Staphylococcus aureus is a major human and animal pathogen, colonizing diverse ecological niches within its hosts. Predicting whether an isolate will infect a specific host and its subsequent clinical fate remains unknown. In this study, we investigated the S. aureus pangenome using a curated set of 356 strains, spanning a wide range of hosts, origins, and clinical display and antibiotic resistance profiles. We used genome-wide association study (GWAS) and random forest (RF) algorithms to discriminate strains based on their origins and clinical sources. Here, we show that the presence of sak and scn can discriminate strains based on their host specificity, while other genes such as mecA are often associated with virulent outcomes. Both GWAS and RF indicated the importance of intergenic regions (IGRs) and coding DNA sequence (CDS) but not sRNAs in forecasting an outcome. Additional transcriptomic analyses performed on the most prevalent clonal complex 8 (CC8) clonal types, in media mimicking nasal colonization or bacteremia, indicated three RNAs as potential RNA markers to forecast infection, followed by 30 others that could serve as infection severity predictors. Our report shows that genetic association and transcriptomics are complementary approaches that will be combined in a single analytical framework to improve our understanding of bacterial pathogenesis and ultimately identify potential predictive molecular markers.IMPORTANCE Predicting the outcome of bacterial colonization and infections, based on extensive genomic and transcriptomic data from a given pathogen, would be of substantial help for clinicians in treating and curing patients. In this report, genome-wide association studies and random forest algorithms have defined gene combinations that differentiate human from animal strains, colonization from diseases, and nonsevere from severe diseases, while it revealed the importance of IGRs and CDS, but not small RNAs (sRNAs), in anticipating an outcome. In addition, transcriptomic analyses performed on the most prevalent clonal types, in media mimicking either nasal colonization or bacteremia, revealed significant differences and therefore potent RNA markers. Overall, the use of both genomic and transcriptomic data in a single analytical framework can enhance our understanding of bacterial pathogenesis. Staphylococcus aureus is a widespread bacterium, colonizing around 30% of humans  offers the possibility of detecting nucleic acid determinants  that could discriminate the clinical onset of a strain. Taking advantage of hundreds of high-quality S. aureus genomes available from both animal and human isolates around the world, we searched for genetic determinants that could predict host preference and, for human strains, colonization, virulence, and disease phenotypes. Knowing the clinical outcome of each isolate included in our data set, we used genome-wide and transcriptome sequencing (RNA-seq) technologies to identify molecular determinants or approaches that could forecast the fate of a colonization or an infection by S. aureus. We performed genome-wide association studies followed by the use of the random forest (RF) algorithm to define gene combinations that could differentiate human from animal strains, colonization from diseases, and nonsevere from severe diseases. We found that intergenic regions (IGRs) and coding DNA sequences (CDSs) were critical for accurate strain classification and should serve as templates for future machine learning (ML) strategies. To test whether our approach could be improved by using clonal complex (CC) matrices, we created a second data set solely composed of the most prevalent clonal complex (CC8). However, the reduction in number of available isolates was detrimental for an accurate prediction of unknown strains. Finally, a transcriptomics approach employed under conditions that mimic transition from colonization to bacteremia enabled the identification of several transcripts, including mRNAs and sRNAs, that are specifically expressed in colonizing or infecting strains. Those transcripts could therefore serve as predictive biomarkers to evaluate disease acuteness.f humans  along wif humans . In humacteremia , 5. A keutbreaks . There iutbreaks , althougirulence , on idenirulence , on subtirulence , on the irulence , on phylirulence , or, recirulence , 14. Beiirulence . Whereasirulence , a breacirulence  and highirulence  suggestsirulence . Unravel. aureus . Interes. aureus . Recentl. aureus . Additio. aureus . DespiteS. aureus genomes were collected and analyzed. After two independent rounds of reviews devoted to the exclusion of irrelevant studies, 266 sequenced S. aureus genomes from humans with reliable clinical information were conserved. Then, we added 27 new S. aureus genomes sequenced in our laboratory and collected from different facilities, as well as 63 genomes sequenced from animal strains, to obtain a final data set of 356 strains  analyses. MLST analysis predicted 97 different sequence types belonging to 41 