Molecular adaptations in response to exercise training are associated with tissue-specific transcriptomic and epigenomic signatures.

Regular exercise has many physical and brain health benefits, yet the molecular mechanisms mediating exercise effects across tissues remain poorly understood. Here we analyzed 400 high-quality DNA methylation, ATAC-seq, and RNA-seq datasets from eight tissues from control and endurance exercise-trained (EET) rats. Integration of baseline datasets mapped the gene location dependence of epigenetic control features and identified differing regulatory landscapes in each tissue. The transcriptional responses to 8 weeks of EET showed little overlap across tissues and predominantly comprised tissue-type enriched genes. We identified sex differences in the transcriptomic and epigenomic changes induced by EET. However, the sex-biased gene responses were linked to shared signaling pathways. We found that many G protein-coupled receptor-encoding genes are regulated by EET, suggesting a role for these receptors in mediating the molecular adaptations to training across tissues. Our findings provide new insights into the mechanisms underlying EET-induced health benefits across organs.

MousiPLIER: A Mouse Pathway-Level Information Extractor Model.

High-throughput gene expression profiling measures individual gene expression across conditions. However, genes are regulated in complex networks, not as individual entities, limiting the interpretability of gene expression data. Machine learning models that incorporate prior biological knowledge are a powerful tool to extract meaningful biology from gene expression data. Pathway-level information extractor (PLIER) is an unsupervised machine learning method that defines biological pathways by leveraging the vast amount of published transcriptomic data. PLIER converts gene expression data into known pathway gene sets, termed latent variables (LVs), to substantially reduce data dimensionality and improve interpretability. In the current study, we trained the first mouse PLIER model on 190,111 mouse brain RNA-sequencing samples, the greatest amount of training data ever used by PLIER. We then validated the mousiPLIER approach in a study of microglia and astrocyte gene expression across mouse brain aging. mousiPLIER identified biological pathways that are significantly associated with aging, including one latent variable (LV41) corresponding to striatal signal. To gain further insight into the genes contained in LV41, we performed k-means clustering on the training data to identify studies that respond strongly to LV41. We found that the variable was relevant to striatum and aging across the scientific literature. Finally, we built a Web server (http://mousiplier.greenelab.com/) for users to easily explore the learned latent variables. Taken together, this study defines mousiPLIER as a method to uncover meaningful biological processes in mouse brain transcriptomic studies.

MousiPLIER: A Mouse Pathway-Level Information Extractor Model.

High throughput gene expression profiling is a powerful approach to generate hypotheses on the underlying causes of biological function and disease. Yet this approach is limited by its ability to infer underlying biological pathways and burden of testing tens of thousands of individual genes. Machine learning models that incorporate prior biological knowledge are necessary to extract meaningful pathways and generate rational hypothesis from the vast amount of gene expression data generated to date. We adopted an unsupervised machine learning method, Pathway-level information extractor (PLIER), to train the first mouse PLIER model on 190,111 mouse brain RNA-sequencing samples, the greatest amount of training data ever used by PLIER. mousiPLER converted gene expression data into a latent variables that align to known pathway or cell maker gene sets, substantially reducing data dimensionality and improving interpretability. To determine the utility of mousiPLIER, we applied it to a mouse brain aging study of microglia and astrocyte transcriptomic profiling. We found a specific set of latent variables that are significantly associated with aging, including one latent variable (LV41) corresponding to striatal signal. We next performed k-means clustering on the training data to identify studies that respond strongly to LV41, finding that the variable is relevant to striatum and aging across the scientific literature. Finally, we built a web server (http://mousiplier.greenelab.com/) for users to easily explore the learned latent variables. Taken together this study provides proof of concept that mousiPLIER can uncover meaningful biological processes in mouse transcriptomic studies.

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-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.

LRRC15+ myofibroblasts dictate the stromal setpoint to suppress tumour immunity.

