of the esophageal microenvironment, identifying bacterial strain-specific signatures with high relevance to progression of metaplasia.

BACKGROUND: Gene copy number variations (CNVs) contribute to genetic diversity and disease prevalence across populations. Substantial efforts have been made to decipher the relationship between CNVs and pathogenesis but with limited success. RESULTS: We have developed a novel computational framework X-CNV ( www.unimd.org/XCNV ), to predict the pathogenicity of CNVs by integrating more than 30 informative features such as allele frequency (AF), CNV length, CNV type, and some deleterious scores. Notably, over 14 million CNVs across various ethnic groups, covering nearly 93% of the human genome, were unified to calculate the AF. X-CNV, which yielded area under curve (AUC) values of 0.96 and 0.94 in training and validation sets, was demonstrated to outperform other available tools in terms of CNV pathogenicity prediction. A meta-voting prediction (MVP) score was developed to quantitively measure the pathogenic effect, which is based on the probabilistic value generated from the XGBoost algorithm. The proposed MVP score demonstrated a high discriminative power in determining pathogenetic CNVs for inherited traits/diseases in different ethnic groups. CONCLUSIONS: The ability of the X-CNV framework to quantitatively prioritize functional, deleterious, and disease-causing CNV on a genome-wide basis outperformed current CNV-annotation tools and will have broad utility in population genetics, disease-association studies, and diagnostic screening.

BACKGROUND: The plasticity of monocytes enables them to exert multiple roles during an immune response, including promoting immune tolerance. How monocytes alter their functions to convey immune tolerance in the context of lower respiratory tract infections in humans is not well understood. Here, we sought to identify epigenetic and transcriptomic features of cytokine production capacity in circulating monocytes during community-acquired pneumonia (CAP). METHODS: Circulating CD14+ monocytes were obtained from the blood of CAP patients included in a longitudinal, observational cohort study, on hospitalization (acute stage, n=75), and from the same patients after a 1-month follow-up (recovery stage, n=56). Age and sex-matched non-infectious participants were included as controls (n=41). Ex vivo cytokine production after lipopolysaccharide (LPS) exposure was assessed by multiplex assay. Transcriptomes of circulating monocytes were generated by RNA-sequencing, and DNA methylation levels in the same monocytes were measured by reduced representation bisulfite sequencing. Data were integrated by fitting projection-to-latent-structure models, and signatures derived by partial least squares discrimination. RESULTS: Monocytes captured during the acute stage exhibited impaired TNF, IL-1β, IL-6, and IL-10 production after ex vivo stimulation with LPS, relative to controls. IL-6 production was not resolved in recovery monocytes. Multivariate analysis of RNA-sequencing data identified 2938 significantly altered RNA transcripts in acute-stage monocytes (fold expression ≤-1.5 or ≥1.5; adjusted p ≤ 0.01), relative to controls. Comparing DNA methylation levels in circulating monocytes of CAP patients to controls revealed minimal differences, specifically in DNAse hypersensitive sites (HS) of acute-stage monocytes. Data integration identified a cholesterol biosynthesis gene signature and DNAse HS axis of IL-1β and IL-10 production (R2 =0.51). CONCLUSIONS: Circulating monocytes obtained from CAP patients during the acute stage exhibited impaired cytokine production capacities, indicative of reprogramming to a state of immune tolerance, which was not fully resolved after 1 month. Our split-sample study showed that 51% of the immune tolerance phenotype can be explained, at least in part, by coordinated shifts in cholesterol biosynthesis gene expression and DNAse HS methylation levels. A multi-scale model identified an epigenetic and transcriptomic signature of immune tolerance in monocytes, with implications for future interventions in immunosuppression. TRIAL REGISTRATION: NCT number NCT02928367.

