PhosSNP for systematic analysis of genetic polymorphisms that influence protein phosphorylation

VIPpred: a novel model for predicting variant impact on phosphorylation events driving carcinogenesis

Disrupted protein phosphorylation due to genetic variation is a widespread phenomenon that triggers oncogenic transformation of healthy cells. However, few relevant phosphorylation disruption events have been verified due to limited biological experimental methods. Because of the lack of reliable benchmark datasets, current bioinformatics methods primarily use sequence-based traits to study variant impact on phosphorylation (VIP). Here, we increased the number of experimentally supported VIP events from less than 30 to 740 by manually curating and reanalyzing multi-omics data from 916 patients provided by the Clinical Proteomic Tumor Analysis Consortium. To predict VIP events in cancer cells, we developed VIPpred, a machine learning method characterized by multidimensional features that exhibits robust performance across different cancer types. Our method provided a pan-cancer landscape of VIP events, which are enriched in cancer-related pathways and cancer driver genes. We found that variant-induced increases in phosphorylation events tend to inhibit the protein degradation of oncogenes and promote tumor suppressor protein degradation. Our work provides new insights into phosphorylation-related cancer biology as well as novel avenues for precision therapy.

Identification of molecular signatures and molecular dynamics simulation of highly deleterious missense variants of key autophagy regulator beclin 1: a computational based approach

Beclin 1 is a key autophagy regulator that also plays significant roles in other intracellular processes such as vacuolar protein sorting. Beclin 1 protein functions as a scaffold in the formation of a multiprotein assemblage during autophagy. Beclin 1 is involved in various diseases such as cancers, neurodegenerative and autophagy-related disorders. In this study, we have used various in silico tools to scan beclin 1 at the molecular level to find its molecular signatures. We have predicted and analysed deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) of beclin 1 causing alterations in its structure and also affecting its interactions with other proteins. In total, twelve coding region deleterious variants were predicted using sequence-based tools and nine were predicted using various structure-based tools. The molecular dynamics (MD) simulations revealed an altered stability of the native structure due to the introduction of mutations. Destabilization of beclin 1 ECD domain was observed due to nsSNPs W300R and E302K. Beclin 1 deleterious nsSNPs were predicted to show significant effects on beclin 1 interactions with ATG14L1, UVRAG and VPS34 proteins and were also predicted to alter the protein-protein interface of beclin 1 complexes. Additionally, beclin 1 was predicted to have thirty-one potential phosphorylation and three ubiquitination sites. In conclusion, the molecular details of beclin 1 could help in the better understanding of its functioning. The study of nsSNPs and their effect on beclin 1 and its interactions might aid in understanding the basis of anomalies caused due to beclin 1 dysfunction.Communicated by Ramaswamy H. Sarma.

RMDisease V2.0: an updated database of genetic variants that affect RNA modifications with disease and trait implication

Recent advances in epitranscriptomics have unveiled functional associations between RNA modifications (RMs) and multiple human diseases, but distinguishing the functional or disease-related single nucleotide variants (SNVs) from the majority of 'silent' variants remains a major challenge. We previously developed the RMDisease database for unveiling the association between genetic variants and RMs concerning human disease pathogenesis. In this work, we present RMDisease v2.0, an updated database with expanded coverage. Using deep learning models and from 873 819 experimentally validated RM sites, we identified a total of 1 366 252 RM-associated variants that may affect (add or remove an RM site) 16 different types of RNA modifications (m6A, m5C, m1A, m5U, Ψ, m6Am, m7G, A-to-I, ac4C, Am, Cm, Um, Gm, hm5C, D and f5C) in 20 organisms (human, mouse, rat, zebrafish, maize, fruit fly, yeast, fission yeast, Arabidopsis, rice, chicken, goat, sheep, pig, cow, rhesus monkey, tomato, chimpanzee, green monkey and SARS-CoV-2). Among them, 14 749 disease- and 2441 trait-associated genetic variants may function via the perturbation of epitranscriptomic markers. RMDisease v2.0 should serve as a useful resource for studying the genetic drivers of phenotypes that lie within the epitranscriptome layer circuitry, and is freely accessible at: www.rnamd.org/rmdisease2.

Genome-Wide Identification of Associations of Circulating Molecules With Spontaneous Coronary Artery Dissection and Aortic Aneurysm and Dissection

Circulating proteins play functional roles in various biological processes and disease pathogenesis. The aim of this study was to highlight circulating proteins associated with aortic aneurysm and dissection (AAD) and spontaneous coronary artery dissection (SCAD). We examined the associations of circulating molecule levels with SCAD by integrating data from a genome-wide association study (GWAS) of CanSCAD and 7 pQTL studies. Mendelian randomization (MR) analysis was applied to examine the associations between circulating molecule levels and AAD by using data from UK Biobank GWAS and pQTL studies. The SCAD-associated SNPs in 1q21.2 were strongly associated with circulating levels of extracellular matrix protein 1 (ECM1) and 25 other proteins (encoded by CTSS, CAT, CNDP1, KNG1, SLAMF7, TIE1, CXCL1, MBL2, ESD, CXCL16, CCL14, KCNE5, CST7, PSME1, GPC3, MAP2K4, SPOCK3, LRPPRC, CLEC4M, NOG, C1QTNF9, CX3CL1, SCP2D1, SERPINF2, and FN1). These proteins were enriched in biological processes such as regulation of peptidase activity and regulation of cellular protein metabolic processes. Proteins (FGF6, FGF9, HGF, BCL2L1, and VEGFA) involved in the Ras signaling pathway were identified to be related to AAD. In addition, SCAD- and AAD-associated SNPs were associated with cytokine and lipid levels. MR analysis showed that circulating ECM1, SPOCK3 and IL1b levels were associated with AAD. Circulating levels of low-density lipoprotein cholesterol and small very-low-density lipoprotein particles were strongly associated with AAD. The present study found associations between circulating proteins and lipids and SCAD and AAD. Circulating ECM1 and low-density lipoprotein cholesterol may play a role in the pathology of SCAD and AAD.

PhosVarDeep: deep-learning based prediction of phospho-variants using sequence information

Human DNA sequencing has revealed numerous single nucleotide variants associated with complex diseases. Researchers have shown that these variants have potential effects on protein function, one of which is to disrupt protein phosphorylation. Based on conventional machine learning algorithms, several computational methods for predicting phospho-variants have been developed, but their performance still leaves considerable room for improvement. In recent years, deep learning has been successfully applied in biological sequence analysis with its efficient sequence pattern learning ability, which provides a powerful tool for improving phospho-variant prediction based on protein sequence information. In the study, we present PhosVarDeep, a novel unified deep-learning framework for phospho-variant prediction. PhosVarDeep takes reference and variant sequences as inputs and adopts a Siamese-like CNN architecture containing two identical subnetworks and a prediction module. In each subnetwork, general phosphorylation sequence features are extracted by a pre-trained sequence feature encoding network and then fed into a CNN module for capturing variant-aware phosphorylation sequence features. After that, a prediction module is introduced to integrate the outputs of the two subnetworks and generate the prediction results of phospho-variants. Comprehensive experimental results on phospho-variant data demonstrates that our method significantly improves the prediction performance of phospho-variants and compares favorably with existing conventional machine learning methods.

