GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier.

BACKGROUND: Lysine glutarylation (Kglu) is one of the most important Post-translational modifications (PTMs), which plays significant roles in various cellular functions, including metabolism, mitochondrial processes, and translation. Therefore, accurate identification of the Kglu site is important for elucidating protein molecular function. Due to the time-consuming and expensive limitations of traditional biological experiments, computational-based Kglu site prediction research is gaining more and more attention. RESULTS: In this paper, we proposed GBDT_KgluSite, a novel Kglu site prediction model based on GBDT and appropriate feature combinations, which achieved satisfactory performance. Specifically, seven features including sequence-based features, physicochemical property-based features, structural-based features, and evolutionary-derived features were used to characterize proteins. NearMiss-3 and Elastic Net were applied to address data imbalance and feature redundancy issues, respectively. The experimental results show that GBDT_KgluSite has good robustness and generalization ability, with accuracy and AUC values of 93.73%, and 98.14% on five-fold cross-validation as well as 90.11%, and 96.75% on the independent test dataset, respectively. CONCLUSION: GBDT_KgluSite is an effective computational method for identifying Kglu sites in protein sequences. It has good stability and generalization ability and could be useful for the identification of new Kglu sites in the future. The relevant code and dataset are available at https://github.com/flyinsky6/GBDT_KgluSite .

Hexachlorocyclohexane impairs human sperm motility by affecting lysine glutarylation and mitochondrial functions.

Decreased sperm motility is a leading cause of male infertility and persistent organic pollutants are known to contribute significantly to the development of this disease. The effects of organochlorine pesticides such as hexachlorocyclohexane (HCH) on human sperm function and their mechanisms of action have received much attention, but are still not fully understood. Herein, we discovered that HCH has a concentration- and time-dependent inhibitory effect on human sperm motility in vitro. Moreover, HCH could reduce the levels of lysine glutarylation (Kglu) and glucose-6-phosphate dehydrogenase activity in sperm. Meanwhile, HCH could increase reactive oxygen species and thereby lead to mitochondrial depolarization and the down-regulation of adenosine triphosphate levels. In particular, we observed that sodium glutarate (Na-glu), the precursor of glutaryl-CoA, could alleviate the inhibitory effect of HCH on sperm Kglu levels, whereas the ROS scavenger N-acetyl-L-cysteine (NAC) had no effect. Intriguingly, both Na-glu and NAC were able to partially inhibit the HCH-induced increase in sperm ROS levels and impaired sperm motility. In conclusion, we propose that HCH inhibits sperm Kglu, leading to the disruption of mitochondrial energy metabolism, which in turn adversely affects sperm motility.

DeepDN_iGlu: prediction of lysine glutarylation sites based on attention residual learning method and DenseNet.

As a key issue in orchestrating various biological processes and functions, protein post-translational modification (PTM) occurs widely in the mechanism of protein's function of animals and plants. Glutarylation is a type of protein-translational modification that occurs at active ε-amino groups of specific lysine residues in proteins, which is associated with various human diseases, including diabetes, cancer, and glutaric aciduria type I. Therefore, the issue of prediction for glutarylation sites is particularly important. This study developed a brand-new deep learning-based prediction model for glutarylation sites named DeepDN_iGlu via adopting attention residual learning method and DenseNet. The focal loss function is utilized in this study in place of the traditional cross-entropy loss function to address the issue of a substantial imbalance in the number of positive and negative samples. It can be noted that DeepDN_iGlu based on the deep learning model offers a greater potential for the glutarylation site prediction after employing the straightforward one hot encoding method, with Sensitivity (Sn), Specificity (Sp), Accuracy (ACC), Mathews Correlation Coefficient (MCC), and Area Under Curve (AUC) of 89.29%, 61.97%, 65.15%, 0.33 and 0.80 accordingly on the independent test set. To the best of the authors' knowledge, this is the first time that DenseNet has been used for the prediction of glutarylation sites. DeepDN_iGlu has been deployed as a web server (https://bioinfo.wugenqiang.top/~smw/DeepDN_iGlu/) that is available to make glutarylation site prediction data more accessible.

