Mechanism of cargo recognition by retromer-linked SNX-BAR proteins.

Endocytic recycling of internalized transmembrane proteins is essential for many important physiological processes. Recent studies have revealed that retromer-related Sorting Nexin family (SNX)-Bin/Amphiphysin/Rvs (BAR) proteins can directly recognize cargoes like cation-independent mannose 6-phosphate receptor (CI-MPR) and Insulin-like growth factor 1 receptor (IGF1R); however, it remains poorly understood how SNX-BARs select specific cargo proteins and whether they recognize additional ligands. Here, we discovered that the binding between SNX-BARs and CI-MPR or IGF1R is mediated by the phox-homology (PX) domain of SNX5 or SNX6 and a bipartite motif, termed SNX-BAR-binding motif (SBM), in the cargoes. Using this motif, we identified over 70 putative SNX-BAR ligands, many of which play critical roles in apoptosis, cell adhesion, signal transduction, or metabolite homeostasis. Remarkably, SNX-BARs could cooperate with both SNX27 and retromer in the recycling of ligands encompassing the SBM, PDZ-binding motif, or both motifs. Overall, our studies establish that SNX-BARs function as a direct cargo-selecting module for a large set of transmembrane proteins transiting the endosome, in addition to their roles in phospholipid recognition and biogenesis of tubular structures.

iEKPD 2.0: an update with rich annotations for eukaryotic protein kinases, protein phosphatases and proteins containing phosphoprotein-binding domains.

Here, we described the updated database iEKPD 2.0 (http://iekpd.biocuckoo.org) for eukaryotic protein kinases (PKs), protein phosphatases (PPs) and proteins containing phosphoprotein-binding domains (PPBDs), which are key molecules responsible for phosphorylation-dependent signalling networks and participate in the regulation of almost all biological processes and pathways. In total, iEKPD 2.0 contained 197 348 phosphorylation regulators, including 109 912 PKs, 23 294 PPs and 68 748 PPBD-containing proteins in 164 eukaryotic species. In particular, we provided rich annotations for the regulators of eight model organisms, especially humans, by compiling and integrating the knowledge from 100 widely used public databases that cover 13 aspects, including cancer mutations, genetic variations, disease-associated information, mRNA expression, DNA & RNA elements, DNA methylation, molecular interactions, drug-target relations, protein 3D structures, post-translational modifications, protein expressions/proteomics, subcellular localizations and protein functional annotations. Compared with our previously developed EKPD 1.0 (∼0.5 GB), iEKPD 2.0 contains ∼99.8 GB of data with an ∼200-fold increase in data volume. We anticipate that iEKPD 2.0 represents a more useful resource for further study of phosphorylation regulators.

iUUCD 2.0: an update with rich annotations for ubiquitin and ubiquitin-like conjugations.

Here, we described the updated database iUUCD 2.0 (http://iuucd.biocuckoo.org/) for ubiquitin-activating enzymes (E1s), ubiquitin-conjugating enzymes (E2s), ubiquitin-protein ligases (E3s), deubiquitinating enzymes (DUBs), ubiquitin/ubiquitin-like binding domains (UBDs) and ubiquitin-like domains (ULDs), which act as key regulators in modulating ubiquitin and ubiquitin-like (UB/UBL) conjugations. In total, iUUCD 2.0 contained 136 512 UB/UBL regulators, including 1230 E1s, 5636 E2s, 93 343 E3s, 9548 DUBs, 30 173 UBDs and 11 099 ULDs in 148 eukaryotic species. In particular, we provided rich annotations for regulators of eight model organisms, especially in humans, by compiling and integrating the knowledge from nearly 70 widely used public databases that cover cancer mutations, single nucleotide polymorphisms (SNPs), mRNA expression, DNA and RNA elements, protein-protein interactions, protein 3D structures, disease-associated information, drug-target relations, post-translational modifications, DNA methylation and protein expression/proteomics. Compared with our previously developed UUCD 1.0 (∼0.41 GB), iUUCD 2.0 has a size of ∼32.1 GB of data with a >75-fold increase in data volume. We anticipate that iUUCD 2.0 can be a more useful resource for further study of UB/UBL conjugations.

