Search

Sex bias exists in the development and progression of nonreproductive organ cancers, but the underlying mechanisms are enigmatic. Studies so far have focused largely on sexual dimorphisms in cancer biology and socioeconomic factors. Here, we establish a role for CD8 + T cell–dependent antitumor immunity in mediating sex differences in tumor aggressiveness, which is driven by the gonadal androgen but not sex chromosomes. A male bias exists in the frequency of intratumoral antigen-experienced Tcf7 /TCF1 + progenitor exhausted CD8 + T cells that are devoid of effector activity as a consequence of intrinsic androgen receptor (AR) function. Mechanistically, we identify a novel sex-specific regulon in progenitor exhausted CD8 + T cells and a pertinent contribution from AR as a direct transcriptional transactivator of Tcf7 /TCF1. The T cell–intrinsic function of AR in promoting CD8 + T cell exhaustion in vivo was established using multiple approaches including loss-of-function studies with CD8-specific Ar knockout mice. Moreover, ablation of the androgen-AR axis rewires the tumor microenvironment to favor effector T cell differentiation and potentiates the efficacy of anti–PD-1 immune checkpoint blockade. Collectively, our findings highlight androgen-mediated promotion of CD8 + T cell dysfunction in cancer and imply broader opportunities for therapeutic development from understanding sex disparities in health and disease.

The ongoing rise in opioid use disorder (OUD) has made it imperative to better model the individual variation within the human population that contributes to OUD vulnerability. Using animal models that capture such variation can be a useful tool. Individual variation in novelty-induced locomotion is predictive of substance use disorder (SUD) propensity. In this model, rats are characterized as high-responders (HR) or low-responders (LR) using a median split based on distance travelled during a locomotor test, and HR rats are generally found to exhibit a more SUD vulnerable behavioral phenotype.The HR/LR model has commonly been used to assess behaviors in male rats using psychostimulants, with limited knowledge of the predictive efficacy of this model in females or the use of an opioid as the reward. In the current study, we assessed several behaviors across the different phases of drug addiction (heroin taking, refraining, and seeking) in over 500 male and female heterogeneous stock rats run at two geographically separate locations. Rats were characterized as HRs or LRs within each sex for analysis.Overall, females exhibit a more OUD vulnerable phenotype relative to males. Additionally, the HR/LR model was predictive of OUD-like behaviors in male, but not female rats. Furthermore, phenotypes did not differ in anxiety-related behaviors, reacquisition of heroin-taking, or punished heroin-taking behavior in either sex.These results emphasize the importance of assessing females in models of individual variation in SUD and highlight limitations in using the HR/LR model to assess OUD propensity.

The advent of high throughput spatial transcriptomics (HST) has allowed for unprecedented characterization of spatially distinct cell communities within a tissue sample. While a wide range of computational tools exist for detecting cell communities in HST data, none allow for characterization of community connectivity, i.e., the relative similarity of cells within and between found communities – an analysis task that can elucidate cellular dynamics in important settings such as the tumor microenvironment. To address this gap, we introduce the concept of analysis of community connectivity (ACC), which entails not only labeling distinct cell communities within a tissue sample, but understanding the relative similarity of cells within and between communities. We develop a Bayesian multi-layer network model called BANYAN for integration of spatial and gene expression information to achieve ACC. We use BANYAN to implement ACC in invasive ductal carcinoma, and uncover distinct community structure relevant to the interaction of cell types within the tumor microenvironment. Next, we show how ACC can help clarify ambiguous annotations in a human white adipose tissue sample. Finally, we demonstrate BANYAN’s ability to recover community connectivity structure via a simulation study based on real sagittal mouse brain HST data.AvailabilityAn R package banyan is available at https://github.com/carter-allen/banyan.Contactchung.911@osu.eduSupplementary informationSupplementary data are available online.Author SummaryThe proliferation of spatial transcriptomics technologies have prompted the development of numerous statistical models for characterizing the makeup of a tissue sample in terms of distinct cell sub-populations. However, existing methods regard inferred sub-populations as static entities and do not offer any ability to discover the relative similarity of cells within and between communities, thereby obfuscating the true interactive nature of cells in a tissue sample. We develop BANYAN: a statistical model for implementing analysis of community connectivity (ACC), i.e., the process of inferring the similarity of cells within and between cell sub-populations. We demonstrate the utility of ACC through the analysis of a publicly available breast cancer data set, which revealed distinct community structure between tumor suppressive and invasive cancer cell sub-populations. We then showed how ACC may help elucidate ambiguous sub-population annotations in a publicly available human white adipose tissue data set. Finally, we implement a simulation study to validate BANYAN’s ability to recover true community connectivity structure in HST data.