Why SNP rs3755955 is associated with human bone mineral density? A molecular and cellular study in bone cells

SNP rs3755955 (major/minor allele: G/A) located in Iduronidase-Alpha-L- (IDUA) gene was reported to be significant for human bone mineral density (BMD). This follow-up study was to uncover the underlying association mechanism through molecular and cellular functional assays relevant to bone. We tested the effects of single nucleotide polymorphisms (SNP) rs3755955 (defined allele G as wild-type and allele A as variant-type) on osteoblastic and osteoclastic functions, as well as protein phosphorylation in stably transfected human fetal osteoblast (hFOB) cell and mononuclear-macrophage (RAW264.7) cell. In hFOB cells, transfection with variant-type IDUA significantly decreased osteoblastic gene expression (OPN, COL1A1 and RANKL) (p < 0.01), impeded cell proliferation (p < 0.05), stimulated cell apoptosis (p < 0.001) and decreased ALP enzyme activity, as compared with that of wild-type IDUA transfection. In RAW264.7 cells, transfection with variant-type IDUA significantly inhibited cell apoptosis (p < 0.01), promoted osteoclastic precursor cell migration (p < 0.0001), growth (p < 0.01), osteoclastic gene expression (TRAP, RANK, Inte-αv and Cath-K) (p < 0.05) and TRAP enzyme activity (p < 0.001), as compared with that of wild-type IDUA transfection. In both hFOB and RAW264.7 cells, the total protein and IDUA protein-specific phosphorylation levels were significantly reduced by variant IDUA transfection, as compared with that of wild-type IDUA transfection (p < 0.05). Variant allele A of phosSNP rs3755955 in IDUA gene regulates protein phosphorylation, inhibits osteoblast function and promotes osteoclastic activity. The SNP rs3755955 could alter IDUA protein phosphorylation, significantly regulates human osteoblastic and osteoclastic gene expression, and influences the growth, differentiation and activity of osteoblast and osteoclast, hence to affect BMD.

PhosSNPs-Regulated Gene Network and Pathway Significant for Rheumatoid Arthritis

OBJECTIVES

Peripheral blood mononuclear cells (PBMCs) are critical for immunity and participate in multiple human diseases, including rheumatoid arthritis (RA). PhosSNPs are nonsynonymous SNPs influencing protein phosphorylation, thus probably modulate cell signaling and gene expression. We aimed to identify phosSNPs-regulated gene network/pathway potentially significant for RA.

METHODS

We collected genome-wide phosSNP genotyping data and transcriptome-wide mRNA expression data from PBMCs of a Chinese sample. We discovered and verified with public datasets differentially expressed genes (DEGs) associated with RA, and replicated RA-associated SNPs in our study sample. We performed a targeted expression quantitative trait locus (eQTL) study on significant phosSNPs and DEGs.

RESULTS

We identified 29 nominally significant eQTL phosSNPs and 83 target genes, and constructed comprehensive regulatory/interaction networks, highlighting the vital effects of two eQTL phosSNPs (rs371513 and rs4824675, FDR <0.05) and four critical node genes (HSPA4, NDUFA2, MRPL15, and ATP5O). Besides, two node/key genes NDUFA2 and ATP5O, regulated by rs371513, were significantly enriched in mitochondrial oxidative phosphorylation pathway. Besides, four pairs of eQTL effects were replicated independently in whole blood and/or transformed fibroblasts.

CONCLUSIONS

The findings delineated a potential role of protein phosphorylation and genetic variations in RA and warranted the significant roles of phosSNPs in regulating RA-associated genes expression in PBMCs. The results pointed out the relevance and significance of oxidative phosphorylation pathway to RA.

RMDisease: a database of genetic variants that affect RNA modifications, with implications for epitranscriptome pathogenesis

Deciphering the biological impacts of millions of single nucleotide variants remains a major challenge. Recent studies suggest that RNA modifications play versatile roles in essential biological mechanisms, and are closely related to the progression of various diseases including multiple cancers. To comprehensively unveil the association between disease-associated variants and their epitranscriptome disturbance, we built RMDisease, a database of genetic variants that can affect RNA modifications. By integrating the prediction results of 18 different RNA modification prediction tools and also 303,426 experimentally-validated RNA modification sites, RMDisease identified a total of 202,307 human SNPs that may affect (add or remove) sites of eight types of RNA modifications (m⁶A, m⁵C, m¹A, m⁵U, Ψ, m⁶Am, m⁷G and Nm). These include 4,289 disease-associated variants that may imply disease pathogenesis functioning at the epitranscriptome layer. These SNPs were further annotated with essential information such as post-transcriptional regulations (sites for miRNA binding, interaction with RNA-binding proteins and alternative splicing) revealing putative regulatory circuits. A convenient graphical user interface was constructed to support the query, exploration and download of the relevant information. RMDisease should make a useful resource for studying the epitranscriptome impact of genetic variants via multiple RNA modifications with emphasis on their potential disease relevance. RMDisease is freely accessible at: www.xjtlu.edu.cn/biologicalsciences/rmd.

KSP: an integrated method for predicting catalyzing kinases of phosphorylation sites in proteins

BACKGROUND

Protein phosphorylation by kinases plays crucial roles in various biological processes including signal transduction and tumorigenesis, thus a better understanding of protein phosphorylation events in cells is fundamental for studying protein functions and designing drugs to treat diseases caused by the malfunction of phosphorylation. Although a large number of phosphorylation sites in proteins have been identified using high-throughput phosphoproteomic technologies, their specific catalyzing kinases remain largely unknown. Therefore, computational methods are urgently needed to predict the kinases that catalyze the phosphorylation of these sites.

RESULTS

We developed KSP, a new algorithm for predicting catalyzing kinases for experimentally identified phosphorylation sites in human proteins. KSP constructs a network based on known protein-protein interactions and kinase-substrate relationships. Based on the network, it computes an affinity score between a phosphorylation site and kinases, and returns the top-ranked kinases of the score as candidate catalyzing kinases. When tested on known kinase-substrate pairs, KSP outperforms existing methods including NetworKIN, iGPS, and PKIS.

CONCLUSIONS

We developed a novel accurate tool for predicting catalyzing kinases of known phosphorylation sites. It can work as a complementary network approach for sequence-based phosphorylation site predictors.

Using phosphoproteomics data to understand cellular signaling: a comprehensive guide to bioinformatics resources

Mass spectrometry-based phosphoproteomics is becoming an essential methodology for the study of global cellular signaling. Numerous bioinformatics resources are available to facilitate the translation of phosphopeptide identification and quantification results into novel biological and clinical insights, a critical step in phosphoproteomics data analysis. These resources include knowledge bases of kinases and phosphatases, phosphorylation sites, kinase inhibitors, and sequence variants affecting kinase function, and bioinformatics tools that can predict phosphorylation sites in addition to the kinase that phosphorylates them, infer kinase activity, and predict the effect of mutations on kinase signaling. However, these resources exist in silos and it is challenging to select among multiple resources with similar functions. Therefore, we put together a comprehensive collection of resources related to phosphoproteomics data interpretation, compared the use of tools with similar functions, and assessed the usability from the standpoint of typical biologists or clinicians. Overall, tools could be improved by standardization of enzyme names, flexibility of data input and output format, consistent maintenance, and detailed manuals.

