PhosphoPOINT: a comprehensive human kinase interactome and phospho-protein database

Integrated proteogenomic characterization of ampullary adenocarcinoma

Ampullary adenocarcinoma (AMPAC) is a rare and heterogeneous malignancy. Here we performed a comprehensive proteogenomic analysis of 198 samples from Chinese AMPAC patients and duodenum patients. Genomic data illustrate that 4q loss causes fatty acid accumulation and cell proliferation. Proteomic analysis has revealed three distinct clusters (C-FAM, C-AD, C-CC), among which the most aggressive cluster, C-AD, is associated with the poorest prognosis and is characterized by focal adhesion. Immune clustering identifies three immune clusters and reveals that immune cluster M1 (macrophage infiltration cluster) and M3 (DC cell infiltration cluster), which exhibit a higher immune score compared to cluster M2 (CD4+ T-cell infiltration cluster), are associated with a poor prognosis due to the potential secretion of IL-6 by tumor cells and its consequential influence. This study provides a comprehensive proteogenomic analysis for seeking for better understanding and potential treatment of AMPAC.

Proteogenomic Approaches for Diseasome Studies

During the last three decades, technological advancements in high-throughput next-generation sequencing have resulted in an increased understanding of proteomic and genomic data, aptly termed proteogenomics. Efforts in developing such approaches have not only been limited but also focused on protein identification and subcellular localization. These approaches, however, have also been explored for their broad understanding of how genomics/transcriptomics data have yielded measures, for example, gene expression regulation/signal cascading and diseasome studies. In this review, we discuss methods and tools developed through sequence-centric integrative modeling of proteogenomic approaches.

Proteogenomic characterization of skull-base chordoma

Skull-base chordoma is a rare, aggressive bone cancer with a high recurrence rate. Despite advances in genomic studies, its molecular characteristics and effective therapies remain unknown. Here, we conduct integrative genomics, transcriptomics, proteomics, and phosphoproteomics analyses of 187 skull-base chordoma tumors. In our study, chromosome instability is identified as a prognostic predictor and potential therapeutic target. Multi-omics data reveals downstream effects of chromosome instability, with RPRD1B as a putative target for radiotherapy-resistant patients. Chromosome 1q gain, associated with chromosome instability and upregulated mitochondrial functions, lead to poorer clinical outcomes. Immune subtyping identify an immune cold subtype linked to chromosome 9p/10q loss and immune evasion. Proteomics-based classification reveals subtypes (P-II and P-III) with high chromosome instability and immune cold features, with P-II tumors showing increased invasiveness. These findings, confirmed in 17 paired samples, provide insights into the biology and treatment of skull-base chordoma.

Proteomic characterization identifies clinically relevant subgroups of soft tissue sarcoma

Soft tissue sarcoma is a broad family of mesenchymal malignancies exhibiting remarkable histological diversity. We portray the proteomic landscape of 272 soft tissue sarcomas representing 12 major subtypes. Hierarchical classification finds the similarity of proteomic features between angiosarcoma and epithelial sarcoma, and elevated expression of SHC1 in AS and ES is correlated with poor prognosis. Moreover, proteomic clustering classifies patients of soft tissue sarcoma into 3 proteomic clusters with diverse driven pathways and clinical outcomes. In the proteomic cluster featured with the high cell proliferation rate, APEX1 and NPM1 are found to promote cell proliferation and drive the progression of cancer cells. The classification based on immune signatures defines three immune subtypes with distinctive tumor microenvironments. Further analysis illustrates the potential association between immune evasion markers (PD-L1 and CD80) and tumor metastasis in soft tissue sarcoma. Overall, this analysis uncovers sarcoma-type-specific changes in proteins, providing insights about relationships of soft tissue sarcoma.

Evolutionary proteogenomic landscape from pre-invasive to invasive lung adenocarcinoma

Lung adenocarcinoma follows a stepwise progression from pre-invasive to invasive. However, there remains a knowledge gap regarding molecular events from pre-invasive to invasive. Here, we conduct a comprehensive proteogenomic analysis comprising whole-exon sequencing, RNA sequencing, and proteomic and phosphoproteomic profiling on 98 pre-invasive and 99 invasive lung adenocarcinomas. The deletion of chr4q12 contributes to the progression from pre-invasive to invasive adenocarcinoma by downregulating SPATA18, thus suppressing mitophagy and promoting cell invasion. Proteomics reveals diverse enriched pathways in normal lung tissues and pre-invasive and invasive adenocarcinoma. Proteomic analyses identify three proteomic subtypes, which represent different stages of tumor progression. We also illustrate the molecular characterization of four immune clusters, including endothelial cells, B cells, DCs, and immune depression subtype. In conclusion, this comprehensive proteogenomic study characterizes the molecular architecture and hallmarks from pre-invasive to invasive lung adenocarcinoma, guiding the way to a deeper understanding of the tumorigenesis and progression of this disease.

Protein Kinases in Obesity, and the Kinase-Targeted Therapy

The action of protein kinases and protein phosphatases is essential for multiple physiological responses. Each protein kinase displays its own unique substrate specificity and a regulatory mechanism that may be modulated by association with other proteins. Protein kinases are classified as dual-specificity kinases and dual-specificity phosphatases. Dual-specificity phosphatases are important signal transduction enzymes that regulate various cellular processes in coordination with protein kinases and play an important role in obesity. Impairment of insulin signaling in obesity is largely mediated by the activation of the inhibitor of kappa B-kinase beta and the c-Jun N-terminal kinase (JNK). Oxidative stress and endoplasmic reticulum (ER) stress activate the JNK pathway which suppresses insulin biosynthesis. Adenosine monophosphate (AMP)-activated protein kinase (AMPK) and mammalian target of rapamycin (mTOR) are important for proper regulation of glucose metabolism in mammals at both the hormonal and cellular levels. Additionally, obesity-activated calcium/calmodulin dependent-protein kinase II/p38 suppresses insulin-induced protein kinase B phosphorylation by activating the ER stress effector, activating transcription factor-4. To alleviate lipotoxicity and insulin resistance, promising targets are pharmacologically inhibited. Nifedipine, calcium channel blocker, stimulates lipogenesis and adipogenesis by downregulating AMPK and upregulating mTOR, which thereby enhances lipid storage. Contrary to the nifedipine, metformin activates AMPK, increases fatty acid oxidation, suppresses fatty acid synthesis and deposition, and thus alleviates lipotoxicity. Obese adults with vascular endothelial dysfunction have greater endothelial cells activation of unfolded protein response stress sensors, RNA-dependent protein kinase-like ER eukaryotic initiation factor-2 alpha kinase (PERK), and activating transcription factor-6. The transcriptional regulation of adipogenesis in obesity is influenced by AGC (protein kinase A (PKA), PKG, PKC) family signaling kinases. Obesity may induce systemic oxidative stress and increase reactive oxygen species in adipocytes. An increase in intracellular oxidative stress can promote PKC-β activation. Activated PKC-β induces growth factor adapter Shc phosphorylation. Shc-generated peroxides reduce mitochondrial oxygen consumption and enhance triglyceride accumulation and lipotoxicity. Liraglutide attenuates mitochondrial dysfunction and reactive oxygen species generation. Co-treatment of antiobesity and antidiabetic herbal compound, berberine with antipsychotic drug olanzapine decreases the accumulation of triglyceride. While low-dose rapamycin, metformin, amlexanox, thiazolidinediones, and saroglitazar protect against insulin resistance, glucagon-like peptide-1 analog liraglutide inhibits palmitate-induced inflammation by suppressing mTOR complex 1 (mTORC1) activity and protects against lipotoxicity.

