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.     

Reaction intensification for biocatalytic production of polyphenolic natural product di-C-β-glucosides

Polyphenolic aglycones featuring two sugars individually attached via C-glycosidic linkage (di-C-glycosides) represent a rare class of plant natural products with unique physicochemical properties and biological activities. Natural scarcity of such di-C-glycosides limits their use-inspired exploration as pharmaceutical ingredients. Here, we show a biocatalytic process technology for reaction-intensified production of the di-C-β-glucosides of two representative phenol substrates, phloretin (a natural flavonoid) and phenyl-trihydroxyacetophenone (a phenolic synthon for synthesis), from sucrose. The synthesis proceeds via an iterative two-fold C-glycosylation of the respective aglycone, supplied as inclusion complex with 2-hydroxypropyl β-cyclodextrin for enhanced water solubility of up to 50 mmol/L, catalyzed by a kumquat di-C-glycosyltransferase (di-CGT), and it uses UDP-Glc provided in situ from sucrose by a soybean sucrose synthase, with catalytic amounts (≤3 mol%) of UDP added. Time course analysis reveals the second C-glycosylation as rate-limiting (0.4–0.5 mmol/L/min) for the di-C-glucoside production. With internal supply from sucrose keeping the UDP-Glc at a constant steady-state concentration (≥50% of the UDP added) during the reaction, the di-C-glycosylation is driven to completion (≥95% yield). Contrary to the mono-C-glucoside intermediate which is stable, the di-C-glucoside requires the addition of reducing agent (10 mmol/L 2-mercaptoethanol) to prevent its decomposition during the synthesis. Both di-C-glucosides are isolated from the reaction mixtures in excellent purity (≥95%), and their expected structures are confirmed by NMR. Collectively, this study demonstrates efficient glycosyltransferase cascade reaction for flexible use in natural product di-C-β-glucoside synthesis from expedient substrates.     

Impacts of iron on phosphate starvation-induced root hair growth in Arabidopsis

In Arabidopsis, phosphate starvation (-Pi)-induced responses of primary root and lateral root growth are documented to be correlated with ambient iron (Fe) status. However, whether and how Fe participates in -Pi-induced root hair growth (RHG) remains unclear. Here, responses of RHG to different Fe concentrations under Pi sufficiency/deficiency were verified. Generally, distinct dosage effects of Fe on RHG appeared at both Pi levels, due to the generation of reactive oxygen species. Following analyses using auxin mutants and the phr1 mutant revealed that auxin and the central regulator PHR1 are required for Fe-triggered RHG under −Pi. A further proteomic study indicated that processes of vesicle trafficking and auxin synthesis and transport were affected by Fe under −Pi, which were subsequently validated by using a vesicle trafficking inhibitor, brefeldin A, and an auxin reporter, R2D2. Moreover, vesicle trafficking-mediated recycling of PIN2, an auxin efflux transporter, was notably affected by Fe under -Pi. Correspondingly, root hairs of pin2 mutant displayed attenuated responses to Fe under -Pi. Together, we propose that Fe affects auxin signalling probably by modulating vesicle trafficking, chiefly the PIN2 recycling, which might work jointly with PHR1 on modulating -Pi-induced RHG.     

A meta-analysis on morphological,physiological and biochemical responses of plants with PGPR inoculation under drought stress

Plant growth-promoting rhizobacteria (PGPR) can help plants to resist drought stress. However, the mechanisms of how PGPR inoculation affect plant status under drought remain incompletely understood. We performed a meta-analysis of plant response to PGPR inoculation by compiling data from 57 PGPR-inoculation studies, including 2, 387 paired observations on morphological, physiological and biochemical parameters under drought and well-watered conditions. We compare the PGPR effect on plants performances among different groups of controls and treatments. Our results reveal that PGPR enables plants to restore themselves from drought-stressed to near a well-watered state, and that C4 plants recover better from drought stress than C3 plants. Furthermore, PGPR is more effective underdrought than well-watered conditions in increasing plant biomass, enhancing photosynthesis and inhibiting oxidant damage, and the responses of C4 plants to the PGPR effect was stronger than that of C3 plants under drought conditions. Additionally, PGPR belonging to different taxa and PGPR with different functional traits have varying degrees of drought-resistance effects on plants. These results are important to improve our understanding of the PGPR beneficial effects on enhanced drought-resistance of plants.