Does ambient air quality standard contribute to green innovation of enterprises in China? Implications for environmental protection and public health.

In the post-COVID-19 era, environmental pollution has been a serious threat to public health. Enterprises are in urgent need of enhancing green technology innovation as the main source of pollutant emissions, and it is necessary for governments to support green innovation of enterprises to reduce pollutant emissions and promote public health. In this context, this paper investigates whether the Ambient Air Quality Standard (AAQS) implemented in 2012 in China contributes to green innovation of enterprises, to provide implications for environmental protection and public health. By using panel data of Chinese A-share listed companies from 2008 to 2020, this study adopts the difference-in-difference model to analyze the policy impact of environmental regulation on green innovation of enterprises and its internal mechanism. The results show that AAQS has significantly improved the green innovation of enterprises. Furthermore, AAQS affects the green innovation of enterprises by virtue of two mechanism paths: compliance cost effect and innovation offset effect. On the one hand, AAQS leads to an increase in production costs of enterprises, thus inhibiting green innovation activities of enterprises. On the other hand, AAQS encourages enterprises to increase R&D investment in green technology, thus enhancing their green innovation. In addition, the impact of AAQS on firms' green innovation has heterogeneous characteristics. Our findings not only enrich the studies of environmental regulation and green innovation of enterprises but also provide policymakers in China and other developing countries with implications for environmental protection and public health improvement.

Cardiac-targeted PIASy gene silencing mediates deSUMOylation of caveolin-3 and prevents ischemia/reperfusion-induced Nav1.5 downregulation and ventricular arrhythmias.

BACKGROUND: Abnormal myocardial Nav1.5 expression and function cause lethal ventricular arrhythmias during myocardial ischemia-reperfusion (I/R). Protein inhibitor of activated STAT Y (PIASy)-mediated caveolin-3 (Cav-3) SUMO modification affects Cav-3 binding to the voltage-gated sodium channel 1.5 (Nav1.5). PIASy activity is increased after myocardial I/R, but it is unclear whether this is attributable to plasma membrane Nav1.5 downregulation and ventricular arrhythmias. METHODS: Using recombinant adeno-associated virus subtype 9 (AAV9), rat cardiac PIASy was silenced using intraventricular injection of PIASy short hairpin RNA (shRNA). After two weeks, rat hearts were subjected to I/R and electrocardiography was performed to assess malignant arrhythmias. Tissues from peri-infarct areas of the left ventricle were collected for molecular biological measurements. RESULTS: PIASy was upregulated by I/R (P < 0.01), with increased SUMO2/3 modification of Cav-3 and reduced membrane Nav1.5 density (P < 0.01). AAV9-PIASy shRNA intraventricular injection into the rat heart downregulated PIASy after I/R, at both mRNA and protein levels (P < 0.05 vs. Scramble-shRNA + I/R group), decreased SUMO-modified Cav-3 levels, enhanced Cav-3 binding to Nav1.5, and prevented I/R-induced decrease of Nav1.5 and Cav-3 co-localization in the intercalated disc and lateral membrane. PIASy silencing in rat hearts reduced I/R-induced fatal arrhythmias, which was reflected by a modest decrease in the duration of ventricular fibrillation (VF; P < 0.05 vs. Scramble-shRNA + I/R group) and a significantly reduced arrhythmia score (P < 0.01 vs. Scramble-shRNA + I/R group). The anti-arrhythmic effects of PIASy silencing were also evidenced by decreased episodes of ventricular tachycardia (VT), sustained VT and VF, especially at the time 5-10 min after ischemia (P < 0.05 vs. Scramble-shRNA + IR group). Using in vitro human embryonic kidney 293 T (HEK293T) cells and isolated adult rat cardiomyocyte models exposed to hypoxia/reoxygenation (H/R), we confirmed that increased PIASy promoted Cav-3 modification by SUMO2/3 and Nav1.5/Cav-3 dissociation after H/R. Mutation of SUMO consensus lysine sites in Cav-3 (K38R or K144R) altered the membrane expression levels of Nav1.5 and Cav-3 before and after H/R in HEK293T cells. CONCLUSIONS: I/R-induced cardiac PIASy activation increased Cav-3 SUMOylation by SUMO2/3 and dysregulated Nav1.5-related ventricular arrhythmias. Cardiac-targeted PIASy silencing mediated Cav-3 deSUMOylation and partially prevented I/R-induced Nav1.5 downregulation in the plasma membrane of cardiomyocytes, and subsequent ventricular arrhythmias in rats. PIASy was identified as a potential therapeutic target for life-threatening arrhythmias in patients with ischemic heart diseases.

mUSP: a high-accuracy map of the in situ crosstalk of ubiquitylation and SUMOylation proteome predicted via the feature enhancement approach.

