An ABHD17-like hydrolase screening system to identify de-S-acylation enzymes of protein substrates in plant cells

Protein S-acylation is an important post-translational modification in eukaryotes, regulating the subcellular localization, trafficking, stability, and activity of substrate proteins. The dynamic regulation of this reversible modification is mediated inversely by protein S-acyltransferases and de-S-acylation enzymes, but the de-S-acylation mechanism remains unclear in plant cells. Here, we characterized a group of putative protein de-S-acylation enzymes in Arabidopsis thaliana, including 11 members of Alpha/Beta Hydrolase Domain-containing Protein 17-like acyl protein thioesterases (ABAPTs). A robust system was then established for the screening of de-S-acylation enzymes of protein substrates in plant cells, based on the effects of substrate localization and confirmed via the protein S-acylation levels. Using this system, the ABAPTs, which specifically reduced the S-acylation levels and disrupted the plasma membrane localization of five immunity-related proteins, were identified respectively in Arabidopsis. Further results indicated that the de-S-acylation of RPM1-Interacting Protein 4, which was mediated by ABAPT8, resulted in an increase of cell death in Arabidopsis and Nicotiana benthamiana, supporting the physiological role of the ABAPTs in plants. Collectively, our current work provides a powerful and reliable system to identify the pairs of plant protein substrates and de-S-acylation enzymes for further studies on the dynamic regulation of plant protein S-acylation.

A robust screening system for ABHD17-like hydrolases was established to identify de-S-acylation enzymes of protein substrates in plant cells.     

Lipidomic alteration of plasma in cured COVID-19 patients using ultra high-performance liquid chromatography with high-resolution mass spectrometry

Background: The pandemic of novel coronavirus disease 2019 (COVID-19) has become a serious public health crisis worldwide. The symptoms of COVID-19 vary from mild to severe among different age groups, but the physiological changes related to COVID-19 are barely understood.Methods: In the present study, a high-resolution mass spectrometry (HRMS)-based lipidomic strategy was used to characterize the endogenous plasma lipids for cured COVID-19 patients with different ages and symptoms. These patients were further divided into two groups: those with severe symptoms or who were elderly and relatively young patients with mild symptoms. In addition, automated lipidomic identification and alignment was conducted by LipidSearch software. Multivariate and univariate analyses were used for differential comparison.Results: Nearly 500 lipid compounds were identified in each cured COVID-19 group through LipidSearch software. At the level of lipid subclasses, patients with severe symptoms or elderly patients displayed dramatic changes in plasma lipidomic alterations, such as increased triglycerides and decreased cholesteryl esters (ChE). Some of these differential lipids might also have essential biological functions. Furthermore, the differential analysis of plasma lipids among groups was performed to provide potential prognostic indicators, and the change in signaling pathways.Conclusions: Dyslipidemia was observed in cured COVID-19 patients due to the viral infection and medical treatment, and the discharged patients should continue to undergo consolidation therapy. This work provides valuable knowledge about plasma lipid markers and potential therapeutic targets of COVID-19 and essential resources for further research on the pathogenesis of COVID-19.     

Proteomic screening and identification of differentially distributed membrane proteins in Escherichia coli

Bacteria show asymmetric subcellular distribution of many proteins involved in diverse cellular processes such as chemotaxis, motility, actin polymerization, chromosome partitioning and cell division. In many cases, the specific subcellular localization of these proteins is critical for proper regulation and function. Although cellular organization of the bacterial cell clearly plays an important role in cell physiology, systematic studies to uncover asymmetrically distributed proteins have not been reported previously. In this study, we undertook a proteomics approach to uncover polar membrane proteins in Escherichia coli. We identified membrane proteins enriched in E. coli minicells using a combination of two-dimensional electrophoresis and mass spectrometry. Among a total of 173 membrane protein spots that were consistently detected, 36 spots were enriched in minicell membranes, whereas 15 spots were more abundant in rod cell membranes. The minicell-enriched proteins included the inner membrane proteins MCPs, AtpA, AtpB, YiaF and AcrA, the membrane-associated FtsZ protein and the outer membrane proteins YbhC, OmpW, Tsx, Pal, FadL, OmpT and BtuB. We immunolocalized two of the minicell-enriched proteins, OmpW and YiaF, and showed that OmpW is a bona fide polar protein whereas YiaF displays a patchy membrane distribution with a polar and septal bias.     

