SPADE: spatial deconvolution for domain specific cell-type estimation.

Understanding gene expression in different cell types within their spatial context is a key goal in genomics research. SPADE (SPAtial DEconvolution), our proposed method, addresses this by integrating spatial patterns into the analysis of cell type composition. This approach uses a combination of single-cell RNA sequencing, spatial transcriptomics, and histological data to accurately estimate the proportions of cell types in various locations. Our analyses of synthetic data have demonstrated SPADE's capability to discern cell type-specific spatial patterns effectively. When applied to real-life datasets, SPADE provides insights into cellular dynamics and the composition of tumor tissues. This enhances our comprehension of complex biological systems and aids in exploring cellular diversity. SPADE represents a significant advancement in deciphering spatial gene expression patterns, offering a powerful tool for the detailed investigation of cell types in spatial transcriptomics.

Semi-reference based cell type deconvolution with application to human metastatic cancers.

Bulk RNA-seq experiments, commonly used to discern gene expression changes across conditions, often neglect critical cell type-specific information due to their focus on average transcript abundance. Recognizing cell type contribution is crucial to understanding phenotype and disease variations. The advent of single-cell RNA sequencing has allowed detailed examination of cellular heterogeneity; however, the cost and analytic caveat prohibits such sequencing for a large number of samples. We introduce a novel deconvolution approach, SECRET, that employs cell type-specific gene expression profiles from single-cell RNA-seq to accurately estimate cell type proportions from bulk RNA-seq data. Notably, SECRET can adapt to scenarios where the cell type present in the bulk data is unrepresented in the reference, thereby offering increased flexibility in reference selection. SECRET has demonstrated superior accuracy compared to existing methods using synthetic data and has identified unknown tissue-specific cell types in real human metastatic cancers. Its versatility makes it broadly applicable across various human cancer studies.

Loss of Endothelial Hypoxia Inducible Factor-Prolyl Hydroxylase 2 Induces Cardiac Hypertrophy and Fibrosis.

Background Cardiac hypertrophy and fibrosis are common adaptive responses to injury and stress, eventually leading to heart failure. Hypoxia signaling is important to the (patho)physiological process of cardiac remodeling. However, the role of endothelial PHD2 (prolyl-4 hydroxylase 2)/hypoxia inducible factor (HIF) signaling in the pathogenesis of cardiac hypertrophy and heart failure remains elusive. Methods and Results Mice with Egln1Tie2Cre (Tie2-Cre-mediated deletion of Egln1 [encoding PHD2]) exhibited left ventricular hypertrophy evident by increased thickness of anterior and posterior wall and left ventricular mass, as well as cardiac fibrosis. Tamoxifen-induced endothelial Egln1 deletion in adult mice also induced left ventricular hypertrophy and fibrosis. Additionally, we observed a marked decrease of PHD2 expression in heart tissues and cardiovascular endothelial cells from patients with cardiomyopathy. Moreover, genetic ablation of Hif2a but not Hif1a in Egln1Tie2Cre mice normalized cardiac size and function. RNA sequencing analysis also demonstrated HIF-2α as a critical mediator of signaling related to cardiac hypertrophy and fibrosis. Pharmacological inhibition of HIF-2α attenuated cardiac hypertrophy and fibrosis in Egln1Tie2Cre mice. Conclusions The present study defines for the first time an unexpected role of endothelial PHD2 deficiency in inducing cardiac hypertrophy and fibrosis in an HIF-2α-dependent manner. PHD2 was markedly decreased in cardiovascular endothelial cells in patients with cardiomyopathy. Thus, targeting PHD2/HIF-2α signaling may represent a novel therapeutic approach for the treatment of pathological cardiac hypertrophy and failure.

Comparison and impact of COVID-19 for patients with cancer: a survival analysis of fatality rate controlling for age, sex and cancer type.