Reversible post-translational modification (PTM) orchestrates various biological processes by changing the properties of proteins. Since many proteins are multiply modified by PTMs, identification of PTM crosstalk site has emerged to be an intriguing topic and attracted much attention. In this study, we systematically deciphered the in situ crosstalk of ubiquitylation and SUMOylation that co-occurs on the same lysine residue. We first collected 3363 ubiquitylation-SUMOylation (UBS) crosstalk site on 1302 proteins and then investigated the prime sequence motifs, the local evolutionary degree and the distribution of structural annotations at the residue and sequence levels between the UBS crosstalk and the single modification sites. Given the properties of UBS crosstalk sites, we thus developed the mUSP classifier to predict UBS crosstalk site by integrating different types of features with two-step feature optimization by recursive feature elimination approach. By using various cross-validations, the mUSP model achieved an average area under the curve (AUC) value of 0.8416, indicating its promising accuracy and robustness. By comparison, the mUSP has significantly better performance with the improvement of 38.41 and 51.48% AUC values compared to the cross-results by the previous single predictor. The mUSP was implemented as a web server available at http://bioinfo.ncu.edu.cn/mUSP/index.html to facilitate the query of our high-accuracy UBS crosstalk results for experimental design and validation.

Mutations of genes including DNMT3A detected by next-generation sequencing in thyroid cancer.

More than 90% of thyroid cancer belongs to the papillary and follicular thyroid carcinomas based on pathological subtypes. Papillary and follicular thyroid carcinoma are generally associated with a good prognosis. In contrast, other pathological subtypes such as poorly-differentiated and anaplastic thyroid carcinoma (PDTC and ATC) have a poor clinical outcome with a short life expectancy. To identify the genetic variations and biomarkers that may potentially distinguish the aggressive form of thyroid cancer, we performed a retrospective analysis of the formalin-fixed paraffin-embedded tumor samples from 50 patients who mainly displayed aggressive thyroid cancer using next-generation sequencing of 416 solid tumor-related genes. We adopted extensive bioinformatic analysis to vigorously remove germline single-nucleotide polymorphism and systematic sequencing errors, and report here that mutation in DNMT3A gene was significantly enriched in patients with PDTC or ATC.

Site-Specific Systematic Analysis of Lysine Modification Crosstalk.

Research has revealed that post-translational modifications (PTMs) that occur at lysine (PLMs) can cooperatively regulate various biological processes by crosstalk. However, the trend of the crosstalk between multiple PLMs and the properties of PLM crosstalk require additional investigation. Here, the crosstalk among acetylation, succinylation, and SUMOylation is systematically studied in a site-specific waz. First, crosstalk between SUMOylation is detected and succinylation is found to be underexpressed, whereas succinylation tends to crosstalk with acetylation and SUMOylation on the same lysine residue while PLM crosstalk is tissue-specific across different species. Further analysis reveals that different PLMs tend to occur crosstalk at diverse subcellular compartments and structural regions, and they participate in distinct biological processes and functions. Additionally, short-term evolutionary analysis shows that there is no additional evolutionary pressure on PLMs crosstalk sites, as found by comparison with singly modified sites. Finally, phylogenetic classification reveals that genes with co-occupied lysine crosstalk are more likely to have higher evolutionary similarity and possess a tendency to cluster in the specific branch. The integrated approach reported here has the potential for large-scale prioritization of in situ crosstalk of PLM candidates and provides a profound understanding of the underlying relationship between different lysine modifications.

Computational prediction of species-specific malonylation sites via enhanced characteristic strategy.