Phosphonate Inhibitors of Pyruvate Dehydrogenase Perturb Homeostasis of Amino Acids and Protein Succinylation in the Brain.

Mitochondrial pyruvate dehydrogenase complex (PDHC) is essential for brain glucose and neurotransmitter metabolism, which is dysregulated in many pathologies. Using specific inhibitors of PDHC in vivo, we determine biochemical and physiological responses to PDHC dysfunction. Dose dependence of the responses to membrane-permeable dimethyl acetylphosphonate (AcPMe2) is non-monotonous. Primary decreases in glutathione and its redox potential, methionine, and ethanolamine are alleviated with increasing PDHC inhibition, the alleviation accompanied by physiological changes. A comparison of 39 brain biochemical parameters after administration of four phosphinate and phosphonate analogs of pyruvate at a fixed dose of 0.1 mmol/kg reveals no primary, but secondary changes, such as activation of 2-oxoglutarate dehydrogenase complex (OGDHC) and decreased levels of glutamate, isoleucine and leucine. The accompanying decreases in freezing time are most pronounced after administration of methyl acetylphosphinate and dimethyl acetylphosphonate. The PDHC inhibitors do not significantly change the levels of PDHA1 expression and phosphorylation, sirtuin 3 and total protein acetylation, but increase total protein succinylation and glutarylation, affecting sirtuin 5 expression. Thus, decreased production of the tricarboxylic acid cycle substrate acetyl-CoA by inhibited PDHC is compensated by increased degradation of amino acids through the activated OGDHC, increasing total protein succinylation/glutarylation. Simultaneously, parasympathetic activity and anxiety indicators decrease.

FCCCSR_Glu: a semi-supervised learning model based on FCCCSR algorithm for prediction of glutarylation sites.

Glutarylation is a post-translational modification which plays an irreplaceable role in various functions of the cell. Therefore, it is very important to accurately identify the glutarylation substrates and its corresponding glutarylation sites. In recent years, many computational methods of glutarylation sites have emerged one after another, but there are still many limitations, among which noisy data and the class imbalance problem caused by the uncertainty of non-glutarylation sites are great challenges. In this study, we propose a new semi-supervised learning algorithm, named FCCCSR, to identify reliable non-glutarylation lysine sites from unlabeled samples as negative samples. FCCCSR first finds core objects from positive samples according to reverse nearest neighbor information, and then clusters core objects based on natural neighbor structure. Finally, reliable negative samples are selected according to clustering result. With FCCCSR algorithm, we propose a new method named FCCCSR_Glu for glutarylation sites identification. In this study, multi-view features are extracted and fused to describe peptides, including amino acid composition, BLOSUM62, amino acid factors and composition of k-spaced amino acid pairs. Then, reliable negative samples selected by FCCCSR and positive samples are combined to establish models and XGBoost optimized by differential evolution algorithm is used as the classifier. On the independent testing dataset, FCCCSR_Glu achieves 85.18%, 98.36%, 94.31% and 0.8651 in sensitivity, specificity, accuracy and Matthew's Correlation Coefficient, respectively, which is superior to state-of-the-art methods in predicting glutarylation sites. Therefore, FCCCSR_Glu can be a useful tool for glutarylation sites prediction and FCCCSR algorithm can effectively select reliable negative samples from unlabeled samples. The data and code are available on https://github.com/xbbxhbc/FCCCSR_Glu.git.

Deep Neural Network Framework Based on Word Embedding for Protein Glutarylation Sites Prediction.