A statistical method for identifying differential gene-gene co-expression patterns

MOTIVATION: To understand cancer etiology, it is important to explore molecular changes in cellular processes from normal state to cancerous state. Because genes interact with each other during cellular processes, carcinogenesis related genes may form differential co-expression patterns with other genes in different cell states. In this study, we develop a statistical method for identifying differential gene-gene co-expression patterns in different cell states. RESULTS: For efficient pattern recognition, we extend the traditional F-statistic and obtain an Expected Conditional F-statistic (ECF-statistic), which incorporates statistical information of location and correlation. We also propose a statistical method for data transformation. Our approach is applied to a microarray gene expression dataset for prostate cancer study. For a gene of interest, our method can select other genes that have differential gene-gene co-expression patterns with this gene in different cell states. The 10 most frequently selected genes, include hepsin, GSTP1 and AMACR, which have recently been proposed to be associated with prostate carcinogenesis. However, genes GSTP1 and AMACR cannot be identified by studying differential gene expression alone. By using tumor suppressor genes TP53, PTEN and RB1, we identify seven genes that also include hepsin, GSTP1 and AMACR. We show that genes associated with cancer may have differential gene-gene expression patterns with many other genes in different cell states. By discovering such patterns, we may be able to identify carcinogenesis related genes.     

SYNCRIP, a member of the heterogeneous nuclear ribonucleoprotein family, is involved in mouse hepatitis virus RNA synthesis

Several cellular proteins, including several heterogeneous nuclear ribonucleoproteins (hnRNPs), have been shown to function as regulatory factors for mouse hepatitis virus (MHV) RNA synthesis as a result of their binding to the 5' and 3' untranslated regions (UTRs) of the viral RNA. Here, we identified another cellular protein, p70, which has been shown by UV cross-linking to bind both the positive- and negative-strand UTRs of MHV RNA specifically. We purified p70 with a a one-step RNA affinity purification procedure with the biotin-labeled 5'-UTR. Matrix-assisted laser desorption ionization (MALDI)-mass spectrometry identified it as synaptotagmin-binding cytoplasmic RNA-interacting protein (SYNCRIP). SYNCRIP is a member of the hnRNP family and localizes largely in the cytoplasm. The p70 was cross-linked to the MHV positive- or negative-strand UTR in vitro and in vivo. The bacterially expressed SYNCRIP was also able to bind to the 5'-UTR of both strands. The SYNCRIP-binding site was mapped to the leader sequence of the 5'-UTR, requiring the UCUAA repeat sequence. To investigate the functional significance of SYNCRIP in MHV replication, we expressed a full-length or a C-terminally truncated form of SYNCRIP in mammalian cells expressing the MHV receptor. The overexpression of either form of SYNCRIP inhibited syncytium formation induced by MHV infection. Furthermore, downregulation of the endogenous SYNCRIP with a specific short interfering RNA delayed MHV RNA synthesis; in contrast, overexpression or downregulation of SYNCRIP did not affect MHV translation. These results suggest that SYNCRIP may be directly involved in MHV RNA replication as a positive regulator. This study identified an additional cellular hnRNP as an MHV RNA-binding protein potentially involved in viral RNA synthesis.     

Structure and expression of mouse alpha 1-acid glycoprotein gene-3 (AGP-3).