AWESOME: a database of SNPs that affect protein post-translational modifications

Protein post-translational modifications (PTMs), including phosphorylation, ubiquitination, methylation, acetylation, glycosylation et al, are very important biological processes. PTM changes in some critical genes, which may be induced by base-pair substitution, are shown to affect the risk of diseases. Recently, large-scale exome-wide association studies found that missense single nucleotide polymorphisms (SNPs) play an important role in the susceptibility for complex diseases or traits. One of the functional mechanisms of missense SNPs is that they may affect PTMs and leads to a protein dysfunction and its downstream signaling pathway disorder. Here, we constructed a database named AWESOME (A Website Exhibits SNP On Modification Event, http://www.awesome-hust.com), which is an interactive web-based analysis tool that systematically evaluates the role of SNPs on nearly all kinds of PTMs based on 20 available tools. We also provided a well-designed scoring system to compare the performance of different PTM prediction tools and help users to get a better interpretation of results. Users can search SNPs, genes or position of interest, filter with specific modifications or prediction methods, to get a comprehensive PTM change induced by SNPs. In summary, our database provides a convenient way to detect PTM-related SNPs, which may potentially be pathogenic factors or therapeutic targets.

Associations between potentially functional CORIN SNPs and serum corin levels in the Chinese Han population

Background

Corin is an important convertase involved in the natriuretic peptide system and may indirectly regulate blood pressure. Genetic factors relate to corin remain unclear. The purpose of the current study was to comprehensively examine the associations among CORIN SNPs, methylations, serum corin levels and hypertension.

Results

We genotyped 9 tag-SNPs in the CORIN gene and measured serum corin levels in 731 new-onset hypertensive cases and 731 age- and sex-matched controls. DNA methylations were tested in 43 individuals. Mendelian randomization was used to investigate the causal associations. Under additive models, we observed associations of rs2289433 (p.Cys13Tyr), rs6823184, rs10517195, rs2271037 and rs12509275 with serum corin levels after adjustment for covariates (P = 0.0399, 0.0238, 0.0016, 0.0148 and 0.0038, respectively). The tag-SNP rs6823184 and SNPs that are in strong linkage disequilibrium with it, i.e., rs10049713, rs6823698 and rs1866689, were associated with CORIN gene expression (P = 2.38 × 10^− 24, 5.94 × 10^− 27, 6.31 × 10^− 27 and 6.30 × 10^− 27, respectively). Neither SNPs nor corin levels was found to be associated with hypertension. SNP rs6823184, which is located in a DNase hypersensitivity cluster, a CpG island and transcription factor binding sites, was significantly associated with cg02955940 methylation levels (P = 1.54 × 10^− 7). A putative causal association between cg02955940 methylation and corin levels was detected (P = 0.0011).

Conclusion

This study identified potentially functional CORIN SNPs that were associated with serum corin level in the Chinese Han population. The effect of CORIN SNPs on corin level may be mediated by DNA methylation.

Phosphorylation-related SNPs influence lipid levels and rheumatoid arthritis risk by altering gene expression and plasma protein levels

Objectives

Phosphorylation-related single-nucleotide polymorphisms (phosSNPs) are missense SNPs that may influence protein phosphorylation. The aim of this study was to evaluate the effect of phosSNPs on lipid levels and RA.

Methods

We examined the association of phosSNPs with lipid levels and RA in large-scale genome-wide association studies (GWAS) and performed random sampling and fgwas analyses to determine whether the phosSNPs associated with lipid levels and RA were significantly enriched. Furthermore, we performed QTL analysis and Mendelian randomization analysis to obtain additional evidence to be associated with the identified phosSNPs and genes.

Results

We found 483 phosSNPs for lipid levels and 243 phosSNPs for RA in the GWAS loci (P &lt; 1.0 × 10−5). SNPs associated with high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, Total cholesterol (TC) and RA were significantly enriched with phosSNPs. Almost all of the identified phosSNPs showed expression quantitative trait loci (eQTL) effects. A total of 48 protein QTLs and 9 metabolite QTLs were found. The phosSNP rs3184504 (p.Trp262Arg) at SH2B3 was significantly associated with RA, SH2B3 expression level, and plasma levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, TC, hypoxanthine and 80 proteins, including beta-2-microglobulin. SH2B3 was differentially expressed between RA cases and controls in peripheral blood mononuclear cells and synovial tissues. Mendelian randomization analysis showed that SH2B3 expression level was significantly associated with TC level and RA. Plasma beta-2-microglobulin level was causally associated with high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, TC levels and RA.

Conclusion

The findings suggested that phosSNPs may play important roles in lipid metabolism and the pathological mechanisms of RA. PhosSNPs may influence lipid levels and RA risk by altering gene expression and plasma protein levels.

Identification of Phosphorylation Associated SNPs for Blood Pressure, Coronary Artery Disease and Stroke from Genome-wide Association Studies

PURPOSE

Phosphorylation-related SNP (phosSNP) is a non-synonymous SNP that might influence protein phosphorylation status. The aim of this study was to assess the effect of phosSNPs on blood pressure (BP), coronary artery disease (CAD) and ischemic stroke (IS).

METHODS

We examined the association of phosSNPs with BP, CAD and IS in shared data from genome-wide association studies (GWAS) and tested if the disease loci were enriched with phosSNPs. Furthermore, we performed quantitative trait locus analysis to find out if the identified phosSNPs have impacts on gene expression, protein and metabolite levels.

RESULTS

We found numerous phosSNPs for systolic BP (count=148), diastolic BP (count=206), CAD (count=20) and IS (count=4). The most significant phosSNPs for SBP, DBP, CAD and IS were rs1801131 in MTHFR, rs3184504 in SH2B3, rs35212307 in WDR12 and rs3184504 in SH2B3, respectively. Our analyses revealed that the associated SNPs identified by the original GWAS were significantly enriched with phosSNPs and many well-known genes predisposing to cardiovascular diseases contain significant phosSNPs. We found that BP, CAD and IS shared for phosSNPs in loci that contain functional genes involve in cardiovascular diseases, e.g., rs11556924 (ZC3HC1), rs1971819 (ICA1L), rs3184504 (SH2B3), rs3739998 (JCAD), rs903160 (SMG6). Four phosSNPs in ADAMTS7 were significantly associated with CAD, including the known functional SNP rs3825807. Moreover, the identified phosSNPs seemed to have the potential to affect transcription regulation and serum levels of numerous cardiovascular diseases-related proteins and metabolites.

CONCLUSION

The findings suggested that phosSNPs may play important roles in BP regulation and the pathological mechanisms of CAD and IS.

Characterization of Plasma Membrane Localization and Phosphorylation Status of Organic Anion Transporting Polypeptide (OATP) 1B1 c.521 T>C Nonsynonymous Single-Nucleotide Polymorphism

PURPOSE

Membrane transport protein organic anion transporting polypeptide (OATP) 1B1 mediates hepatic uptake of many drugs (e.g. statins). The OATP1B1 c.521 T > C (p. V174A) polymorphism has reduced transport activity. Conflicting in vitro results exist regarding whether V174A-OATP1B1 has reduced plasma membrane localization; no such data has been reported in physiologically relevant human liver tissue. Other potential changes, such as phosphorylation, of the V174A-OATP1B1 protein have not been explored. Current studies characterized the plasma membrane localization of V174A-OATP1B1 in genotyped human liver tissue and cell culture and compared the phosphorylation status of V174A- and wild-type (WT)-OATP1B1.