Dendritic cells (DCs) are professional APCs equipped with MHC-restricted Ags, costimulations, and cytokines that effectively prime and differentiate naive T cells into distinct functional subsets. The immune signals that DCs carry reflect the route of Ag uptake and the innate stimuli they received. In the mucosal tissues, owing to the great variety of foreign Ags and inflammatory cues, DCs are predominantly activated and migratory. In the small intestine, CD4 Th17 cells are abundant and have been shown to be regulated by DCs and macrophages. Using a mouse commensal bacteria experimental model, we identified that the early priming step of commensal-driven Th17 cells is controlled by bona fide Zbtb46-expressing DCs. CCR7-dependent migration of type 2 DCs (DC2s) from the small intestine to the mesenteric lymph nodes (MLNs) is essential for the activation of naive CD4 T cells. The migratory DC2 population in the MLNs is almost exclusively Esam+ cells. Single-cell RNA sequencing highlighted the abundance of costimulatory markers (CD40 and OX40) and chemokines (Ccl22 and Cxcl16) on MLN migratory DCs. Further resolution of MLN migratory DC2s revealed that the Th17-polarizing cytokine IL-6 colocalizes with DC2s expressing CD40, Ccl17, and Ccl22. Thus, early Th17 cell differentiation is initiated by a small subset of migratory DC2s in the gut-draining lymph nodes.

High throughput spatial transcriptomics (HST) technologies have allowed for identification of distinct cell sub-populations in tissue samples, i.e., tissue architecture identification. However, existing methods do not allow for simultaneous analysis of multiple HST samples. Moreover, standard tissue architecture identification approaches do not provide uncertainty measures. Finally, no existing frameworks have integrated deep learning with Bayesian statistical models for HST data analyses. To address these gaps, we developed MAPLE: a hybrid deep learning and Bayesian modeling framework for detection of spatially informed cell spot sub-populations, uncertainty quantification, and inference of group effects in multi-sample HST experiments. MAPLE includes an embedded regression model to explain cell sub-population abundance in terms of available covariates such as treatment group, disease status, or tissue region. We demonstrate the capability of MAPLE to achieve accurate, comprehensive, and interpretable tissue architecture inference through four case studies that spanned a variety of organs in both humans and animal models.AvailabilityAn R package maple is available at https://github.com/carter-allen/maple.Contactchung.911@osu.eduSupplementary informationSupplementary data are available online.

Opioid use disorder is a psychological condition that affects over 200,000 people per year in the U.S., causing the Centers for Disease Control and Prevention to label the crisis as a rapidly spreading public health epidemic. The behavioral relationship between opioid exposure and development of opioid use disorder (OUD) varies greatly between individuals, implying existence of sup-populations with varying degrees of opioid vulnerability. However, effective pre-clinical identification of these sub-populations remains challenging due to the complex multivariate measurements employed in animal models of OUD. In this study, we propose a novel non-linear network-based data analysis workflow that employs seven behavioral traits to identify opioid use sub-populations and assesses contributions of behavioral variables to opioid vulnerability and resiliency. Through this analysis workflow we determined how behavioral variables across heroin taking, refraining and seeking interact with one another to identify potentially heroin resilient and vulnerable behavioral sub-populations. Data were collected from over 400 heterogeneous stock rats in two geographically distinct locations. Rats underwent heroin self-administration training, followed by a progressive ratio and heroin-primed reinstatement test. Next, rats underwent extinction training and a cue-induced reinstatement test. To enter the analysis workflow, we integrated data from different cohorts of rats and removed possible batch effects. We then constructed a rat-rat similarity network based on their behavioral patterns and implemented community detection on this similarity network using a Bayesian degree-corrected stochastic block model to uncover sub-populations of rats with differing levels of opioid vulnerability. We identified three statistically distinct clusters corresponding to distinct behavioral sub-populations, vulnerable, resilient and intermediate for heroin use, refraining and seeking. We implement this analysis workflow as an open source R package, named mlsbm.