Computational prediction of methylation types of covalently modified lysine and arginine residues in proteins.

Protein methylation is an essential posttranslational modification (PTM) mostly occurs at lysine and arginine residues, and regulates a variety of cellular processes. Owing to the rapid progresses in the large-scale identification of methylation sites, the available data set was dramatically expanded, and more attention has been paid on the identification of specific methylation types of modification residues. Here, we briefly summarized the current progresses in computational prediction of methylation sites, which provided an accurate, rapid and efficient approach in contrast with labor-intensive experiments. We collected 5421 methyllysines and methylarginines in 2592 proteins from the literature, and classified most of the sites into different types. Data analyses demonstrated that different types of methylated proteins were preferentially involved in different biological processes and pathways, whereas a unique sequence preference was observed for each type of methylation sites. Thus, we developed a predictor of GPS-MSP, which can predict mono-, di- and tri-methylation types for specific lysines, and mono-, symmetric di- and asymmetrical di-methylation types for specific arginines. We critically evaluated the performance of GPS-MSP, and compared it with other existing tools. The satisfying results exhibited that the classification of methylation sites into different types for training can considerably improve the prediction accuracy. Taken together, we anticipate that our study provides a new lead for future computational analysis of protein methylation, and the prediction of methylation types of covalently modified lysine and arginine residues can generate more useful information for further experimental manipulation.

WERAM: a database of writers, erasers and readers of histone acetylation and methylation in eukaryotes.

In this work, we developed a database WERAM (http://weram.biocuckoo.org/) for histone acetyltransferases, histone deacetylases, histone methyltransferases, histone demethylases and acetyl- or methyl-binding proteins, which catalyze, remove and recognize histone acetylation and methylation sites as 'writers', 'erasers' and 'readers', and synergistically determine the 'histone code'. From the scientific literature, we totally collected over 580 experimentally identified histone regulators from eight model organisms, including Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Caenorhabditis elegans, Arabidopsis thaliana, Schizosaccharomyces pombe and Saccharomyces cerevisiae We also collected ∼900 site-specific regulator-histone relations from the eight species. According to the experimental evidence, known histone regulators were classified into distinct families. To computationally detect more proteins in eukaryotes, we constructed hidden Markov model (HMM) profiles for histone regulator families. For families without HMM profiles, we also conducted orthologous searches. Totally, WERAM database contained more than 20 thousand non-redundant histone regulators from 148 eukaryotes. The detailed annotations and classification information of histone regulators were provided, together with site-specific histone substrates if available.

CGDB: a database of circadian genes in eukaryotes.

We report a database of circadian genes in eukaryotes (CGDB, http://cgdb.biocuckoo.org), containing ∼73 000 circadian-related genes in 68 animals, 39 plants and 41 fungi. Circadian rhythm is ∼24 h rhythm in behavioral and physiological processes that exists in almost all organisms on the earth. Defects in the circadian system are highly associated with a number of diseases such as cancers. Although several databases have been established for rhythmically expressed genes, a comprehensive database of cycling genes across phyla is still lacking. From the literature, we collected 1382 genes of which transcript level oscillations were validated using methods such as RT-PCR, northern blot and in situ hybridization. Given that many genes exhibit different oscillatory patterns in different tissues/cells within an organism, we have included information regarding the phase and amplitude of the oscillation, as well as the tissue/cells in which the oscillation was identified. Using these well characterized cycling genes, we have then conducted an orthologous search and identified ∼45 000 potential cycling genes from 148 eukaryotes. Given that significant effort has been devoted to identifying cycling genes by transcriptome profiling, we have also incorporated these results, a total of over 26 000 genes, into our database.

Systematic characterization of small RNAome during zebrafish early developmental stages.