METHODS

Localization of V174A- and WT-OATP1B1 were determined in OATP1B1 c.521 T > C genotyped human liver tissue (n = 79) by immunohistochemistry and in transporter-overexpressing human embryonic kidney (HEK) 293 and HeLa cells by surface biotinylation and confocal microscopy. Phosphorylation and transport of OATP1B1 was determined using ³²P-orthophosphate labeling and [³H]estradiol-17β-glucuronide accumulation, respectively.

RESULTS

All three methods demonstrated predominant plasma membrane localization of both V174A- and WT-OATP1B1 in human liver tissue and in cell culture. Compared to WT-OATP1B1, the V174A-OATP1B1 has significantly increased phosphorylation and reduced transport.

CONCLUSIONS

We report novel findings of increased phosphorylation, but not impaired membrane localization, in association with the reduced transport function of the V174A-OATP1B1.

SNPs rs10224002 in PRKAG2 may disturb gene expression and consequently affect hypertension

Genome-wide association studies have identified a large number of genetic loci for blood pressure in European populations. The associations in other populations are needed to determine. The purpose of this study was to examine the associations between the single nucleotide polymorphisms (SNPs) identified in European populations and hypertension in the Chinese Han population, and highlight the potential roles. Seven tag-SNPs were genotyped in 857 hypertension cases and 927 controls to test the associations. The intronic SNP rs10224002 (PRKAG2) which could affect DNase and regulatory motif was associated with hypertension (P = 0.024). This SNP was also found to be associated with coronary artery disease and stroke. We searched for potential functional variants by bioinformatics analysis and found various kinds of variants such as missense mutations, phosphorylation-related SNPs and SNPs that have regulatory potentials in the blood pressure loci. We performed expression quantitative trait locus (eQTL) and differential expression analyses for the identified variants and genes. eQTL analysis found that rs10224002 was associated with PRKAG2 gene expression in peripheral blood (P = 0.0016). PRKAG2 was differentially expressed between hypertension cases and controls (P = 0.0133), coronary artery disease cases and controls (P = 0.02112) and stroke cases and controls (P = 0.0059). Our study demonstrated that SNPs rs10224002 may be associated with hypertension in the Chinese Han population and PRKAG2 may play a role in the etiology of hypertension and cardiovascular diseases.

Detecting post-translational modification signatures as potential biomarkers in clinical mass spectrometry

INTRODUCTION

Numerous diseases are caused by changes in post-translational modifications (PTMs). Therefore, the number of clinical proteomics studies that include the analysis of PTMs is increasing. Combining complementary information-for example changes in protein abundance, PTM levels, with the genome and transcriptome (proteogenomics)-holds great promise for discovering important drivers and markers of disease, as variations in copy number, expression levels, or mutations without spatial/functional/isoform information is often insufficient or even misleading. Areas covered: We discuss general considerations, requirements, pitfalls, and future perspectives in applying PTM-centric proteomics to clinical samples. This includes samples obtained from a human subject, for instance (i) bodily fluids such as plasma, urine, or cerebrospinal fluid, (ii) primary cells such as reproductive cells, blood cells, and (iii) tissue samples/biopsies. Expert commentary: PTM-centric discovery proteomics can substantially contribute to the understanding of disease mechanisms by identifying signatures with potential diagnostic or even therapeutic relevance but may require coordinated efforts of interdisciplinary and eventually multi-national consortia, such as initiated in the cancer moonshot program. Additionally, robust and standardized mass spectrometry (MS) assays-particularly targeted MS, MALDI imaging, and immuno-MALDI-may be transferred to the clinic to improve patient stratification for precision medicine, and guide therapies.

Detection of Putative Functional Single Nucleotide Polymorphisms in Blood Pressure Loci and Validation of Association Between Single Nucleotide Polymorphism in WBP1L and Hypertension in the Chinese Han Population

We have performed a gene-based association study and detected several important blood pressure (BP)-associated genes. In this study, we explored functional variants in these genes by bioinformatics analysis and validated the associations between the functional single nucleotide polymorphisms (SNPs) and hypertension with public data and our in-house data of 857 cases and 927 controls. We found various functional variants in the BP-associated genes, including missense mutations and phosphorylation-related SNPs. Most of these SNPs were associated with expressions of the local genes. Some of these SNPs were associated with coronary artery disease or ischemic stroke. The associations between 12 functional SNPs in 7 genes and BP were validated (P < 5 × 10). The intronic SNP rs176185, which may influence promoter histone, enhancer histone, DNase and regulatory motifs and showed cis-eQTL effect on WBP1L, was associated with hypertension in the Chinese Han population (P = 0.0119). Our study detected plenty of potential functional SNPs in the BP-associated genes and demonstrated that rs176185 may be associated with hypertension in the Chinese Han population.

Why SNP rs227584 is associated with human BMD and fracture risk? A molecular and cellular study in bone cells

A large number of SNPs significant for osteoporosis (OP) had been identified by genome-wide association studies. However, the underlying association mechanisms were largely unknown. From the perspective of protein phosphorylation, gene expression regulation, and bone cell activity, this study aims to illustrate association mechanisms for representative SNPs of interest. We utilized public databases and bioinformatics tool to identify OP-associated SNPs which potentially influence protein phosphorylation (phosSNPs). Associations with hip/spine BMD, as well as fracture risk, in human populations for one significant phosSNP, that is, rs227584 (major/minor allele: C/A, EAS population) located in C17orf53 gene, were suggested in prior meta-analyses. Specifically, carriers of allele C had significant higher BMD and lower risk of low-trauma fractures than carriers of A. We pursued to test the molecular and cellular functions of rs227584 in bone through osteoblastic cell culture and multiple assays. We identified five phosSNPs significant for OP (P < 0.01). The osteoblastic cells, which was transfected with wild-type C17orf53 (allele C at rs227584, P126), demonstrated specific interaction with NEK2 kinase, increased expression levels of osteoblastic genes significantly (OPN, OCN, COL1A1, P < 0.05), and promoted osteoblast growth and ALP activity, in contrast to those transfected with mutant C17orf53 (allele A at rs227584, T126). In the light of the consistent evidences between the present functional study in human bone cells and the prior association studies in human populations, we conclude that the SNP rs227584, via altering protein-kinase interaction, regulates osteoblastic gene expression, influences osteoblast growth and activity, hence to affect BMD and fracture risk in humans.

Proteomic analysis and prediction of amino acid variations that influence protein posttranslational modifications

Accumulative studies have indicated that amino acid variations through changing the type of residues of the target sites or key flanking residues could directly or indirectly influence protein posttranslational modifications (PTMs) and bring about a detrimental effect on protein function. Computational mutation analysis can greatly narrow down the efforts on experimental work. To increase the utilization of current computational resources, we first provide an overview of computational prediction of amino acid variations that influence protein PTMs and their functional analysis. We also discuss the challenges that are faced while developing novel in silico approaches in the future. The development of better methods for mutation analysis-related protein PTMs will help to facilitate the development of personalized precision medicine.

m6ASNP: a tool for annotating genetic variants by m6A function

Background

Large-scale genome sequencing projects have identified many genetic variants for diverse diseases. A major goal of these projects is to characterize these genetic variants to provide insight into their function and roles in diseases. N6-methyladenosine (m6A) is one of the most abundant RNA modifications in eukaryotes. Recent studies have revealed that aberrant m6A modifications are involved in many diseases.