SnapKin: a snapshot deep learning ensemble for kinase-substrate prediction from phosphoproteomics data

A major challenge in mass spectrometry-based phosphoproteomics lies in identifying the substrates of kinases, as currently only a small fraction of substrates identified can be confidently linked with a known kinase. Machine learning techniques are promising approaches for leveraging large-scale phosphoproteomics data to computationally predict substrates of kinases. However, the small number of experimentally validated kinase substrates (true positive) and the high data noise in many phosphoproteomics datasets together limit their applicability and utility. Here, we aim to develop advanced kinase-substrate prediction methods to address these challenges. Using a collection of seven large phosphoproteomics datasets, and both traditional and deep learning models, we first demonstrate that a 'pseudo-positive' learning strategy for alleviating small sample size is effective at improving model predictive performance. We next show that a data resampling-based ensemble learning strategy is useful for improving model stability while further enhancing prediction. Lastly, we introduce an ensemble deep learning model ('SnapKin') by incorporating the above two learning strategies into a 'snapshot' ensemble learning algorithm. We propose SnapKin, an ensemble deep learning method, for predicting substrates of kinases from large-scale phosphoproteomics data. We demonstrate that SnapKin consistently outperforms existing methods in kinase-substrate prediction. SnapKin is freely available at https://github.com/PYangLab/SnapKin.

Integrative Proteomic Analysis Reveals the Cytoskeleton Regulation and Mitophagy Difference Between Ischemic Cardiomyopathy and Dilated Cardiomyopathy

Ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM) are the two primary etiologies of end-stage heart failure. However, there remains a dearth of comprehensive understanding the global perspective and the dynamics of the proteome and phosphoproteome in ICM and DCM, which hinders the profound comprehension of pivotal biological characteristics as well as differences in signal transduction activation mechanisms between these two major types of heart failure. We conducted high-throughput quantification proteomics and phosphoproteomics analysis of clinical heart tissues with ICM or DCM, which provided us the system-wide molecular insights into pathogenesis of clinical heart failure in both ICM and DCM. Both protein and phosphorylation expression levels exhibit distinct separation between heart failure and normal control heart tissues, highlighting the prominent characteristics of ICM and DCM. By integrating with omics results, Western blots, phosphosite-specific mutation, chemical intervention, and immunofluorescence validation, we found a significant activation of the PRKACA-GSK3β signaling pathway in ICM. This signaling pathway influenced remolding of the microtubule network and regulated the critical actin filaments in cardiac construction. Additionally, DCM exhibited significantly elevated mitochondria energy supply injury compared to ICM, which induced the ROCK1-vimentin signaling pathway activation and promoted mitophagy. Our study not only delineated the major distinguishing features between ICM and DCM but also revealed the crucial discrepancy in the mechanisms between ICM and DCM. This study facilitates a more profound comprehension of pathophysiologic heterogeneity between ICM and DCM and provides a novel perspective to assist in the discovery of potential therapeutic targets for different types of heart failure.

Proteogenomics of different urothelial bladder cancer stages reveals distinct molecular features for papillary cancer and carcinoma in situ

The progression of urothelial bladder cancer (UC) is a complicated multi-step process. We perform a comprehensive multi-omics analysis of 448 samples from 190 UC patients, covering the whole spectrum of disease stages and grades. Proteogenomic integration analysis indicates the mutations of HRAS regulated mTOR signaling to form urothelial papilloma rather than papillary urothelial cancer (PUC). DNA damage is a key signaling pathway in the progression of carcinoma in situ (CIS) and related to APOBEC signature. Glucolipid metabolism increase and lower immune cell infiltration are associated with PUC compared to CIS. Proteomic analysis distinguishes the origins of invasive tumors (PUC-derived and CIS-derived), related to distinct clinical prognosis and molecular features. Additionally, loss of RBPMS, associated with CIS-derived tumors, is validated to increase the activity of AP-1 and promote metastasis. This study reveals the characteristics of two distinct branches (PUC and CIS) of UC progression and may eventually benefit clinical practice.

Deep Learning in Phosphoproteomics: Methods and Application in Cancer Drug Discovery

Protein phosphorylation is a key post-translational modification (PTM) that is a central regulatory mechanism of many cellular signaling pathways. Several protein kinases and phosphatases precisely control this biochemical process. Defects in the functions of these proteins have been implicated in many diseases, including cancer. Mass spectrometry (MS)-based analysis of biological samples provides in-depth coverage of phosphoproteome. A large amount of MS data available in public repositories has unveiled big data in the field of phosphoproteomics. To address the challenges associated with handling large data and expanding confidence in phosphorylation site prediction, the development of many computational algorithms and machine learning-based approaches have gained momentum in recent years. Together, the emergence of experimental methods with high resolution and sensitivity and data mining algorithms has provided robust analytical platforms for quantitative proteomics. In this review, we compile a comprehensive collection of bioinformatic resources used for the prediction of phosphorylation sites, and their potential therapeutic applications in the context of cancer.

Comprehensive proteogenomic characterization of early duodenal cancer reveals the carcinogenesis tracks of different subtypes

The subtypes of duodenal cancer (DC) are complicated and the carcinogenesis process is not well characterized. We present comprehensive characterization of 438 samples from 156 DC patients, covering 2 major and 5 rare subtypes. Proteogenomics reveals LYN amplification at the chromosome 8q gain functioned in the transmit from intraepithelial neoplasia phase to infiltration tumor phase via MAPK signaling, and illustrates the DST mutation improves mTOR signaling in the duodenal adenocarcinoma stage. Proteome-based analysis elucidates stage-specific molecular characterizations and carcinogenesis tracks, and defines the cancer-driving waves of the adenocarcinoma and Brunner's gland subtypes. The drug-targetable alanyl-tRNA synthetase (AARS1) in the high tumor mutation burden/immune infiltration is significantly enhanced in DC progression, and catalyzes the lysine-alanylation of poly-ADP-ribose polymerases (PARP1), which decreases the apoptosis of cancer cells, eventually promoting cell proliferation and tumorigenesis. We assess the proteogenomic landscape of early DC, and provide insights into the molecular features corresponding therapeutic targets.

