Holistic similarity-based prediction of phosphorylation sites for understudied kinases.

Phosphorylation is an essential mechanism for regulating protein activities. Determining kinase-specific phosphorylation sites by experiments involves time-consuming and expensive analyzes. Although several studies proposed computational methods to model kinase-specific phosphorylation sites, they typically required abundant experimentally verified phosphorylation sites to yield reliable predictions. Nevertheless, the number of experimentally verified phosphorylation sites for most kinases is relatively small, and the targeting phosphorylation sites are still unidentified for some kinases. In fact, there is little research related to these understudied kinases in the literature. Thus, this study aims to create predictive models for these understudied kinases. A kinase-kinase similarity network was generated by merging the sequence-, functional-, protein-domain- and 'STRING'-related similarities. Thus, besides sequence data, protein-protein interactions and functional pathways were also considered to aid predictive modelling. This similarity network was then integrated with a classification of kinase groups to yield highly similar kinases to a specific understudied type of kinase. Their experimentally verified phosphorylation sites were leveraged as positive sites to train predictive models. The experimentally verified phosphorylation sites of the understudied kinase were used for validation. Results demonstrate that 82 out of 116 understudied kinases were predicted with adequate performance via the proposed modelling strategy, achieving a balanced accuracy of 0.81, 0.78, 0.84, 0.84, 0.85, 0.82, 0.90, 0.82 and 0.85, for the 'TK', 'Other', 'STE', 'CAMK', 'TKL', 'CMGC', 'AGC', 'CK1' and 'Atypical' groups, respectively. Therefore, this study demonstrates that web-like predictive networks can reliably capture the underlying patterns in such understudied kinases by harnessing relevant sources of similarities to predict their specific phosphorylation sites.

Extraction of microRNA-target interaction sentences from biomedical literature by deep learning approach.

MicroRNA (miRNA)-target interaction (MTI) plays a substantial role in various cell activities, molecular regulations and physiological processes. Published biomedical literature is the carrier of high-confidence MTI knowledge. However, digging out this knowledge in an efficient manner from large-scale published articles remains challenging. To address this issue, we were motivated to construct a deep learning-based model. We applied the pre-trained language models to biomedical text to obtain the representation, and subsequently fed them into a deep neural network with gate mechanism layers and a fully connected layer for the extraction of MTI information sentences. Performances of the proposed models were evaluated using two datasets constructed on the basis of text data obtained from miRTarBase. The validation and test results revealed that incorporating both PubMedBERT and SciBERT for sentence level encoding with the long short-term memory (LSTM)-based deep neural network can yield an outstanding performance, with both F1 and accuracy being higher than 80% on validation data and test data. Additionally, the proposed deep learning method outperformed the following machine learning methods: random forest, support vector machine, logistic regression and bidirectional LSTM. This work would greatly facilitate studies on MTI analysis and regulations. It is anticipated that this work can assist in large-scale screening of miRNAs, thereby revealing their functional roles in various diseases, which is important for the development of highly specific drugs with fewer side effects. Source code and corpus are publicly available at https://github.com/qi29.

dbAMP 2.0: updated resource for antimicrobial peptides with an enhanced scanning method for genomic and proteomic data.

The last 18 months, or more, have seen a profound shift in our global experience, with many of us navigating a once-in-100-year pandemic. To date, COVID-19 remains a life-threatening pandemic with little to no targeted therapeutic recourse. The discovery of novel antiviral agents, such as vaccines and drugs, can provide therapeutic solutions to save human beings from severe infections; however, there is no specifically effective antiviral treatment confirmed for now. Thus, great attention has been paid to the use of natural or artificial antimicrobial peptides (AMPs) as these compounds are widely regarded as promising solutions for the treatment of harmful microorganisms. Given the biological significance of AMPs, it was obvious that there was a significant need for a single platform for identifying and engaging with AMP data. This led to the creation of the dbAMP platform that provides comprehensive information about AMPs and facilitates their investigation and analysis. To date, the dbAMP has accumulated 26 447 AMPs and 2262 antimicrobial proteins from 3044 organisms using both database integration and manual curation of >4579 articles. In addition, dbAMP facilitates the evaluation of AMP structures using I-TASSER for automated protein structure prediction and structure-based functional annotation, providing predictive structure information for clinical drug development. Next-generation sequencing (NGS) and third-generation sequencing have been applied to generate large-scale sequencing reads from various environments, enabling greatly improved analysis of genome structure. In this update, we launch an efficient online tool that can effectively identify AMPs from genome/metagenome and proteome data of all species in a short period. In conclusion, these improvements promote the dbAMP as one of the most abundant and comprehensively annotated resources for AMPs. The updated dbAMP is now freely accessible at http://awi.cuhk.edu.cn/dbAMP.

dbPTM in 2022: an updated database for exploring regulatory networks and functional associations of protein post-translational modifications.