In recent years, much research has found that dysregulation of glutarylation is associated with many human diseases, such as diabetes, cancer, and glutaric aciduria type I. Therefore, glutarylation identification and characterization are essential tasks for determining modification-specific proteomics. This study aims to propose a novel deep neural network framework based on word embedding techniques for glutarylation sites prediction. Multiple deep neural network models are implemented to evaluate the performance of glutarylation sites prediction. Furthermore, an extensive experimental comparison of word embedding techniques is conducted to utilize the most efficient method for improving protein sequence data representation. The results suggest that the proposed deep neural networks not only improve protein sequence representation but also work effectively in glutarylation sites prediction by obtaining a higher accuracy and confidence rate compared to the previous work. Moreover, embedding techniques were proven to be more productive than the pre-trained word embedding techniques for glutarylation sequence representation. Our proposed method has significantly outperformed all traditional performance metrics compared to the advanced integrated vector support, with accuracy, specificity, sensitivity, and correlation coefficient of 0.79, 0.89, 0.59, and 0.51, respectively. It shows the potential to detect new glutarylation sites and uncover the relationships between glutarylation and well-known lysine modification.

Delayed Impact of 2-Oxoadipate Dehydrogenase Inhibition on the Rat Brain Metabolism Is Linked to Protein Glutarylation.

Background: The DHTKD1-encoded 2-oxoadipate dehydrogenase (OADH) oxidizes 2-oxoadipate-a common intermediate of the lysine and tryptophan catabolism. The mostly low and cell-specific flux through these pathways, and similar activities of OADH and ubiquitously expressed 2-oxoglutarate dehydrogenase (OGDH), agree with often asymptomatic phenotypes of heterozygous mutations in the DHTKD1 gene. Nevertheless, OADH/DHTKD1 are linked to impaired insulin sensitivity, cardiovascular disease risks, and Charcot-Marie-Tooth neuropathy. We hypothesize that systemic significance of OADH relies on its generation of glutaryl residues for protein glutarylation. Using pharmacological inhibition of OADH and the animal model of spinal cord injury (SCI), we explore this hypothesis. Methods: The weight-drop model of SCI, a single intranasal administration of an OADH-directed inhibitor trimethyl adipoyl phosphonate (TMAP), and quantification of the associated metabolic changes in the rat brain employ established methods. Results: The TMAP-induced metabolic changes in the brain of the control, laminectomized (LE) and SCI rats are long-term and (patho)physiology-dependent. Increased glutarylation of the brain proteins, proportional to OADH expression in the control and LE rats, represents a long-term consequence of the OADH inhibition. The proportionality suggests autoglutarylation of OADH, supported by our mass-spectrometric identification of glutarylated K155 and K818 in recombinant human OADH. In SCI rats, TMAP increases glutarylation of the brain proteins more than OADH expression, inducing a strong perturbation in the brain glutathione metabolism. The redox metabolism is not perturbed by TMAP in LE animals, where the inhibition of OADH increases expression of deglutarylase sirtuin 5. The results reveal the glutarylation-imposed control of the brain glutathione metabolism. Glutarylation of the ODP2 subunit of pyruvate dehydrogenase complex at K451 is detected in the rat brain, linking the OADH function to the brain glucose oxidation essential for the redox state. Short-term inhibition of OADH by TMAP administration manifests in increased levels of tryptophan and decreased levels of sirtuins 5 and 3 in the brain. Conclusion: Pharmacological inhibition of OADH affects acylation system of the brain, causing long-term, (patho)physiology-dependent changes in the expression of OADH and sirtuin 5, protein glutarylation and glutathione metabolism. The identified glutarylation of ODP2 subunit of pyruvate dehydrogenase complex provides a molecular mechanism of the OADH association with diabetes.

ProtTrans-Glutar: Incorporating Features From Pre-trained Transformer-Based Models for Predicting Glutarylation Sites.