Integrative proteogenomic characterization of early esophageal cancer

Esophageal squamous cell carcinoma (ESCC) is malignant while the carcinogenesis is still unclear. Here, we perform a comprehensive multi-omics analysis of 786 trace-tumor-samples from 154 ESCC patients, covering 9 histopathological stages and 3 phases. Proteogenomics elucidates cancer-driving waves in ESCC progression, and reveals the molecular characterization of alcohol drinking habit associated signatures. We discover chromosome 3q gain functions in the transmit from nontumor to intraepithelial neoplasia phases, and find TP53 mutation enhances DNA replication in intraepithelial neoplasia phase. The mutations of AKAP9 and MCAF1 upregulate glycolysis and Wnt signaling, respectively, in advanced-stage ESCC phase. Six major tracks related to different clinical features during ESCC progression are identified, which is validated by an independent cohort with another 256 samples. Hyperphosphorylated phosphoglycerate kinase 1 (PGK1, S203) is considered as a drug target in ESCC progression. This study provides insight into the understanding of ESCC molecular mechanism and the development of therapeutic targets.

Proteogenomics of diffuse gliomas reveal molecular subtypes associated with specific therapeutic targets and immune-evasion mechanisms

Diffuse gliomas are devastating brain tumors. Here, we perform a proteogenomic profiling of 213 retrospectively collected glioma tumors. Proteogenomic analysis reveals the downstream biological events leading by EGFR-, IDH1-, TP53-mutations. The comparative analysis illustrates the distinctive features of GBMs and LGGs, indicating CDK2 inhibitor might serve as a promising drug target for GBMs. Further proteogenomic integrative analysis combined with functional experiments highlight the cis-effect of EGFR alterations might lead to glioma tumor cell proliferation through ERK5 medicates nucleotide synthesis process. Proteome-based stratification of gliomas defines 3 proteomic subgroups (S-Ne, S-Pf, S-Im), which could serve as a complement to WHO subtypes, and would provide the essential framework for the utilization of specific targeted therapies for particular glioma subtypes. Immune clustering identifies three immune subtypes with distinctive immune cell types. Further analysis reveals higher EGFR alteration frequencies accounts for elevation of immune check point protein: PD-L1 and CD70 in T-cell infiltrated tumors.

Proteogenomic insights into the biology and treatment of pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with poor prognosis. Proteogenomic characterization and integrative proteomic analysis provide a functional context to annotate genomic abnormalities with prognostic value.

METHODS

We performed an integrated multi-omics analysis, including whole-exome sequencing, RNA-seq, proteomic, and phosphoproteomic analysis of 217 PDAC tumors with paired non-tumor adjacent tissues. In vivo functional experiments were performed to further illustrate the biological events related to PDAC tumorigenesis and progression.

RESULTS

A comprehensive proteogenomic landscape revealed that TP53 mutations upregulated the CDK4-mediated cell proliferation process and led to poor prognosis in younger patients. Integrative multi-omics analysis illustrated the proteomic and phosphoproteomic alteration led by genomic alterations such as KRAS mutations and ADAM9 amplification of PDAC tumorigenesis. Proteogenomic analysis combined with in vivo experiments revealed that the higher amplification frequency of ADAM9 (8p11.22) could drive PDAC metastasis, though downregulating adhesion junction and upregulating WNT signaling pathway. Proteome-based stratification of PDAC revealed three subtypes (S-I, S-II, and S-III) related to different clinical and molecular features. Immune clustering defined a metabolic tumor subset that harbored FH amplicons led to better prognosis. Functional experiments revealed the role of FH in altering tumor glycolysis and in impacting PDAC tumor microenvironments. Experiments utilizing both in vivo and in vitro assay proved that loss of HOGA1 promoted the tumor growth via activating LARP7-CDK1 pathway.

CONCLUSIONS

This proteogenomic dataset provided a valuable resource for researchers and clinicians seeking for better understanding and treatment of PDAC.

Phosphoproteomics of extracellular vesicles integrated with multiomics analysis reveals novel kinase networks for lung cancer

Phosphorylation regulates the functions of proteins and aberrant phosphorylation often leads to a variety of diseases, including cancers. Extracellular vesicles (EVs) are important messengers in the microenvironment and their proteome contributes to cancer genesis and metastasis, while the kinases that driving EVs proteins' phosphorylation are less known. Clinical tissue samples from 13 patients with non-small-cell lung cancer (NSCLC) were utilized to isolate cancer EVs and adjacent normal EVs. Through quantitative phosphoproteomics analysis, 2473 phosphorylation sites on 1567 proteins were successfully identified and quantified. Accordingly, 152 kinases were identified, and 25 of them were differentially expressed. Based on Tied Diffusion through Interacting Events (TieDIE) algorithm, we integrated genomic and transcriptomic data sets of NSCLC from TCGA with our phosphoproteome data set to construct signaling networks. Through database integration and multiomics enrichment analysis, a compact network of 234 nodes with 1599 edges was constructed, which consisted of 34 transcription factors, 33 kinases, 63 aberrant genes, and 172 linking proteins. Rarely studied phosphorylation sites were specifically enriched. Key phosphoproteins of network nodes were validated in patients' EVs, including MAPK6^S189 , IKBKE^S172 , SRC^Y530 , CDK7^S164 , and CDK1^T14 . These networks depict intrinsic signal-regulation derived from EVs' phosphoproteins, providing a comprehensive and pathway-based strategy for in-depth lung cancer research.

Integrative proteomic characterization of trace FFPE samples in early-stage gastrointestinal cancer

Background

The surveillance and therapy of early-stage cancer would be better for patients’ prognosis. However, the extreme trace amount of tissue samples in different stages have limited in portraying the characterization of early-stage cancer. Therefore, we focused on and presented comprehensive proteomic and phosphoproproteomic profiling of the trace FFPE samples from early-stage gastrointestinal cancer, and then explored the potential biomarkers of early-stage gastrointestinal cancer.

Methods

In this study, a quantitative proteomic method with chromatography with mass spectrometry (LC-MS/MS) was used to analyse the proteomic difference between the trace early-stage esophageal squamous cell carcinoma (EESCC) and early-stage duodenum adenocarcinoma cancer (EDAC).