Protein post-translational modifications (PTMs) play an important role in different cellular processes. In view of the importance of PTMs in cellular functions and the massive data accumulated by the rapid development of mass spectrometry (MS)-based proteomics, this paper presents an update of dbPTM with over 2 777 000 PTM substrate sites obtained from existing databases and manual curation of literature, of which more than 2 235 000 entries are experimentally verified. This update has manually curated over 42 new modification types that were not included in the previous version. Due to the increasing number of studies on the mechanism of PTMs in the past few years, a great deal of upstream regulatory proteins of PTM substrate sites have been revealed. The updated dbPTM thus collates regulatory information from databases and literature, and merges them into a protein-protein interaction network. To enhance the understanding of the association between PTMs and molecular functions/cellular processes, the functional annotations of PTMs are curated and integrated into the database. In addition, the existing PTM-related resources, including annotation databases and prediction tools are also renewed. Overall, in this update, we would like to provide users with the most abundant data and comprehensive annotations on PTMs of proteins. The updated dbPTM is now freely accessible at https://awi.cuhk.edu.cn/dbPTM/.

Multi-Omics Characterization of E3 Regulatory Patterns in Different Cancer Types.

Ubiquitination, a post-translational modification, refers to the covalent attachment of ubiquitin molecules to substrates. This modification plays a critical role in diverse cellular processes such as protein degradation. The specificity of ubiquitination for substrates is regulated by E3 ubiquitin ligases. Dysregulation of ubiquitination has been associated with numerous diseases, including cancers. In our study, we first investigated the protein expression patterns of E3 ligases across 12 cancer types. Our findings indicated that E3 ligases tend to be up-regulated and exhibit reduced tissue specificity in tumors. Moreover, the correlation of protein expression between E3 ligases and substrates demonstrated significant changes in cancers, suggesting that E3-substrate specificity alters in tumors compared to normal tissues. By integrating transcriptome, proteome, and ubiquitylome data, we further characterized the E3-substrate regulatory patterns in lung squamous cell carcinoma. Our analysis revealed that the upregulation of the SKP2 E3 ligase leads to excessive degradation of BRCA2, potentially promoting tumor cell proliferation and metastasis. Furthermore, the upregulation of E3 ubiquitin-protein ligase TRIM33 was identified as a biomarker associated with a favorable prognosis by inhibiting the cell cycle. This work exemplifies how leveraging multi-omics data to analyze E3 ligases across various cancers can unveil prognosis biomarkers and facilitate the identification of potential drug targets for cancer therapy.

Antidementia medication acetylcholinesterase inhibitors have therapeutic benefits on osteoporotic bone by attenuating osteoclastogenesis and bone resorption.

This study was designed to determine whether the use of acetylcholinesterase inhibitors (AChEIs), a group of drugs that stimulate acetylcholine receptors and are used to treat Alzheimer's disease (AD), is associated with osteoporosis protection and inhibition of osteoclast differentiation and function. Firstly, we examined the effects of AChEIs on RANKL-induced osteoclast differentiation and function with osteoclastogenesis and bone resorption assays. Next, we investigated the impacts of AChEIs on RANKL-induced nuclear factor κB and NFATc1 activation and expression of osteoclast marker proteins CA-2, CTSK and NFATc1, and dissected the MAPK signaling in osteoclasts in vitro by using luciferase assay and Western blot. Finally, we assessed the in vivo efficacy of AChEIs using an ovariectomy-induced osteoporosis mouse model, which was analyzed using microcomputed tomography, in vivo osteoclast and osteoblast parameters were assessed using histomorphometry. We found that Donepezil and Rivastigmine inhibited RANKL-induced osteoclastogenesis and impaired osteoclastic bone resorption. Moreover, AChEIs reduced the RANKL-induced transcription of Nfatc1, and expression of osteoclast marker genes to varying degrees (mainly Donepezil and Rivastigmine but not Galantamine). Furthermore, AChEIs variably inhibited RANKL-induced MAPK signaling accompanied by downregulation of AChE transcription. Finally, AChEIs protected against OVX-induced bone loss mainly by inhibiting osteoclast activity. Taken together, AChEIs (mainly Donepezil and Rivastigmine) exerted a positive effect on bone protection by inhibiting osteoclast function through MAPK and NFATc1 signaling pathways through downregulating AChE. Our findings have important clinical implications that elderly patients with dementia who are at risk of developing osteoporosis may potentially benefit from therapy with the AChEI drugs. Our study may influence drug choice in those patients with both AD and osteoporosis.

