大鼠肾髓质活体微循环及观察方法

采用肾乳头暴露方法活体观察Sprague-Dawley大鼠肾髓质微循环。结果发现:正常成年大鼠肾乳头可暴露1.1±0.5mm; 乳头表面直血管数29.8±6.3;升、降支比例3.4:1。升支平均直径13.68±6.13μm,降支10.8±2.57μm。肾乳头连续暴露观察10h,其微循环未发生明显病理性改变。说明这一方法可以用于肾髓质微循环活体研究。     

Fatty acyl-CoA binding activity of the nuclear thyroid hormone receptor

Long-chain fatty acids and their acyl-CoA esters are potent inhibitors of nuclear thyroid hormone (T₃) receptor in vitro. In the present study, we obtained evidence for acyl-CoA binding activity in the nuclear extract from rat liver. The activity sedimented at a position (3.5 S) identical with that of the T₃ receptor, and the two activities sedimented together. Similarly, they coeluted on DEAE-Sephadex. After partial purification of the receptor, it was again inhibited strongly by acyl-CoAs. Heat stability and a partial trypsin digestion of the receptor both suggested that the action site of oleoyl-CoA overlapped the T₃-binding domain of the receptor. In addition, thyroid hormone receptor β1, synthesized in vitro, bound oleoyl-CoA specifically and its T₃-binding activity was inhibited. The dissociation constant for oleoyl-CoA binding to the partially purified receptor was 1.2 × 10^?7 M. This value as well as its molecular size distinguished the nuclear binding sites from the cytoplasmic fatty acid/acyl-CoA binding proteins. Oleoyl-CoA had no effect on the glucocorticoid receptor, another member of the nuclear hormone-receptor superfamily. From these results, we propose that thyroid hormone receptor is a specific acyl-CoA binding protein of the cell nucleus.     

TBC1D25 alleviates nonalcoholic steatohepatitis by inhibiting abnormal lipid accumulation and inflammation

Nonalcoholic fatty liver disease (NAFLD) is a strong stimulant of cardiovascular diseases, affecting one-quarter of the world's population. TBC1 domain family member 25 (TBC1D25) regulates the development of myocardial hypertrophy and cerebral ischemia–reperfusion injury; however, its effect on NAFLD/nonalcoholic steatohepatitis (NASH) has not been reported. In this study, we demonstrated that TBC1D25 expression is upregulated in NASH. TBC1D25 deficiency aggravated hepatic steatosis, inflammation, and fibrosis in NASH. In vitro tests revealed that TBC1D25 overexpression restrained NASH responses. Subsequent mechanistic validation experiments demonstrated that TBC1D25 interfered with NASH progression by inhibiting abnormal lipid accumulation and inflammation. TBC1D25 deficiency significantly promoted NASH occurrence and development. Therefore, TBC1D25 may potentially be used as a clinical therapeutic target for NASH treatment.     

LEP and LEPR are possibly a double-edged sword for wound healing

Wound healing is a complex and error-prone process. Wound healing in adults often leads to the formation of scars, a type of fibrotic tissue that lacks skin appendages. Hypertrophic scars and keloids can also form when the wound-healing process goes wrong. Leptin (Lep) and leptin receptors (LepRs) have recently been shown to affect multiple stages of wound healing. This effect, however, is paradoxical for scarless wound healing. On the one hand, Lep exerts pro-inflammatory and profibrotic effects; on the other hand, Lep can regulate hair follicle growth. This paper summarises the role of Lep and LepRs on cells in different stages of wound healing, briefly introduces the process of wound healing and Lep and LepRs, and examines the possibility of promoting scarless wound healing through spatiotemporal, systemic, and local regulation of Lep levels and the binding of Lep and LepRs.     

Evaluation of housekeeping genes as references for quantitative real-time PCR analysis of European eel,Anguilla anguilla