Lysine glutarylation is a post-translational modification (PTM) that plays a regulatory role in various physiological and biological processes. Identifying glutarylated peptides using proteomic techniques is expensive and time-consuming. Therefore, developing computational models and predictors can prove useful for rapid identification of glutarylation. In this study, we propose a model called ProtTrans-Glutar to classify a protein sequence into positive or negative glutarylation site by combining traditional sequence-based features with features derived from a pre-trained transformer-based protein model. The features of the model were constructed by combining several feature sets, namely the distribution feature (from composition/transition/distribution encoding), enhanced amino acid composition (EAAC), and features derived from the ProtT5-XL-UniRef50 model. Combined with random under-sampling and XGBoost classification method, our model obtained recall, specificity, and AUC scores of 0.7864, 0.6286, and 0.7075 respectively on an independent test set. The recall and AUC scores were notably higher than those of the previous glutarylation prediction models using the same dataset. This high recall score suggests that our method has the potential to identify new glutarylation sites and facilitate further research on the glutarylation process.

Roles of Negatively Charged Histone Lysine Acylations in Regulating Nucleosome Structure and Dynamics.

The nucleosome, the basic repeating unit of chromatin, is a dynamic structure that consists of DNA and histones. Insights derived from biochemical and biophysical approaches have revealed that histones posttranslational modifications (PTMs) are key regulators of nucleosome structure and dynamics. Mounting evidence suggests that the newly identified negatively charged histone lysine acylations play significant roles in altering nucleosome and chromatin dynamics, subsequently affecting downstream DNA-templated processes including gene transcription and DNA damage repair. Here, we present an overview of the dynamic changes of nucleosome and chromatin structures in response to negatively charged histone lysine acylations, including lysine malonylation, lysine succinylation, and lysine glutarylation.

Deglutarylation of glutaryl-CoA dehydrogenase by deacylating enzyme SIRT5 promotes lysine oxidation in mice.

A wide range of protein acyl modifications has been identified on enzymes across various metabolic processes; however, the impact of these modifications remains poorly understood. Protein glutarylation is a recently identified modification that can be nonenzymatically driven by glutaryl-CoA. In mammalian systems, this unique metabolite is only produced in the lysine and tryptophan oxidative pathways. To better understand the biology of protein glutarylation, we studied the relationship between enzymes within the lysine/tryptophan catabolic pathways, protein glutarylation, and regulation by the deglutarylating enzyme sirtuin 5 (SIRT5). Here, we identify glutarylation on the lysine oxidation pathway enzyme glutaryl-CoA dehydrogenase (GCDH) and show increased GCDH glutarylation when glutaryl-CoA production is stimulated by lysine catabolism. Our data reveal that glutarylation of GCDH impacts its function, ultimately decreasing lysine oxidation. We also demonstrate the ability of SIRT5 to deglutarylate GCDH, restoring its enzymatic activity. Finally, metabolomic and bioinformatic analyses indicate an expanded role for SIRT5 in regulating amino acid metabolism. Together, these data support a feedback loop model within the lysine/tryptophan oxidation pathway in which glutaryl-CoA is produced, in turn inhibiting GCDH function via glutaryl modification of GCDH lysine residues and can be relieved by SIRT5 deacylation activity.

iGluK-Deep: computational identification of lysine glutarylation sites using deep neural networks with general pseudo amino acid compositions.

Lysine glutarylation is a post-translation modification which plays an important regulatory role in a variety of physiological and enzymatic processes including mitochondrial functions and metabolic processes both in eukaryotic and prokaryotic cells. This post-translational modification influences chromatin structure and thereby results in global regulation of transcription, defects in cell-cycle progression, DNA damage repair, and telomere silencing. To better understand the mechanism of lysine glutarylation, its identification in a protein is necessary, however, experimental methods are time-consuming and labor-intensive. Herein, we propose a new computational prediction approach to supplement experimental methods for identification of lysine glutarylation site prediction by deep neural networks and Chou's Pseudo Amino Acid Composition (PseAAC). We employed well-known deep neural networks for feature representation learning and classification of peptide sequences. Our approach opts raw pseudo amino acid compositions and obsoletes the need to separately perform costly and cumbersome feature extraction and selection. Among the developed deep learning-based predictors, the standard neural network-based predictor demonstrated highest scores in terms of accuracy and all other performance evaluation measures and outperforms majority of previously reported predictors without requiring expensive feature extraction process. iGluK-Deep:Computational Identification of lysine glutarylationsites using deep neural networks with general Pseudo Amino Acid Compositions Sheraz Naseer, Rao Faizan Ali, Yaser Daanial Khan, P.D.D DominicCommunicated by Ramaswamy H. Sarma.