A large-scale investigation and identification of methicillin-resistant Staphylococcus aureus based on peaks binning of matrix-assisted laser desorption ionization-time of flight MS spectra.

Recent studies have demonstrated that the matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) could be used to detect superbugs, such as methicillin-resistant Staphylococcus aureus (MRSA). Due to an increasingly clinical need to classify between MRSA and methicillin-sensitive Staphylococcus aureus (MSSA) efficiently and effectively, we were motivated to develop a systematic pipeline based on a large-scale dataset of MS spectra. However, the shifting problem of peaks in MS spectra induced a low effectiveness in the classification between MRSA and MSSA isolates. Unlike previous works emphasizing on specific peaks, this study employs a binning method to cluster MS shifting ions into several representative peaks. A variety of bin sizes were evaluated to coalesce drifted or shifted MS peaks to a well-defined structured data. Then, various machine learning methods were performed to carry out the classification between MRSA and MSSA samples. Totally 4858 MS spectra of unique S. aureus isolates, including 2500 MRSA and 2358 MSSA instances, were collected by Chang Gung Memorial Hospitals, at Linkou and Kaohsiung branches, Taiwan. Based on the evaluation of Pearson correlation coefficients and the strategy of forward feature selection, a total of 200 peaks (with the bin size of 10 Da) were identified as the marker attributes for the construction of predictive models. These selected peaks, such as bins 2410-2419, 2450-2459 and 6590-6599 Da, have indicated remarkable differences between MRSA and MSSA, which were effective in the prediction of MRSA. The independent testing has revealed that the random forest model can provide a promising prediction with the area under the receiver operating characteristic curve (AUC) at 0.8450. When comparing to previous works conducted with hundreds of MS spectra, the proposed scheme demonstrates that incorporating machine learning method with a large-scale dataset of clinical MS spectra may be a feasible means for clinical physicians on the administration of correct antibiotics in shorter turn-around-time, which could reduce mortality, avoid drug resistance and shorten length of stay in hospital in the future.

A representation and deep learning model for annotating ubiquitylation sentences stating E3 ligase - substrate interaction.

BACKGROUND: Ubiquitylation is an important post-translational modification of proteins that not only plays a central role in cellular coding, but is also closely associated with the development of a variety of diseases. The specific selection of substrate by ligase E3 is the key in ubiquitylation. As various high-throughput analytical techniques continue to be applied to the study of ubiquitylation, a large amount of ubiquitylation site data, and records of E3-substrate interactions continue to be generated. Biomedical literature is an important vehicle for information on E3-substrate interactions in ubiquitylation and related new discoveries, as well as an important channel for researchers to obtain such up to date data. The continuous explosion of ubiquitylation related literature poses a great challenge to researchers in acquiring and analyzing the information. Therefore, automatic annotation of these E3-substrate interaction sentences from the available literature is urgently needed. RESULTS: In this research, we proposed a model based on representation and attention mechanism based deep learning methods, to automatic annotate E3-substrate interaction sentences in biomedical literature. Focusing on the sentences with E3 protein inside, we applied several natural language processing methods and a Long Short-Term Memory (LSTM)-based deep learning classifier to train the model. Experimental results had proved the effectiveness of our proposed model. And also, the proposed attention mechanism deep learning method outperforms other statistical machine learning methods. We also created a manual corpus of E3-substrate interaction sentences, in which the E3 proteins and substrate proteins are also labeled, in order to construct our model. The corpus and model proposed by our research are definitely able to be very useful and valuable resource for advancement of ubiquitylation-related research. CONCLUSION: Having the entire manual corpus of E3-substrate interaction sentences readily available in electronic form will greatly facilitate subsequent text mining and machine learning analyses. Automatic annotating ubiquitylation sentences stating E3 ligase-substrate interaction is significantly benefited from semantic representation and deep learning. The model enables rapid information accessing and can assist in further screening of key ubiquitylation ligase substrates for in-depth studies.