MOTIVATION: Protein malonylation is a novel post-translational modification (PTM) which orchestrates a variety of biological processes. Annotation of malonylation in proteomics is the first-crucial step to decipher its physiological roles which are implicated in the pathological processes. Comparing with the expensive and laborious experimental research, computational prediction can provide an accurate and effective approach to the identification of many types of PTMs sites. However, there is still no online predictor for lysine malonylation. RESULTS: By searching from literature and database, a well-prepared up-to-data benchmark datasets were collected in multiple organisms. Data analyses demonstrated that different organisms were preferentially involved in different biological processes and pathways. Meanwhile, unique sequence preferences were observed for each organism. Thus, a novel malonylation site online prediction tool, called MaloPred, which can predict malonylation for three species, was developed by integrating various informative features and via an enhanced feature strategy. On the independent test datasets, AUC (area under the receiver operating characteristic curves) scores are obtained as 0.755, 0.827 and 0.871 for Escherichia coli ( E.coli ), Mus musculus ( M.musculus ) and Homo sapiens ( H.sapiens ), respectively. The satisfying results suggest that MaloPred can provide more instructive guidance for further experimental investigation of protein malonylation. AVAILABILITY AND IMPLEMENTATION: http://bioinfo.ncu.edu.cn/MaloPred.aspx . CONTACT: jdqiu@ncu.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Accurate in silico prediction of species-specific methylation sites based on information gain feature optimization.

As one of the most important reversible types of post-translational modification, protein methylation catalyzed by methyltransferases carries many pivotal biological functions as well as many essential biological processes. Identification of methylation sites is prerequisite for decoding methylation regulatory networks in living cells and understanding their physiological roles. Experimental methods are limitations of labor-intensive and time-consuming. While in silicon approaches are cost-effective and high-throughput manner to predict potential methylation sites, but those previous predictors only have a mixed model and their prediction performances are not fully satisfactory now. Recently, with increasing availability of quantitative methylation datasets in diverse species (especially in eukaryotes), there is a growing need to develop a species-specific predictor. Here, we designed a tool named PSSMe based on information gain (IG) feature optimization method for species-specific methylation site prediction. The IG method was adopted to analyze the importance and contribution of each feature, then select the valuable dimension feature vectors to reconstitute a new orderly feature, which was applied to build the finally prediction model. Finally, our method improves prediction performance of accuracy about 15% comparing with single features. Furthermore, our species-specific model significantly improves the predictive performance compare with other general methylation prediction tools. Hence, our prediction results serve as useful resources to elucidate the mechanism of arginine or lysine methylation and facilitate hypothesis-driven experimental design and validation. AVAILABILITY AND IMPLEMENTATION: The tool online service is implemented by C# language and freely available at http://bioinfo.ncu.edu.cn/PSSMe.aspx CONTACT: jdqiu@ncu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.

A homology-based pipeline for global prediction of post-translational modification sites.

The pathways of protein post-translational modifications (PTMs) have been shown to play particularly important roles for almost any biological process. Identification of PTM substrates along with information on the exact sites is fundamental for fully understanding or controlling biological processes. Alternative computational strategies would help to annotate PTMs in a high-throughput manner. Traditional algorithms are suited for identifying the common organisms and tissues that have a complete PTM atlas or extensive experimental data. While annotation of rare PTMs in most organisms is a clear challenge. In this work, to this end we have developed a novel homology-based pipeline named PTMProber that allows identification of potential modification sites for most of the proteomes lacking PTMs data. Cross-promotion E-value (CPE) as stringent benchmark has been used in our pipeline to evaluate homology to known modification sites. Independent-validation tests show that PTMProber achieves over 58.8% recall with high precision by CPE benchmark. Comparisons with other machine-learning tools show that PTMProber pipeline performs better on general predictions. In addition, we developed a web-based tool to integrate this pipeline at http://bioinfo.ncu.edu.cn/PTMProber/index.aspx. In addition to pre-constructed prediction models of PTM, the website provides an extensional functionality to allow users to customize models.

SuccFind: a novel succinylation sites online prediction tool via enhanced characteristic strategy.

UNLABELLED: Lysine succinylation orchestrates a variety of biological processes. Annotation of succinylation in proteomes is the first-crucial step to decipher physiological roles of succinylation implicated in the pathological processes. In this work, we developed a novel succinylation site online prediction tool, called SuccFind, which is constructed to predict the lysine succinylation sites based on two major categories of characteristics: sequence-derived features and evolutionary-derived information of sequence and via an enhanced feature strategy for further optimizations. The assessment results obtained from cross-validation suggest that SuccFind can provide more instructive guidance for further experimental investigation of protein succinylation. AVAILABILITY AND IMPLEMENTATION: A user-friendly server is freely available on the web at: http://bioinfo.ncu.edu.cn/SuccFind.aspx. CONTACT: jdqiu@ncu.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Systematic Analysis of the Genetic Variability That Impacts SUMO Conjugation and Their Involvement in Human Diseases.