The genome of Mus domesticus has multiple genes of the alpha 1-acid glycoprotein (AGP). Two cDNA clones were identified corresponding to AGP-1 and AGP-2. Moreover, two alleles of AGP-1 exist in inbred mice. The genomic DNA of the AGP-2 gene has been cloned and studied. Here we report the genomic organization of three M. domesticus AGP genes, the sequence analysis of the AGP-3 genomic DNA, and the expression of the AGP-3 gene. The major structural differences between AGP-2 and AGP-3 genes are located in introns 1 and 5. The low level of AGP-3 mRNA can be detected by the polymerase chain reaction (PCR). The molecular basis of the low level expression of AGP-3 and the possible classification of AGP-3 as a pseudogene are discussed.     

典型煤层水微生物产甲烷潜力及其群落结构研究

内蒙古自治区二连盆地、海拉尔盆地是我国重要的煤层气产区,其中生物成因煤层气是煤层气的重要来源,但复杂物质转化产甲烷相关微生物群落结构及功能尚不清楚。【目的】研究煤层水中的微生物代谢挥发性脂肪酸产甲烷的生理特征及群落特征。【方法】以内蒙古自治区二连盆地和海拉尔盆地的四口煤层气井水作为接种物,分别添加乙酸钠、丙酸钠和丁酸钠厌氧培养;定期监测挥发性脂肪酸降解过程中甲烷和底物的变化趋势,应用高通量测序技术,分析原始煤层气井水及稳定期产甲烷菌液的微生物群落结构。【结果】除海拉尔盆地H303煤层气井微生物不能代谢丙酸外,其他样品均具备代谢乙酸、丙酸和丁酸产生甲烷的能力,其生理生态参数存在显著差异,产甲烷延滞期依次是乙酸<丁酸<丙酸;最大比产甲烷速率和底物转化效率依次是丙酸<乙酸<丁酸。富集培养后,古菌群落结构与煤层气井水的来源显著相关,二连盆地优势古菌为氢营养型产甲烷古菌Methanocalculus (相对丰度13.5%–63.4%)和复合营养型产甲烷古菌Methanosarcina (7.9%–51.3%),海拉尔盆地的优势古菌为氢营养型产甲烷古菌Methanobact...     

On the centrality and uniqueness of species from the network perspective

Identifying important species for maintaining ecosystem functions is a challenge in ecology. Since species are components of food webs, one way to conceptualize and quantify species importance is from a network perspective. The importance of a species can be quantified by measuring the centrality of its position in a food web, because a central node may have greater influence on others in the network. A species may also be important because it has a unique network position, such that its loss cannot be easily compensated. Therefore, for a food web to be robust, we hypothesize that central species must be functionally redundant in terms of their network position. In this paper, we test our hypothesis by analysing the Prince William Sound ecosystem. We found that species centrality and uniqueness are negatively correlated, and such an observation is also carried over to other food webs.     

Type I and II β-turns prediction using NMR chemical shifts

A method for predicting type I and II β-turns using nuclear magnetic resonance (NMR) chemical shifts is proposed. Isolated β-turn chemical-shift data were collected from 1,798 protein chains. One-dimensional statistical analyses on chemical-shift data of three classes β-turn (type I, II, and VIII) showed different distributions at four positions, (i) to (i + 3). Considering the central two residues of type I β-turns, the mean values of C_ο, C_α, H_N, and N_H chemical shifts were generally (i + 1) > (i + 2). The mean values of C_β and H_α chemical shifts were (i + 1) < (i + 2). The distributions of the central two residues in type II and VIII β-turns were also distinguishable by trends of chemical shift values. Two-dimensional cluster analyses on chemical-shift data show positional distributions more clearly. Based on these propensities of chemical shift classified as a function of position, rules were derived using scoring matrices for four consecutive residues to predict type I and II β-turns. The proposed method achieves an overall prediction accuracy of 83.2 and 84.2 % with the Matthews correlation coefficient values of 0.317 and 0.632 for type I and II β-turns, indicating that its higher accuracy for type II turn prediction. The results show that it is feasible to use NMR chemical shifts to predict the β-turn types in proteins. The proposed method can be incorporated into other chemical-shift based protein secondary structure prediction methods.