Fast and slow muscle fiber transcriptome dynamics with lifelong endurance exercise.

We investigated fast and slow muscle fiber transcriptome exercise dynamics among three groups of men: lifelong exercisers (LLE, n = 8, 74 ± 1 yr), old healthy nonexercisers (OH, n = 9, 75 ± 1 yr), and young exercisers (YE, n = 8, 25 ± 1 yr). On average, LLE had exercised ∼4 day·wk-1 for ∼8 h·wk-1 over 53 ± 2 years. Muscle biopsies were obtained pre- and 4 h postresistance exercise (3 × 10 knee extensions at 70% 1-RM). Fast and slow fiber size and function were assessed preexercise with fast and slow RNA-seq profiles examined pre- and postexercise. LLE fast fiber size was similar to OH, which was ∼30% smaller than YE (P < 0.05) with contractile function variables among groups, resulting in lower power in LLE (P < 0.05). LLE slow fibers were ∼30% larger and more powerful compared with YE and OH (P < 0.05). At the transcriptome level, fast fibers were more responsive to resistance exercise compared with slow fibers among all three cohorts (P < 0.05). Exercise induced a comprehensive biological response in fast fibers (P < 0.05) including transcription, signaling, skeletal muscle cell differentiation, and metabolism with vast differences among the groups. Fast fibers from YE exhibited a growth and metabolic signature, with LLE being primarily metabolic, and OH showing a strong stress-related response. In slow fibers, only LLE exhibited a biological response to exercise (P < 0.05), which was related to ketone and lipid metabolism. The divergent exercise transcriptome signatures provide novel insight into the molecular regulation in fast and slow fibers with age and exercise and suggest that the ∼5% weekly exercise time commitment of the lifelong exercisers provided a powerful investment for fast and slow muscle fiber metabolic health at the molecular level.NEW & NOTEWORTHY This study provides the first insights into fast and slow muscle fiber transcriptome dynamics with lifelong endurance exercise. The fast fibers were more responsive to exercise with divergent transcriptome signatures among young exercisers (growth and metabolic), lifelong exercisers (metabolic), and old healthy nonexercisers (stress). Only lifelong exercisers had a biological response in slow fibers (metabolic). These data provide novel insights into fast and slow muscle fiber health at the molecular level with age and exercise.

Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data.

Resolving chromatin-remodeling-linked gene expression changes at cell-type resolution is important for understanding disease states. Here we describe MAGICAL (Multiome Accessibility Gene Integration Calling and Looping), a hierarchical Bayesian approach that leverages paired single-cell RNA sequencing and single-cell transposase-accessible chromatin sequencing from different conditions to map disease-associated transcription factors, chromatin sites, and genes as regulatory circuits. By simultaneously modeling signal variation across cells and conditions in both omics data types, MAGICAL achieved high accuracy on circuit inference. We applied MAGICAL to study Staphylococcus aureus sepsis from peripheral blood mononuclear single-cell data that we generated from subjects with bloodstream infection and uninfected controls. MAGICAL identified sepsis-associated regulatory circuits predominantly in CD14 monocytes, known to be activated by bacterial sepsis. We addressed the challenging problem of distinguishing host regulatory circuit responses to methicillin-resistant and methicillin-susceptible S. aureus infections. Although differential expression analysis failed to show predictive value, MAGICAL identified epigenetic circuit biomarkers that distinguished methicillin-resistant from methicillin-susceptible S. aureus infections.

Automated single-cell omics end-to-end framework with data-driven batch inference.

To facilitate single cell multi-omics analysis and improve reproducibility, we present SPEEDI (Single-cell Pipeline for End to End Data Integration), a fully automated end-to-end framework for batch inference, data integration, and cell type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDI's data-driven batch inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed at https://speedi.princeton.edu/.

Integrated single-cell multiome analysis reveals muscle fiber-type gene regulatory circuitry modulated by endurance exercise.