Preparation of Affinity Purified Antibodies against ε-Glutaryl-Lysine Residues in Proteins for Investigation of Glutarylated Proteins in Animal Tissues.

The glutarylation of lysine residues in proteins attracts attention as a possible mechanism of metabolic regulation, perturbed in pathologies. The visualization of protein glutarylation by antibodies specific to ε-glutaryl-lysine residues may be particularly useful to reveal pathogenic mutations in the relevant enzymes. We purified such antibodies from the rabbit antiserum, obtained after sequential immunization with two artificially glutarylated proteins, using affinity chromatography on ε-glutaryl-lysine-containing sorbents. Employing these anti(ε-glutaryl-lysine)-antibodies for the immunoblotting analysis of rat tissues and mitochondria has demonstrated the sample-specific patterns of protein glutarylation. The study of the protein glutarylation in rat tissue homogenates revealed a time-dependent fragmentation of glutarylated proteins in these preparations. The process may complicate the investigation of potential changes in the acylation level of specific protein bands when studying time-dependent effects of the acylation regulators. In the rat brain, the protein glutarylation, succinylation and acetylation patterns obtained upon the immunoblotting of the same sample with the corresponding antibodies are shown to differ. Specific combinations of molecular masses of major protein bands in the different acylation patterns confirm the selectivity of the anti(ε-glutaryl-lysine)-antibodies obtained in this work. Hence, our affinity-purified anti(ε-glutaryllysine)-antibodies provide an effective tool to characterize protein glutarylation, revealing its specific pattern, compared to acetylation and succinylation, in complex protein mixtures.

Functions and Mechanisms of Lysine Glutarylation in Eukaryotes.

Lysine glutarylation (Kglu) is a newly discovered post-translational modification (PTM), which is considered to be reversible, dynamic, and conserved in prokaryotes and eukaryotes. Recent developments in the identification of Kglu by mass spectrometry have shown that Kglu is mainly involved in the regulation of metabolism, oxidative damage, chromatin dynamics and is associated with various diseases. In this review, we firstly summarize the development history of glutarylation, the biochemical processes of glutarylation and deglutarylation. Then we focus on the pathophysiological functions such as glutaric acidemia 1, asthenospermia, etc. Finally, the current computational tools for predicting glutarylation sites are discussed. These emerging findings point to new functions for lysine glutarylation and related enzymes, and also highlight the mechanisms by which glutarylation regulates diverse cellular processes.

iGlu_AdaBoost: Identification of Lysine Glutarylation Using the AdaBoost Classifier.

Lysine glutarylation is a newly reported post-translational modification (PTM) that plays significant roles in regulating metabolic and mitochondrial processes. Accurate identification of protein glutarylation is the primary task to better investigate molecular functions and various applications. Due to the common disadvantages of the time-consuming and expensive nature of traditional biological sequencing techniques as well as the explosive growth of protein data, building precise computational models to rapidly diagnose glutarylation is a popular and feasible solution. In this work, we proposed a novel AdaBoost-based predictor called iGlu_AdaBoost to distinguish glutarylation and non-glutarylation sequences. Here, the top 37 features were chosen from a total of 1768 combined features using Chi2 following incremental feature selection (IFS) to build the model, including 188D, the composition of k-spaced amino acid pairs (CKSAAP), and enhanced amino acid composition (EAAC). With the help of the hybrid-sampling method SMOTE-Tomek, the AdaBoost algorithm was performed with satisfactory recall, specificity, and AUC values of 87.48%, 72.49%, and 0.89 over 10-fold cross validation as well as 72.73%, 71.92%, and 0.63 over independent test, respectively. Further feature analysis inferred that positively charged amino acids RK play critical roles in glutarylation recognition. Our model presented the well generalization ability and consistency of the prediction results of positive and negative samples, which is comparable to four published tools. The proposed predictor is an efficient tool to find potential glutarylation sites and provides helpful suggestions for further research on glutarylation mechanisms and concerned disease treatments.