Protein function has been observed to rely on select essential sites instead of requiring all sites to be indispensable. Small ubiquitin-related modifier (SUMO) conjugation or sumoylation, which is a highly dynamic reversible process and its outcomes are extremely diverse, ranging from changes in localization to altered activity and, in some cases, stability of the modified, has shown to be especially valuable in cellular biology. Motivated by the significance of SUMO conjugation in biological processes, we report here on the first exploratory assessment whether sumoylation related genetic variability impacts protein functions as well as the occurrence of diseases related to SUMO. Here, we defined the SUMOAMVR as sumoylation related amino acid variations that affect sumoylation sites or enzymes involved in the process of connectivity, and categorized four types of potential SUMOAMVRs. We detected that 17.13% of amino acid variations are potential SUMOAMVRs and 4.83% of disease mutations could lead to SUMOAMVR with our system. More interestingly, the statistical analysis demonstrates that the amino acid variations that directly create new potential lysine sumoylation sites are more likely to cause diseases. It can be anticipated that our method can provide more instructive guidance to identify the mechanisms of genetic diseases.

Progress and challenges in predicting protein methylation sites.

Protein methylation catalyzed by methyltransferases carries many important biological functions. Methylation and their regulatory enzymes are involved in a variety of human disease states, raising the possibility that abnormally methylated proteins can be disease markers and methyltransferases are potential therapeutic targets. Identification of methylation sites is a prerequisite for decoding methylation regulatory networks in living cells and understanding their physiological roles that have been implicated in the pathological processes. Due to various limitations of experimental methods, in silico approaches for identifying novel methylation sites have become increasingly popular. In this review, we summarize the progress in the prediction of protein methylation sites from the dataset, feature representation, prediction algorithm and online resources in the past ten years. We also discuss the challenges that are faced while developing novel predictors in the future. The development and application of methylation site prediction is a promising field of systematic biology, provided that protein methyltransferases, species and functional information will be taken into account.

Proteomic analysis and prediction of human phosphorylation sites in subcellular level reveal subcellular specificity.

MOTIVATION: Protein phosphorylation is the most common post-translational modification (PTM) regulating major cellular processes through highly dynamic and complex signaling pathways. Large-scale comparative phosphoproteomic studies have frequently been done on whole cells or organs by conventional bottom-up mass spectrometry approaches, i.e at the phosphopeptide level. Using this approach, there is no way to know from where the phosphopeptide signal originated. Also, as a consequence of the scale of these studies, important information on the localization of phosphorylation sites in subcellular compartments (SCs) is not surveyed. RESULTS: Here, we present a first account of the emerging field of subcellular phosphoproteomics where a support vector machine (SVM) approach was combined with a novel algorithm of discrete wavelet transform (DWT) to facilitate the identification of compartment-specific phosphorylation sites and to unravel the intricate regulation of protein phosphorylation. Our data reveal that the subcellular phosphorylation distribution is compartment type dependent and that the phosphorylation displays site-specific sequence motifs that diverge between SCs. AVAILABILITY AND IMPLEMENTATION: The method and database both are available as a web server at: http://bioinfo.ncu.edu.cn/SubPhos.aspx. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PredHydroxy: computational prediction of protein hydroxylation site locations based on the primary structure.

Compared to well-known and extensively studied protein phosphorylation, protein hydroxylation attracts much less attention and the molecular mechanism of the hydroxylation is still incompletely understood. And yet annotation of hydroxylation in proteomes is a first-critical step toward decoding protein function and understanding their physiological roles that have been implicated in the pathological processes and providing useful information for the drug designs of various diseases related with hydroxylation. In this work, we present a novel method called PredHydroxy to automate the prediction of the proline and lysine hydroxylation sites based on position weight amino acids composition, 8 high-quality amino acid indices and support vector machines. The PredHydroxy achieved a promising performance with an area under the receiver operating characteristic curve (AUC) of 82.72% and a Matthew's correlation coefficient (MCC) of 69.03% for hydroxyproline as well as an AUC of 87.41% and a MCC of 66.68% for hydroxylysine in jackknife cross-validation. The results obtained from both the cross validation and independent tests suggest that the PredHydroxy might be a powerful and complementary tool for further experimental investigation of protein hydroxylation. Feature analyses demonstrate that hydroxylation and non-hydroxylation have distinct location-specific differences; alpha and turn propensity is of importance for the hydroxylation of proline and lysine residues. A user-friendly server is freely available on the web at: .