Findings

In this study, we present a user-friendly web server called "m6ASNP" that is dedicated to the identification of genetic variants that target m6A modification sites. A random forest model was implemented in m6ASNP to predict whether the methylation status of an m6A site is altered by the variants that surround the site. In m6ASNP, genetic variants in a standard variant call format (VCF) are accepted as the input data, and the output includes an interactive table that contains the genetic variants annotated by m6A function. In addition, statistical diagrams and a genome browser are provided to visualize the characteristics and to annotate the genetic variants.

Conclusions

We believe that m6ASNP is a very convenient tool that can be used to boost further functional studies investigating genetic variants. The web server "m6ASNP" is implemented in JAVA and PHP and is freely available at [60].

PhosphoPICK-SNP: quantifying the effect of amino acid variants on protein phosphorylation

Motivation

Genome-wide association studies are identifying single nucleotide variants (SNVs) linked to various diseases, however the functional effect caused by these variants is often unknown. One potential functional effect, the loss or gain of protein phosphorylation sites, can be induced through variations in key amino acids that disrupt or introduce valid kinase binding patterns. Current methods for predicting the effect of SNVs on phosphorylation operate on the sequence content of reference and variant proteins. However, consideration of the amino acid sequence alone is insufficient for predicting phosphorylation change, as context factors determine kinase-substrate selection.

Results

We present here a method for quantifying the effect of SNVs on protein phosphorylation through an integrated system of motif analysis and context-based assessment of kinase targets. By predicting the effect that known variants across the proteome have on phosphorylation, we are able to use this background of proteome-wide variant effects to quantify the significance of novel variants for modifying phosphorylation. We validate our method on a manually curated set of phosphorylation change-causing variants from the primary literature, showing that the method predicts known examples of phosphorylation change at high levels of specificity. We apply our approach to data-sets of variants in phosphorylation site regions, showing that variants causing predicted phosphorylation loss are over-represented among disease-associated variants.

Availability and Implementation

The method is freely available as a web-service at the website http://bioinf.scmb.uq.edu.au/phosphopick/snp.

Contact

m.boden@uq.edu.au.

Supplementary information

Supplementary data are available at Bioinformatics online.

Functional annotation of Alzheimer's disease associated loci revealed by GWASs

Genome-wide association studies (GWASs) discovered a number of SNPs and genes associated with Alzheimer's disease (AD). However, how these SNPs and genes influence the liability to AD is not fully understood. We deployed computational approaches to explore the function and action mechanisms of AD -related SNPs and genes identified by GWASs, including the effects of 195 GWAS lead SNPs and 338 proxy SNPs on miRNAs binding and protein phosphorylation, their RegulomeDB and 3DSNP scores, and gene ontology, pathway enrichment and protein-protein interaction network of 126 AD-associated genes. Our computational analysis identified 6 lead SNPs (rs10119, rs1048699, rs148763909, rs610932, rs6857 and rs714948) and 2 proxy SNPs (rs12539172 and rs2847655) that potentially impacted the miRNA binding. Lead SNP rs2296160 and proxy SNPs rs679620 and rs2228145 were identified as PhosSNPs potentially influencing protein phosphorylation. AD-associated genes showed enrichment of “regulation of beta-amyloid formation”, “regulation of neurofibrillary tangle assembly”, “leukocyte mediated immunity” and “protein-lipid complex assembly” signaling pathway. Protein-protein interaction network and functional module analyses identified highly-interconnected “hub” genes (APOE, PICALM, BIN1, ABCA7, CD2AP, CLU, CR1, MS4A4E and MS4A6A) and bottleneck genes (APOE, TOMM40, NME8, PICALM, CD2AP, ZCWPW1, FAM180B, GAB2 and PTK2B) that created three tight subnetworks. Our results provided the targets for further experimental assessment and further insight on AD pathophysiology.

Gender-Specific Associations between CHGB Genetic Variants and Schizophrenia in a Korean Population

PURPOSE

Schizophrenia is a devastating mental disorder and is known to be affected by genetic factors. The chromogranin B (CHGB), a member of the chromogranin gene family, has been proposed as a candidate gene associated with the risk of schizophrenia. The secretory pathway for peptide hormones and neuropeptides in the brain is regulated by chromogranin proteins. The aim of this study was to investigate the potential associations between genetic variants of CHGB and schizophrenia susceptibility.

MATERIALS AND METHODS

In the current study, 15 single nucleotide polymorphisms of CHGB were genotyped in 310 schizophrenia patients and 604 healthy controls.

RESULTS

Statistical analysis revealed that two genetic variants (non-synonymous rs910122; rs2821 in 3'-untranslated region) were associated with schizophrenia [minimum p=0.002; odds ratio (OR)=0.72], even after correction for multiple testing (p(corr)=0.02). Since schizophrenia is known to be differentially expressed between sexes, additional analysis for sex was performed. As a result, these two genetic variants (rs910122 and rs2821) and a haplotype (ht3) showed significant associations with schizophrenia in male subjects (p(corr)=0.02; OR=0.64), whereas the significance disappeared in female subjects (p>0.05).

CONCLUSION

Although this study has limitations including a small number of samples and lack of functional study, our results suggest that genetic variants of CHGB may have sex-specific effects on the risk of schizophrenia and provide useful preliminary information for further study.

Methods, Tools and Current Perspectives in Proteogenomics

With combined technological advancements in high-throughput next-generation sequencing and deep mass spectrometry-based proteomics, proteogenomics, i.e. the integrative analysis of proteomic and genomic data, has emerged as a new research field. Early efforts in the field were focused on improving protein identification using sample-specific genomic and transcriptomic sequencing data. More recently, integrative analysis of quantitative measurements from genomic and proteomic studies have identified novel insights into gene expression regulation, cell signaling, and disease. Many methods and tools have been developed or adapted to enable an array of integrative proteogenomic approaches and in this article, we systematically classify published methods and tools into four major categories, (1) Sequence-centric proteogenomics; (2) Analysis of proteogenomic relationships; (3) Integrative modeling of proteogenomic data; and (4) Data sharing and visualization. We provide a comprehensive review of methods and available tools in each category and highlight their typical applications.

Computational analyses of type 2 diabetes-associated loci identified by genome-wide association studies

BACKGROUND

Genome-wide association studies (GWAS) of type 2 diabetes (T2D) have discovered a number of loci that contribute to susceptibility to the disease. Future challenges include elucidation of functional mechanisms through which these GWAS-identified loci modulate T2D disease risk. The aim of the present study was to comprehensively characterize T2D associated single nucleotide polymorphisms (SNPs) and genes through computational approaches.

METHODS

Computational biology approaches used in the present study included comparative genomic analyses and functional annotation using GWAS3D and RegulomeDB, investigation of the effects of T2D-associated SNPs on miRNA binding and protein phosphorylation, and gene ontology, pathway enrichment, protein-protein interaction (PPI) networks and functional module analysis of T2D-associated genes from previously published GWAS.

RESULTS

Computational analysis identified a number of T2D GWAS-associated SNPs that were located at protein binding sites, including CCCTC-binding factor (CTCF), E1A binding protein p300 (EP300), hepatocyte nuclear factor 4alpha (HNF4A), transcription factor 7 like 2 (TCF7L2), forkhead box A1 (FOXA1) and A2 (FOXA2), and potentially affected the binding of miRNAs and protein phosphorylation. Pathway enrichment analysis confirmed two well-known maturity onset diabetes of the young and T2D pathways, whereas PPI network analysis identified highly interconnected "hub" genes, such as TCF7L2, melatonin receptor 1B (MTNR1B), and solute carrier family 30 (zinc transporter), member 8 (SLC30A8), that created two tight subnetworks.