Computational Identification of Lysine Glutarylation Sites Using Positive-Unlabeled Learning.

BACKGROUND: As a new type of protein acylation modification, lysine glutarylation has been found to play a crucial role in metabolic processes and mitochondrial functions. To further explore the biological mechanisms and functions of glutarylation, it is significant to predict the potential glutarylation sites. In the existing glutarylation site predictors, experimentally verified glutarylation sites are treated as positive samples and non-verified lysine sites as the negative samples to train predictors. However, the non-verified lysine sites may contain some glutarylation sites which have not been experimentally identified yet. METHODS: In this study, experimentally verified glutarylation sites are treated as the positive samples, whereas the remaining non-verified lysine sites are treated as unlabeled samples. A bioinformatics tool named PUL-GLU was developed to identify glutarylation sites using a positive-unlabeled learning algorithm. RESULTS: Experimental results show that PUL-GLU significantly outperforms the current glutarylation site predictors. Therefore, PUL-GLU can be a powerful tool for accurate identification of protein glutarylation sites. CONCLUSION: A user-friendly web-server for PUL-GLU is available at http://bioinform.cn/pul_glu/.

Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features.

Post Translational Modification (PTM) is defined as the alteration of protein sequence upon interaction with different macromolecules after the translation process. Glutarylation is considered one of the most important PTMs, which is associated with a wide range of cellular functioning, including metabolism, translation, and specified separate subcellular localizations. During the past few years, a wide range of computational approaches has been proposed to predict Glutarylation sites. However, despite all the efforts that have been made so far, the prediction performance of the Glutarylation sites has remained limited. One of the main challenges to tackle this problem is to extract features with significant discriminatory information. To address this issue, we propose a new machine learning method called BiPepGlut using the concept of a bi-peptide-based evolutionary method for feature extraction. To build this model, we also use the Extra-Trees (ET) classifier for the classification purpose, which, to the best of our knowledge, has never been used for this task. Our results demonstrate BiPepGlut is able to significantly outperform previously proposed models to tackle this problem. BiPepGlut achieves 92.0%, 84.8%, 95.6%, 0.82, and 0.88 in accuracy, sensitivity, specificity, Matthew's Correlation Coefficient, and F1-score, respectively. BiPepGlut is implemented as a publicly available online predictor.

Identification of Malonylation, Succinylation, and Glutarylation in Serum Proteins of Acute Myocardial Infarction Patients.

PURPOSE: To identify protein malonylation, succinylation, and glutarylation in human and rat serum. EXPERIMENTAL DESIGN: Immunoprecipitation coupled with MS/MS is employed to compare the relative abundance of malonylation, succinylation, and glutarylation of serum protein in acute myocardial infarction human and rat. RESULTS: One hundred thirty and 48 unique malonylated, succinylated, or glutarylated peptides are found in human and rat serum, respectively. Succinylation is the most predominant modification. The most modified protein is albumin. Abundance of serum protein succinylation and glutarylation is significantly (p < 0.05) lower in the peripheral serum of ST-segment elevation myocardial infarction patients compared with healthy volunteers, which is also observed in acute myocardial infarction rats. CONCLUSIONS AND CLINICAL RELEVANCE: Malonylation, succinylation, and glutarylation widely exist in mammalian serum proteins, and may reveal novel mechanism of acute myocardial infarction.

Glutarylation of Histone H4 Lysine 91 Regulates Chromatin Dynamics.

