Ncapg dynamically coordinates the myogenesis of fetal bovine tissue by adjusting chromatin accessibility

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. NCAPG is a subunit of condensin I that plays a crucial role in chromatin condensation during mitosis. NCAPG has been demonstrated to be associated with farm animal growth traits. However, its role in regulating myoblast differentiation is still unclear. We used myoblasts derived from fetal bovine tissue as an in vitro model and found that NCAPG was expressed during myogenic differentiation in the cytoplasm and nucleus. Silencing NCAPG prolonged the mitosis and impaired the differentiation due to increased myoblast apoptosis. After 1.5 days of differentiation, silencing NCAPG enhanced muscle-specific gene expression. An assay for transposase-accessible chromatinhigh throughput sequencing (ATAC-seq) revealed that silencing NCAPG altered chromatin accessibility to activating protein 1 (AP-1) and its subunits. Knocking down the expression of the AP-1 subunits fos-related antigen 2 (FOSL2) or junB proto-oncogene (JUNB) enhanced part of the muscle-specific gene expression. In conclusion, our data provide valuable evidence about NCAPG’s function in myogenesis, as well as its potential role in gene expression

Numerical study of inflow equivalence ratio inhomogeneity on oblique detonation formation in hydrogen-air mixtures

In this study, numerical simulations using Euler equations with detailed chemistry are performed to investigate the effect of fuel-air composition inhomogeneity on the oblique detonation wave (ODW) initiation in hydrogen-air mixtures. This study aims for a better understanding of oblique detonation wave engine performance under practical operating conditions, among those is the inhomogeneous mixing of fuel and air giving rise to a variation of the equivalence ratio (ER) in the incoming combustible flow. This work focuses primarily on how a variable equivalence ratio in the inflow mixture affects both the formation and characteristic parameters of the oblique detonation wave. In this regard, the present simulation imposes initially a lateral linear distribution of the mixture equivalence ratio within the initiation region. The variation is either from fuel-lean or fuel-rich to the uniform stoichiometric mixture condition above the oblique shock wave. The obtained numerical results illustrate that the reaction surface is distorted in the cases of low mixture equivalence ratio. The so-called “V-shaped” flame is observed but differed from previous results that it is not coupled with any compression or shock wave. Analyzing the temperature and species density evolution also shows that the fuel-lean and fuel-rich inhomogeneity have different effects on the combustion features in the initiation region behind the oblique shock wave. Two characteristic quantities, namely the initiation length and the ODW surface position, are defined to describe quantitatively the effects of mixture equivalence ratio inhomogeneity. The results show that the initiation length is mainly determined by the mixture equivalence ratio in the initiation region. Additional computations are performed by reversing ER distribution, i.e., with the linear variation above the initiation region of uniform stoichiometric condition and results also demonstrate that the ODW position is effectively determined by the ER variation before the ODW, which has in turn only negligible effect on the initiation length

bta-miR-23a Regulates the Myogenic Differentiation of Fetal Bovine Skeletal Muscle-Derived Progenitor Cells by Targeting MDFIC Gene

miR-23a, a member of the miR-23a/24-2/27a cluster, has been demonstrated to play pivotal roles in many cellular activities. However, the mechanisms of how bta-miR-23a controls the myogenic differentiation (MD) of PDGFRalpha(-) bovine progenitor cells (bPCs) remain poorly understood. In the present work, bta-miR-23a expression was increased during the MD of (PDGFRalpha-) bPCs. Moreover, bta-miR-23a overexpression significantly promoted the MD of (PDGFRalpha-) bPCs. Luciferase reporter assays showed that the 3\u27-UTR region of MDFIC (MyoD family inhibitor domain containing) could be a promising target of bta-miR-23a, which resulted in its post-transcriptional down-regulation. Additionally, the knockdown of MDFIC by siRNA facilitated the MD of (PDGFRalpha-) bPCs, while the overexpression of MDFIC inhibited the activating effect of bta-miR-23a during MD. Of note, MDFIC might function through the interaction between MyoG transcription factor and MEF2C promoter. This study reveals that bta-miR-23a can promote the MD of (PDGFRalpha-) bPCs through post-transcriptional downregulation of MDFIC

BMP4 and rosiglitazone improves adipogenesis of bovine fetal muscle derived progenitor cells

peer reviewedIntramuscular fat (IMF) content is one of the most important factors determining beef quality and price. Intramuscular adipocytes develop from mesenchymal stem cells (MSCs) in mesoderm. The mechanisms of preadipocytes differentiate into mature adipocytes to a great extent are clear, but the commitment of MSCs to preadipocytes is largely unknown. In this study, the Platelet-derived growth factor receptor α (PDGFRα) positive progenitor cells were isolated from the longissimus dorsi muscle (LM) of fetal bovine and induced adipogenesis. To optimize the in vitro IMF differentiation model, the effects of bone morphogenic protein 4 (BMP4) and rosiglitazone during differentiation were studied. Comparing with control group, progenitor cells treated with BMP4 or rosiglitazone accumulated more intracellular lipid. Furthermore, the mRNA expression level of adipocyte-specific genes also increased significantly in BMP4 or rosiglitazone treated cells. The result indicated that BMP4 and rosiglitazone could promote adipogenesis and be applied in adipogenic differentiation of fetal bovine derived progenitor cells

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Effects of boundary layer on wedge-induced oblique detonation structures in hydrogen-air mixtures