BACKGROUND: During early vertebrate development, various small non-coding RNAs (sRNAs) such as MicroRNAs (miRNAs) and Piwi-interacting RNAs (piRNAs) are dynamically expressed for orchestrating the maternal-to-zygotic transition (MZT). Systematic analysis of expression profiles of zebrafish small RNAome will be greatly helpful for understanding the sRNA regulation during embryonic development. RESULTS: We first determined the expression profiles of sRNAs during eight distinct stages of early zebrafish development by sRNA-seq technology. Integrative analyses with a new computational platform of CSZ (characterization of small RNAome for zebrafish) demonstrated an sRNA class transition from piRNAs to miRNAs as development proceeds. We observed that both the abundance and diversity of miRNAs are gradually increased, while the abundance is enhanced more dramatically than the diversity during development. However, although both the abundance and diversity of piRNAs are gradually decreased, the diversity was firstly increased then rapidly decreased. To evaluate the computational accuracy, the expression levels of four known miRNAs were experimentally validated. We also predicted 25 potentially novel miRNAs, whereas two candidates were verified by Northern blots. CONCLUSIONS: Taken together, our analyses revealed the piRNA to miRNA transition as a conserved mechanism in zebrafish, although two different types of sRNAs exhibit distinct expression dynamics in abundance and diversity, respectively. Our study not only generated a better understanding for sRNA regulations in early zebrafish development, but also provided a useful platform for analyzing sRNA-seq data. The CSZ was implemented in Perl and freely downloadable at: http://csz.biocuckoo.org.

The BMAL1/HIF2A heterodimer modulates circadian variations of myocardial injury.

Acute myocardial infarction stands as a prominent cause of morbidity and mortality worldwide1-6. Clinical studies have demonstrated that the severity of cardiac injury following myocardial infarction exhibits a circadian pattern, with larger infarct sizes and poorer outcomes in patients experiencing morning onset myocardial infarctions7-14. However, the molecular mechanisms that govern circadian variations of myocardial injury remain unclear. Here, we show that BMAL114-20, a core circadian transcription factor, orchestrates diurnal variability in myocardial injury. Unexpectedly, BMAL1 modulates circadian-dependent cardiac injury by forming a transcriptionally active heterodimer with a non-canonical partner, hypoxia-inducible factor 2 alpha (HIF2A)6,21-23, in a diurnal manner. Substantiating this finding, we determined the cryo-EM structure of the BMAL1/HIF2A/DNA complex, revealing a previously unknown capacity for structural rearrangement within BMAL1, which enables the crosstalk between circadian rhythms and hypoxia signaling. Furthermore, we identified amphiregulin (AREG) as a rhythmic transcriptional target of the BMAL1/HIF2A heterodimer, critical for regulating circadian variations of myocardial injury. Finally, pharmacologically targeting the BMAL1/HIF2A-AREG pathway provides effective cardioprotection, with maximum efficacy when aligned with the pathway's circadian trough. Our findings not only uncover a novel mechanism governing the circadian variations of myocardial injury but also pave the way for innovative circadian-based treatment strategies, potentially shifting current treatment paradigms for myocardial infarction.

MitoTrace: A Computational Framework for Analyzing Mitochondrial Variation in Single-Cell RNA Sequencing Data.

Genetic variation in the mitochondrial genome is linked to important biological functions and various human diseases. Recent progress in single-cell genomics has established single-cell RNA sequencing (scRNAseq) as a popular and powerful technique to profile transcriptomics at the cellular level. While most studies focus on deciphering gene expression, polymorphisms including mitochondrial variants can also be readily inferred from scRNAseq. However, limited attention has been paid to investigate the single-cell landscape of mitochondrial variants, despite the rapid accumulation of scRNAseq data in the community. In addition, a diploid context is assumed for most variant calling tools, which is not appropriate for mitochondrial heteroplasmies. Here, we introduce MitoTrace, an R package for the analysis of mitochondrial genetic variation in bulk and scRNAseq data. We applied MitoTrace to several publicly accessible data sets and demonstrated its ability to robustly recover genetic variants from scRNAseq data. We also validated the applicability of MitoTrace to scRNAseq data from diverse platforms. Overall, MitoTrace is a powerful and user-friendly tool to investigate mitochondrial variants from scRNAseq data.

PIM1 promotes hepatic conversion by suppressing reprogramming-induced ferroptosis and cell cycle arrest.