CONCLUSIONS

The results provide objectives and clues for future experimental studies and further insights into the molecular pathogenesis of T2D.

Mutation-Structure-Function Relationship Based Integrated Strategy Reveals the Potential Impact of Deleterious Missense Mutations in Autophagy Related Proteins on Hepatocellular Carcinoma (HCC): A Comprehensive Informatics Approach

Autophagy, an evolutionary conserved multifaceted lysosome-mediated bulk degradation system, plays a vital role in liver pathologies including hepatocellular carcinoma (HCC). Post-translational modifications (PTMs) and genetic variations in autophagy components have emerged as significant determinants of autophagy related proteins. Identification of a comprehensive spectrum of genetic variations and PTMs of autophagy related proteins and their impact at molecular level will greatly expand our understanding of autophagy based regulation. In this study, we attempted to identify high risk missense mutations that are highly damaging to the structure as well as function of autophagy related proteins including LC3A, LC3B, BECN1 and SCD1. Number of putative structural and functional residues, including several sites that undergo PTMs were also identified. In total, 16 high-risk SNPs in LC3A, 18 in LC3B, 40 in BECN1 and 43 in SCD1 were prioritized. Out of these, 2 in LC3A (K49A, K51A), 1 in LC3B (S92C), 6 in BECN1 (S113R, R292C, R292H, Y338C, S346Y, Y352H) and 6 in SCD1 (Y41C, Y55D, R131W, R135Q, R135W, Y151C) coincide with potential PTM sites. Our integrated analysis found LC3B Y113C, BECN1 I403T, SCD1 R126S and SCD1 Y218C as highly deleterious HCC-associated mutations. This study is the first extensive in silico mutational analysis of the LC3A, LC3B, BECN1 and SCD1 proteins. We hope that the observed results will be a valuable resource for in-depth mechanistic insight into future investigations of pathological missense SNPs using an integrated computational platform.

Impact of SNPs on Protein Phosphorylation Status in Rice (Oryza sativa L.)

Single nucleotide polymorphisms (SNPs) are widely used in functional genomics and genetics research work. The high-quality sequence of rice genome has provided a genome-wide SNP and proteome resource. However, the impact of SNPs on protein phosphorylation status in rice is not fully understood. In this paper, we firstly updated rice SNP resource based on the new rice genome Ver. 7.0, then systematically analyzed the potential impact of Non-synonymous SNPs (nsSNPs) on the protein phosphorylation status. There were 3,897,312 SNPs in Ver. 7.0 rice genome, among which 9.9% was nsSNPs. Whilst, a total 2,508,261 phosphorylated sites were predicted in rice proteome. Interestingly, we observed that 150,197 (39.1%) nsSNPs could influence protein phosphorylation status, among which 52.2% might induce changes of protein kinase (PK) types for adjacent phosphorylation sites. We constructed a database, SNP_rice, to deposit the updated rice SNP resource and phosSNPs information. It was freely available to academic researchers at http://bioinformatics.fafu.edu.cn. As a case study, we detected five nsSNPs that potentially influenced heterotrimeric G proteins phosphorylation status in rice, indicating that genetic polymorphisms showed impact on the signal transduction by influencing the phosphorylation status of heterotrimeric G proteins. The results in this work could be a useful resource for future experimental identification and provide interesting information for better rice breeding.

Association of LRRK2 R1628P variant with Parkinson's disease in Ethnic Han-Chinese and subgroup population

Recent studies have linked certain single nucleotide polymorphisms in the leucine-rich repeat kinase 2 (LRRK2) gene with Parkinson’s disease (PD). The R1628P variant of LRRK2 may be a specific risk factor for PD in ethnic Han-Chinese populations. This study is to elucidate the epidemiological feature of R1628P in ethnic Han-Chinese population with PD. A comprehensive meta-analysis was performed to evaluate the precise association between R1628P variant and the risk for PD in ethnic Han-Chinese and subgroups stratified by gender, onset age, or family history. The analysis assessing the role of R1628P on the risk of PD in ethnic Han-Chinese supported a significant association, and the odds ratio was 1.86. We further estimate the specific prevalence in relevant ethnic Han-Chinese subgroups. After stratifying the eligible data by gender, onset age, or family history, significant associations were found in all male, female, early-onset, late-onset, familial and sporadic subgroups, and the odds ratio were 1.90, 1.94, 2.12, 1.75, 6.71 and 1.81 respectively. In conclusion, our meta-analysis suggests that R1628P variant of LRRK2 has a significant association with the risk of PD in ethnic Han-Chinese and subgroup population.

Prioritizing functional phosphorylation sites based on multiple feature integration

Protein phosphorylation is an important type of post-translational modification that is involved in a variety of biological activities. Most phosphorylation events occur on serine, threonine and tyrosine residues in eukaryotes. In recent years, many phosphorylation sites have been identified as a result of advances in mass-spectrometric techniques. However, a large percentage of phosphorylation sites may be non-functional. Systematically prioritizing functional sites from a large number of phosphorylation sites will be increasingly important for the study of their biological roles. This study focused on exploring the intrinsic features of functional phosphorylation sites to predict whether a phosphosite is likely to be functional. We found significant differences in the distribution of evolutionary conservation, kinase association, disorder score, and secondary structure between known functional and background phosphorylation datasets. We built four different types of classifiers based on the most representative features and found that their performances were similar. We also prioritized 213,837 human phosphorylation sites from a variety of phosphorylation databases, which will be helpful for subsequent functional studies. All predicted results are available for query and download on our website (Predict Functional Phosphosites, PFP, http://pfp.biosino.org/).

Parkinson-Related LRRK2 Mutation R1628P Enables Cdk5 Phosphorylation of LRRK2 and Upregulates Its Kinase Activity

Background

Recent studies have linked certain single nucleotide polymorphisms in the leucine-rich repeat kinase 2 (LRRK2) gene with Parkinson’s disease (PD). Among the mutations, LRRK2 c.4883G>C (R1628P) variant was identified to have a significant association with the risk of PD in ethnic Han-Chinese populations. But the molecular pathological mechanisms of R1628P mutation in PD is still unknown.

Principle Findings

Unlike other LRRK2 mutants in the Roc-COR-Kinase domain, the R1628P mutation didn’t alter the LRRK2 kinase activity and promote neuronal death directly. LRRK2 R1628P mutation increased the binding affinity of LRRK2 with Cyclin-dependent kinase 5 (Cdk5). Interestingly, R1628P mutation turned its adjacent amino acid residue S1627 on LRRK2 protein to a novel phosphorylation site of Cdk5, which could be defined as a typical type II (+) phosphorylation-related single nucleotide polymorphism. Importantly, we showed that the phosphorylation of S1627 by Cdk5 could activate the LRRK2 kinase, and neurons ectopically expressing R1628P displayed a higher sensitivity to 1-methyl-4-phenylpyridinium, a bioactive metabolite of environmental toxin MPTP, in a Cdk5-dependent manner.