Recent single-cell studies of cancer in both mice and humans have identified the emergence of a myofibroblast population specifically marked by the highly restricted leucine-rich-repeat-containing protein 15 (LRRC15)1-3. However, the molecular signals that underlie the development of LRRC15+ cancer-associated fibroblasts (CAFs) and their direct impact on anti-tumour immunity are uncharacterized. Here in mouse models of pancreatic cancer, we provide in vivo genetic evidence that TGFß receptor type 2 signalling in healthy dermatopontin+ universal fibroblasts is essential for the development of cancer-associated LRRC15+ myofibroblasts. This axis also predominantly drives fibroblast lineage diversity in human cancers. Using newly developed Lrrc15-diphtheria toxin receptor knock-in mice to selectively deplete LRRC15+ CAFs, we show that depletion of this population markedly reduces the total tumour fibroblast content. Moreover, the CAF composition is recalibrated towards universal fibroblasts. This relieves direct suppression of tumour-infiltrating CD8+ T cells to enhance their effector function and augments tumour regression in response to anti-PDL1 immune checkpoint blockade. Collectively, these findings demonstrate that TGFß-dependent LRRC15+ CAFs dictate the tumour-fibroblast setpoint to promote tumour growth. These cells also directly suppress CD8+ T cell function and limit responsiveness to checkpoint blockade. Development of treatments that restore the homeostatic fibroblast setpoint by reducing the population of pro-disease LRRC15+ myofibroblasts may improve patient survival and response to immunotherapy.

Non-negative Independent Factor Analysis disentangles discrete and continuous sources of variation in scRNA-seq data.

MOTIVATION: Single-cell RNA-seq analysis has emerged as a powerful tool for understanding inter-cellular heterogeneity. Due to the inherent noise of the data, computational techniques often rely on dimensionality reduction (DR) as both a pre-processing step and an analysis tool. Ideally, DR should preserve the biological information while discarding the noise. However, if the DR is to be used directly to gain biological insight it must also be interpretable-that is the individual dimensions of the reduction should correspond to specific biological variables such as cell-type identity or pathway activity. Maximizing biological interpretability necessitates making assumption about the data structures and the choice of the model is critical. RESULTS: We present a new probabilistic single-cell factor analysis model, Non-negative Independent Factor Analysis (NIFA), that incorporates different interpretability inducing assumptions into a single modeling framework. The key advantage of our NIFA model is that it simultaneously models uni- and multi-modal latent factors, and thus isolates discrete cell-type identity and continuous pathway activity into separate components. We apply our approach to a range of datasets where cell-type identity is known, and we show that NIFA-derived factors outperform results from ICA, PCA, NMF and scCoGAPS (an NMF method designed for single-cell data) in terms of disentangling biological sources of variation. Studying an immunotherapy dataset in detail, we show that NIFA is able to reproduce and refine previous findings in a single analysis framework and enables the discovery of new clinically relevant cell states. AVAILABILITY AND IMPLEMENTATION: NFIA is a R package which is freely available at GitHub (https://github.com/wgmao/NIFA). The test dataset is archived at https://zenodo.org/record/6286646. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Antibody Responses to SARS-CoV-2 Following an Outbreak Among Marine Recruits With Asymptomatic or Mild Infection.

We investigated serological responses following a SARS-CoV-2 outbreak in spring 2020 on a US Marine recruit training base. 147 participants that were isolated during an outbreak of respiratory illness were enrolled in this study, with visits approximately 6 and 10 weeks post-outbreak (PO). This cohort is comprised of young healthy adults, ages 18-26, with a high rate of asymptomatic infection or mild symptoms, and therefore differs from previously reported longitudinal studies on humoral responses to SARS-CoV-2, which often focus on more diverse age populations and worse clinical presentation. 80.9% (119/147) of the participants presented with circulating IgG antibodies against SARS-CoV-2 spike (S) receptor-binding domain (RBD) at 6 weeks PO, of whom 97.3% (111/114) remained positive, with significantly decreased levels, at 10 weeks PO. Neutralizing activity was detected in all sera from SARS-CoV-2 IgG positive participants tested (n=38) at 6 and 10 weeks PO, without significant loss between time points. IgG and IgA antibodies against SARS-CoV-2 RBD, S1, S2, and the nucleocapsid (N) protein, as well neutralization activity, were generally comparable between those participants that had asymptomatic infection or mild disease. A multiplex assay including S proteins from SARS-CoV-2 and related zoonotic and human endemic betacoronaviruses revealed a positive correlation for polyclonal cross-reactivity to S after SARS-CoV-2 infection. Overall, young adults that experienced asymptomatic or mild SARS-CoV-2 infection developed comparable humoral responses, with no decrease in neutralizing activity at least up to 10 weeks after infection.