Results

We identified ~ 6000 proteins and > 10,000 phosphosites in single trace FFPE samples. Comparative analysis disclosed the diverse proteomic features of tumor tissues compared with paired normal tissue of EESCC and EDAC, and revealed the difference of EESCC and EDAC was derived from their origin normal tissue. The distinct separation of EESCC and EDAC illustrated the functions of cell cycle (RB1 T373, EGFR T693) in EESCC, and the positive impacts of apoptosis, metabolic processes (MTOR and MTOR S1261) in EDAC. Furthermore, we deconvoluted the immune infiltration of early-stage gastrointestinal cancer, in which higher immune cell signatures were detected in EDAC, and showed the specific cytokines in EESCC and EDAC. We performed kinases-substates relationship analysis and elucidated the specific proteomic kinase characterization of EESCC and EDAC, and proposed the medicative effects and corresponding drugs for EESCC and EDAC at the clinic.

Conclusion

We disclosed the specific immune characterization of the early-stage gastrointestinal cancer, and presented potential makers of EESCC (EGFR, PDGFRB, CDK4, WEE1) and EDAC (MTOR, MAP2K1, MAPK3). This study represents a major stepping stone towards investigating the carcinogenesis mechanism of gastrointestinal cancer, and providing a rich resource for medicative strategy in the clinic.

Graphical Abstract

Text Mining and Machine Learning Protocol for Extracting Human-Related Protein Phosphorylation Information from PubMed

In the modern health care research, protein phosphorylation has gained an enormous attention from the researchers across the globe and requires automated approaches to process a huge volume of data on proteins and their modifications at the cellular level. The data generated at the cellular level is unique as well as arbitrary, and an accumulation of massive volume of information is inevitable. Biological research has revealed that a huge array of cellular communication aided by protein phosphorylation and other similar mechanisms imply different and diverse meanings. This led to a collection of huge volume of data to understand the biological functions of human evolution, especially for combating diseases in a better way. Text mining, an automated approach to mine the information from an unstructured data, finds its application in extracting protein phosphorylation information from the biomedical literature databases such as PubMed. This chapter outlines a recent text mining protocol that applies natural language parsing (NLP) for named entity recognition and text processing, and support vector machines (SVM), a machine learning algorithm for classifying the processed text related human protein phosphorylation. We discuss on evaluating the text mining system which is the outcome of the protocol on three corpora, namely, human Protein Phosphorylation (hPP) corpus, Integrated Protein Literature Information and Knowledge corpus (iProLink), and Phosphorylation Literature corpus (PLC). We also present a basic understanding on the chemistry and biology that drive the protein phosphorylation process in a human body. We believe that this basic understanding will be useful to advance the existing text mining systems for extracting protein phosphorylation information from PubMed.

Is DNA repair controlled by a biological logic circuit?

The possible utilization of biological logic circuit(s) in the integration and regulation of DNA repair is discussed. The author believes this mode of regulation likely applies to many other areas of cell biology; however, there are currently more experimental data to support its involvement in the control of DNA repair. Sequential logic processes always require a clock to orchestrate the orderly processing of events. In the proposed hypothesis, the pulses in the expression of p53 serve this function. Given the many advantages of logic type control, one would expect that in the course of ~ 3 billion years of evolution, where single cell life forms were likely the only forms of life, a biological logic type control system would have evolved to control at least some biological processes. Several other required components in addition to the 'clock' have been identified, such as; a method to temporarily inactivate repair processes when they are not required (e.g. the reversible inactivation of MGMT, a suicide repair protein, by phosphorylation); this prevents complex DNA repair systems with potentially overlapping repair functions from interfering with each other.

ConsensusPathDB 2022: molecular interactions update as a resource for network biology

Molecular interactions are key drivers of biological function. Providing interaction resources to the research community is important since they allow functional interpretation and network-based analysis of molecular data. ConsensusPathDB (http://consensuspathdb.org) is a meta-database combining interactions of diverse types from 31 public resources for humans, 16 for mice and 14 for yeasts. Using ConsensusPathDB, researchers commonly evaluate lists of genes, proteins and metabolites against sets of molecular interactions defined by pathways, Gene Ontology and network neighborhoods and retrieve complex molecular neighborhoods formed by heterogeneous interaction types. Furthermore, the integrated protein–protein interaction network is used as a basis for propagation methods. Here, we present the 2022 update of ConsensusPathDB, highlighting content growth, additional functionality and improved database stability. For example, the number of human molecular interactions increased to 859 848 connecting 200 499 unique physical entities such as genes/proteins, metabolites and drugs. Furthermore, we integrated regulatory datasets in the form of transcription factor–, microRNA– and enhancer–gene target interactions, thus providing novel functionality in the context of overrepresentation and enrichment analyses. We specifically emphasize the use of the integrated protein–protein interaction network as a scaffold for network inferences, present topological characteristics of the network and discuss strengths and shortcomings of such approaches.

Adipocytes disrupt the translational programme of acute lymphoblastic leukaemia to favour tumour survival and persistence

The specific niche adaptations that facilitate primary disease and Acute Lymphoblastic Leukaemia (ALL) survival after induction chemotherapy remain unclear. Here, we show that Bone Marrow (BM) adipocytes dynamically evolve during ALL pathogenesis and therapy, transitioning from cellular depletion in the primary leukaemia niche to a fully reconstituted state upon remission induction. Functionally, adipocyte niches elicit a fate switch in ALL cells towards slow-proliferation and cellular quiescence, highlighting the critical contribution of the adipocyte dynamic to disease establishment and chemotherapy resistance. Mechanistically, adipocyte niche interaction targets posttranscriptional networks and suppresses protein biosynthesis in ALL cells. Treatment with general control nonderepressible 2 inhibitor (GCN2ib) alleviates adipocyte-mediated translational repression and rescues ALL cell quiescence thereby significantly reducing the cytoprotective effect of adipocytes against chemotherapy and other extrinsic stressors. These data establish how adipocyte driven restrictions of the ALL proteome benefit ALL tumours, preventing their elimination, and suggest ways to manipulate adipocyte-mediated ALL resistance.

Inositol treatment inhibits medulloblastoma through suppression of epigenetic-driven metabolic adaptation

Deregulation of chromatin modifiers plays an essential role in the pathogenesis of medulloblastoma, the most common paediatric malignant brain tumour. Here, we identify a BMI1-dependent sensitivity to deregulation of inositol metabolism in a proportion of medulloblastoma. We demonstrate mTOR pathway activation and metabolic adaptation specifically in medulloblastoma of the molecular subgroup G4 characterised by a BMI1High;CHD7Low signature and show this can be counteracted by IP6 treatment. Finally, we demonstrate that IP6 synergises with cisplatin to enhance its cytotoxicity in vitro and extends survival in a pre-clinical BMI1High;CHD7Low xenograft model.

