Endogenous ligand of the APJ receptor Apelin-13 inhibits cell apoptosis and oxidative stress of cardiomyocytes.

The present study aimed to investigate the effect of Apelin-13 on nicotine-induced injuries of cardiomyocytes. To establish an H9c2 cell model of nicotine-induced apoptosis, H9c2 cells were divided into the control group, nicotine group, and Apelin-13+nicotine group. The apoptosis rate of H9c2 cells was then detected by flow cytometry. Later, the expressions of indicators related to apoptosis, oxidative stress, and inflammatory responses were measured via Western blotting and quantitative real-time polymerase chain reaction (qRT-PCR). The results revealed that the expression of B-cell lymphoma-2 (Bcl-2) was remarkably down-regulated (P<0.01), while the apoptosis rate and the expressions of apoptosis-related proteins (Bcl-2-associated X protein (Bax) and cysteinyl aspartate specific proteinase-3 (Caspase-3)) were significantly up-regulated (P<0.01) in the nicotine group. However, the variation trends of Bcl-2, Bax, and Caspase-3 in the Apelin-13+nicotine group were contrary to those in the nicotine group (P<0.01). Additionally, the expressions of interleukin-1 beta (IL-1ß) and tumor necrosis factor-alpha (TNF-α) obviously declined (P<0.01), while those of superoxide dismutase 1 (SOD1) and SOD2 dramatically rose in the Apelin-13+nicotine group (P<0.01). Furthermore, Apelin-13 treatment evidently elevated the expressions of phosphorylated protein kinase B (p-AKT) and phosphorylated phosphatidylinositol 3-kinase (PI3K). In conclusion, Apelin-13 inhibits nicotine-induced apoptosis and oxidative stress in H9c2 cells via the PI3K/AKT signaling pathway.

Mitochondrion-Targeted Triphenylphosphonium-Based Kresoxim-Methyl Analogues: Synthesis, Fungicidal Activity, and Action Mechanism Approach.

ß-Methoxyacrylate fungicides as complex III Qo site inhibitors play a crucial role in the control of crop diseases. In this study, the triphenylphosphonium (TPP)-driven mitochondrion-targeting strategy was used to modify the kresoxim-methyl scaffold at the toxicophore or side chain to develop novel mitochondrion-targeted QoI fungicides. These kresoxim-methyl analogues exhibited different fungicidal activities, depending on the position of TPP conjugation and the linker length. Among them, 2A-5 and 2C-4 showed excellent characteristics superior to kresoxim-methyl as candidate fungicides, in which the activity enhancement against Phytophthora capsici was the most remarkable, with an EC50 value of about 5 µM. Notably, both hyphal and zoospore structures of the pathogens were severely damaged after treatment with them. The action mechanism approach revealed that they might cause a significant decrease in ATP synthesis and ROS outbreak in different ways. The results also provided a new insight into the contribution of targeting group TPP to the fungicidal activity in TPP-driven fungicides.

iPCD: A Comprehensive Data Resource of Regulatory Proteins in Programmed Cell Death.

Programmed cell death (PCD) is an essential biological process involved in many human pathologies. According to the continuous discovery of new PCD forms, a large number of proteins have been found to regulate PCD. Notably, post-translational modifications play critical roles in PCD process and the rapid advances in proteomics have facilitated the discovery of new PCD proteins. However, an integrative resource has yet to be established for maintaining these regulatory proteins. Here, we briefly summarize the mainstream PCD forms, as well as the current progress in the development of public databases to collect, curate and annotate PCD proteins. Further, we developed a comprehensive database, with integrated annotations for programmed cell death (iPCD), which contained 1,091,014 regulatory proteins involved in 30 PCD forms across 562 eukaryotic species. From the scientific literature, we manually collected 6493 experimentally identified PCD proteins, and an orthologous search was then conducted to computationally identify more potential PCD proteins. Additionally, we provided an in-depth annotation of PCD proteins in eight model organisms, by integrating the knowledge from 102 additional resources that covered 16 aspects, including post-translational modification, protein expression/proteomics, genetic variation and mutation, functional annotation, structural annotation, physicochemical property, functional domain, disease-associated information, protein-protein interaction, drug-target relation, orthologous information, biological pathway, transcriptional regulator, mRNA expression, subcellular localization and DNA and RNA element. With a data volume of 125 GB, we anticipate that iPCD can serve as a highly useful resource for further analysis of PCD in eukaryotes.

GPS-Uber: a hybrid-learning framework for prediction of general and E3-specific lysine ubiquitination sites.