Amyloid ß peptide promotes bone formation by regulating Wnt/ß-catenin signaling and the OPG/RANKL/RANK system.

BACKGROUND: Amyloid ß peptide (Aß) is involved in osteoporosis, but the effects of Aß on osteoblast and bone formation remain unclear. In this study, we investigated the effect of Aß on bone formation. METHODS: An animal model of osteoporosis was established by ovariectomy in C57BL/6 mice. The mice received intraperitoneal injection of Aß. The effect of Aß on the osteogenic differentiation of human bone marrow stromal stem cells (hBMSCs) and differentiation of both pre-osteoblasts and pre-osteoclasts in a co-culture system were investigated. RESULTS: In the animal study, intraperitoneal injection of Aß for 8 weeks promoted early and late osteogenic differentiation of hBMSCs. Aß treatment significantly elevated osterix+ (osteoblastic) cells but decreased TRAP+ cells (osteoclasts) in the distal femur bone. In vitro study showed that Aß treatment significantly enhanced matrix mineralization and osteogenic markers (Runx2 and osteocalcin). Aß treatment activated Wnt/ß-catenin signaling in hBMSCs. The effect of Aß was blocked by DKK1 (a Wnt/ß-catenin inhibitor) treatment. In the co-culture system, Aß treatment significantly increased the ALP activities of MC3T3-E1 cells (pre-osteoblasts) but reduced the TRAP+ RAW264.7 cells (pre-osteoclasts). Aß treatment upregulated TCF1 and OPG proteins in MC3T3-E1 cells. Aß treatment upregulated IκB-α but downregulated NFATc1protein in RAW264.7 cells. These effects were blocked by XAV-939 (a Wnt signaling antagonist), and then rescued by additional Wnt3a (a Wnt agonist). CONCLUSION: Aß treatment simultaneously promoted osteogenic differentiation via Wnt/ß-catenin signaling, and inhibited osteoclasts differentiation via the OPG/RANKL/RANK system, suggesting Aß is a positive regulator of osteoblast differentiation and bone formation.

Casein kinase 1 epsilon facilitates cartilage destruction in osteoarthritis through JNK pathway.

Osteoarthritis (OA) is a high-morbidity skeletal disease worldwide and the exact mechanisms underlying OA pathogenesis are not fully understood. Casein kinase 1 epsilon (CK1ε) is a serine/threonine protein kinase, but its relationship with OA is still unknown. We demonstrated that CK1ε was upregulated in articular cartilage of human patients with OA and mice with experimentally induced OA. Activity of CK1ε, demonstrated by analysis of phosphorylated substrates, was significantly elevated in interleukin (IL)-1ß-induced OA-mimicking chondrocytes. CK1ε inhibitor or CK1ε short hairpin RNA (shRNA) partially blocked matrix metalloproteinase (MMP) expression by primary chondrocytes induced by IL-1ß, and also inhibited cartilage destruction in knee joints of experimental OA model mice. Conversely, overexpression of CK1ε promoted chondrocyte catabolism. Previous studies indicated that CK1ε was involved in canonical Wnt/ß-catenin signaling and noncanonical Wnt/c-Jun N-terminal kinase (JNK) signaling pathway. Interestingly, the activity of JNK but not ß-catenin decreased after CK1ε knockdown in IL-1ß-treated chondrocytes in vitro, and JNK inhibition reduced MMP expression in chondrocytes overexpressing CK1ε, which illustrated that CK1ε-mediated OA was based on JNK pathway. In conclusion, our results demonstrate that CK1ε promotes OA development, and inhibition of CK1ε could be a potential strategy for OA treatment in the future.

Isotope Labeling Strategies for Acylcarnitines Profile in Biological Samples by Liquid Chromatography-Mass Spectrometry.