Endurance exercise is an important health modifier. We studied cell-type specific adaptations of human skeletal muscle to acute endurance exercise using single-nucleus (sn) multiome sequencing in human vastus lateralis samples collected before and 3.5 hours after 40 min exercise at 70% VO2max in four subjects, as well as in matched time of day samples from two supine resting circadian controls. High quality same-cell RNA-seq and ATAC-seq data were obtained from 37,154 nuclei comprising 14 cell types. Among muscle fiber types, both shared and fiber-type specific regulatory programs were identified. Single-cell circuit analysis identified distinct adaptations in fast, slow and intermediate fibers as well as LUM-expressing FAP cells, involving a total of 328 transcription factors (TFs) acting at altered accessibility sites regulating 2,025 genes. These data and circuit mapping provide single-cell insight into the processes underlying tissue and metabolic remodeling responses to exercise.

Blood RNA alternative splicing events as diagnostic biomarkers for infectious disease.

Assays detecting blood transcriptome changes are studied for infectious disease diagnosis. Blood-based RNA alternative splicing (AS) events, which have not been well characterized in pathogen infection, have potential normalization and assay platform stability advantages over gene expression for diagnosis. Here, we present a computational framework for developing AS diagnostic biomarkers. Leveraging a large prospective cohort of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whole-blood RNA sequencing (RNA-seq) data, we identify a major functional AS program switch upon viral infection. Using an independent cohort, we demonstrate the improved accuracy of AS biomarkers for SARS-CoV-2 diagnosis compared with six reported transcriptome signatures. We then optimize a subset of AS-based biomarkers to develop microfluidic PCR diagnostic assays. This assay achieves nearly perfect test accuracy (61/62 = 98.4%) using a naive principal component classifier, significantly more accurate than a gene expression PCR assay in the same cohort. Therefore, our RNA splicing computational framework enables a promising avenue for host-response diagnosis of infection.

A methylation clock model of mild SARS-CoV-2 infection provides insight into immune dysregulation.

DNA methylation comprises a cumulative record of lifetime exposures superimposed on genetically determined markers. Little is known about methylation dynamics in humans following an acute perturbation, such as infection. We characterized the temporal trajectory of blood epigenetic remodeling in 133 participants in a prospective study of young adults before, during, and after asymptomatic and mildly symptomatic SARS-CoV-2 infection. The differential methylation caused by asymptomatic or mildly symptomatic infections was indistinguishable. While differential gene expression largely returned to baseline levels after the virus became undetectable, some differentially methylated sites persisted for months of follow-up, with a pattern resembling autoimmune or inflammatory disease. We leveraged these responses to construct methylation-based machine learning models that distinguished samples from pre-, during-, and postinfection time periods, and quantitatively predicted the time since infection. The clinical trajectory in the young adults and in a diverse cohort with more severe outcomes was predicted by the similarity of methylation before or early after SARS-CoV-2 infection to the model-defined postinfection state. Unlike the phenomenon of trained immunity, the postacute SARS-CoV-2 epigenetic landscape we identify is antiprotective.

Multi-omic identification of key transcriptional regulatory programs during endurance exercise training.

Transcription factors (TFs) play a key role in regulating gene expression and responses to stimuli. We conducted an integrated analysis of chromatin accessibility, DNA methylation, and RNA expression across eight rat tissues following endurance exercise training (EET) to map epigenomic changes to transcriptional changes and determine key TFs involved. We uncovered tissue-specific changes and TF motif enrichment across all omic layers, differentially accessible regions (DARs), differentially methylated regions (DMRs), and differentially expressed genes (DEGs). We discovered distinct routes of EET-induced regulation through either epigenomic alterations providing better access for TFs to affect target genes, or via changes in TF expression or activity enabling target gene response. We identified TF motifs enriched among correlated epigenomic and transcriptomic alterations, DEGs correlated with exercise-related phenotypic changes, and EET-induced activity changes of TFs enriched for DEGs among their gene targets. This analysis elucidates the unique transcriptional regulatory mechanisms mediating diverse organ effects of EET.