As an important post-translational modification, lysine ubiquitination participates in numerous biological processes and is involved in human diseases, whereas the site specificity of ubiquitination is mainly decided by ubiquitin-protein ligases (E3s). Although numerous ubiquitination predictors have been developed, computational prediction of E3-specific ubiquitination sites is still a great challenge. Here, we carefully reviewed the existing tools for the prediction of general ubiquitination sites. Also, we developed a tool named GPS-Uber for the prediction of general and E3-specific ubiquitination sites. From the literature, we manually collected 1311 experimentally identified site-specific E3-substrate relations, which were classified into different clusters based on corresponding E3s at different levels. To predict general ubiquitination sites, we integrated 10 types of sequence and structure features, as well as three types of algorithms including penalized logistic regression, deep neural network and convolutional neural network. Compared with other existing tools, the general model in GPS-Uber exhibited a highly competitive accuracy, with an area under curve values of 0.7649. Then, transfer learning was adopted for each E3 cluster to construct E3-specific models, and in total 112 individual E3-specific predictors were implemented. Using GPS-Uber, we conducted a systematic prediction of human cancer-associated ubiquitination events, which could be helpful for further experimental consideration. GPS-Uber will be regularly updated, and its online service is free for academic research at http://gpsuber.biocuckoo.cn/.

Atg9-centered multi-omics integration reveals new autophagy regulators in Saccharomyces cerevisiae.

In Saccharomyces cerevisiae, Atg9 is an important autophagy-related (Atg) protein, and interacts with hundreds of other proteins. How many Atg9-interacting proteins are involved in macroautophagy/autophagy is unclear. Here, we conducted a multi-omic profiling of Atg9-dependent molecular landscapes during nitrogen starvation-induced autophagy, and identified 290 and 256 genes to be markedly regulated by ATG9 in transcriptional and translational levels, respectively. Unexpectedly, we found most of known Atg proteins and autophagy regulators that interact with Atg9 were not significantly changed in the mRNA or protein level during autophagy. Based on a hypothesis that proteins with similar molecular characteristics might have similar functions, we developed a new method named inference of functional interacting partners (iFIP) to integrate the transcriptomic, proteomic and interactomic data, and predicted 42 Atg9-interacting proteins to be potentially involved in autophagy, including 15 known Atg proteins or autophagy regulators. We validated 2 Atg9-interacting partners, Glo3 and Scs7, to be functional in both bulk and selective autophagy. The mRNA and protein expressions but not subcellular localizations of Glo3 and Scs7 were affected with or without ATG9 during autophagy, whereas the colocalizations of the 2 proteins and Atg9 were markedly enhanced at early stages of the autophagic process. Further analyses demonstrated that Glo3 but not Scs7 regulates the retrograde transport of Atg9 during autophagy. A working model was illustrated to highlight the importance of the Atg9 interactome. Taken together, our study not only provided a powerful method for analyzing the multi-omics data, but also revealed 2 new players that regulate autophagy.Abbreviations: ALP: alkaline phosphatase; Arf1: ADP-ribosylation factor 1; Atg: autophagy-related; Co-IP: co-immunoprecipitation; Cvt: cytoplasm-to-vacuole targeting; DEM: differentially expressed mRNA; DEP: differentially expressed protein; DIC: differential interference contrast; E-ratio: enrichment ratio; ER: endoplasmic reticulum; ES: enrichment score; FC: fold change; FPKM: fragments per kilobase of exon per million fragments mapped; GAP: GTPase-activating protein; GFP: green fluorescent protein; GO: gene ontology; GSEA: gene set enrichment analysis; GST: glutathione S-transferase; HA: hemagglutinin; iFIP: inference of functional interacting partners; KO: knockout; LR: logistic regression; OE: over-expression; PAS: phagophore assembly site; PPI: protein-protein interaction; RFP: red fluorescence protein; RNA-seq: RNA sequencing; RT-PCR: real-time polymerase chain reaction; SCC: Spearman's correlation coefficient; SD-N: synthetic minimal medium lacking nitrogen; THANATOS: The Autophagy, Necrosis, ApopTosis OrchestratorS; Vsn: variance stabilization normalization; WT: wild-type.

Cell Adhesion and Migration Behaviors on Patterned Thermoresponsive Microgel Stripes.

Cell adhesion and migration behaviors are influenced by their surrounding environments. In this work, patterned poly(N-isopropylacrylamide-styrene) (pNIPAAmSt) microgel stripes, which can provide a more complex environment, were fabricated on polyethyleneimine (PEI)-precoated glass substrate, and their effects on cell adhesion and migration behavior were investigated. The results showed that patterned cell layers can be formed on the space regions either by selective adhesion of the cells or by detaching the attached cells from the thermoresponsive microgel stripes via mild temperature stimuli. The migration behavior of the cells was affected by both the cell types and the width of the microgel stripes. The wider the microgel stripe, the harder it is for the cells to migrate, and the longer the patterned cell layers can be conserved. It is worth noting that the cell-cell interaction in the cocultured system plays a key role in the cell migration process. This work may offer a platform to study the adhesion and migration behavior of mono/cocultured cells in a more controllable system.