Acylcarnitines are closely related to many metabolic diseases and likely to be good biomarkers for clinical diagnosis. Studies on acylcarnitines may help deepen the understanding of their functions associated with disease processes. However, the diversity of their structures and lack of commercial standards make them difficult to be fully detected. In this work, a highly efficient isotope labeling strategy was developed to detect acylcarnitines in biological samples using liquid chromatography-mass spectrometry (LC-MS). A pair of reagents with or without deuterium labeling tags containing both a positive charge and a primary amine group were synthesized, which were used to label the carboxyl group of acylcarnitines. The reaction was performed under mild conditions to avoid hydrolysis of acylcarnitines. The reaction products had two positive charges, forming a double charge peak in MS spectra and with mass-to-charge ratio difference (Δ m/ z) of 4.0251 Da between the light- and heavy-labeled products. The feature made it possible to distinguish signals of acylcarnitines from other carboxyl metabolites with Δ m/ z of 8.0502 Da between their light- and heavy-labeled products. On the basis of the characteristics and the tandem mass spectrometry (MS/MS) spectra, a total of 108 acylcarnitines were discovered and identified in urine samples. Our established approach will be very helpful for the studies of diseases associated with acylcarnitines metabolism.

BMI1 activates WNT signaling in colon cancer by negatively regulating the WNT antagonist IDAX.

Aberrant activation of Wnt signaling plays a critical role in the development of colon cancer. BMI, a component of the polycomb repressive complex (PRC1), is upregulated in various types of cancer and contributes to epigenetic silencing of tumor suppressors. In this study, we showed that BMI1 is upregulated in colon cancer tissues and cell lines. Overexpression of BMI1 in primary epithelial colon cells promotes cellular growth and activates WNT pathway, while BMI1 silencing in colon cancer cells represses these effects. We also found that BMI1 binds to the promoter of IDAX, a Wnt antagonist, and decreases its transcription. Expression of IDAX is downregulated in colon cancer tissues and cell lines and negatively correlated with BMI1 in colon cancer tissues. Furthermore, Silencing of IDAX counteracts the effects of BMI1 suppression, while its overexpression reverses oncogenic effects of BMI1. Together, these findings indicate that BMI1-mediated IDAX epigenetic suppression is crucial for enhancement of colon carcinogenesis, suggesting that BMI1∖IDAX axis as a potential novel diagnostic and therapeutic target of colon cancer.

Comprehensive Analysis of Acylcarnitine Species in db/db Mouse Using a Novel Method of High-Resolution Parallel Reaction Monitoring Reveals Widespread Metabolic Dysfunction Induced by Diabetes.

Acylcarnitines are exerting a variety of biological functions depending on the differences in lengths, saturation levels, and conjugation groups, which to a great extent contribute to the challenges of acylcarnitines quantifications due to various kinds of isomers. Here, we describe a novel method by using high-resolution parallel reaction monitoring (PRM) liquid chromatography-tandem mass spectrometry (LC-MS/MS). Both reversed-phase and normal-phase column were used in order to get accurate, reliable, widespread quantification of acylcarnitines, and without tedious sample preparation procedure. The method provided the most comprehensive acylcarnitine profile with high-resolution MS and MS/MS confirmation to date. A total of 117 acylcarnitines were detected from plasma and urine samples. The application of targeted profiling of acylcarnitines in db/m+ control and db/db diabetic mice indicated incomplete amino acid and fatty acid oxidation on diabetic mice. Interestingly, the reduction of medium odd-numbered chain acylcarnitines in urine samples was first observed between db/m+ and db/db mice. The high-resolution PRM method makes it possible to monitor the widespread metabolic changes of the acylcarnitines in response to stimuli. Besides, the accurate MS and MS/MS spectra data of the 117 acylcarnitines could be used as mass spectrometric resources for the identification of acylcarnitines.

Urinary profiling of cis-diol-containing metabolites in rats with bisphenol A exposure by liquid chromatography-mass spectrometry and isotope labeling.

Exposure to bisphenol A (BPA), an environmental contaminant, has been linked to metabolic disorders. However, there are no reports describing the effects of BPA on the profiling of cis-diol metabolites. It is challenge to detect these metabolites in biological samples because of their low abundance, high polarity and serious matrix interference. In this study, a chemical isotope-labeling method was applied to solve these problems. Acetone and deuterated acetone (acetone-d6) were used as chemical tags to label the rat urine samples, respectively. The light and heavy labeling products were recognized using the ShiftedIonsFinder software. The selected cis-diol metabolite signals were used to build a data set. The data set was applied to evaluate the changes in the urinary profiling of cis-diol-containing metabolites in rats with BPA exposure. The results showed that chromatographic separation and mass spectrometry detection of cis-diol metabolites were improved after acetone labeling. Using this method, the cis-diol metabolites were recognized easily from the urine samples. By comparing different dose administration on rats, the influence of BPA exposure on cis-diol metabolites was investigated. The analytes showing noticeable differences were identified. It was found that high-dose BPA exposure had strong effects on the cis-diol compound metabolism. The influences were mostly related to the metabolism of galactose and nucleoside and its analogues. The disturbance of the galactose metabolism by BPA is reported for the first time, to the best of our knowledge. This may have some implications for exploring the toxic effects of BPA exposure.