BACKGROUND: Merkel cell carcinoma (MCC) is a rare but aggressive skin cancer with limited treatment possibilities. Merkel cell tumors display with neuroendocrine features and Merkel cell polyomavirus (MCPyV) infection in the majority (80%) of patients. Although loss of histone H3 lysine 27 trimethylation (H3K27me3) has been shown during MCC tumorigenesis, epigenetic dysregulation has largely been overlooked. METHODS: We conducted global DNA methylation profiling of clinically annotated MCC primary tumors, metastatic skin tumors, metastatic lymph node tumors, paired normal tissues, and two human MCC cell lines using the Illumina Infinium EPIC DNA methylation BeadArray platform. RESULTS: Significant differential DNA methylation patterns across the genome are revealed between the four tissue types, as well as based on MCPyV status. Furthermore, 964 genes directly regulated by promoter or gene body DNA methylation were identified with high enrichment in neuro-related pathways. Finally, our findings suggest that loss of H3K27me3 occupancy in MCC is attributed to KDM6B and EZHIP overexpression as a consequence of promoter DNA hypomethylation. CONCLUSIONS: We have demonstrated specific DNA methylation patterns for primary MCC tumors, metastatic MCCs, and adjacent-normal tissues. We have also identified DNA methylation markers that not only show potential diagnostic or prognostic utility in MCC management, but also correlate with MCC tumorigenesis, MCPyV expression, neuroendocrine features, and H3K27me3 status. The identification of DNA methylation alterations in MCC supports the need for further studies to understand the clinical implications of epigenetic dysregulation and potential therapeutic targets in MCC.

BACKGROUND: Tumor response to therapy is affected by both the cell types and the cell states present in the tumor microenvironment. This is true for many cancer treatments, including immune checkpoint inhibitors (ICIs). While it is well-established that ICIs promote T cell activation, their broader impact on other intratumoral immune cells is unclear; this information is needed to identify new mechanisms of action and improve ICI efficacy. Many preclinical studies have begun using single-cell analysis to delineate therapeutic responses in individual immune cell types within tumors. One major limitation to this approach is that therapeutic mechanisms identified in preclinical models have failed to fully translate to human disease, restraining efforts to improve ICI efficacy in translational research. METHOD: We previously developed a computational transfer learning approach called projectR to identify shared biology between independent high-throughput single-cell RNA-sequencing (scRNA-seq) datasets. In the present study, we test this algorithm's ability to identify conserved and clinically relevant transcriptional changes in complex tumor scRNA-seq data and expand its application to the comparison of scRNA-seq datasets with additional data types such as bulk RNA-seq and mass cytometry. RESULTS: We found a conserved signature of NK cell activation in anti-CTLA-4 responsive mouse and human tumors. In human metastatic melanoma, we found that the NK cell activation signature associates with longer overall survival and is predictive of anti-CTLA-4 (ipilimumab) response. Additional molecular approaches to confirm the computational findings demonstrated that human NK cells express CTLA-4 and bind anti-CTLA-4 antibodies independent of the antibody binding receptor (FcR) and that similar to T cells, CTLA-4 expression by NK cells is modified by cytokine-mediated and target cell-mediated NK cell activation. CONCLUSIONS: These data demonstrate a novel application of our transfer learning approach, which was able to identify cell state transitions conserved in preclinical models and human tumors. This approach can be adapted to explore many questions in cancer therapeutics, enhance translational research, and enable better understanding and treatment of disease.

BACKGROUND: Human pancreatic islets are a central focus of research in metabolic studies. Transcriptomics is frequently used to interrogate alterations in cultured human islet cells using single-cell RNA-sequencing (scRNA-seq). We introduce single-nucleus RNA-sequencing (snRNA-seq) as an alternative approach for investigating transplanted human islets. METHODS: The Nuclei EZ protocol was used to obtain nuclear preparations from fresh and frozen human islet cells. Such preparations were first used to generate snRNA-seq datasets and compared to scRNA-seq output obtained from cells from the same donor. Finally, we employed snRNA-seq to obtain the transcriptomic profile of archived human islets engrafted in immunodeficient animals. RESULTS: We observed virtually complete concordance in identifying cell types and gene proportions as well as a strong association of global and islet cell type gene signatures between scRNA-seq and snRNA-seq applied to fresh and frozen cultured or transplanted human islet samples. CONCLUSIONS: We propose snRNA-seq as a reliable strategy to probe transcriptomic profiles of freshly harvested or frozen sources of transplanted human islet cells especially when scRNA-seq is not ideal.