Eels are important aquaculture species for which an increasing number of reference genes are being identified and applied. In this study, five housekeeping genes [RPL7 (ribosomal protein L7), 18 S (18 S ribosomal RNA), EF1A (elongation factor 1α), ACTB (β-actin) and GAPDH (glyceraldehyde-3-phosphate dehydrogenase)] were chosen to evaluate their reliability as reference genes for quantitative real-time PCR (qPCR) for the study of Anguilla anguilla. The expression of the selected genes in different eel tissues was determined using qPCR at different growth stages or upon challenge by Anguillid herpesvirus (AngHV), and the expression levels of these genes were then compared and evaluated using the geNorm and NormFinder algorithms. Then, RefFinder was used to comprehensively rank the examined housekeeping genes. Interestingly, the expression of the evaluated housekeeping genes exhibited tissue-dependent and treatment-dependent variations. In different growth periods A. anguilla tissues, the most stable genes were the following: ACTB in mucus; 18 S in skin and kidney; RPL7 in muscle, gill, intestine and brain; EF1A in heart and liver; and GAPDH in spleen. In contrast, in AngHV-challenged A. anguilla tissues, the most stable genes were the following: 18 S in mucus; RPL7 in skin, gill, heart, spleen, kidney and intestine; EF1A in muscle and liver; and ACTB in brain. Further comparison analysis indicated that the expression of RPL7 and EF1A was stable in multiple A. anguilla tissues in different growth periods and in eels challenged by AngHV. Nonetheless, the expression level of GAPDH in eel tissues was lower, and it was unstable in several tissues. These results indicated that the selection of reference genes for qPCR analysis in A. anguilla should be made in accordance with experimental parameters, and both RPL7 and EF1A could be used as reference genes for qPCR study of A. anguilla at different growth stages or upon challenge by AngHV. The reference genes identified in this study could improve the accuracy of qPCR data and facilitate further studies aimed at understanding the biology of eels.     

Integrating sample similarities into latent class analysis: a tree-structured shrinkage approach

This paper is concerned with using multivariate binary observations to estimate the probabilities of unobserved classes with scientific meanings. We focus on the setting where additional information about sample similarities is available and represented by a rooted weighted tree. Every leaf in the given tree contains multiple samples. Shorter distances over the tree between the leaves indicate a priori higher similarity in class probability vectors. We propose a novel data integrative extension to classical latent class models with tree-structured shrinkage. The proposed approach enables (1) borrowing of information across leaves, (2) estimating data-driven leaf groups with distinct vectors of class probabilities, and (3) individual-level probabilistic class assignment given the observed multivariate binary measurements. We derive and implement a scalable posterior inference algorithm in a variational Bayes framework. Extensive simulations show more accurate estimation of class probabilities than alternatives that suboptimally use the additional sample similarity information. A zoonotic infectious disease application is used to illustrate the proposed approach. The paper concludes by a brief discussion on model limitations and extensions.     

Associating somatic mutation with clinical outcomes through kernel regression and optimal transport

Somatic mutations in cancer patients are inherently sparse and potentially high dimensional. Cancer patients may share the same set of deregulated biological processes perturbed by different sets of somatically mutated genes. Therefore, when assessing the associations between somatic mutations and clinical outcomes, gene-by-gene analysis is often under-powered because it does not capture the complex disease mechanisms shared across cancer patients. Rather than testing genes one by one, an intuitive approach is to aggregate somatic mutation data of multiple genes to assess their joint association with clinical outcomes. The challenge is how to aggregate such information. Building on the optimal transport method, we propose a principled approach to estimate the similarity of somatic mutation profiles of multiple genes between tumor samples, while accounting for gene–gene similarities defined by gene annotations or empirical mutational patterns. Using such similarities, we can assess the associations between somatic mutations and clinical outcomes by kernel regression. We have applied our method to analyze somatic mutation data of 17 cancer types and identified at least five cancer types, where somatic mutations are associated with overall survival, progression-free interval, or cytolytic activity.     

Functional data analysis with covariate-dependent mean and covariance structures

Functional data analysis has emerged as a powerful tool in response to the ever-increasing resources and efforts devoted to collecting information about response curves or anything that varies over a continuum. However, limited progress has been made with regard to linking the covariance structures of response curves to external covariates, as most functional models assume a common covariance structure. We propose a new functional regression model with covariate-dependent mean and covariance structures. Particularly, by allowing variances of random scores to be covariate-dependent, we identify eigenfunctions for each individual from the set of eigenfunctions that govern the variation patterns across all individuals, resulting in high interpretability and prediction power. We further propose a new penalized quasi-likelihood procedure that combines regularization and B-spline smoothing for model selection and estimation and establish the convergence rate and asymptotic normality of the proposed estimators. The utility of the developed method is demonstrated via simulations, as well as an analysis of the Avon Longitudinal Study of Parents and Children concerning parental effects on the growth curves of their offspring, which yields biologically interesting results.