Downfalls of Chemical Probes Acting at the Kinase ATP-Site: CK2 as a Case Study

Protein kinases are a large class of enzymes with numerous biological roles and many have been implicated in a vast array of diseases, including cancer and the novel coronavirus infection COVID-19. Thus, the development of chemical probes to selectively target each kinase is of great interest. Inhibition of protein kinases with ATP-competitive inhibitors has historically been the most widely used method. However, due to the highly conserved structures of ATP-sites, the identification of truly selective chemical probes is challenging. In this review, we use the Ser/Thr kinase CK2 as an example to highlight the historical challenges in effective and selective chemical probe development, alongside recent advances in the field and alternative strategies aiming to overcome these problems. The methods utilised for CK2 can be applied to an array of protein kinases to aid in the discovery of chemical probes to further understand each kinase's biology, with wide-reaching implications for drug development.

GasPhos: Protein Phosphorylation Site Prediction Using a New Feature Selection Approach with a GA-Aided Ant Colony System

Protein phosphorylation is one of the most important post-translational modifications, and many biological processes are related to phosphorylation, such as DNA repair, transcriptional regulation and signal transduction and, therefore, abnormal regulation of phosphorylation usually causes diseases. If we can accurately predict human phosphorylation sites, this could help to solve human diseases. Therefore, we developed a kinase-specific phosphorylation prediction system, GasPhos, and proposed a new feature selection approach, called Gas, based on the ant colony system and a genetic algorithm and used performance evaluation strategies focused on different kinases to choose the best learning model. Gas uses the mean decrease Gini index (MDGI) as a heuristic value for path selection and adopts binary transformation strategies and new state transition rules. GasPhos can predict phosphorylation sites for six kinases and showed better performance than other phosphorylation prediction tools. The disease-related phosphorylated proteins that were predicted with GasPhos are also discussed. Finally, Gas can be applied to other issues that require feature selection, which could help to improve prediction performance. GasPhos is available at http://predictor.nchu.edu.tw/GasPhos.

Current Challenges and Implications of Proteogenomic Approaches in Prostate Cancer

In the recent past, next-generation sequencing (NGS) approaches have heralded the omics era. With NGS data burgeoning, there arose a need to disseminate the omic data better. Proteogenomics has been vividly used for characterising the functions of candidate genes and is applied in ascertaining various diseased phenotypes, including cancers. However, not much is known about the role and application of proteogenomics, especially Prostate Cancer (PCa). In this review, we outline the need for proteogenomic approaches, their applications and their role in PCa.

Bioinformatics toolbox for exploring protein phosphorylation network

A clear systematic delineation of the interactions between phosphorylation sites on substrates and their effector kinases plays a fundamental role in revealing cellular activities, understanding signaling modulation mechanisms and proposing novel hypotheses. The emergence of bioinformatics tools contributes to studying phosphorylation network. Some of them feature the visualization of network, enabling more effective trace of the underlying biological problems in a clear and succinct way. In this review, we aimed to provide a toolbox for exploring phosphorylation network. We first systematically surveyed 19 tools that are available for exploring phosphorylation networks, and subsequently comparatively analyzed and summarized these tools to guide tool selection in terms of functionality, data sources, performance, network visualization and implementation, and finally briefly discussed the application cases of these tools. In different scenarios, the conclusion on the suitability of a tool for a specific user may vary. Nevertheless, easily accessible bioinformatics tools are proved to facilitate biological findings. Hopefully, this work might also assist non-specialists, students, as well as computational scientists who aim at developing novel tools in the field of phosphorylation modification.

EPSD: a well-annotated data resource of protein phosphorylation sites in eukaryotes

As an important post-translational modification (PTM), protein phosphorylation is involved in the regulation of almost all of biological processes in eukaryotes. Due to the rapid progress in mass spectrometry-based phosphoproteomics, a large number of phosphorylation sites (p-sites) have been characterized but remain to be curated. Here, we briefly summarized the current progresses in the development of data resources for the collection, curation, integration and annotation of p-sites in eukaryotic proteins. Also, we designed the eukaryotic phosphorylation site database (EPSD), which contained 1 616 804 experimentally identified p-sites in 209 326 phosphoproteins from 68 eukaryotic species. In EPSD, we not only collected 1 451 629 newly identified p-sites from high-throughput (HTP) phosphoproteomic studies, but also integrated known p-sites from 13 additional databases. Moreover, we carefully annotated the phosphoproteins and p-sites of eight model organisms by integrating the knowledge from 100 additional resources that covered 15 aspects, including phosphorylation regulator, genetic variation and mutation, functional annotation, structural annotation, physicochemical property, functional domain, disease-associated information, protein-protein interaction, drug-target relation, orthologous information, biological pathway, transcriptional regulator, mRNA expression, protein expression/proteomics and subcellular localization. We anticipate that the EPSD can serve as a useful resource for further analysis of eukaryotic phosphorylation. With a data volume of 14.1 GB, EPSD is free for all users at http://epsd.biocuckoo.cn/.

Mining protein phosphorylation information from biomedical literature using NLP parsing and Support Vector Machines

BACKGROUND

Extraction of protein phosphorylation information from biomedical literature has gained much attention because of the importance in numerous biological processes.

OBJECTIVE

In this study, we propose a text mining methodology which consists of two phases, NLP parsing and SVM classification to extract phosphorylation information from literature.

METHODS

First, using NLP parsing we divide the data into three base-forms depending on the biomedical entities related to phosphorylation and further classify into ten sub-forms based on their distribution with phosphorylation keyword. Next, we extract the phosphorylation entity singles/pairs/triplets and apply SVM to classify the extracted singles/pairs/triplets using a set of features applicable to each sub-form.

RESULTS

The performance of our methodology was evaluated on three corpora namely PLC, iProLink and hPP corpus. We obtained promising results of >85% F-score on ten sub-forms of training datasets on cross validation test. Our system achieved overall F-score of 93.0% on iProLink and 96.3% on hPP corpus test datasets. Furthermore, our proposed system achieved best performance on cross corpus evaluation and outperformed the existing system with recall of 90.1%.

CONCLUSIONS

The performance analysis of our unique system on three corpora reveals that it extracts protein phosphorylation information efficiently in both non-organism specific general datasets such as PLC and iProLink, and human specific dataset such as hPP corpus.

Advances in kinome research of parasitic worms - implications for fundamental research and applied biotechnological outcomes

Protein kinases are enzymes that play essential roles in the regulation of many cellular processes. Despite expansions in the fields of genomics, transcriptomics and bioinformatics, there is limited information on the kinase complements (kinomes) of most eukaryotic organisms, including parasitic worms that cause serious diseases of humans and animals. The biological uniqueness of these worms and the draft status of their genomes pose challenges for the identification and classification of protein kinases using established tools. In this article, we provide an account of kinase biology, the roles of kinases in diseases and their importance as drug targets, and drug discovery efforts in key socioeconomically important parasitic worms. In this context, we summarise methods and resources commonly used for the curation, identification, classification and functional annotation of protein kinase sequences from draft genomes; review recent advances made in the characterisation of the worm kinomes; and discuss the implications of these advances for investigating kinase signalling and developing small-molecule inhibitors as new anti-parasitic drugs.