Protein kinase-mediated phosphorylation plays a critical role in many biological processes. However, the identification of key regulatory kinases is still a great challenge. Here, we develop a trans-omics-based method, central kinase inference, to predict potentially key kinases by integrating quantitative transcriptomic and phosphoproteomic data. Using known kinases associated with anti-cancer drug resistance, the accuracy of our method denoted by the area under the curve is 5.2% to 29.5% higher than Kinase-Substrate Enrichment Analysis. We further use this method to analyze trans-omic data in hepatocyte maturation and hepatic reprogramming of human dermal fibroblasts, uncovering 5 kinases as regulators in the two processes. Further experiments reveal that a serine/threonine kinase, PIM1, promotes hepatic conversion and protects human dermal fibroblasts from reprogramming-induced ferroptosis and cell cycle arrest. This study not only reveals new regulatory kinases, but also provides a helpful method that might be extended to predict central kinases involved in other biological processes.

Model-based analysis uncovers mutations altering autophagy selectivity in human cancer.

Autophagy can selectively target protein aggregates, pathogens, and dysfunctional organelles for the lysosomal degradation. Aberrant regulation of autophagy promotes tumorigenesis, while it is far less clear whether and how tumor-specific alterations result in autophagic aberrance. To form a link between aberrant autophagy selectivity and human cancer, we establish a computational pipeline and prioritize 222 potential LIR (LC3-interacting region) motif-associated mutations (LAMs) in 148 proteins. We validate LAMs in multiple proteins including ATG4B, STBD1, EHMT2 and BRAF that impair their interactions with LC3 and autophagy activities. Using a combination of transcriptomic, metabolomic and additional experimental assays, we show that STBD1, a poorly-characterized protein, inhibits tumor growth via modulating glycogen autophagy, while a patient-derived W203C mutation on LIR abolishes its cancer inhibitory function. This work suggests that altered autophagy selectivity is a frequently-used mechanism by cancer cells to survive during various stresses, and provides a framework to discover additional autophagy-related pathways that influence carcinogenesis.

An integrative multi-omics approach uncovers the regulatory role of CDK7 and CDK4 in autophagy activation induced by silica nanoparticles.

Dysfunction of macroautophagy/autophagy has been postulated as a major cellular toxicological response to nanomaterials. It has been reported that excessive autophagy activation, induced by silica nanoparticles (SiNPs), contributes to autophagy dysfunction, whereas little is known how SiNPs trigger autophagy activation. Here, we treated normal rat kidney (NRK) cells using 3 different sizes of SiNPs (16, 29, and 51 nm) and observed that 16-nm SiNPs, with a final concentration of 60 µg/mL, dramatically induce autophagy activation without reducing cell viability. We further conducted a transcriptomic, proteomic, and phosphoproteomic profiling, and detected 23 autophagy-related (Atg) genes and 35 autophagy regulators regulated on at least one omic layer. To identify key regulators from the multi-omics data, we developed a new algorithm of computational prediction of master autophagy-regulating kinases (cMAK) to detect 21 candidates and revealed the CDK7-CDK4 cascade to be functional. The silence or inhibition of Cdk7 or Cdk4 significantly attenuated autophagic activation but not influenced autophagic flux blockage induced by 16-nm SiNPs. Further computational modeling indicated that the CDK7-CDK4 signaling axis potentially triggers autophagy activation by phosphorylating RB1 (RB transcriptional corepressor 1), activating two critical transcription factors, E2F1 (E2F transcription factor 1) and FOXO3 (forkhead box O3), and enhancing the transcriptional levels of at least 8 Atg genes and autophagy regulators in response to SiNPs. Our studies not only established a powerful method for predicting regulatory kinases from the multi-omics data but also revealed a potential mechanism of SiNP-triggered autophagy activation through modulating the CDK7-CDK4 cascade.Abbreviations: 3-MA: 3-methyladenine; Atg: autophagy-related; BECN1: beclin 1; CCK-8: cell counting kit-8; CDK4: cyclin dependent kinase 4; CDK7: cyclin dependent kinase 7; cMAK: computational prediction of master autophagy-regulating kinases; CQ: chloroquine; DMEM: Dulbecco's modified Eagle's medium; DMSO: dimethyl sulfoxide; E-ratio: enrichment ratio; E2F1: E2F transcription factor 1; EBSS: Earle's balanced salt solution; ER: endoplasmic reticulum; FOXO3: forkhead box O3; FPKM: fragments per kilobase of exon per million fragments mapped; GO: gene ontology; H2O2: hydrogen peroxide; iGPS: in vivo GPS; KEGG: Kyoto Encyclopedia of Genes and Genomes; LC-MS/MS: liquid chromatography-tandem mass spectrometry; LDH: lactate dehydrogenase; MAP1LC3B/LC3: microtubule associated protein 1 light chain 3 beta; NRK: normal rat kidney; p-site: phosphorylation site; PBS: phosphate-buffered saline; PDI: polydispersity index; PTM: post-translational modification; QKS: quantitative kinase state; RB1: RB transcriptional corepressor 1; RBHs: reciprocal best hits; RNA-Seq: RNA sequencing; ROS: reactive oxygen species; rSiNPs: SiNPs fluorescently labeled with rhodamine B; SEM: scanning electronic microscopy; SiNPs: silica nanoparticles; siRNA: small interfering RNA; SQSTM1/p62: sequestosome 1; ssKSR: site-specific kinase-substrate relation; TEM: transmission electron microscopy; tfLC3: mRFP-GFP tandem fluorescent-tagged LC3.