clonal complexes  (n = 27), such as CC133 (n = 7), CC151 (n = 6), and CC130 (n = 3)  . Eleven  (n = 3) . Based o (n = 3) .10.1128/msystems.00378-22.2FIG S2FIG S2, PDF file, 0.1 MB.Minimum spanning tree representing the repartition and diversity of our data set. The size of the circles is a function of the number of clonal isolates. Colors indicate the clonal complex diversity of the data set. Download Copyright © 2022 Sassi et al.2022Sassi et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the S. aureus species, was derived from the 356 genomes listed in n = 7,338), 2,813 unique genes were identified which corresponded to ~8 singletons per genome  , indicating that more than half of the predicted sRNA genes are among a variable set of accessory genes, inherited laterally , indicating high variability in those regions.The size of the pangenome, which includes coding sequences, sRNA genes, and intergenic regions (IGRs) of the aterally . Only onaterally . Third, 10.1128/msystems.00378-22.4TABLE S2Table S2, XLSX file, 10.5 MB.Analysis of the pangenome using Panaroo. Download Copyright © 2022 Sassi et al.2022Sassi et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the P < 0.01) were found between animal and human isolates for genes encoding the mobilome  . The genomes of human isolates significantly contain more genes (P < 0.01) assigned to the mobilome  than those of the animal isolates (mean number of genes = 33). Within human isolates, significant differences (P < 0.01) were also found within this category between colonization and nonsevere infection isolates. The genomes of nonsevere infection isolates contain more genes from the mobilome (mean number of genes = 56) than colonization isolates (mean number of genes = 43) . Conversely, no significant differences were found between isolates from colonization versus global infection (severe and nonsevere), from colonization versus severe infections, and from nonsevere versus severe infections . Together, these findings show that significant stochastic variation at the mobilome level occurs between livestock and human isolates but also between colonization and nonsevere isolates.To address whether livestock-, colonization-, or infection-associated (severe and nonsevere) genomes encode determinants specific to their origins, an in-depth functional analysis was conducted on the pangenome. To that aim, each genome was annotated using the COG database , and eacPseudomonas aeruginosa  ruginosa , but usiruginosa . Second,ruginosa . Overallruginosa  and S4.10.1128/msystems.00378-22.5TABLE S3Table S3, XLSX file, 0.04 MB.Identification of significant differences based on host preference or clinical status using Scoary. Download Copyright © 2022 Sassi et al.2022Sassi et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/msystems.00378-22.6TABLE S4Table S4, XLSX file, 0.2 MB.Identification of significant differences based on host preference or clinical status using pyseer. Download Copyright © 2022 Sassi et al.2022Sassi et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the n = 48, Bonferroni’s P < 10−5), with the virulence genes sak and scn being among the most significant . Other genes with strong P values  included recT (a recombinase gene), group 3780, or group 3965. The second approach with pyseer identified 75 genes significantly associated with human isolates, with scn and sak being again among the most significant  and a total of seven genes in common with Scoary (genes coding for 2 transposases and 3 phage proteins besides scn and sak)  able to infect both animals and humans can harbor these two genes  . Since sse genes . This anP values up to 5.50 × 10−29 for cluster 15862, located upstream from a putative nonheme iron containing ferritin that belongs to the ferric uptake regulator-like protein PerR and member of the FUR family , colonizations and nonsevere infections , and colonizations and severe infections  but not between nonsevere and severe infections. pyseer revealed 6 to 47 genes associated with a clinical category (P < 10−5) . Similarly, mecA was again identified  when colonizations were compared with severe infections.Strains isolated from humans were further classified based on clinical status. GWAS was also performed to investigate colonization versus infection, colonization versus nonsevere infection, colonization versus severe infection, and nonsevere versus severe genetic traits. For CDS, Scoary identified significant differences in clinical status  between  < 10−5) . Twenty- < 10−5)  and S4. mecA  were identified using Scoary between animal and human strains, while none were found for the other categories of strains (P < 0.0001) associated with one of the groups of strains  was located in sbnE (NCTC8325_85880_T_C) (We then investigated whether single nucleotide polymorphisms (SNP) were more appropriate for discriminating isolates from colonization and infection and ultimately to be correlated with clinical outputs. For this purpose, a total of 26,577 core genome SNPs, identified in our data set, were