Oblique detonation wave (ODW) structures are studied widely in recent years, but most of them are solved by the Euler equations without considering viscosity and then effects of boundary layer. In this study, the Navier-Stokes Equations are used to simulate the wedge-induced ODWs in hydrogen-air mixtures, and the two types of ODW transition structures at different incident Mach number M-i are analyzed to clarify the effects of viscosity and hence the boundary layer. Results show that the effect of boundary layer on ODW structures should be classified by the types of ODW transition patterns. As for the smooth transition pattern of ODW at high Mach numbers, the effect of boundary layer can be neglected, but for the abrupt transition pattern of ODW at low Mach numbers, the effect of boundary layer is large and it changes the ODW structure greatly. Resulting from the interaction of shock and boundary layer, a recirculation zone is formed within the viscous ODW layer at M-i = 7, which leads to the phenomenon that the straight oblique shock wave evolves into two sections, with the downstream one having a larger shock angle. Additionally, the corresponding transition position moves upstream, and the initiation length becomes only one third of that in inviscid ODW. The great importance of considering viscosity in ODW simulations and future designs of combustor of oblique detonation engine has been addressed. (C) 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved

Numerical investigation of oblique detonations induced by a finite wedge in a stoichiometric hydrogen-air mixture

Two-dimensional, oblique detonation waves (ODWs) in a stoichiometric hydrogen-air mixture are simulated using the reactive Euler equations with a detailed chemical reaction model. This study focuses the effects of expansion waves on the initiation, which is modeled by a finite-length wedge. Numerical results demonstrate that the expansion wave may quench the ODW if it interacts with the initiation region, and the critical position is found to be dependent on the incident Mach number M-0. The critical position moves upstream in the case of high M-0, and downstream in the case of low M-0. Furthermore, ODW structures show different behaviors when the expansion wave is near its critical position. In the case of M-0 = 10, the structure is featured by a stationary but decoupled shock and reactive surface, while a transient downstream-moving ODW is observed in the case of M-0 = 7. By decreasing the turning angle, the former one keeps the same, while the later one becomes also stationary. These differences are related with the initiation mechanisms of two ODW structures, demonstrating that the structure of wave-controlled initiation is more sensitive to the expansion waves than the kinetic-controlled initiation

Initiation of oblique detonation waves induced by a blunt wedge in stoichiometric hydrogen-air mixtures

Two-dimensional, oblique detonation waves (ODWs) in a stoichiometric hydrogen-air mixture are simulated with the reactive Euler equations using a detailed chemical reaction model. This study focuses on blunt wedge induced ODWs, which are not only influenced by inflow parameters but also the size of the blunt body. With the inflow parameters of flight altitude of 30 km and flight Mach number M-0 of 8-10, the numerical results demonstrate that the blunt wedge is crucial to initiate the ODW. In the case of M-0 = 10, the straight wedge without the blunt forebody can initiate the detonation. However, decreasing M-0 causes the failure of initiation, which can be compensated by increasing the radius R-0 of the blunt forebody. By adjusting R-0, two initiation procedures are observed and distinguished: one is the wedge-induced initiation and the other is the blunt forebody-induced initiation. Although both have been independently studied before, in this study, their coexistence is demonstrated, and the mechanism is analyzed for the first time. A theoretical analysis based on the classic initiation theory is performed to elucidate the initiation mechanism, giving a good agreement between the critical radius with numerical results. (C) 2019 Elsevier Masson SAS. All rights reserved

A Multimodal Protein Representation Framework for Quantifying Transferability Across Biochemical Downstream Tasks

Abstract Proteins are the building blocks of life, carrying out fundamental functions in biology. In computational biology, an effective protein representation facilitates many important biological quantifications. Most existing protein representation methods are derived from self‐supervised language models designed for text analysis. Proteins, however, are more than linear sequences of amino acids. Here, a multimodal deep learning framework for incorporating ≈1 million protein sequence, structure, and functional annotation (MASSA) is proposed. A multitask learning process with five specific pretraining objectives is presented to extract a fine‐grained protein‐domain feature. Through pretraining, multimodal protein representation achieves state‐of‐the‐art performance in specific downstream tasks such as protein properties (stability and fluorescence), protein‒protein interactions (shs27k/shs148k/string/skempi), and protein‒ligand interactions (kinase, DUD‐E), while achieving competitive results in secondary structure and remote homology tasks. Moreover, a novel optimal‐transport‐based metric with rich geometry awareness is introduced to quantify the dynamic transferability from the pretrained representation to the related downstream tasks, which provides a panoramic view of the step‐by‐step learning process. The pairwise distances between these downstream tasks are also calculated, and a strong correlation between the inter‐task feature space distributions and adaptability is observed

Bridging the Gap between Target-Based and Cell-Based Drug Discovery with a Graph Generative Multitask Model

The development of new drugs is crucial for protecting humans from disease. In the past several decades, target-based screening has been one of the most popular methods for developing new drugs. This method efficiently screens potential inhibitors of a target protein in vitro, but it frequently fails in vivo due to insufficient activity of the selected drugs. There is a need for accurate computational methods to bridge this gap. Here, we present a novel graph multi-task deep learning model to identify compounds with both target inhibitory and cell active (MATIC) properties. On a carefully curated SARS-CoV-2 data set, the proposed MATIC model shows advantages compared with the traditional method in screening effective compounds in vivo. Following this, we investigated the interpretability of the model and discovered that the learned features for target inhibition (in vitro) or cell active (in vivo) tasks are different with molecular property correlations and atom functional attention. Based on these findings, we utilized a Monte Carlo-based reinforcement learning generative model to generate novel multiproperty compounds with both in vitro and in vivo efficacy, thus bridging the gap between target-based and cell-based drug discovery. The tool is freely accessible at https://github.com/SIAT-code/MATIC