OBJECTIVES: Prior research has reported an increased risk of fatality for patients with cancer, but most studies investigated the risk by comparing cancer to non-cancer patients among COVID-19 infections, where cancer might have contributed to the increased risk. This study is to understand COVID-19's imposed HR of fatality while controlling for covariates, such as age, sex, metastasis status and cancer type. METHODS: We conducted survival analyses of 4606 cancer patients with COVID-19 test results from 16 March to 11 October 2020 in UK Biobank and estimated the overall HR of fatality with and without COVID-19 infection. We also examined the HRs of 13 specific cancer types with at least 100 patients using a stratified analysis. RESULTS: COVID-19 resulted in an overall HR of 7.76 (95% CI 5.78 to 10.40, p<10-10) by following 4606 patients with cancer for 21 days after the tests. The HR varied among cancer type, with over a 10-fold increase in fatality rate (false discovery rate ≤0.02) for melanoma, haematological malignancies, uterine cancer and kidney cancer. Although COVID-19 imposed a higher risk for localised versus distant metastasis cancers, those of distant metastases yielded higher overall fatality rates due to their multiplicative effects. DISCUSSION: The results confirmed prior reports for the increased risk of fatality for patients with COVID-19 plus hematological malignancies and demonstrated similar findings of COVID-19 on melanoma, uterine, and kidney cancers. CONCLUSION: The results highlight the heightened risk that COVID-19 imposes on localised and haematological cancer patients and the necessity to vaccinate uninfected patients with cancer promptly, particularly for the cancer types most influenced by COVID-19. Results also suggest the importance of timely care for patients with localised cancer, whether they are infected by COVID-19 or not.

The Club Cell Marker SCGB1A1 Downstream of FOXA2 is Reduced in Asthma.

Human SCGB1A1 protein has been shown to be significantly reduced in BAL, sputum, and serum from humans with asthma as compared with healthy individuals. However, the mechanism of this reduction and its functional impact have not been entirely elucidated. By mining online datasets, we found that the mRNA of SCGB1A1 was significantly repressed in brushed human airway epithelial cells from individuals with asthma, and this repression appeared to be associated with reduced expression of FOXA2. Consistently, both Scgb1A1 and FoxA2 were downregulated in an ovalbumin-induced mouse model of asthma. Furthermore, compared with wild-type mice, Scgb1a1 knockout mice had increased airway hyperreactivity and inflammation when they were exposed to ovalbumin, confirming the antiinflammatory role of Scgb1a1 in protection against asthma phenotypes. To search for potential asthma-related stimuli of SCGB1A1 repression, we tested T-helper cell type 2 cytokines. Both IL-4 and IL-13 repressed epithelial expression of SCGB1A1 and FOXA2. Importantly, infection of epithelial cells with human rhinovirus similarly reduced expression of these two genes, which suggests that FOXA2 may be the common regulator of SCGB1A1. To establish the causal role of reduced FOXA2 in SCGB1A1 repression, we demonstrated that FOXA2 was required for SCGB1A1 expression at baseline. FOXA2 overexpression was sufficient to drive promoter activity and expression of SCGB1A1 and was also able to restore the repressed SCGB1A1 expression in IL-13-treated or rhinovirus-infected cells. Taken together, these findings suggest that low levels of epithelial SCGB1A1 in asthma are caused by reduced FOXA2 expression.

Model Selection and Estimation with Quantal-Response Data in Benchmark Risk Assessment.

This article describes several approaches for estimating the benchmark dose (BMD) in a risk assessment study with quantal dose-response data and when there are competing model classes for the dose-response function. Strategies involving a two-step approach, a model-averaging approach, a focused-inference approach, and a nonparametric approach based on a PAVA-based estimator of the dose-response function are described and compared. Attention is raised to the perils involved in data "double-dipping" and the need to adjust for the model-selection stage in the estimation procedure. Simulation results are presented comparing the performance of five model selectors and eight BMD estimators. An illustration using a real quantal-response data set from a carcinogenecity study is provided.

Information-theoretic model-averaged benchmark dose analysis in environmental risk assessment.

An important objective in environmental risk assessment is estimation of minimum exposure levels, called Benchmark Doses (BMDs), that induce a pre-specified Benchmark Response (BMR) in a dose-response experiment. In such settings, representations of the risk are traditionally based on a specified parametric model. It is a well-known concern, however, that existing parametric estimation techniques are sensitive to the form employed for modeling the dose response. If the chosen parametric model is in fact misspecified, this can lead to inaccurate low-dose inferences. Indeed, avoiding the impact of model selection was one early motivating issue behind development of the BMD technology. Here, we apply a frequentist model averaging approach for estimating benchmark doses, based on information-theoretic weights. We explore how the strategy can be used to build one-sided lower confidence limits on the BMD, and we study the confidence limits' small-sample properties via a simulation study. An example from environmental carcinogenicity testing illustrates the calculations. It is seen that application of this information-theoretic, model averaging methodology to benchmark analysis can improve environmental health planning and risk regulation when dealing with low-level exposures to hazardous agents.