Conclusion

Our data indicate that Parkinson-related LRRK2 mutation R1628P leads to Cdk5 phosphorylation of LRRK2 at S1627, which would upregulate the kinase activity of LRRK2 and consequently cause neuronal death.

Identification of IDUA and WNT16 Phosphorylation-Related Non-Synonymous Polymorphisms for Bone Mineral Density in Meta-Analyses of Genome-Wide Association Studies

Protein phosphorylation regulates a wide variety of cellular processes. Thus, we hypothesize that single-nucleotide polymorphisms (SNPs) that may modulate protein phosphorylation could affect osteoporosis risk. Based on a previous conventional genome-wide association (GWA) study, we conducted a three-stage meta-analysis targeting phosphorylation-related SNPs (phosSNPs) for femoral neck (FN)-bone mineral density (BMD), total hip (HIP)-BMD, and lumbar spine (LS)-BMD phenotypes. In stage 1, 9593 phosSNPs were meta-analyzed in 11,140 individuals of various ancestries. Genome-wide significance (GWS) and suggestive significance were defined by α = 5.21 × 10(-6) (0.05/9593) and 1.00 × 10(-4), respectively. In stage 2, nine stage 1-discovered phosSNPs (based on α = 1.00 × 10(-4)) were in silico meta-analyzed in Dutch, Korean, and Australian cohorts. In stage 3, four phosSNPs that replicated in stage 2 (based on α = 5.56 × 10(-3), 0.05/9) were de novo genotyped in two independent cohorts. IDUA rs3755955 and rs6831280, and WNT16 rs2707466 were associated with BMD phenotypes in each respective stage, and in three stages combined, achieving GWS for both FN-BMD (p = 8.36 × 10(-10), p = 5.26 × 10(-10), and p = 3.01 × 10(-10), respectively) and HIP-BMD (p = 3.26 × 10(-6), p = 1.97 × 10(-6), and p = 1.63 × 10(-12), respectively). Although in vitro studies demonstrated no differences in expressions of wild-type and mutant forms of IDUA and WNT16B proteins, in silico analyses predicts that WNT16 rs2707466 directly abolishes a phosphorylation site, which could cause a deleterious effect on WNT16 protein, and that IDUA phosSNPs rs3755955 and rs6831280 could exert indirect effects on nearby phosphorylation sites. Further studies will be required to determine the detailed and specific molecular effects of these BMD-associated non-synonymous variants.

How Intrinsic Molecular Dynamics Control Intramolecular Communication in Signal Transducers and Activators of Transcription Factor STAT5

Signal Transducer and Activator of Transcription STAT5 is a key mediator of cell proliferation, differentiation and survival. While STAT5 activity is tightly regulated in normal cells, its constitutive activation directly contributes to oncogenesis and is associated with a broad range of hematological and solid tumor cancers. Therefore the development of compounds able to modulate pathogenic activation of this protein is a very challenging endeavor. A crucial step of drug design is the understanding of the protein conformational features and the definition of putative binding site(s) for such modulators. Currently, there is no structural data available for human STAT5 and our study is the first footprint towards the description of structure and dynamics of this protein. We investigated structural and dynamical features of the two STAT5 isoforms, STAT5a and STAT5b, taken into account their phosphorylation status. The study was based on the exploration of molecular dynamics simulations by different analytical methods. Despite the overall folding similarity of STAT5 proteins, the MD conformations display specific structural and dynamical features for each protein, indicating first, sequence-encoded structural properties and second, phosphorylation-induced effects which contribute to local and long-distance structural rearrangements interpreted as allosteric event. Further examination of the dynamical coupling between distant sites provides evidence for alternative profiles of the communication pathways inside and between the STAT5 domains. These results add a new insight to the understanding of the crucial role of intrinsic molecular dynamics in mediating intramolecular signaling in STAT5. Two pockets, localized in close proximity to the phosphotyrosine-binding site and adjacent to the channel for communication pathways across STAT5, may constitute valid targets to develop inhibitors able to modulate the function-related communication properties of this signaling protein.

Current strategies for mutation detection in phenotype-driven screens utilising next generation sequencing

Mutagenesis-based screens in mice are a powerful discovery platform to identify novel genes or gene functions associated with disease phenotypes. An N-ethyl-N-nitrosourea (ENU) mutagenesis screen induces single nucleotide variants randomly in the mouse genome. Subsequent phenotyping of mutant and wildtype mice enables the identification of mutated pathways resulting in phenotypes associated with a particular ENU lesion. This unbiased approach to gene discovery conducts the phenotyping with no prior knowledge of the functional mutations. Before the advent of affordable next generation sequencing (NGS), ENU variant identification was a limiting step in gene characterization, akin to ‘finding a needle in a haystack’. The emergence of a reliable reference genome alongside advances in NGS has propelled ENU mutation discovery from an arduous, time-consuming exercise to an effective and rapid form of mutation discovery. This has permitted large mouse facilities worldwide to use ENU for novel mutation discovery in a high-throughput manner, helping to accelerate basic science at the mechanistic level. Here, we describe three different strategies used to identify ENU variants from NGS data and some of the subsequent steps for mutation characterisation.

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.

A Comparative Analysis and Review of lysyl Residues Affected by Posttranslational Modifications

Post-translational modification is the most common mechanism of regulating protein function. If phosphorylation is considered a key event in many signal transduction pathways, other modifications must be considered as well. In particular the side chain of lysine residues is a target of different modifications; notably acetylation, methylation, ubiquitylation, sumoylation, neddylation, etc. Mass spectrometry approaches combining highly sensitive instruments and specific enrichment strategies have enabled the identification of modified sites on a large scale. Here we make a comparative analysis of the most representative lysine modifications (ubiquitylation, acetylation, sumoylation and methylation) identified in the human proteome. This review focuses on conserved amino acids, secondary structures preference, subcellular localization of modified proteins, and signaling pathways where these modifications are implicated. We discuss specific differences and similarities between these modifications, characteristics of the crosstalk among lysine post translational modifications, and single nucleotide polymorphisms that could influence lysine post-translational modifications in humans.

Detection and analysis of disease-associated single nucleotide polymorphism influencing post-translational modification

Post-translational modification (PTM) plays a crucial role in biological functions and corresponding disease developments. Discovering disease-associated non-synonymous SNPs (nsSNPs) altering PTM sites can help to estimate the various PTM candidates involved in diseases, therefore, an integrated analysis between SNPs, PTMs and diseases is necessary. However, only a few types of PTMs affected by nsSNPs have been studied without considering disease-association until now. In this study, we developed a new database called PTM-SNP which contains a comprehensive collection of human nsSNPs that affect PTM sites, together with disease information. Total 179,325 PTM-SNPs were collected by aligning missense SNPs and stop-gain SNPs on PTM sites (position 0) or their flanking region (position -7 to 7). Disease-associated SNPs from GWAS catalogs were also matched with detected PTM-SNP to find disease associated PTM-SNPs. Our result shows PTM-SNPs are highly associated with diseases, compared with other nsSNP sites and functional classes including near gene, intron and so on. PTM-SNP can provide an insight about discovering important PTMs involved in the diseases easily through the web site. PTM-SNP is freely available at http://gcode.kaist.ac.kr/ptmsnp.