Phosphorylation of SNX27 by MAPK11/14 links cellular stress-signaling pathways with endocytic recycling.

Endocytosed proteins can be delivered to lysosomes for degradation or recycled to either the trans-Golgi network or the plasma membrane. It remains poorly understood how the recycling versus degradation of cargoes is determined. Here, we show that multiple extracellular stimuli, including starvation, LPS, IL-6, and EGF treatment, can strongly inhibit endocytic recycling of multiple cargoes through the activation of MAPK11/14. The stress-induced kinases in turn directly phosphorylate SNX27, a key regulator of endocytic recycling, at serine 51 (Ser51). Phosphorylation of SNX27 at Ser51 alters the conformation of its cargo-binding pocket and decreases the interaction between SNX27 and cargo proteins, thereby inhibiting endocytic recycling. Our study indicates that endocytic recycling is highly dynamic and can crosstalk with cellular stress-signaling pathways. Suppression of endocytic recycling and enhancement of receptor lysosomal degradation serve as new mechanisms for cells to cope with stress and save energy.

HybridSucc: A Hybrid-learning Architecture for General and Species-specific Succinylation Site Prediction.

As an important protein acylation modification, lysine succinylation (Ksucc) is involved in diverse biological processes, and participates in human tumorigenesis. Here, we collected 26,243 non-redundant known Ksucc sites from 13 species as the benchmark data set, combined 10 types of informative features, and implemented a hybrid-learning architecture by integrating deep-learning and conventional machine-learning algorithms into a single framework. We constructed a new tool named HybridSucc, which achieved area under curve (AUC) values of 0.885 and 0.952 for general and human-specific prediction of Ksucc sites, respectively. In comparison, the accuracy of HybridSucc was 17.84%-50.62% better than that of other existing tools. Using HybridSucc, we conducted a proteome-wide prediction and prioritized 370 cancer mutations that change Ksucc states of 218 important proteins, including PKM2, SHMT2, and IDH2. We not only developed a high-profile tool for predicting Ksucc sites, but also generated useful candidates for further experimental consideration. The online service of HybridSucc can be freely accessed for academic research at http://hybridsucc.biocuckoo.org/.

GPS 5.0: An Update on the Prediction of Kinase-specific Phosphorylation Sites in Proteins.

In eukaryotes, protein phosphorylation is specifically catalyzed by numerous protein kinases (PKs), faithfully orchestrates various biological processes, and reversibly determines cellular dynamics and plasticity. Here we report an updated algorithm of Group-based Prediction System (GPS) 5.0 to improve the performance for predicting kinase-specific phosphorylation sites (p-sites). Two novel methods, position weight determination (PWD) and scoring matrix optimization (SMO), were developed. Compared with other existing tools, GPS 5.0 exhibits a highly competitive accuracy. Besides serine/threonine or tyrosine kinases, GPS 5.0 also supports the prediction of dual-specificity kinase-specific p-sites. In the classical module of GPS 5.0, 617 individual predictors were constructed for predicting p-sites of 479 human PKs. To extend the application of GPS 5.0, a species-specific module was implemented to predict kinase-specific p-sites for 44,795 PKs in 161 eukaryotes. The online service and local packages of GPS 5.0 are freely available for academic research at http://gps.biocuckoo.cn.

