PhosphoSitePlus (http://www.phosphosite.org) is an open, comprehensive, manually curated and interactive resource for studying experimentally observed post-translational modifications, primarily of human and mouse proteins. It encompasses 1,30,000 non-redundant modification sites, primarily phosphorylation, ubiquitinylation and acetylation. The interface is designed for clarity and ease of navigation. From the home page, users can launch simple or complex searches and browse high-throughput data sets by disease, tissue or cell line. Searches can be restricted by specific treatments, protein types, domains, cellular components, disease, cell types, cell lines, tissue and sequences or motifs. A few clicks of the mouse will take users to substrate pages or protein pages with sites, sequences, domain diagrams and molecular visualization of side-chains known to be modified; to site pages with information about how the modified site relates to the functions of specific proteins and cellular processes and to curated information pages summarizing the details from one record. PyMOL and Chimera scripts that colorize reactive groups on residues that are modified can be downloaded. Features designed to facilitate proteomic analyses include downloads of modification sites, kinase-substrate data sets, sequence logo generators, a Cytoscape plugin and BioPAX download to enable pathway visualization of the kinase-substrate interactions in PhosphoSitePlus®.

PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse

The liver is an essential metabolic organ, and its metabolic function is controlled by insulin and other metabolic hormones. Glucose is converted into pyruvate through glycolysis in the cytoplasm, and pyruvate is subsequently oxidized in the mitochondria to generate ATP through the TCA cycle and oxidative phosphorylation. In the fed state, glycolytic products are used to synthesize fatty acids through de novo lipogenesis. Long-chain fatty acids are incorporated into triacylglycerol, phospholipids, and/or cholesterol esters in hepatocytes. These complex lipids are stored in lipid droplets and membrane structures, or secreted into the circulation as very low-density lipoprotein particles. In the fasted state, the liver secretes glucose through both glycogenolysis and gluconeogenesis. During pronged fasting, hepatic gluconeogenesis is the primary source for endogenous glucose production. Fasting also promotes lipolysis in adipose tissue, resulting in release of nonesterified fatty acids which are converted into ketone bodies in hepatic mitochondria though β-oxidation and ketogenesis. Ketone bodies provide a metabolic fuel for extrahepatic tissues. Liver energy metabolism is tightly regulated by neuronal and hormonal signals. The sympathetic system stimulates, whereas the parasympathetic system suppresses, hepatic gluconeogenesis. Insulin stimulates glycolysis and lipogenesis but suppresses gluconeogenesis, and glucagon counteracts insulin action. Numerous transcription factors and coactivators, including CREB, FOXO1, ChREBP, SREBP, PGC-1α, and CRTC2, control the expression of the enzymes which catalyze key steps of metabolic pathways, thus controlling liver energy metabolism. Aberrant energy metabolism in the liver promotes insulin resistance, diabetes, and nonalcoholic fatty liver diseases.

Energy metabolism in the liver

According to Genome Sequencing Project statistics (http://www.ncbi.nlm.nih.gov/genomes/static/gpstat.html), as of Feb 16, 2012, complete gene sequences have become available for 2816 viruses, 1117 prokaryotes, and 36 eukaryotes.1–2 The availability of full genome sequences has greatly facilitated biological research in many fields, and has greatly contributed to the growth of proteomics.

Proteins are important because they are the direct bio-functional molecules in the living organisms. The term “proteomics” was coined from merging “protein” and “genomics” in the 1990s.3–4 As a post-genomic discipline, proteomics encompasses efforts to identify and quantify all the proteins of a proteome, including expression, cellular localization, interactions, post-translational modifications (PTMs), and turnover as a function of time, space and cell type, thus making the full investigation of a proteome more challenging than sequencing a genome. There are possibly 100,000 protein forms encoded by the approximate 20,235 genes of the human genome,5 and determining the explicit function of each form will be a challenge.

The progress of proteomics has been driven by the development of new technologies for peptide/protein separation, mass spectrometry analysis, isotope labeling for quantification, and bioinformatics data analysis. Mass spectrometry has emerged as a core tool for large-scale protein analysis. In the past decade, there has been a rapid advance in the resolution, mass accuracy, sensitivity and scan rate of mass spectrometers used to analyze proteins. In addition, hybrid mass analyzers have been introduced recently (e.g. Linear Ion Trap-Orbitrap series6–7) which have significantly improved proteomic analysis.