A phosphorylation-related variant ADD1-rs4963 modifies the risk of colorectal cancer

It is well-established that abnormal protein phosphorylation could play an essential role in tumorgenesis by disrupting a variety of physiological processes such as cell growth, signal transduction and cell motility. Moreover, increasing numbers of phosphorylation-related variants have been identified in association with cancers. ADD1 (α-adducin), a versatile protein expressed ubiquitously in eukaryotes, exerts an important influence on membrane cytoskeleton, cell proliferation and cell-cell communication. Recently, a missense variant at the codon of ADD1's phosphorylation site, rs4963 (Ser586Cys), was reported to modify the risk of non-cardia gastric cancer. To explore the role of ADD1-rs4963 in colorectal cancer (CRC), we conducted a case-control study with a total of 1054 CRC cases and 1128 matched controls in a Chinese population. After adjustment for variables including age, gender, smoking and drinking, it was demonstrated that this variant significantly conferred susceptibility to CRC (G versus C: OR = 1.16, 95% CI = 1.03-1.31, P = 0.016; CG versus CC: OR = 1.25, 95% CI = 1.02-1.55, P = 0.036; GG versus CC: OR = 1.35, 95% CI = 1.06-1.72, P = 0.015). We further investigated the interaction of ADD1-rs4963 with smoking or drinking exposure, but found no significant result. This study is the first report of an association between ADD1 and CRC risk, promoting our knowledge of the genetics of CRC.

Biological databases for human research

The completion of the Human Genome Project lays a foundation for systematically studying the human genome from evolutionary history to precision medicine against diseases. With the explosive growth of biological data, there is an increasing number of biological databases that have been developed in aid of human-related research. Here we present a collection of human-related biological databases and provide a mini-review by classifying them into different categories according to their data types. As human-related databases continue to grow not only in count but also in volume, challenges are ahead in big data storage, processing, exchange and curation.

Phosphorylation site prediction in plants

Protein phosphorylation events on serine, threonine, and tyrosine residues are the most pervasive protein covalent bond modifications in plant signaling. Both low and high throughput studies reveal the importance of phosphorylation in plant molecular biology. Although becoming more and more common, the proteome-wide screening on phosphorylation by experiments remains time consuming and costly. Therefore, in silico prediction methods are proposed as a complementary analysis tool to enhance the phosphorylation site identification, develop biological hypothesis, or help experimental design. These methods build statistical models based on the experimental data, and they do not have some of the technical-specific bias, which may have advantage in proteome-wide analysis. More importantly computational methods are very fast and cheap to run, which makes large-scale phosphorylation identifications very practical for any types of biological study. Thus, the phosphorylation prediction tools become more and more popular. In this chapter, we will focus on plant specific phosphorylation site prediction tools, with essential illustration of technical details and application guidelines. We will use Musite, PhosPhAt and PlantPhos as the representative tools. We will present the results on the prediction of the Arabidopsis protein phosphorylation events to give users a general idea of the performance range of the three tools, together with their strengths and limitations. We believe these prediction tools will contribute more and more to the plant phosphorylation research community.

A multiscale statistical mechanical framework integrates biophysical and genomic data to assemble cancer networks

Functional interpretation of genomic variation is critical to understanding human disease but it remains difficult to predict the effects of specific mutations on protein interaction networks and the phenotypes they regulate. We describe an analytical framework based on multiscale statistical mechanics that integrates genomic and biophysical data to model the human SH2-phosphoprotein network in normal and cancer cells. We apply our approach to data in The Cancer Genome Atlas (TCGA) and test model predictions experimentally. We find that mutations in phosphoproteins often create new interactions but that mutations in SH2 domains result almost exclusively in loss of interactions. Some of these mutations eliminate all interactions but many cause more selective loss, thereby rewiring specific edges in highly connected subnetworks. Moreover, idiosyncratic mutations appear to be as functionally consequential as recurrent mutations. By synthesizing genomic, structural, and biochemical data our framework represents a new approach to the interpretation of genetic variation.

Superresolution imaging reveals structural features of EB1 in microtubule plus-end tracking

A method is introduced for optically imaging intracellular protein interactions at nanometer spatial resolution in live cells using photoactivatable complementary fluorescent (PACF) proteins. The PACF approach established here will enable the simultaneous visualization of homo- and heterodimeric interactions at the single live-cell level.

Visualization of specific molecules and their interactions in real time and space is essential to delineate how cellular dynamics and the signaling circuit are orchestrated. Spatial regulation of conformational dynamics and structural plasticity of protein interactions is required to rewire signaling circuitry in response to extracellular cues. We introduce a method for optically imaging intracellular protein interactions at nanometer spatial resolution in live cells, using photoactivatable complementary fluorescent (PACF) proteins. Subsets of complementary fluorescent protein molecules were activated, localized, and then bleached; this was followed by the assembly of superresolution images from aggregate position of sum interactive molecules. Using PACF, we obtained precise localization of dynamic microtubule plus-end hub protein EB1 dimers and their distinct distributions at the leading edges and in the cell bodies of migrating cells. We further delineated the structure–function relationship of EB1 by generating EB1-PACF dimers (EB1^wt:EB1^wt, EB1^wt:EB1^mt, and EB1^mt:EB1^mt) and imaging their precise localizations in culture cells. Surprisingly, our analyses revealed critical role of a previously uncharacterized EB1 linker region in tracking microtubule plus ends in live cells. Thus PACF provides a unique approach to delineating spatial dynamics of homo- or heterodimerized proteins at the nanometer scale and establishes a platform to report the precise regulation of protein interactions in space and time in live cells.

Current trend of annotating single nucleotide variation in humans--A case study on SNVrap

As high throughput methods, such as whole genome genotyping arrays, whole exome sequencing (WES) and whole genome sequencing (WGS), have detected huge amounts of genetic variants associated with human diseases, function annotation of these variants is an indispensable step in understanding disease etiology. Large-scale functional genomics projects, such as The ENCODE Project and Roadmap Epigenomics Project, provide genome-wide profiling of functional elements across different human cell types and tissues. With the urgent demands for identification of disease-causal variants, comprehensive and easy-to-use annotation tool is highly in demand. Here we review and discuss current progress and trend of the variant annotation field. Furthermore, we introduce a comprehensive web portal for annotating human genetic variants. We use gene-based features and the latest functional genomics datasets to annotate single nucleotide variation (SNVs) in human, at whole genome scale. We further apply several function prediction algorithms to annotate SNVs that might affect different biological processes, including transcriptional gene regulation, alternative splicing, post-transcriptional regulation, translation and post-translational modifications. The SNVrap web portal is freely available at http://jjwanglab.org/snvrap.

De novo identification and quantification of single amino-acid variants in human brain

The detection of single amino-acid variants (SAVs) usually depends on single-nucleotide polymorphisms (SNPs) database. Here, we describe a novel method that discovers SAVs at proteome level independent of SNPs data. Using mass spectrometry-based de novo sequencing algorithm, peptide-candidates are identified and compared with theoretical protein database to generate SAVs under pairing strategy, which is followed by database re-searching to control false discovery rate. In human brain tissues, we can confidently identify known and novel protein variants with diverse origins. Combined with DNA/RNA sequencing, we verify SAVs derived from DNA mutations, RNA alternative splicing, and unknown post-transcriptional mechanisms. Furthermore, quantitative analysis in human brain tissues reveals several tissue-specific differential expressions of SAVs. This approach provides a novel access to high-throughput detection of protein variants, which may offer the potential for clinical biomarker discovery and mechanistic research.