Decreased MALL expression negatively impacts colorectal cancer patient survival

The aim of the present study was to determine whether MALL expression is associated with colon cancer progression and patient survival. MALL mRNA expression was reduced in the tumor tissues of 70% of the colon cancer patients and 75% of the rectal cancer patients as compared to their normal tissues. MALL protein was also significantly reduced in the tumor tissues of colon cancer patients (P < 0.001). Increased LOH and methylation of MALL was observed in tumor tissues as compared to normal tissues. Reduced MALL expression was associated with vessin invasion, disease recurrence and metastasis or death (P ≤ 0.027). Furthermore, patients with MALL-negative tumors had significantly decreased overall survival (OS) and disease-free survival (DFS) (P < 0.008 and P < 0.011, respectively). Univariate analysis indicated that MALL expression was significantly associated with OS and DFS. Finally, overexpression of MALL suppressed HCT116 and SW480 cell proliferation and inhibited HCT116 migration. MALL may play a role in colorectal cancer progression as suppression of its expression in tumor tissues negatively impacts colorectal cancer patient survival. Further analyses are required to determine if reduced MALL expression is due to LOH and/or methylation.

Recent Advances in Substrate Identification of Protein Kinases in Plants and Their Role in Stress Management

Protein phosphorylation-dephosphorylation is a well-known regulatory mechanism in biological systems and has become one of the significant means of protein function regulation, modulating most of the biological processes. Protein kinases play vital role in numerous cellular processes. Kinases transduce external signal into responses such as growth, immunity and stress tolerance through phosphorylation of their target proteins. In order to understand these cellular processes at the molecular level, one needs to be aware of the different substrates targeted by protein kinases. Advancement in tools and techniques has bestowed practice of multiple approaches that enable target identification of kinases. However, so far none of the methodologies has been proved to be as good as a panacea for the substrate identification. In this review, the recent advances that have been made in the identifications of putative substrates and the implications of these kinases and their substrates in stress management are discussed.

Functional phosphatome requirement for protein homeostasis, networked mitochondria, and sarcomere structure in C. elegans muscle

BACKGROUND

Skeletal muscle is central to locomotion and metabolic homeostasis. The laboratory worm Caenorhabditis elegans has been developed into a genomic model for assessing the genes and signals that regulate muscle development and protein degradation. Past work has identified a receptor tyrosine kinase signalling network that combinatorially controls autophagy, nerve signal to muscle to oppose proteasome-based degradation, and extracellular matrix-based signals that control calpain and caspase activation. The last two discoveries were enabled by following up results from a functional genomic screen of known regulators of muscle. Recently, a screen of the kinome requirement for muscle homeostasis identified roughly 40% of kinases as required for C. elegans muscle health; 80 have identified human orthologues and 53 are known to be expressed in skeletal muscle. To complement this kinome screen, here, we screen most of the phosphatases in C. elegans.

METHODS

RNA interference was used to knockdown phosphatase-encoding genes. Knockdown was first conducted during development with positive results also knocked down only in fully developed adult muscle. Protein homeostasis, mitochondrial structure, and sarcomere structure were assessed using transgenic reporter proteins. Genes identified as being required to prevent protein degradation were also knocked down in conditions that blocked proteasome or autophagic degradation. Genes identified as being required to prevent autophagic degradation were also assessed for autophagic vesicle accumulation using another transgenic reporter. Lastly, bioinformatics were used to look for overlap between kinases and phosphatases required for muscle homeostasis, and the prediction that one phosphatase was required to prevent mitogen-activated protein kinase activation was assessed by western blot.

RESULTS

A little over half of all phosphatases are each required to prevent abnormal development or maintenance of muscle. Eighty-six of these phosphatases have known human orthologues, 57 of which are known to be expressed in human skeletal muscle. Of the phosphatases required to prevent abnormal muscle protein degradation, roughly half are required to prevent increased autophagy.

CONCLUSIONS

A significant portion of both the kinome and phosphatome are required for establishing and maintaining C. elegans muscle health. Autophagy appears to be the most commonly triggered form of protein degradation in response to disruption of phosphorylation-based signalling. The results from these screens provide measurable phenotypes for analysing the combined contribution of kinases and phosphatases in a multi-cellular organism and suggest new potential regulators of human skeletal muscle for further analysis.

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.

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.

Coupling an EML4-ALK-centric interactome with RNA interference identifies sensitizers to ALK inhibitors

Patients with lung cancers harboring anaplastic lymphoma kinase (ALK) gene fusions benefit from treatment with ALK inhibitors, but acquired resistance inevitably arises. A better understanding of proximal ALK signaling mechanisms may identify sensitizers to ALK inhibitors that disrupt the balance between prosurvival and proapoptotic effector signals. Using affinity purification coupled with mass spectrometry in an ALK fusion lung cancer cell line (H3122), we generated an ALK signaling network and investigated signaling activity using tyrosine phosphoproteomics. We identified a network of 464 proteins composed of subnetworks with differential response to ALK inhibitors. A small hairpin RNA screen targeting 407 proteins in this network revealed 64 and 9 proteins that when knocked down sensitized cells to crizotinib and alectinib, respectively. Among these, knocking down fibroblast growth factor receptor substrate 2 (FRS2) or coiled-coil and C2 domain-containing protein 1A (CC2D1A), both scaffolding proteins, sensitized multiple ALK fusion cell lines to the ALK inhibitors crizotinib and alectinib. Collectively, our data set provides a resource that enhances our understanding of signaling and drug resistance networks consequent to ALK fusions and identifies potential targets to improve the efficacy of ALK inhibitors in patients.

Functional characterization of EI24-induced autophagy in the degradation of RING-domain E3 ligases

Historically, the ubiquitin-proteasome system (UPS) and autophagy pathways were believed to be independent; however, recent data indicate that these pathways engage in crosstalk. To date, the players mediating this crosstalk have been elusive. Here, we show experimentally that EI24 (EI24, autophagy associated transmembrane protein), a key component of basal macroautophagy/autophagy, degrades 14 physiologically important E3 ligases with a RING (really interesting new gene) domain, whereas 5 other ligases were not degraded. Based on the degradation results, we built a statistical model that predicts the RING E3 ligases targeted by EI24 using partial least squares discriminant analysis. Of 381 RING E3 ligases examined computationally, our model predicted 161 EI24 targets. Those targets are primarily involved in transcription, proteolysis, cellular bioenergetics, and apoptosis and regulated by TP53 and MTOR signaling. Collectively, our work demonstrates that EI24 is an essential player in UPS-autophagy crosstalk via degradation of RING E3 ligases. These results indicate a paradigm shift regarding the fate of E3 ligases.