Benchmarking transcriptional host response signatures for infection diagnosis.

Identification of host transcriptional response signatures has emerged as a new paradigm for infection diagnosis. For clinical applications, signatures must robustly detect the pathogen of interest without cross-reacting with unintended conditions. To evaluate the performance of infectious disease signatures, we developed a framework that includes a compendium of 17,105 transcriptional profiles capturing infectious and non-infectious conditions and a standardized methodology to assess robustness and cross-reactivity. Applied to 30 published signatures of infection, the analysis showed that signatures were generally robust in detecting viral and bacterial infections in independent data. Asymptomatic and chronic infections were also detectable, albeit with decreased performance. However, many signatures were cross-reactive with unintended infections and aging. In general, we found robustness and cross-reactivity to be conflicting objectives, and we identified signature properties associated with this trade-off. The data compendium and evaluation framework developed here provide a foundation for the development of signatures for clinical application. A record of this paper's transparent peer review process is included in the supplemental information.

Multi-objective optimization identifies a specific and interpretable COVID-19 host response signature.

The identification of a COVID-19 host response signature in blood can increase the understanding of SARS-CoV-2 pathogenesis and improve diagnostic tools. Applying a multi-objective optimization framework to both massive public and new multi-omics data, we identified a COVID-19 signature regulated at both transcriptional and epigenetic levels. We validated the signature's robustness in multiple independent COVID-19 cohorts. Using public data from 8,630 subjects and 53 conditions, we demonstrated no cross-reactivity with other viral and bacterial infections, COVID-19 comorbidities, or confounders. In contrast, previously reported COVID-19 signatures were associated with significant cross-reactivity. The signature's interpretation, based on cell-type deconvolution and single-cell data analysis, revealed prominent yet complementary roles for plasmablasts and memory T cells. Although the signal from plasmablasts mediated COVID-19 detection, the signal from memory T cells controlled against cross-reactivity with other viral infections. This framework identified a robust, interpretable COVID-19 signature and is broadly applicable in other disease contexts. A record of this paper's transparent peer review process is included in the supplemental information.

Pre-infection antiviral innate immunity contributes to sex differences in SARS-CoV-2 infection.

Male sex is a major risk factor for SARS-CoV-2 infection severity. To understand the basis for this sex difference, we studied SARS-CoV-2 infection in a young adult cohort of United States Marine recruits. Among 2,641 male and 244 female unvaccinated and seronegative recruits studied longitudinally, SARS-CoV-2 infections occurred in 1,033 males and 137 females. We identified sex differences in symptoms, viral load, blood transcriptome, RNA splicing, and proteomic signatures. Females had higher pre-infection expression of antiviral interferon-stimulated gene (ISG) programs. Causal mediation analysis implicated ISG differences in number of symptoms, levels of ISGs, and differential splicing of CD45 lymphocyte phosphatase during infection. Our results indicate that the antiviral innate immunity set point causally contributes to sex differences in response to SARS-CoV-2 infection. A record of this paper's transparent peer review process is included in the supplemental information.

High-resolution transcriptomics informs glial pathology in human temporal lobe epilepsy.