examined. Only four significant SNPs (Bonferroni’s  strains . In cont strains . One hun880_T_C) , which icsoR, a gene that regulates copper resistance mechanisms and is often found on plasmids . Trees were generated (i) to distinguish animal from human strains or (ii) to differentiate human isolates based on the clinical status of isolation, namely, colonization versus infections, colonization versus nonsevere infections, colonization versus severe infections, and nonsevere versus severe infections. Overall, the accuracy was high  in separating strains isolated from animals from strains isolated from humans, reaching 93% (error rate of 7%) when using only IGRs  were ranked 4th, 5th, and 2nd, respectively. With the IGR matrices, an even better correlation was observed with clusters 15862, 15817, 24735, and 16230  classification algorithms known to smartly deal with correlation and interaction among features in genomics. RF is a widely used ensemble machine learning (ML) method that aims to aggregate several classification trees using bagging , 39. In nly IGRs . Howeverked 15th , whereasnd 16230 , being and 16230 . Similarnd 16230 , was rannd 16230 . Overallnd 16230 .10.1128/msystems.00378-22.9TABLE S7Table S7, XLSX file, 0.01 MB.List of genes flanking IGRs that belong to the top five variables of most importance for random forest. Download Copyright © 2022 Sassi et al.2022Sassi et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the https://biochpharma.univ-rennes1.fr/supplemental-data).To verify the robustness of the RF classification algorithms estimated on the training set, we calculated error rates using the testing set (out of bag). As for the training set, the algorithm was more powerful distinguishing animal strains from human strains than distinguishing strains in the other categories . An ~7 tTogether, these results indicate that the RF methodology is efficient for classifying strains regarding their environmental isolation status and revealed that the use of IGRs is important to improve discrimination. Additionally, the out-of-bag error rates indicate that RF is suitable for a good prediction accuracy in distinguishing animal from human isolates.n = 356), ~43% of the core genome is devoted to metabolism. The ST8 pan-sRNome was composed of 613 sRNA genes. Five hundred six belong to the core genome  worldwide and also because we already had 80 ST8 genomes in our main data set. They were classified into three subgroups according to clinical data: colonization, nonsevere infections, and severe infections . The 80 database ), while n = 3) from the one that yielded septic shock (n = 3) according to the Third International Consensus Definitions for Sepsis and Septic Shock . Among genes involved in virulence, srtB  was also overexpressed in septic shock strains. Together, this transcriptomic analysis performed under nose-mimicking and blood infection-mimicking conditions showed that some genes are differentially expressed between the different categories and that septic shock strains exhibited higher transcript levels of a few virulence factors in SNM3. lrgA and lrgB are promising targets that could be used as primary tools to discriminate strains and, therefore, ultimately predict infections.Based on the conditions used above, we searched for gene expression variations that could discriminate the clinical origin of the studied isolates. We pooled appropriate RNA-seq data in the categories of colonization, infections, bacteremia without organ failure, and septic shock. Additionally, we applied a DESeq cutoff of 2 and a riations . First, bscesses , was incectively . More diectively . In BMM,10.1128/msystems.00378-22.10TABLE S8Table S8, XLSX file, 0.2 MB.List of genes differentially expressed based on culture conditions (SNM3 versus BMM) and list of potential RNA biomarkers as a function of clinical status of strains that belong to ST8. Download Copyright © 2022 Sassi et al.2022Sassi et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the S. aureus mortality is impacted by a variety of host-related factors, such as age and comorbidities  is a virulence factor acting as a thrombolytic agent, which leads to tissue damage and improves bacterial invasiveness. The SAK protein has a dual role in human illnesses, promoting skin infections while, surprisingly, decreasing disease severity (scn) helps the bacteria escape attack from the host immune system . This suggests that a small set of genes may be sufficient to distinguish strains based on their genomic sequences. However, predictive assessment showed limitations as soon as the predictions went beyond host specificity. This lack of accuracy seems correlated with the size of the data set. In this retrospective work, a main limitation lies in the absence of a precise description of strain isolation conditions. From a set of thousands of S. aureus genome sequences available from the literature that we analyzed, only 356 met the necessary requirements (n = 80) illustrates the importance of a large collection, as the error