DAPPLE 2: a Tool for the Homology-Based Prediction of Post-Translational Modification Sites

The post-translational modification of proteins is critical for regulating their function. Although many post-translational modification sites have been experimentally determined, particularly in certain model organisms, experimental knowledge of these sites is severely lacking for many species. Thus, it is important to be able to predict sites of post-translational modification in such species. Previously, we described DAPPLE, a tool that facilitates the homology-based prediction of one particular post-translational modification, phosphorylation, in an organism of interest using known phosphorylation sites from other organisms. Here, we describe DAPPLE 2, which expands and improves upon DAPPLE in three major ways. First, it predicts sites for many post-translational modifications (20 different types) using data from several sources (15 online databases). Second, it has the ability to make predictions approximately 2-7 times faster than DAPPLE depending on the database size and the organism of interest. Third, it simplifies and accelerates the process of selecting predicted sites of interest by categorizing them based on gene ontology terms, keywords, and signaling pathways. We show that DAPPLE 2 can successfully predict known human post-translational modification sites using, as input, known sites from species that are either closely (e.g., mouse) or distantly (e.g., yeast) related to humans. DAPPLE 2 can be accessed at http://saphire.usask.ca/saphire/dapple2 .

PredPhos: an ensemble framework for structure-based prediction of phosphorylation sites

Background

Post-translational modifications (PTMs) occur on almost all proteins and often strongly affect the functions of modified proteins. Phosphorylation is a crucial PTM mechanism with important regulatory functions in biological systems. Identifying the potential phosphorylation sites of a target protein may increase our understanding of the molecular processes in which it takes part.

Results

In this paper, we propose PredPhos, a computational method that can accurately predict both kinase-specific and non-kinase-specific phosphorylation sites by using optimally selected properties. The optimal combination of features was selected from a set of 153 novel structural neighborhood properties by a two-step feature selection method consisting of a random forest algorithm and a sequential backward elimination method. To overcome the imbalanced problem, we adopt an ensemble method, which combines bootstrap resampling technique, support vector machine-based fusion classifiers and majority voting strategy. We evaluate the proposed method using both tenfold cross validation and independent test. Results show that our method achieves a significant improvement on the prediction performance for both kinase-specific and non-kinase-specific phosphorylation sites.

Conclusions

The experimental results demonstrate that the proposed method is quite effective in predicting phosphorylation sites. Promising results are derived from the new structural neighborhood properties, the novel way of feature selection, as well as the ensemble method.

Unraveling Kinase Activation Dynamics Using Kinase-Substrate Relationships from Temporal Large-Scale Phosphoproteomics Studies

In response to stimuli, biological processes are tightly controlled by dynamic cellular signaling mechanisms. Reversible protein phosphorylation occurs on rapid time-scales (milliseconds to seconds), making it an ideal carrier of these signals. Advances in mass spectrometry-based proteomics have led to the identification of many tens of thousands of phosphorylation sites, yet for the majority of these the kinase is unknown and the underlying network topology of signaling networks therefore remains obscured. Identifying kinase substrate relationships (KSRs) is therefore an important goal in cell signaling research. Existing consensus sequence motif based prediction algorithms do not consider the biological context of KSRs, and are therefore insensitive to many other mechanisms guiding kinase-substrate recognition in cellular contexts. Here, we use temporal information to identify biologically relevant KSRs from Large-scale In Vivo Experiments (KSR-LIVE) in a data-dependent and automated fashion. First, we used available phosphorylation databases to construct a repository of existing experimentally-predicted KSRs. For each kinase in this database, we used time-resolved phosphoproteomics data to examine how its substrates changed in phosphorylation over time. Although substrates for a particular kinase clustered together, they often exhibited a different temporal pattern to the phosphorylation of the kinase. Therefore, although phosphorylation regulates kinase activity, our findings imply that substrate phosphorylation likely serve as a better proxy for kinase activity than kinase phosphorylation. KSR-LIVE can thereby infer which kinases are regulated within a biological context. Moreover, KSR-LIVE can also be used to automatically generate positive training sets for the subsequent prediction of novel KSRs using machine learning approaches. We demonstrate that this approach can distinguish between Akt and Rps6kb1, two kinases that share the same linear consensus motif, and provide evidence suggesting IRS-1 S265 as a novel Akt site. KSR-LIVE is an open-access algorithm that allows users to dissect phosphorylation signaling within a specific biological context, with the potential to be included in the standard analysis workflow for studying temporal high-throughput signal transduction data.

FAM20A binds to and regulates FAM20C localization

Mutations in the Family with sequence similarity (FAM) 20 gene family are associated with mineralized tissue phenotypes in humans. Among these genes, FAM20A mutations are associated with Amelogenesis Imperfecta (AI) with gingival hyperplasia and nephrocalcinosis, while FAM20C mutations cause Raine syndrome, exhibiting bone and craniofacial/dental abnormalities. Although it has been demonstrated that Raine syndrome associated-FAM20C mutants prevented FAM20C kinase activity and secretion, overexpression of the catalytically inactive D478A FAM20C mutant was detected in both cell extracts and the media. This suggests that FAM20C secretion doesn’t require its kinase activity, and that another molecule(s) may control the secretion. In this study, we found that extracellular FAM20C localization was increased when wild-type (WT), but not AI-forms of FAM20A was co-transfected. On the other hand, extracellular FAM20C was absent in the conditioned media of mouse embryonic fibroblasts (MEFs) derived from Fam20a knock-out (KO) mouse, while it was detected in the media from WT MEFs. We also showed that cells with the conditioned media of Fam20a WT MEFs mineralized, but those with the conditioned media of KO MEFs failed to mineralize in vitro. Our data thus demonstrate that FAM20A controls FAM20C localization that may assist in the extracellular function of FAM20C in mineralized tissues.