The pathophysiology of epilepsy underlies a complex network dysfunction between neurons and glia, the molecular cell type-specific contributions of which remain poorly defined in the human disease. In this study, we validated a method that simultaneously isolates neuronal (NEUN +), astrocyte (PAX6 + NEUN-), and oligodendroglial progenitor (OPC) (OLIG2 + NEUN-) enriched nuclei populations from non-diseased, fresh-frozen human neocortex and then applied it to characterize the distinct transcriptomes of such populations isolated from electrode-mapped temporal lobe epilepsy (TLE) surgical samples. Nuclear RNA-seq confirmed cell type specificity and informed both common and distinct pathways associated with TLE in astrocytes, OPCs, and neurons. Compared to postmortem control, the transcriptome of epilepsy astrocytes showed downregulation of mature astrocyte functions and upregulation of development-related genes. To gain further insight into glial heterogeneity in TLE, we performed single cell transcriptomics (scRNA-seq) on four additional human TLE samples. Analysis of the integrated TLE dataset uncovered a prominent subpopulation of glia that express a hybrid signature of both reactive astrocyte and OPC markers, including many cells with a mixed GFAP + OLIG2 + phenotype. A further integrated analysis of this TLE scRNA-seq dataset and a previously published normal human temporal lobe scRNA-seq dataset confirmed the unique presence of hybrid glia only in TLE. Pseudotime analysis revealed cell transition trajectories stemming from this hybrid population towards both OPCs and reactive astrocytes. Immunofluorescence studies in human TLE samples confirmed the rare presence of GFAP + OLIG2 + glia, including some cells with proliferative activity, and functional analysis of cells isolated directly from these samples disclosed abnormal neurosphere formation in vitro. Overall, cell type-specific isolation of glia from surgical epilepsy samples combined with transcriptomic analyses uncovered abnormal glial subpopulations with de-differentiated phenotype, motivating further studies into the dysfunctional role of reactive glia in temporal lobe epilepsy.

Multi-omics profiling of single nuclei from frozen archived postmortem human pituitary tissue.

Concomitant profiling of transcriptome and chromatin accessibility in isolated nuclei can reveal gene regulatory control mechanisms in health and disease. We report a single nucleus multi-omics analysis protocol optimized for frozen archived postmortem human pituitaries that is also effective for frozen ovine and murine pituitaries and human skeletal muscle biopsies. Its main advantages are that (1) it is not limited to fresh tissue, (2) it avoids tissue dissociation-induced transcriptional changes, and (3) it includes a novel, automated quality control pipeline. For complete details on the use and execution of this protocol, please refer to Ruf-Zamojski et al. (2021) and Zhang et al. (2022).

Asymptomatic SARS-CoV-2 Infection Is Associated With Higher Levels of Serum IL-17C, Matrix Metalloproteinase 10 and Fibroblast Growth Factors Than Mild Symptomatic COVID-19.

Young adults infected with SARS-CoV-2 are frequently asymptomatic or develop only mild disease. Because capturing representative mild and asymptomatic cases require active surveillance, they are less characterized than moderate or severe cases of COVID-19. However, a better understanding of SARS-CoV-2 asymptomatic infections might shed light into the immune mechanisms associated with the control of symptoms and protection. To this aim, we have determined the temporal dynamics of the humoral immune response, as well as the serum inflammatory profile, of mild and asymptomatic SARS-CoV-2 infections in a cohort of 172 initially seronegative prospectively studied United States Marine recruits, 149 of whom were subsequently found to be SARS-CoV-2 infected. The participants had blood samples taken, symptoms surveyed and PCR tests for SARS-CoV-2 performed periodically for up to 105 days. We found similar dynamics in the profiles of viral load and in the generation of specific antibody responses in asymptomatic and mild symptomatic participants. A proteomic analysis using an inflammatory panel including 92 analytes revealed a pattern of three temporal waves of inflammatory and immunoregulatory mediators, and a return to baseline for most of the inflammatory markers by 35 days post-infection. We found that 23 analytes were significantly higher in those participants that reported symptoms at the time of the first positive SARS-CoV-2 PCR compared with asymptomatic participants, including mostly chemokines and cytokines associated with inflammatory response or immune activation (i.e., TNF-α, TNF-ß, CXCL10, IL-8). Notably, we detected 7 analytes (IL-17C, MMP-10, FGF-19, FGF-21, FGF-23, CXCL5 and CCL23) that were higher in asymptomatic participants than in participants with symptoms; these are known to be involved in tissue repair and may be related to the control of symptoms. Overall, we found a serum proteomic signature that differentiates asymptomatic and mild symptomatic infections in young adults, including potential targets for developing new therapies and prognostic tests.