Integrated omics in Drosophila uncover a circadian kinome.

Most organisms on the earth exhibit circadian rhythms in behavior and physiology, which are driven by endogenous clocks. Phosphorylation plays a central role in timing the clock, but how this contributes to overt rhythms is unclear. Here we conduct phosphoproteomics in conjunction with transcriptomic and proteomic profiling using fly heads. By developing a pipeline for integrating multi-omics data, we identify 789 (~17%) phosphorylation sites with circadian oscillations. We predict 27 potential circadian kinases to participate in phosphorylating these sites, including 7 previously known to function in the clock. We screen the remaining 20 kinases for effects on circadian rhythms and find an additional 3 to be involved in regulating locomotor rhythm. We re-construct a signal web that includes the 10 circadian kinases and identify GASKET as a potentially important regulator. Taken together, we uncover a circadian kinome that potentially shapes the temporal pattern of the entire circadian molecular landscapes.

DeepPhagy: a deep learning framework for quantitatively measuring autophagy activity in Saccharomyces cerevisiae.

Seeing is believing. The direct observation of GFP-Atg8 vacuolar delivery under confocal microscopy is one of the most useful end-point measurements for monitoring yeast macroautophagy/autophagy. However, manually labelling individual cells from large-scale sets of images is time-consuming and labor-intensive, which has greatly hampered its extensive use in functional screens. Herein, we conducted a time-course analysis of nitrogen starvation-induced autophagy in wild-type and knockout mutants of 35 AuTophaGy-related (ATG) genes in Saccharomyces cerevisiae and obtained 1,944 confocal images containing > 200,000 cells. We manually labelled 8,078 autophagic and 18,493 non-autophagic cells as a benchmark dataset and developed a new deep learning tool for autophagy (DeepPhagy), which exhibited superior accuracy in recognizing autophagic cells compared to other existing methods, with an area under the curve (AUC) value of 0.9710 from 10-fold cross-validations. We further used DeepPhagy to automatically analyze all the images and quantitatively classified the autophagic phenotypes of the 35 atg knockout mutants into 3 classes. The high consistency in our computational and biochemical results indicated the reliability of DeepPhagy for measuring autophagic activity. Moreover, we used DeepPhagy to analyze 3 additional types of autophagic phenotypes, including the targeting of Atg1-GFP to the vacuole, the vacuolar delivery of GFP-Atg19, and the disintegration of autophagic bodies indicated by GFP-Atg8, all with satisfying accuracies. Taken together, our study not only enables the GFP-Atg8 fluorescence assay to become a quantitative measurement for analyzing autophagic phenotypes in S. cerevisiae but also demonstrates that deep learning-based methods could potentially be applied to different types of autophagy.Abbreviations:Ac: accuracy; ALP: alkaline phosphatase; ALR: autophagic lysosomal reformation; ATG: AuTophaGy-related; AUC: area under the curve; CNN: convolutional neural network; Cvt: cytoplasm-to-vacuole targeting; DeepPhagy: deep learning for autophagy; fc_2: second fully connected; GFP: green fluorescent protein; MAP1LC3/LC3: microtubule-associated protein 1 light chain 3 beta; HAT: histone acetyltransferase; HemI: Heat map Illustrator; JRE: Java Runtime Environment; KO: knockout; LRN: local response normalization; MCC: Mathew Correlation Coefficient; OS: operating system; PAS: phagophore assembly site; PC: principal component; PCA: principal component analysis; PPI: protein-protein interaction; Pr: precision; QPSO: Quantum-behaved Particle Swarm Optimization; ReLU: rectified linear unit; RF: random forest; ROC: receiver operating characteristic; ROI: region of interest; SD: systematic derivation; SGD: stochastic gradient descent; Sn: sensitivity; Sp: specificity; SRG: seeded region growing; t-SNE: t-distributed stochastic neighbor embedding; 2D: 2-dimensional; WT: wild-type.