Amyloid ß Peptide Enhances RANKL-Induced Osteoclast Activation through NF-κB, ERK, and Calcium Oscillation Signaling.

Osteoporosis and Alzheimer's disease (AD) are common chronic degenerative disorders which are strongly associated with advanced age. We have previously demonstrated that amyloid beta peptide (Aß), one of the pathological hallmarks of AD, accumulated abnormally in osteoporotic bone specimens in addition to having an activation effect on osteoclast (Bone 2014,61:164-75). However, the underlying molecular mechanisms remain unclear. Activation of NF-κB, extracellular signal-regulated kinase (ERK) phosphorylates, and calcium oscillation signaling pathways by receptor activator NF-κB ligand (RANKL) plays a pivotal role in osteoclast activation. Targeting this signaling to modulate osteoclast function has been a promising strategy for osteoclast-related diseases. In this study, we investigated the effects of Aß on RANKL-induced osteoclast signaling pathways in vitro. In mouse bone marrow monocytes (BMMs), Aß exerted no effect on RANKL-induced osteoclastogenesis but promoted osteoclastic bone resorption. In molecular levels, Aß enhanced NF-κB activity and IκB-α degradation, activated ERK phosphorylation and stimulated calcium oscillation, thus leading to upregulation of NFAT-c1 expression during osteoclast activation. Taken together, our data demonstrate that Aß enhances RANKL-induced osteoclast activation through IκB-α degradation, ERK phosphorylation, and calcium oscillation signaling pathways and that Aß may be a promising agent in the treatment of osteoclast-related disease such as osteoporosis.

Synthesis and antiproliferative activity of 2,7-diamino l0-(3,5-dimethoxy)benzyl-9(10H)-acridone derivatives as potent telomeric G-quadruplex DNA ligands.

A novel series of l0-(3,5-dimethoxy)benzyl-9(10H)-acridone derivatives with terminal ammonium substituents at C2 and C7 positions on the acridone ring were successfully synthesized as antiproliferation agents. The biologic activity of the acridone compounds against leukemia CCRF-CEM cells demonstrated that some of the compounds displayed good antiproliferative activity, among which compound 6a containing dimethylamine substituents at the terminal C2 and C7 positions exhibited the highest cytotoxicity with IC50 at 0.3µM. In addition compound 6a showed little toxicity against normal 293T cells proliferation with IC50 more than 100µM. Further study indicated that compound 6a had strong binding activity to human telomeric G-quadruplex DNA, as detected by mass spectrometry, CD spectroscopy, UV absorption, FRET and fluorescence quenching assays. Our data suggested that the activity of 6a might be associated with its stabilization of G-quadruplex DNA, which can be developed as potent antitumor agent.

Rapid assessment of the coenzyme Q10 redox state using ultrahigh performance liquid chromatography tandem mass spectrometry.

An improved method for accurate and rapid assessment of the coenzyme Q10 (CoQ10) redox state using ultrahigh performance liquid chromatography tandem mass spectrometry was described, with particular attention given to the instability of the reduced form of CoQ10 during sample preparation, chromatographic separation and mass spectrometric detection. As highly lipophilic compounds in complex biological matrices, both reduced and oxidized forms of CoQ10 were extracted simultaneously from the tissue samples by methanol which is superior to ethanol and isopropanol. After centrifugation, the supernatants were immediately separated on a C18 column with isocratic elution using methanol containing 2 mM ammonium acetate as a non-aqueous mobile phase, and detected by positive electrospray ionization tandem mass spectrometry in multiple reaction monitoring (MRM) mode. Ammonium acetate as an additive in methanol provided enhanced mass spectrometric responses for both forms of CoQ10, primarily due to stable formation of adduct ions [M + NH4](+), which served as precursor ions in positive ionization MRM transitions. The assay showed a linear range of 8.6-8585 ng mL(-1) for CoQ10H2 and 8.6-4292 ng mL(-1) for CoQ10. The limits of detection (LODs) were 7.0 and 1.0 ng mL(-1) and limits of quantification (LOQs) were 15.0 and 5.0 ng mL(-1) for CoQ10H2 and CoQ10, respectively. This rapid extractive and analytical method could avoid artificial auto-oxidation of the reduced form of CoQ10, enabling the native redox state assessment. This reliable method was also successfully applied for the measurement of the CoQ10 redox state in liver tissues of mice exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin, revealing the down-regulated mitochondrial electron transport chain.