Background Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19) through direct lysis of infected lung epithelial cells, which releases damage-associated molecular patterns and induces a pro-inflammatory cytokine milieu causing systemic inflammation. Anti-viral and anti-inflammatory agents have shown limited therapeutic efficacy. Soluble CD24 (CD24Fc) blunts the broad inflammatory response induced by damage-associated molecular patterns via binding to extracellular high mobility group box 1 and heat shock proteins, as well as regulating the downstream Siglec10-Src homology 2 domain–containing phosphatase 1 pathway. A recent randomized phase III trial evaluating CD24Fc for patients with severe COVID-19 (SAC-COVID; NCT04317040) demonstrated encouraging clinical efficacy. Methods Using a systems analytical approach, we studied peripheral blood samples obtained from patients enrolled at a single institution in the SAC-COVID trial to discern the impact of CD24Fc treatment on immune homeostasis. We performed high dimensional spectral flow cytometry and measured the levels of a broad array of cytokines and chemokines to discern the impact of CD24Fc treatment on immune homeostasis in patients with COVID-19. Results Twenty-two patients were enrolled, and the clinical characteristics from the CD24Fc vs. placebo groups were matched. Using high-content spectral flow cytometry and network-level analysis, we found that patients with severe COVID-19 had systemic hyper-activation of multiple cellular compartments, including CD8+ T cells, CD4+ T cells, and CD56+ natural killer cells. Treatment with CD24Fc blunted this systemic inflammation, inducing a return to homeostasis in NK and T cells without compromising the anti-Spike protein antibody response. CD24Fc significantly attenuated the systemic cytokine response and diminished the cytokine coexpression and network connectivity linked with COVID-19 severity and pathogenesis. Conclusions Our data demonstrate that CD24Fc rapidly down-modulates systemic inflammation and restores immune homeostasis in SARS-CoV-2-infected individuals, supporting further development of CD24Fc as a novel therapeutic against severe COVID-19.

In studies of infant growth, an important research goal is to identify latent clusters of infants with delayed motor development-a risk factor for adverse outcomes later in life. However, there are numerous statistical challenges in modeling motor development: the data are typically skewed, exhibit intermittent missingness, and are correlated across repeated measurements over time. Using data from the Nurture study, a cohort of approximately 600 mother-infant pairs, we develop a flexible Bayesian mixture model for the analysis of infant motor development. First, we model developmental trajectories using matrix skew-normal distributions with cluster-specific parameters to accommodate dependence and skewness in the data. Second, we model the cluster-membership probabilities using a Polya-Gamma data-augmentation scheme, which improves predictions of the cluster-membership allocations. Lastly, we impute missing responses from conditional multivariate skew-normal distributions. Bayesian inference is achieved through straightforward Gibbs sampling. Through simulation studies, we show that the proposed model yields improved inferences over models that ignore skewness or adopt conventional imputation methods. We applied the model to the Nurture data and identified two distinct developmental clusters, as well as detrimental effects of food insecurity on motor development. These findings can aid investigators in targeting interventions during this critical early-life developmental window.

High throughput spatial transcriptomics (HST) is a rapidly emerging class of experimental technologies that allow for profiling gene expression in tissue samples at or near single-cell resolution while retaining the spatial location of each sequencing unit within the tissue sample. Through analyzing HST data, we seek to identify sub-populations of cells within a tissue sample that may inform biological phenomena. Existing computational methods either ignore the spatial heterogeneity in gene expression profiles, fail to account for important statistical features such as skewness, or are heuristic-based network clustering methods that lack the inferential benefits of statistical modeling. To address this gap, we develop SPRUCE: a Bayesian spatial multivariate finite mixture model based on multivariate skew-normal distributions, which is capable of identifying distinct cellular sub-populations in HST data. We further implement a novel combination of Pólya-Gamma data augmentation and spatial random effects to infer spatially correlated mixture component membership probabilities without relying on approximate inference techniques. Via a simulation study, we demonstrate the detrimental inferential effects of ignoring skewness or spatial correlation in HST data. Using publicly available human brain HST data, SPRUCE outperforms existing methods in recovering expertly annotated brain layers. Finally, our application of SPRUCE to human breast cancer HST data indicates that SPRUCE can distinguish distinct cell populations within the tumor microenvironment. An R package spruce for fitting the proposed models is available through The Comprehensive R Archive Network.