PTMD: A Database of Human Disease-associated Post-translational Modifications.

Various posttranslational modifications (PTMs) participate in nearly all aspects of biological processes by regulating protein functions, and aberrant states of PTMs are frequently implicated in human diseases. Therefore, an integral resource of PTM-disease associations (PDAs) would be a great help for both academic research and clinical use. In this work, we reported PTMD, a well-curated database containing PTMs that are associated with human diseases. We manually collected 1950 known PDAs in 749 proteins for 23 types of PTMs and 275 types of diseases from the literature. Database analyses show that phosphorylation has the largest number of disease associations, whereas neurologic diseases have the largest number of PTM associations. We classified all known PDAs into six classes according to the PTM status in diseases and demonstrated that the upregulation and presence of PTM events account for a predominant proportion of disease-associated PTM events. By reconstructing a disease-gene network, we observed that breast cancers have the largest number of associated PTMs and AKT1 has the largest number of PTMs connected to diseases. Finally, the PTMD database was developed with detailed annotations and can be a useful resource for further analyzing the relations between PTMs and human diseases. PTMD is freely accessible at http://ptmd.biocuckoo.org.

THANATOS: an integrative data resource of proteins and post-translational modifications in the regulation of autophagy.

Macroautophagy/autophagy is a highly conserved process for degrading cytoplasmic contents, determines cell survival or death, and regulates the cellular homeostasis. Besides ATG proteins, numerous regulators together with various post-translational modifications (PTMs) are also involved in autophagy. In this work, we collected 4,237 experimentally identified proteins regulated in autophagy and cell death pathways from the literature. Then we computationally identified potential orthologs of known proteins, and developed a comprehensive database of The Autophagy, Necrosis, ApopTosis OrchestratorS (THANATOS, http://thanatos.biocuckoo.org ), containing 191,543 proteins potentially associated with autophagy and cell death pathways in 164 eukaryotes. We performed an evolutionary analysis of ATG genes, and observed that ATGs required for the autophagosome formation are highly conserved across eukaryotes. Further analyses revealed that known cancer genes and drug targets were overrepresented in human autophagy proteins, which were significantly associated in a number of signaling pathways and human diseases. By reconstructing a human kinase-substrate phosphorylation network for ATG proteins, our results confirmed that phosphorylation play a critical role in regulating autophagy. In total, we mapped 65,015 known sites of 11 types of PTMs to collected proteins, and revealed that all types of PTM substrates were enriched in human autophagy. In addition, we observed multiple types of PTM regulators such as protein kinases and ubiquitin E3 ligases or adaptors were significantly associated with human autophagy, and again the results emphasized the importance of PTM regulations in autophagy. We anticipated THANATOS can be a useful resource for further studies.

PLMD: An updated data resource of protein lysine modifications.

Post-translational modifications (PTMs) occurring at protein lysine residues, or protein lysine modifications (PLMs), play critical roles in regulating biological processes. Due to the explosive expansion of the amount of PLM substrates and the discovery of novel PLM types, here we greatly updated our previous studies, and presented a much more integrative resource of protein lysine modification database (PLMD). In PLMD, we totally collected and integrated 284,780 modification events in 53,501 proteins across 176 eukaryotes and prokaryotes for up to 20 types of PLMs, including ubiquitination, acetylation, sumoylation, methylation, succinylation, malonylation, glutarylation, glycation, formylation, hydroxylation, butyrylation, propionylation, crotonylation, pupylation, neddylation, 2-hydroxyisobutyrylation, phosphoglycerylation, carboxylation, lipoylation and biotinylation. Using the data set, a motif-based analysis was performed for each PLM type, and the results demonstrated that different PLM types preferentially recognize distinct sequence motifs for the modifications. Moreover, various PLMs synergistically orchestrate specific cellular biological processes by mutual crosstalks with each other, and we totally found 65,297 PLM events involved in 90 types of PLM co-occurrences on the same lysine residues. Finally, various options were provided for accessing the data, while original references and other annotations were also present for each PLM substrate. Taken together, we anticipated the PLMD database can serve as a useful resource for further researches of PLMs. PLMD 3.0 was implemented in PHP + MySQL and freely available at http://plmd.biocuckoo.org.