“Bottom-up” protein analysis refers to the characterization of proteins by analysis of peptides released from the protein through proteolysis. When bottom-up is performed on a mixture of proteins it is called shotgun proteomics,8–10 a name coined by the Yates lab because of its analogy to shotgun genomic sequencing.11 Shotgun proteomics provides an indirect measurement of proteins through peptides derived from proteolytic digestion of intact proteins. In a typical shotgun proteomics experiment, the peptide mixture is fractionated and subjected to LC-MS/MS analysis. Peptide identification is achieved by comparing the tandem mass spectra derived from peptide fragmentation with theoretical tandem mass spectra generated from in silico digestion of a protein database. Protein inference is accomplished by assigning peptide sequences to proteins. Because peptides can be either uniquely assigned to a single protein or shared by more than one protein, the identified proteins may be further scored and grouped based on their peptides. In contrast, another strategy, termed ‘top-down’ proteomics, is used to characterize intact proteins (Figure 1). The top-down approach has some potential advantages for PTM and protein isoform determination and has achieved notable success. Intact proteins have been measured up to 200 kDa,12 and a large scale study has identified more than 1,000 proteins by multi-dimensional separations from complex samples.13 However, the top-down method has significant limitations compared with shotgun proteomics due to difficulties with protein fractionation, protein ionization and fragmentation in the gas phase. By relying on the analysis of peptides, which are more easily fractionated, ionized and fragmented, shotgun proteomics can be more universally adopted for protein analysis. In fact, a hybrid of bottom-up and top-down methodologies and instrumentation has been introduced as middle-down proteomics.14 Essentially, middle-down proteomics analyzes larger peptide fragments than bottom-up proteomics, minimizing peptide redundancy between proteins. Additionally the large peptide fragments yield similar advantages as top-down proteomics, such as gaining further insight into post-translational modifications, without the analytical challenges of analyzing intact proteins. Shotgun proteomics has become a workhorse for the analysis of proteins and their modifications and will be increasingly combined with top-down methods in the future.



Figure 1

Proteomic strategies: bottom-up vs. top-down vs. middle-down. The bottom-up approach analyzes proteolytic peptides. The top-down method measures the intact proteins. The middle-down strategy analyzes larger peptides resulted from limited digestion or ...



In the past decade shotgun proteomics has been widely used by biologists for many different research experiments, advancing biological discoveries. Some applications include, but are not limited to, proteome profiling, protein quantification, protein modification, and protein-protein interaction. There have been several reviews nicely summarizing mass spectrometry history,15 protein quantification with mass spectrometry,16 its biological applications,5,17–26 and many recent advances in methodology.27–32 In this review, we try to provide a full and updated survey of shotgun proteomics, including the fundamental techniques and applications that laid the foundation along with those developed and greatly improved in the past several years.

Protein Analysis by Shotgun/Bottom-up Proteomics

Silent information regulator 2 (Sir2) proteins (sirtuins) are nicotinamide adenine dinucleotide-dependent deacetylases that regulate important biological processes. Mammals have seven sirtuins, Sirt1 to Sirt7. Four of them (Sirt4 to Sirt7) have no detectable or very weak deacetylase activity. We found that Sirt5 is an efficient protein lysine desuccinylase and demalonylase in vitro. The preference for succinyl and malonyl groups was explained by the presence of an arginine residue (Arg(105)) and tyrosine residue (Tyr(102)) in the acyl pocket of Sirt5. Several mammalian proteins were identified with mass spectrometry to have succinyl or malonyl lysine modifications. Deletion of Sirt5 in mice appeared to increase the level of succinylation on carbamoyl phosphate synthase 1, which is a known target of Sirt5. Thus, protein lysine succinylation may represent a posttranslational modification that can be reversed by Sirt5 in vivo.

/pdf/sirt5-is-a-nad-dependent-protein-lysine-demalonylase-and-3765xep097.pdf

Sirt5 is a NAD-dependent protein lysine demalonylase and desuccinylase.