DataRemix: a universal data transformation for optimal inference from gene expression datasets.

MOTIVATION: RNA-seq technology provides unprecedented power in the assessment of the transcription abundance and can be used to perform a variety of downstream tasks such as inference of gene-correlation network and eQTL discovery. However, raw gene expression values have to be normalized for nuisance biological variation and technical covariates, and different normalization strategies can lead to dramatically different results in the downstream study. RESULTS: We describe a generalization of singular value decomposition-based reconstruction for which the common techniques of whitening, rank-k approximation and removing the top k principal components are special cases. Our simple three-parameter transformation, DataRemix, can be tuned to reweigh the contribution of hidden factors and reveal otherwise hidden biological signals. In particular, we demonstrate that the method can effectively prioritize biological signals over noise without leveraging external dataset-specific knowledge, and can outperform normalization methods that make explicit use of known technical factors. We also show that DataRemix can be efficiently optimized via Thompson sampling approach, which makes it feasible for computationally expensive objectives such as eQTL analysis. Finally, we apply our method to the Religious Orders Study and Memory and Aging Project dataset, and we report what to our knowledge is the first replicable trans-eQTL effect in human brain. AVAILABILITYAND IMPLEMENTATION: DataRemix is an R package which is freely available at GitHub (https://github.com/wgmao/DataRemix). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Pathway-level information extractor (PLIER) for gene expression data.

A major challenge in gene expression analysis is to accurately infer relevant biological insights, such as variation in cell-type proportion or pathway activity, from global gene expression studies. We present pathway-level information extractor (PLIER) ( https://github.com/wgmao/PLIER and http://gobie.csb.pitt.edu/PLIER ), a broadly applicable solution for this problem that outperforms available cell proportion inference algorithms and can automatically identify specific pathways that regulate gene expression. Our method improves interstudy replicability and reveals biological insights when applied to trans-eQTL (expression quantitative trait loci) identification.

RERconverge: an R package for associating evolutionary rates with convergent traits.

MOTIVATION: When different lineages of organisms independently adapt to similar environments, selection often acts repeatedly upon the same genes, leading to signatures of convergent evolutionary rate shifts at these genes. With the increasing availability of genome sequences for organisms displaying a variety of convergent traits, the ability to identify genes with such convergent rate signatures would enable new insights into the molecular basis of these traits. RESULTS: Here we present the R package RERconverge, which tests for association between relative evolutionary rates of genes and the evolution of traits across a phylogeny. RERconverge can perform associations with binary and continuous traits, and it contains tools for visualization and enrichment analyses of association results. AVAILABILITY AND IMPLEMENTATION: RERconverge source code, documentation and a detailed usage walk-through are freely available at https://github.com/nclark-lab/RERconverge. Datasets for mammals, Drosophila and yeast are available at https://bit.ly/2J2QBnj. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Design, synthesis, and biological evaluation of pyrrolobenzodiazepine-containing hypoxia-activated prodrugs.

The ability of various pyrrolobenzodiazepine(PBD)-containing cytotoxic compounds to function as hypoxia-activated prodrugs was assessed. These molecules incorporated a 1-methyl-2-nitro-1H-imidazole hypoxia-activated trigger (present in the clinically evaluated compound TH-302) in a manner that masked a reactive imine moiety required for cytotoxic activity. Incubation of the prodrugs with cytochrome P450-reductase under normoxic and hypoxic conditions revealed that some, but not all, were efficient substrates for the enzyme. In these experiments, prodrugs derived from PBD-monomers underwent rapid conversion to the parent cytotoxic compounds under low-oxygen conditions while related PBD-dimers did not. The ability of a given prodrug to function as an efficient cytochrome P450-reductase substrate correlated with the ratio of cytotoxic potencies measured for the compound against NCI460 cells under normoxic and hypoxic conditions.