An Atlas of the Human Kinome Reveals the Mutational Landscape Underlying Dysregulated Phosphorylation Cascades in Cancer

Kinase inhibitors are used widely to treat various cancers, but adaptive reprogramming of kinase cascades and activation of feedback loop mechanisms often contribute to therapeutic resistance. Determining comprehensive, accurate maps of kinase circuits may therefore help elucidate mechanisms of response and resistance to kinase inhibitor therapies. In this study, we identified and validated phosphorylatable target sites across human cell and tissue types to generate PhosphoAtlas, a map of 1,733 functionally interconnected proteins comprising the human phospho-reactome. A systematic curation approach was used to distill protein phosphorylation data cross-referenced from 38 public resources. We demonstrated how a catalog of 2,617 stringently verified heptameric peptide regions at the catalytic interface of kinases and substrates could expose mutations that recurrently perturb specific phospho-hubs. In silico mapping of 2,896 nonsynonymous tumor variants identified from thousands of tumor tissues also revealed that normal and aberrant catalytic interactions co-occur frequently, showing how tumors systematically hijack, as well as spare, particular subnetworks. Overall, our work provides an important new resource for interrogating the human tumor kinome to strategically identify therapeutically actionable kinase networks that drive tumorigenesis. Cancer Res; 76(7); 1733-45. ©2016 AACR.

The exploration of network motifs as potential drug targets from post-translational regulatory networks

Phosphorylation and proteolysis are among the most common post-translational modifications (PTMs), and play critical roles in various biological processes. More recent discoveries imply that the crosstalks between these two PTMs are involved in many diseases. In this work, we construct a post-translational regulatory network (PTRN) consists of phosphorylation and proteolysis processes, which enables us to investigate the regulatory interplays between these two PTMs. With the PTRN, we identify some functional network motifs that are significantly enriched with drug targets, some of which are further found to contain multiple proteins targeted by combinatorial drugs. These findings imply that the network motifs may be used to predict targets when designing new drugs. Inspired by this, we propose a novel computational approach called NetTar for predicting drug targets using the identified network motifs. Benchmarking results on real data indicate that our approach can be used for accurate prediction of novel proteins targeted by known drugs.

Integrated analysis of global proteome, phosphoproteome, and glycoproteome enables complementary interpretation of disease-related protein networks

Multi-dimensional proteomic analyses provide different layers of protein information, including protein abundance and post-translational modifications. Here, we report an integrated analysis of protein expression, phosphorylation, and N-glycosylation by serial enrichments of phosphorylation and N-glycosylation (SEPG) from the same tissue samples. On average, the SEPG identified 142,106 unmodified peptides of 8,625 protein groups, 18,846 phosphopeptides (15,647 phosphosites), and 4,019 N-glycopeptides (2,634 N-glycosites) in tumor and adjacent normal tissues from three gastric cancer patients. The combined analysis of these data showed that the integrated analysis additively improved the coverages of gastric cancer-related protein networks; phosphoproteome and N-glycoproteome captured predominantly low abundant signal proteins, and membranous or secreted proteins, respectively, while global proteome provided abundances for general population of the proteome. Therefore, our results demonstrate that the SEPG can serve as an effective approach for multi-dimensional proteome analyses, and the holistic profiles of protein expression and PTMs enabled improved interpretation of disease-related networks by providing complementary information.

A reverse-engineering approach to dissect post-translational modulators of transcription factor's activity from transcriptional data

Background

Transcription factors (TFs) act downstream of the major signalling pathways functioning as master regulators of cell fate. Their activity is tightly regulated at the transcriptional, post-transcriptional and post-translational level. Proteins modifying TF activity are not easily identified by experimental high-throughput methods.

Results

We developed a computational strategy, called Differential Multi-Information (DMI), to infer post-translational modulators of a transcription factor from a compendium of gene expression profiles (GEPs). DMI is built on the hypothesis that the modulator of a TF (i.e. kinase/phosphatases), when expressed in the cell, will cause the TF target genes to be co-expressed. On the contrary, when the modulator is not expressed, the TF will be inactive resulting in a loss of co-regulation across its target genes. DMI detects the occurrence of changes in target gene co-regulation for each candidate modulator, using a measure called Multi-Information. We validated the DMI approach on a compendium of 5,372 GEPs showing its predictive ability in correctly identifying kinases regulating the activity of 14 different transcription factors.

Conclusions

DMI can be used in combination with experimental approaches as high-throughput screening to efficiently improve both pathway and target discovery. An on-line web-tool enabling the user to use DMI to identify post-transcriptional modulators of a transcription factor of interest che be found at http://dmi.tigem.it.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0700-3) contains supplementary material, which is available to authorized users.

Combinations of Kinase Inhibitors Protecting Myoblasts against Hypoxia

Cell-based therapies to treat skeletal muscle disease are limited by the poor survival of donor myoblasts, due in part to acute hypoxic stress. After confirming that the microenvironment of transplanted myoblasts is hypoxic, we screened a kinase inhibitor library in vitro and identified five kinase inhibitors that protected myoblasts from cell death or growth arrest in hypoxic conditions. A systematic, combinatorial study of these compounds further improved myoblast viability, showing both synergistic and additive effects. Pathway and target analysis revealed CDK5, CDK2, CDC2, WEE1, and GSK3β as the main target kinases. In particular, CDK5 was the center of the target kinase network. Using our recently developed statistical method based on elastic net regression we computationally validated the key role of CDK5 in cell protection against hypoxia. This method provided a list of potential kinase targets with a quantitative measure of their optimal amount of relative inhibition. A modified version of the method was also able to predict the effect of combinations using single-drug response data. This work is the first step towards a broadly applicable system-level strategy for the pharmacology of hypoxic damage.

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.

Plasma phosphoproteome and differential plasma phosphoproteins with opisthorchis viverrini-related cholangiocarcinoma

This study was conducted to investigate the plasma phosphoproteome and differential plasma phosphoproteins in cases of of Opisthorchis viverrini (OV)-related cholangiocarcinoma (CCA). Plasma phosphoproteomes from CCA patients (10) and non-CCA subjects (5 each for healthy subjects and OV infection) were investigated using gel-based and solution-based LC-MS/MS. Phosphoproteins in plasma samples were enriched and analyzed by LC-MS/MS. STRAP, PANTHER, iPath, and MeV programs were applied for the identification of their functions, signaling and metabolic pathways; and for the discrimination of potential biomarkers in CCA patients and non-CCA subjects, respectively. A total of 90 and 60 plasma phosphoproteins were identified by gel-based and solution-based LC-MS/MS, respectively. Most of the phosphoproteins were cytosol proteins which play roles in several cellular processes, signaling pathways, and metabolic pathways (STRAP, PANTHER, and iPath analysis). The absence of serine/arginine repetitive matrix protein 3 (A6NNA2), tubulin tyrosine ligase-like family, member 6, and biorientation of chromosomes in cell division protein 1-like (Q8NFC6) in plasma phosphoprotein were identified as potential biomarkers for the differentiation of healthy subjects from patients with CCA and OV infection. To differentiate CCA from OV infection, the absence of both serine/threonine-protein phosphatase 2A 56 kDa regulatory subunit beta isoform and coiled-coil domain-containing protein 126 precursor (Q96EE4) were then applied. A combination of 5 phosphoproteins may new alternative choices for CCA diagnosis.