Single nucleus transcriptome and chromatin accessibility of postmortem human pituitaries reveal diverse stem cell regulatory mechanisms.

Despite their importance in tissue homeostasis and renewal, human pituitary stem cells (PSCs) are incompletely characterized. We describe a human single nucleus RNA-seq and ATAC-seq resource from pediatric, adult, and aged postmortem pituitaries (snpituitaryatlas.princeton.edu) and characterize cell-type-specific gene expression and chromatin accessibility programs for all major pituitary cell lineages. We identify uncommitted PSCs, committing progenitor cells, and sex differences. Pseudotime trajectory analysis indicates that early-life PSCs are distinct from the other age groups. Linear modeling of same-cell multiome data identifies regulatory domain accessibility sites and transcription factors that are significantly associated with gene expression in PSCs compared with other cell types and within PSCs. We identify distinct deterministic mechanisms that contribute to heterogeneous marker expression within PSCs. These findings characterize human stem cell lineages and reveal diverse mechanisms regulating key PSC genes and cell type identity.

Skeletal muscle transcriptome response to a bout of endurance exercise in physically active and sedentary older adults.

Age-related declines in cardiorespiratory fitness and physical function are mitigated by regular endurance exercise in older adults. This may be due, in part, to changes in the transcriptional program of skeletal muscle following repeated bouts of exercise. However, the impact of chronic exercise training on the transcriptional response to an acute bout of endurance exercise has not been clearly determined. Here, we characterized baseline differences in muscle transcriptome and exercise-induced response in older adults who were active/endurance trained or sedentary. RNA-sequencing was performed on vastus lateralis biopsy specimens obtained before, immediately after, and 3 h following a bout of endurance exercise (40 min of cycling at 60%-70% of heart rate reserve). Using a recently developed bioinformatics approach, we found that transcript signatures related to type I myofibers, mitochondria, and endothelial cells were higher in active/endurance-trained adults and were associated with key phenotypic features including V̇o2peak, ATPmax, and muscle fiber proportion. Immune cell signatures were elevated in the sedentary group and linked to visceral and intermuscular adipose tissue mass. Following acute exercise, we observed distinct temporal transcriptional signatures that were largely similar among groups. Enrichment analysis revealed catabolic processes were uniquely enriched in the sedentary group at the 3-h postexercise timepoint. In summary, this study revealed key transcriptional signatures that distinguished active and sedentary adults, which were associated with difference in oxidative capacity and depot-specific adiposity. The acute response signatures were consistent with beneficial effects of endurance exercise to improve muscle health in older adults irrespective of exercise history and adiposity.NEW & NOTEWORTHY Muscle transcript signatures associated with oxidative capacity and immune cells underlie important phenotypic and clinical characteristics of older adults who are endurance trained or sedentary. Despite divergent phenotypes, the temporal transcriptional signatures in response to an acute bout of endurance exercise were largely similar among groups. These data provide new insight into the transcriptional programs of aging muscle and the beneficial effects of endurance exercise to promote healthy aging in older adults.

Anti-invasive efficacy and survival benefit of the YAP-TEAD inhibitor verteporfin in preclinical glioblastoma models.