Histone posttranslational modifications (PTMs) regulate chromatin structure and dynamics during various DNA-associated processes. Here, we report that lysine glutarylation (Kglu) occurs at 27 lysine residues on human core histones. Using semi-synthetic glutarylated histones, we show that an evolutionarily conserved Kglu at histone H4K91 destabilizes nucleosome in vitro. In Saccharomyces cerevisiae, the replacement of H4K91 by glutamate that mimics Kglu influences chromatin structure and thereby results in a global upregulation of transcription and defects in cell-cycle progression, DNA damage repair, and telomere silencing. In mammalian cells, H4K91glu is mainly enriched at promoter regions of highly expressed genes. A downregulation of H4K91glu is tightly associated with chromatin condensation during mitosis and in response to DNA damage. The cellular dynamics of H4K91glu is controlled by Sirt7 as a deglutarylase and KAT2A as a glutaryltransferase. This study designates a new histone mark (Kglu) as a new regulatory mechanism for chromatin dynamics.

Lysine glutarylation in human sperm is associated with progressive motility.

STUDY QUESTION: Is there a role for lysine glutarylation (Kglu), a newly identified protein post-translational modification (PTM), in human sperm? SUMMARY ANSWER: Kglu occurs in several proteins located in the tail of human sperm, and it was reduced in asthenozoospermic (A) men and positively correlated with progressive motility of human sperm, indicating its important role in maintaining sperm motility. WHAT IS KNOWN ALREADY: Since mature sperm are almost transcriptionally silent, PTM is regarded as an important pathway in regulating sperm function. However, only phosphorylation has been extensively studied in mature sperm to date. Protein lysine modification (PLM), a hot spot of PTMs, was rarely studied except for a few reports on lysine methylation and acetylation. As a newly identified PLM, Kglu has not been well characterized, especially in mature sperm. STUDY DESIGN, SIZE, DURATION: Sperm samples were obtained from normozoospermic (N) men and A men who visited the reproductive medical center between February 2016 and January 2018. In total, 61 N men and 59 A men were recruited to participate in the study. PARTICIPANTS/MATERIALS, SETTING, METHODS: Kglu was examined by immunoblotting and immunofluorescence assays using a previously qualified pan-anti-glutaryllysine antibody that recognizes glutaryllysine in a wide range of sequence contexts (both in histones and non-histone substrates) but not the structurally similar malonyllysine and succinyllysine. The immunofluorescence assay was imaged using laser scanning confocal microscopy and super-resolution structured illumination microscopy. Sperm motility parameters were examined by computer-assisted sperm analysis. MAIN RESULTS AND THE ROLE OF CHANCE: Kglu occurs in several proteins (20-150 kDa) located in the tail of human sperm, especially in the middle piece and the latter part of the principal piece. Sperm Kglu was modulated by regulatory systems (enzymes and glutaryl-CoA) similar to those in HeLa cells. The mean level of sperm Kglu was significantly reduced in A men compared with N men (P < 0.001) and was positively correlated with progressive motility (P < 0.001). The sodium glutarate-induced elevation of Kglu levels in A men with lower Kglu levels in sperm significantly improved the progressive motility (P < 0.001). Furthermore, the reduced sperm Kglu levels in A men was accompanied by an increase in sperm glutaryl-CoA dehydrogenase (a regulatory enzyme of Kglu). LARGE SCALE DATA: N/A. LIMITATIONS, REASONS FOR CAUTION: Although the present study indicated the involvement of sperm Kglu in maintaining progressive motility of human sperm, the underlying mechanism needs to be investigated further. WIDER IMPLICATIONS OF THE FINDINGS: The findings of this study provide an insight into the novel role of Kglu in human sperm and suggest that abnormality of sperm PLMs may be one of the causes of asthenozoospermia. STUDY FUNDING/COMPETING INTEREST(S): National Natural Science Foundation of China (81 771 644 to T.L.; 31 671 204 to X.Z. and 81 871 207 to H.C.); National Basic Research Program of China (973 Program, 2015CB943003 to X.Z.); Natural Science Foundation of Jiangxi, China (20171ACB21006 and 20161BAB204167 to T.L.; 20165BCB18001 to X.Z.). The authors have no conflicts of interest to declare.