Class I lysine deacetylases facilitate glucocorticoid-induced transcription.

Nuclear receptors use lysine acetyltransferases and lysine deacetylases (KDACs) in regulating transcription through histone acetylation. Lysine acetyltransferases interact with steroid receptors upon binding of an agonist and are recruited to target genes. KDACs have been shown to interact with steroid receptors upon binding to an antagonist. We have shown previously that KDAC inhibitors (KDACis) potently repress the mouse mammary tumor virus promoter through transcriptional mechanisms and impair the ability of the glucocorticoid receptor (GR) to activate it, suggesting that KDACs can play a positive role in GR transactivation. In the current study, we extended this analysis to the entire GR transcriptome and found that the KDACi valproic acid impairs the ability of agonist-bound GR to activate about 50% of its target genes. This inhibition is largely due to impaired transcription rather than defective GR processing and was also observed using a structurally distinct KDACi. Depletion of KDAC1 expression mimicked the effects of KDACi in over half of the genes found to be impaired in GR transactivation. Simultaneous depletion of KDACs 1 and 2 caused full or partial impairment of several more GR target genes. Altogether we found that Class I KDAC activity facilitates GR-mediated activation at a sizable fraction of GR-activated target genes and that KDAC1 alone or in coordination with KDAC2 is required for efficient GR transactivation at many of these target genes. Finally, our work demonstrates that KDACi exposure has a significant impact on GR signaling and thus has ramifications for the clinical use of these drugs.

[Study on reliability and validity of the Tinnitus Evaluation Questionnaire].

OBJECTIVE: To investigate the value of the Tinnitus Evaluation Questionnaire (TEQ) in clinical application. METHODS: Cronbach's α coefficient was used to examine the reliability of the TEQ internal consistency. Examined the re-measured reliability by the correlation coefficient by two doctors' 1 - 3 hours interval questionnaires' scores. And inspected criteria validity according to the correlation coefficient of the TEQ and Tinnitus Handicap Inventory (THI). RESULTS: In the 202 tinnitus patients, the TEQ Cronbach's α coefficient was 0.76 and re-measured reliability was 0.938. The THI correlation coefficient was 0.769. Among which, 99 patients feel tinnitus alleviated obviously after the treatment, the TEQ scores were significantly lower than that before the treatment (t = 21.42, P < 0.001). CONCLUSIONS: The TEQ reflects the severity of tinnitus completely, and has preferable reliability and validity. The characteristics are concise, practical and exact. It is worthy of clinical application.

Gene expression analysis at the intersection of ploidy and hybridity in maize.

Heterosis and polyploidy are two important aspects of plant evolution. To examine these issues, we conducted a global gene expression study of a maize ploidy series as well as a set of tetraploid inbred and hybrid lines. This gene expression analysis complements an earlier phenotypic study of these same materials. We find that ploidy change affects a large fraction of the genome, albeit at low levels; gene expression changes rarely exceed 2-fold and are typically not statistically significant. The most common gene expression profile we detected is greater than linear increase from monoploid to diploid, and reductions from diploid to triploid and from triploid to tetraploid, a trend that mirrors plant stature. When examining heterosis in tetraploid maize lines, we found a large fraction of the genome impacted but the majority of changes were not statistically significant at 2-fold or less. Non-additive expression was common in the hybrids, and the extent of non-additivity increased both in number and magnitude from duplex to quadruplex hybrids. Overall, we find that gene expression trends mirror observations from the phenotypic studies; however, obvious mechanistic connections remain unknown.

An optical approach for drug screening based on light-harvesting conjugated polyelectrolytes.

Drugs turn the light off: Conjugated polyelectrolytes (CPEs) have been used in fluorescent assays for real-time screening of small molecules that prevent the RNA-protein complexation that is important for virus replication and thereby can be considered potential initial candidates for drug discovery (see picture).