Spatially resolved transcriptomics provides a new way to define spatial contexts and understand biological functions in complex diseases. Although some computational frameworks can characterize spatial context via various clustering methods, the detailed spatial architectures and functional zonation often cannot be revealed and localized due to the limited capacities of associating spatial information. We present RESEPT, a deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics. Given inputs as gene expression or RNA velocity, RESEPT learns a three-dimensional embedding with a spatial retained graph neural network from the spatial transcriptomics. The embedding is then visualized by mapping as color channels in an RGB image and segmented with a supervised convolutional neural network model. Based on a benchmark of sixteen 10x Genomics Visium spatial transcriptomics datasets on the human cortex, RESEPT infers and visualizes the tissue architecture accurately. It is noteworthy that, for the in-house AD samples, RESEPT can localize cortex layers and cell types based on a pre-defined region-or cell-type-specific genes and furthermore provide critical insights into the identification of amyloid-beta plaques in Alzheimer’s disease. Interestingly, in a glioblastoma sample analysis, RESEPT distinguishes tumor-enriched, non-tumor, and regions of neuropil with infiltrating tumor cells in support of clinical and prognostic cancer applications.

High throughput spatial transcriptomics (HST) is a rapidly emerging class of experimental technologies that allow for profiling gene expression in tissue samples at or near single-cell resolution while retaining the spatial location of each sequencing unit within the tissue sample. Through analyzing HST data, we seek to identify sub-populations within a tissue sample that reflect distinct cell types or states. Existing methods either ignore the spatial heterogeneity in gene expression profiles, fail to account for important statistical features such as skewness, or are heuristic network-based clustering methods that lack the inferential benefits of statistical modeling. To address this gap, we develop SPRUCE: a Bayesian spatial multivariate finite mixture model based on multivariate skew-normal distributions, which is capable of identifying distinct cellular sub-populations in HST data. We further implement a novel combination of Pólya–Gamma data augmentation and spatial random effects to infer spatially correlated mixture component membership probabilities without relying on approximate inference techniques. Via a simulation study, we demonstrate the detrimental inferential effects of ignoring skewness or spatial correlation in HST data. Using publicly available human brain HST data, SPRUCE outperforms existing methods in recovering expertly annotated brain layers. Finally, our application of SPRUCE to human breast cancer HST data indicates that SPRUCE can distinguish distinct cell populations within the tumor microenvironment.

Dendritic cells (DCs) are professional antigen-presenting cells equipped with MHC-restricted antigens, co-stimulations, and cytokines that effectively prime and differentiate naïve T cells into distinct functional subsets. The immune signals that DCs carry reflect the route of antigen uptake and the innate stimuli they received. In the mucosal tissues, owing to the great variety of foreign antigens and inflammatory cues, DCs are predominantly activated and migratory. The gut commensal bacteria contribute to immune health by educating the immune system at this unique location interfacing with the outside environment. In the small intestine, CD4 Th17 cells are abundant and play a critical role in mucosal defense against bacterial and fungal infections. We identified that the early priming step of commensal-driven Th17 cells is controlled by bona fide Zbtb46-expressing DCs. CCR7-dependent migration of DC2s from the small intestine to the mesenteric lymph nodes is essential for the activation of naïve CD4 T cells. The migratory DC2 population in the MLN are almost exclusively Esam+ cells. Single-cell RNA sequencing highlighted the abundance of co-stimulatory markers (CD40 and OX40) and chemokines (Ccl22 and Cxcl16) on MLN migratory DCs. Further resolution of MLN migratory DC2s revealed that the Th17-polarizing cytokine IL-6 colocalizes with DC2s expressing CD40, Ccl17, and Ccl22. Our study indicates that the mucosal Th17 cell response is regulated by a restricted subset of migratory DC2s in the gut draining lymph nodes. Supported by NIAID U01AI125859