KinPred-RNA-kinase activity inference and cancer type classification using machine learning on RNA-seq data.

Kinases as important enzymes can transfer phosphate groups from high-energy and phosphate-donating molecules to specific substrates and play essential roles in various cellular processes. Existing algorithms for kinase activity from phosphorylated proteomics data are often costly, requiring valuable samples. Moreover, methods to extract kinase activities from bulk RNA sequencing data remain undeveloped. In this study, we propose a computational framework KinPred-RNA to derive kinase activities from bulk RNA-sequencing data in cancer samples. KinPred-RNA framework, using the extreme gradient boosting (XGBoost) regression model, outperforms random forest regression, multiple linear regression, and support vector machine regression models in predicting kinase activities from cancer-related RNA sequencing data. Efficient gene signatures from the LINCS-L1000 dataset were used as inputs for KinPred-RNA. The results highlight its potential to be related to biological function. In conclusion, KinPred RNA constitutes a significant advance in cancer research by potentially facilitating the identification of cancer.

Pyroptosis-based risk score predicts prognosis and drug sensitivity in lung adenocarcinoma.

Pyroptosis is a recently identified form of programmed cell death; however, its role in lung adenocarcinoma (LUAD) remains unclear. Therefore, we set out to explore the prognostic potential of pyroptosis-related genes in LUAD. The pyroptosis-related risk score (PRRS) was developed by least absolute shrinkage and selection operator Cox regression and multivariate Cox regression. We found that PRRS was an independent prognostic factor for LUAD. LUAD patients in the high-PRRS group showed a significantly shorter overall survival (OS) and enriched in cell proliferation-related pathways. Then pathway enrichment analyses, mutation profile, tumor microenvironment, and drug sensitivity analysis were further studied in PRRS stratified LUAD patients. Tumor purity (TP) analyses revealed that L-PRRS LUAD patients had a lower TP, and patients in L-TP + L-PRRS subgroup had the most prolonged OS. Mutation analyses suggested that the L-PRRS LUAD patients had a lower tumor mutation burden (TMB), and patients in H-TMB + L-PRRS subgroup had the most prolonged OS. Drug sensitivity analyses showed that PRRS was significantly negatively correlated with the sensitivity of cisplatin, besarotene, etc., while it was significantly positively correlated with the sensitivity of kin001-135. Eventually, a nomogram was constructed based on PRRS and clinical characters of LUAD. Overall, the pyroptosis-related signature is helpful for prognostic prediction and in guiding treatment for LUAD patients.

KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites.

The purpose of this work is to enhance KinasePhos, a machine learning-based kinase-specific phosphorylation site prediction tool. Experimentally verified kinase-specific phosphorylation data were collected from PhosphoSitePlus, UniProtKB, the GPS 5.0, and Phospho.ELM. In total, 41,421 experimentally verified kinase-specific phosphorylation sites were identified. A total of 1380 unique kinases were identified, including 753 with existing classification information from KinBase and the remaining 627 annotated by building a phylogenetic tree. Based on this kinase classification, a total of 771 predictive models were built at the individual, family, and group levels, using at least 15 experimentally verified substrate sites in positive training datasets. The improved models demonstrated their effectiveness compared with other prediction tools. For example, the prediction of sites phosphorylated by the protein kinase B, casein kinase 2, and protein kinase A families had accuracies of 94.5%, 92.5%, and 90.0%, respectively. The average prediction accuracy for all 771 models was 87.2%. For enhancing interpretability, the SHapley Additive exPlanations (SHAP) method was employed to assess feature importance. The web interface of KinasePhos 3.0 has been redesigned to provide comprehensive annotations of kinase-specific phosphorylation sites on multiple proteins. Additionally, considering the large scale of phosphoproteomic data, a downloadable prediction tool is available at https://awi.cuhk.edu.cn/KinasePhos/download.html or https://github.com/tom-209/KinasePhos-3.0-executable-file.