Virus Database and Online Inquiry System Based on Natural Vectors.

We construct a virus database called VirusDB (http://yaulab.math.tsinghua.edu.cn/VirusDB/) and an online inquiry system to serve people who are interested in viral classification and prediction. The database stores all viral genomes, their corresponding natural vectors, and the classification information of the single/multiple-segmented viral reference sequences downloaded from National Center for Biotechnology Information. The online inquiry system serves the purpose of computing natural vectors and their distances based on submitted genomes, providing an online interface for accessing and using the database for viral classification and prediction, and back-end processes for automatic and manual updating of database content to synchronize with GenBank. Submitted genomes data in FASTA format will be carried out and the prediction results with 5 closest neighbors and their classifications will be returned by email. Considering the one-to-one correspondence between sequence and natural vector, time efficiency, and high accuracy, natural vector is a significant advance compared with alignment methods, which makes VirusDB a useful database in further research.

ImmunoPET helps predicting the efficacy of antibody-drug conjugates targeting TENB2 and STEAP1.

The efficacy of antibody-drug conjugates (ADCs) targeted to solid tumors depends on biological processes that are hard to monitor in vivo. 89Zr-immunoPET of the ADC antibodies could help understand the performance of ADCs in the clinic by confirming the necessary penetration, binding, and internalization. This work studied monomethyl auristatin E (MMAE) ADCs against two targets in metastatic castration-resistant prostate cancer, TENB2 and STEAP1, in four patient-derived tumor models (LuCaP35V, LuCaP70, LuCaP77, LuCaP96.1). Three aspects of ADC biology were measured and compared: efficacy was measured in tumor growth inhibition studies; target expression was measured by immunohistochemistry and flow cytometry; and tumor antibody uptake was measured with 111In-mAbs and gamma counting or with 89Zr-immunoPET. Within each model, the mAb with the highest tumor uptake showed the greatest potency as an ADC. Sensitivity between models varied, with the LuCaP77 model showing weak efficacy despite high target expression and high antibody uptake. Ex vivo analysis confirmed the in vivo results, showing a correlation between expression, uptake and ADC efficacy. We conclude that 89Zr-immunoPET data can demonstrate which ADC candidates achieve the penetration, binding, and internalization necessary for efficacy in tumors sensitive to the toxic payload.

Targeting LGR5+ cells with an antibody-drug conjugate for the treatment of colon cancer.

Cancer stem cells (CSCs) are hypothesized to actively maintain tumors similarly to how their normal counterparts replenish differentiated cell types within tissues, making them an attractive therapeutic target for the treatment of cancer. Because most CSC markers also label normal tissue stem cells, it is unclear how to selectively target them without compromising normal tissue homeostasis. We evaluated a strategy that targets the cell surface leucine-rich repeat-containing G protein-coupled receptor 5 (LGR5), a well-characterized tissue stem cell and CSC marker, with an antibody conjugated to distinct cytotoxic drugs. One antibody-drug conjugate (ADC) demonstrated potent tumor efficacy and safety in vivo. Furthermore, the ADC decreased tumor size and proliferation, translating to improved survival in a genetically engineered model of intestinal tumorigenesis. These data demonstrate that ADCs can be leveraged to exploit differences between normal and cancer stem cells to successfully target gastrointestinal cancers.

Distinguishing proteins from arbitrary amino acid sequences.