BACKGROUND: Glioblastoma (GBM) remains a largely incurable disease as current therapy fails to target the invasive nature of glioma growth in disease progression and recurrence. Here, we use the FDA-approved drug and small molecule Hippo inhibitor Verteporfin (VP) to target YAP-TEAD activity, known to mediate convergent aspects of tumor invasion/metastasis, and assess the drug's efficacy and survival benefit in GBM models. METHODS: Up to 8 low-passage patient-derived GBM cell lines with distinct genomic drivers, including 3 primary/recurrent pairs, were treated with VP or vehicle (VEH) to assess in vitro effects on proliferation, migration, invasion, YAP-TEAD activity, and transcriptomics. Patient-derived orthotopic xenograft (PDX) models were used to assess VP's brain penetrance and effects on tumor burden and survival. RESULTS: VP treatment disturbed YAP/TAZ-TEAD activity; disrupted transcriptome signatures related to invasion, epithelial-to-mesenchymal, and proneural-to-mesenchymal transition, phenocopying TEAD1-knockout effects; and impaired tumor migration/invasion dynamics across primary and recurrent GBM lines. In an aggressive orthotopic PDX GBM model, short-term VP treatment consistently diminished core and infiltrative tumor burden, which was associated with decreased tumor expression of Ki67, nuclear YAP, TEAD1, and TEAD-associated targets EGFR, CDH2, and ITGB1. Finally, long-term VP treatment appeared nontoxic and conferred survival benefit compared to VEH in 2 PDX models: as monotherapy in primary (de novo) GBM and in combination with Temozolomide chemoradiation in recurrent GBM, where VP treatment associated with increased MGMT methylation. CONCLUSIONS: We demonstrate combined anti-invasive and anti-proliferative efficacy for VP with survival benefit in preclinical GBM models, indicating potential therapeutic value of this already FDA-approved drug if repurposed for GBM patients.

Attenuated activation of pulmonary immune cells in mRNA-1273-vaccinated hamsters after SARS-CoV-2 infection.

The mRNA-1273 vaccine is effective against SARS-CoV-2 and was granted emergency use authorization by the FDA. Clinical studies, however, cannot provide the controlled response to infection and complex immunological insight that are only possible with preclinical studies. Hamsters are the only model that reliably exhibits severe SARS-CoV-2 disease similar to that in hospitalized patients, making them pertinent for vaccine evaluation. We demonstrate that prime or prime-boost administration of mRNA-1273 in hamsters elicited robust neutralizing antibodies, ameliorated weight loss, suppressed SARS-CoV-2 replication in the airways, and better protected against disease at the highest prime-boost dose. Unlike in mice and nonhuman primates, low-level virus replication in mRNA-1273-vaccinated hamsters coincided with an anamnestic response. Single-cell RNA sequencing of lung tissue permitted high-resolution analysis that is not possible in vaccinated humans. mRNA-1273 prevented inflammatory cell infiltration and the reduction of lymphocyte proportions, but enabled antiviral responses conducive to lung homeostasis. Surprisingly, infection triggered transcriptome programs in some types of immune cells from vaccinated hamsters that were shared, albeit attenuated, with mock-vaccinated hamsters. Our results support the use of mRNA-1273 in a 2-dose schedule and provide insight into the potential responses within the lungs of vaccinated humans who are exposed to SARS-CoV-2.

Comparing Host Module Activation Patterns and Temporal Dynamics in Infection by Influenza H1N1 Viruses.

Influenza is a serious global health threat that shows varying pathogenicity among different virus strains. Understanding similarities and differences among activated functional pathways in the host responses can help elucidate therapeutic targets responsible for pathogenesis. To compare the types and timing of functional modules activated in host cells by four influenza viruses of varying pathogenicity, we developed a new DYNAmic MOdule (DYNAMO) method that addresses the need to compare functional module utilization over time. This integrative approach overlays whole genome time series expression data onto an immune-specific functional network, and extracts conserved modules exhibiting either different temporal patterns or overall transcriptional activity. We identified a common core response to influenza virus infection that is temporally shifted for different viruses. We also identified differentially regulated functional modules that reveal unique elements of responses to different virus strains. Our work highlights the usefulness of combining time series gene expression data with a functional interaction map to capture temporal dynamics of the same cellular pathways under different conditions. Our results help elucidate conservation of the immune response both globally and at a granular level, and provide mechanistic insight into the differences in the host response to infection by influenza strains of varying pathogenicity.