OBJECTIVES: Food insecurity has been associated with obesity, but previous studies are inconsistent and few included infants. We examined associations between household food security and infant adiposity and assessed the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) and Supplemental Nutrition Assistance Program (SNAP) as effect modifiers. We hypothesized that infants from food-insecure households would have greater adiposity, with attenuation by WIC and SNAP. METHODS: We repeatedly measured 666 infants from the southeastern United States in 2013–2017. We categorized households as high, marginal, low, or very low using the US Household Food Security Survey Module. Outcomes were BMI z score, subscapular and triceps skinfold-for-age z score, the sum of subscapular and triceps skinfolds, the ratio of subscapular and triceps skinfolds, and BMI z score ≥1 (at risk for overweight). We used covariate-adjusted repeated-measures linear and logistic regressions. RESULTS: Of infants, 68.6% were Black and 60.5% had household incomes CONCLUSIONS: In larger and longer studies, researchers should examine food security and adiposity in young children.

ObjectivesTo assess associations between maternal prenatal diet quality and infant adiposity.DesignThe design was a prospective birth cohort.SettingWe used data from the Nurture study, a cohort of women and their infants residing in the southeastern USA.Participants and exposure assessmentBetween 2013 and 2015, we enrolled 860 women between 20 and 36 weeks’ gestation. After reconsenting at delivery and excluding women with implausible calorie intakes, we measured dietary intake using the Block food frequency questionnaire, and assessed diet quality using a modified Alternate Healthy Eating Index 2010 (AHEI-2010), which assessed intake of 10 food categories, including fruits, vegetables, whole grains, nuts/legumes, fats, meats, beverages and sodium (excluding alcohol).OutcomesWe assessed birth weight for gestational age z-score, small and large for gestational age, low birth weight and macrosomia. Outcomes at 6 and 12 months were weight-for-length z-score, sum of subscapular and triceps skinfold thickness (SS+TR) and subscapular-to-triceps skinfold ratio (SS:TR).ResultsAmong mothers, 70.2% were black and 20.9% were white; less than half (45.2%) reported having a high school diploma or less. Among infants, 8.7% were low birth weight and 8.6% were small for gestational age. Unadjusted estimates showed that a higher AHEI-2010 score, was associated with a higher birth weight for gestational z-score (β=0.01; 95% CI 0.002 to 0.02; p=0.02) and a greater likelihood of macrosomia (OR=1.04; 95% CI 1.004 to 1.09; p=0.03). After adjustment, maternal diet quality was not associated with infant adiposity at birth, 6 or 12 months.ConclusionsAlthough poor maternal diet quality during pregnancy was not associated with infant adiposity in our study, maternal diet during pregnancy may still be an important and modifiable factor of public health importance.

In systems biology, inference of functional associations among genes is compelling because the construction of functional association networks facilitates biomarker discovery. Specifically, such gene associations in human can help identify putative biomarkers that can be used as diagnostic tools in treating patients. Although biomedical literature is considered a valuable data source for this task, currently only a limited number of webservers are available for mining gene-gene associations from the vast amount of biomedical literature using text mining techniques. Moreover, these webservers often have limited coverage of biomedical literature and also lack efficient and user-friendly tools to interpret and visualize mined relationships among genes. To address these limitations, we developed GAIL (Gene-gene Association Inference based on biomedical Literature), an interactive webserver that infers human gene-gene associations from Gene Ontology (GO) guided biomedical literature mining and provides dynamic visualization of the resulting association networks and various gene set enrichment analysis tools. We evaluate the utility and performance of GAIL with applications to gene signatures associated with systemic lupus erythematosus and breast cancer. Results show that GAIL allows effective interrogation and visualization of gene-gene networks and their subnetworks, which facilitates biological understanding of gene-gene associations. GAIL is available at http://chunglab.io/GAIL/.