Lysine acetylation is a key mechanism that regulates chromatin structure; aberrant acetylation levels have been linked to the development of several diseases. Acetyl-lysine modifications create docking sites for bromodomains, which are small interaction modules found on diverse proteins, some of which have a key role in the acetylation-dependent assembly of transcriptional regulator complexes. These complexes can then initiate transcriptional programmes that result in phenotypic changes. The recent discovery of potent and highly specific inhibitors for the BET (bromodomain and extra-terminal) family of bromodomains has stimulated intensive research activity in diverse therapeutic areas, particularly in oncology, where BET proteins regulate the expression of key oncogenes and anti-apoptotic proteins. In addition, targeting BET bromodomains could hold potential for the treatment of inflammation and viral infection. Here, we highlight recent progress in the development of bromodomain inhibitors, and their potential applications in drug discovery.

Targeting bromodomains: epigenetic readers of lysine acetylation

Protein lysine acetylation has emerged as a key posttranslational modification in cellular regulation, in particular through the modification of histones and nuclear transcription regulators. We show that lysine acetylation is a prevalent modification in enzymes that catalyze intermediate metabolism. Virtually every enzyme in glycolysis, gluconeogenesis, the tricarboxylic acid (TCA) cycle, the urea cycle, fatty acid metabolism, and glycogen metabolism was found to be acetylated in human liver tissue. The concentration of metabolic fuels, such as glucose, amino acids, and fatty acids, influenced the acetylation status of metabolic enzymes. Acetylation activated enoyl–coenzyme A hydratase/3-hydroxyacyl–coenzyme A dehydrogenase in fatty acid oxidation and malate dehydrogenase in the TCA cycle, inhibited argininosuccinate lyase in the urea cycle, and destabilized phosphoenolpyruvate carboxykinase in gluconeogenesis. Our study reveals that acetylation plays a major role in metabolic regulation.

https://depts.washington.edu/mmcslu/sites/default/files/Regulation%20of%20Cellular%20Metabolism%20by%20Protein%20Lysine%20Acetylation%20-%20Zhao%20et%20al%20-%20Science%202010%20(Supplement%20Included)3.2011.pdf

Regulation of cellular metabolism by protein lysine acetylation.

To clarify the generative mechanism and influencing factors of graduate students’ willingness and behavior of initiative scientific research and innovation, this research builds a quantitative model consisting of six variables: academic interest, regulatory pressure, conditions for improving scientific research and innovation capabilities, willingness to take initiative in scientific research and innovation, the promotion of achievements in scientific research, and initiative scientific research and innovation behavior. In total, 684 valid questionnaires were distributed and collected through WeChat Moments. Descriptive statistical analysis, reliability and validity analysis, factor analysis, path analysis, and conditioning analysis were conducted using the SPSS and structural equation model (SEM). The results showed that: (1) academic interest, regulatory pressure, and conditions for improving scientific research and innovation capabilities have a positive impact on the intention of initiative scientific research and innovation, but the impact of regulatory pressure is extremely insignificant. (2) The willingness to take initiative in scientific research and innovation has a positive and significant impact on initiative scientific research and innovation behavior, and the promotion of achievements in scientific research plays a negative moderating effect. Finally, this manuscript puts forward the suggestions on how to promote the initiative scientific research and innovation behavior of postgraduate students based on the research results.

Research on the Initiative Scientific Research and Innovation Behavior of Postgraduates: Based on the Theory of Planned Behavior

Hainan Tropical Rainforest National Park, as one of the first national parks established in China, should make full use of the gold brand of “Hainan Free Trade Port” and “National Park,” rely on the advantages and potential of system integration and innovation, draw on the experience of national parks in mature free trade ports (FTPs), and build a perfect national park policy system. This study introduces the PMC index model, constructs a national park policy evaluation index system, and conducts a systematic comparative analysis of 14 representative national park policies in Hainan rainforest, Hong Kong, and Singapore. The study finds that the policy of Hainan Tropical Rainforest National Park is not effective enough, the policy in social and cultural fields is relatively lacking, the policy content needs to be further strengthened, and the policy functions need to be improved, etc., and puts forward corresponding suggestions for optimization.

Yaxue Zeng

Papers

Regulation of cellular metabolism by protein lysine acetylation.

Research on the Initiative Scientific Research and Innovation Behavior of Postgraduates: Based on the Theory of Planned Behavior

Policy Optimization for Hainan Tropical Rainforest National Park Based on Quantitative Comparison of Regional Policies of Free Trade Port Areas