What kinds of amino acid sequences could possibly be protein sequences? From all existing databases that we can find, known proteins are only a small fraction of all possible combinations of amino acids. Beginning with Sanger's first detailed determination of a protein sequence in 1952, previous studies have focused on describing the structure of existing protein sequences in order to construct the protein universe. No one, however, has developed a criteria for determining whether an arbitrary amino acid sequence can be a protein. Here we show that when the collection of arbitrary amino acid sequences is viewed in an appropriate geometric context, the protein sequences cluster together. This leads to a new computational test, described here, that has proved to be remarkably accurate at determining whether an arbitrary amino acid sequence can be a protein. Even more, if the results of this test indicate that the sequence can be a protein, and it is indeed a protein sequence, then its identity as a protein sequence is uniquely defined. We anticipate our computational test will be useful for those who are attempting to complete the job of discovering all proteins, or constructing the protein universe.

An integrated approach to identify normal tissue expression of targets for antibody-drug conjugates: case study of TENB2.

BACKGROUND AND PURPOSE: The success of antibody-drug conjugates (ADCs) depends on the therapeutic window rendered by the differential expression between normal and pathological tissues. The ability to identify and visualize target expression in normal tissues could reveal causes for target-mediated clearance observed in pharmacokinetic characterization. TENB2 is a prostate cancer target associated with the progression of poorly differentiated and androgen-independent tumour types, and ADCs specific for TENB2 are candidate therapeutics. The objective of this study was to locate antigen expression of TENB2 in normal tissues, thereby elucidating the underlying causes of target-mediated clearance. EXPERIMENTAL APPROACH: A series of pharmacokinetics, tissue distribution and mass balance studies were conducted in mice using a radiolabelled anti-TENB2 ADC. These data were complemented by non-invasive single photon emission computed tomography - X-ray computed tomography imaging and immunohistochemistry. KEY RESULTS: The intestines were identified as a saturable and specific antigen sink that contributes, at least in part, to the rapid target-mediated clearance of the anti-TENB2 antibody and its drug conjugate in rodents. As a proof of concept, we also demonstrated the selective disposition of the ADC in a tumoural environment in vivo using the LuCaP 77 transplant mouse model. High tumour uptake was observed despite the presence of the antigen sink, and antigen specificity was confirmed by antigen blockade. CONCLUSIONS AND IMPLICATIONS: Our findings provide the anatomical location and biological interpretation of target-mediated clearance of anti-TENB2 antibodies and corresponding drug conjugates. Further investigations may be beneficial in addressing the relative contributions to ADC disposition from antigen expression in both normal and pathological tissues.

Differential effects of predosing on tumor and tissue uptake of an 111In-labeled anti-TENB2 antibody-drug conjugate.

UNLABELLED: TENB2, also known as tomoregulin or transmembrane protein with epidermal growth factor-like and 2 follistatin-like domains, is a transmembrane proteoglycan overexpressed in human prostate tumors. This protein is a promising target for antimitotic monomethyl auristatin E (MMAE)-based antibody-drug conjugate (ADC) therapy. Nonlinear pharmacokinetics in normal mice suggested that antigen expression in normal tissues may contribute to targeted mediated disposition. We evaluated a predosing strategy with unconjugated antibody to block ADC uptake in target-expressing tissues in a mouse model while striving to preserve tumor uptake and efficacy. METHODS: Unconjugated, unlabeled antibody was preadministered to mice bearing the TENB2-expressing human prostate explant model, LuCaP 77, followed by a single administration of (111)In-labeled anti-TENB2-MMAE for biodistribution and SPECT/CT studies. A tumor-growth-inhibition study was conducted to determine the pharmacodynamic consequences of predosing. RESULTS: Preadministration of anti-TENB2 at 1 mg/kg significantly increased blood exposure of the radiolabeled ADC and reduced intestinal, hepatic, and splenic uptake while not affecting tumor accretion. Similar tumor-to-heart ratios were measured by SPECT/CT at 24 h with and without the predose. Consistent with this, the preadministration of 0.75 mg/kg did not interfere with efficacy in a tumor-growth study dosed at 0.75 mg or 2.5 mg of ADC per kilogram. CONCLUSION: Overall, the potential to mask peripheral, nontumor antigen uptake while preserving tumor uptake and efficacy could ameliorate toxicity and may significantly affect future dosing strategies for ADCs.