OBJECTIVES: Infancy represents a critical period for growth and development, and food insecurity during this time may impact later health. Few previous studies have assessed food insecurity and obesity in young children in the US, and even fewer have targeted infants. Moreover, the ability of federal food assistance programs to affect this relation remains unclear. METHODS: We examined 666 infants in the US-based Nurture birth cohort. We conducted home visits when infants were 3, 6, 9, and 12 months. We measured household food insecurity via maternal report using the US Household Food Security Survey Module: Six-Item Short Form. We categorized infants as living in full, marginal, low, or very low food security households. We calculated infant body mass index (BMI) z-score from measured lengths and weights using World Health Organization reference standards. We documented participation in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) and Supplemental Nutrition Assistance Program (SNAP) at each home visit. We used repeated-measures linear regression models with imputed data to examine covariate-adjusted associations between household food security and BMI z-score throughout infancy. RESULTS: Nearly 70% of infants were black and 49% were female. At 3 months, 32.8% of infants were from households with low or very low food security. Infant BMI increased from months 3 to 12 in all food security groups (Figure). However, after adjustment for potential confounders, low household food insecurity (0.18; 95% CI 0.05, 0.32; P = 0.01) and very low household food insecurity (0.22; 95% CI 0.05, 0.38; P = 0.01) were associated with higher infant BMI z-score throughout infancy but marginal household food security was not (0.001; 95% CI −0.13, 0.13; P = 0.99). There was no evidence of effect modification by participation in either WIC (P = 0.36) or SNAP (P = 0.67). CONCLUSIONS: Infants from food insecure households had higher BMIs throughout infancy, and this association was not attenuated by WIC or SNAP participation. About one-third of infants in this cohort were living in food insecure households, which raises substantial concern from both a public health and obesity prevention perspective. FUNDING SOURCES: NIDDK, National Institutes of Health. SUPPORTING TABLES, IMAGES AND/OR GRAPHS

We expressed the bovine herpesvirus 1 (BHV-1) glycoprotein IV (gIV) in bovine cells. The protein expressed was identical in molecular mass and antigenic reactivity to the native gIV protein but was localized in the cytoplasm. Expressing cells were partially resistant to BHV-1, herpes simplex virus, and pseudorabies virus, as shown by a 10- to 1,000-fold-lower number of plaques forming on these cells than on control cells. The level of resistance depended on the level of gIV expression and the type and amount of challenge virus. These data are consistent with previous reports by others that cellular expression of the BHV-1 gIV homologs, herpes simplex virus glycoprotein D, and pseudorabies virus glycoprotein gp50 provide partial resistance against infection with these viruses. We have extended these findings by showing that once BHV-1 enters gIV-expressing cells, it replicates and spreads normally, as shown by the normal size of BHV-1 plaques and the delayed but vigorous synthesis of viral proteins. Our data are consistent with the binding of BHV-1 gIV to a cellular receptor required for initial penetration by all three herpesviruses and interference with the function of that receptor molecule.

A method is described for expressing bovine herpes virus-1 (BHV-1) glycoproteins gI and gIII in eukaryotic cells. These cells are prepared by transfection with plasmids containing the BHV-1 glycoprotein genes. The BHV-1 glycoproteins from these expressing cells as well as from BHV-1 infected cells can be purified by monoclonal antibody affinity chromatography. The BHV-1 expressing cells can be used as targets in immunologic assays and the affinity-purified glycoproteins are suitable for use in in vitro immunologic assays or as immunogens to induce virus neutralizing antibodies.

Summary We expressed the bovine herpesvirus type 1 (BHV-1) glycoproteins, gI and gIII, in bovine cells using a bovine papillomavirus vector. The proteins expressed by these cells had the same M r as the native BHV-1 proteins and monoclonal antibodies detected no differences in their antigenic structure. Cells expressing gI were infected with either BHV-1 or herpes simplex virus type 1 (HSV-1). The number of plaques in gI-expressing cells was similar to that seen with normal fibroblasts infected with BHV-1 or HSV-1. However, BHV-1 or HSV-1 plaques produced in gI-expressing cells were smaller and darker than those seen in normal fibroblasts indicating an interference with cell-to-cell transmission or cellular lysis. Virus growth curves and [35S]methionine labelling of BHV-1-infected gI-expressing cells showed no difference in virus production, virus protein synthesis or cellular protein shutdown when compared to BHV-1-infected normal cells. This led us to conclude that the gI protein may interfere with a cellular protein(s) responsible for the cytopathic effects of BHV-1 infection. Cells expressing gIII were fully susceptible to BHV-1 infection.