rate for predictions dramatically increased. An alternative hypothesis to explain more reliable predictions on the species level would be that strains belonging to a single ST  do not show strong differences in their propensity to cause invasive infection. However, from a positive standpoint, our data suggest that RF models should be implemented as additional information for clinician practitioners and that the collection of an extensive data set may lead to the development of dedicated ML programs based on the presence/absence of a defined set of genes.However, in the present report, analysis of gene content and mutation rate showed some limitations in discriminating strains regarding the clinical status of their hosts and their origins. Therefore, we implemented RF, a well-established statistical method that has two main advantages in our context: (i) RF can be used to rank the importance of the variables using permutations and out-of-bags, and (ii) RF is not limited to linear association and is flexible enough to catch nonlinear as well as interaction relationships between genes and infection status. Based on GWAS results, we explored CDS and IGR content but not the sRNA content. Again, the use of IGRs in combination or not with CDSs showed that these data are relevant to predict host specificity but can also be useful in increasing prediction of clinical status. Our out-of-bag prediction showed that RF was powerful in determining whether a strain was specific to humans or animals. Interestingly, determination of variables of importance indicated a good correlation between GWAS and RF. irements  for subsS. aureus mostly expressed genes implicated in amino acid transport and metabolism in SNM3, consistent with the poor nutritional content of this medium mimicking human nose conditions. Among the virulence factors induced in BMM, the isd operon  was counterbalanced by RNA sequencing data. First, the use of SNM3  and BMM d operon , immune  and spa , and srt protein , are thet system , and thelux pump  is knownlux pump . AlthougS. aureus.To conclude, the use of comparative genomics, GWAS, RF analyses, and transcriptomics studies within a single pilot study identified genomic combinations that distinguish strain host specificity and enabled estimation, through ML algorithms, of whether a colonized individual will be at higher risk to have a severe staphylococcal infection. We also highlight the importance of IGRs when considering the genomic elements. Furthermore, the expression pattern of a gene set may act as a biomarker to forecast strain clinical outcomes. The data from this study contribute to a better understanding of the critical factors that allow a pathogenic strain to establish a staphylococcal infection. In the future, a similar approach focusing on the role of host factors may be useful to provide an extensive prediction of risk associated with Staphylococcus aureus” AND “genomes” to search the PubMed database for studies published from July 2014 to June 2015, which corresponded with the beginning of the present study. Two authors (M. Sassi and Y. Augagneur) independently screened titles and abstracts to identify relevant studies. The search was supplemented by seeking the references of all eligible studies. Human studies were considered eligible when their authors (i) reported extractable and complete data on S. aureus genomes and (ii) documented sufficiently accurate clinical information. To prevent confounding factors, studies dealing with immunodeficient patients were excluded. A restriction on English and French literature was also imposed. To complete our data sets, we obtained strains from the infectious diseases and intensive care units located in our health care facility , from the Victoria Hospital , and from the Lausanne Hospital (Switzerland). Strains from asymptomatic individuals were also recovered from these facilities. Other strains were obtained from the National Reference Laboratory for Staphylococci in Lyon, France. All strains available in our laboratory were confirmed as being S. aureus by using multilocus sequence typing . The second set contained all the isolates typed as ST8 during the construction of the first set, to which we added 10 novel ST8 isolates for transcriptomic studies.Two different sets of genomes were generated during this study. The first one included strains that belong to multiple clonal complexes see . The fire typing  and S. aA typing . Then, tS. aureus strains sequenced in Rennes facilities were grown in brain heart infusion (BHI) broth (Oxoid) at 37°C and under agitation (160 rpm). Genomic DNA was isolated using the Wizard genomic DNA purification kit (Promega) in the presence of lytic enzymes (lysozyme and lysostaphin) in accordance with the manufacturer’s recommendations for Gram-positive bacteria. Subsequently, genomic DNA was precipitated with sodium acetate and washed two times with 70% (vol/vol) ethanol. DNA was sheared using a Covaris M220 focused ultrasonicator to generate an average fragment size of 600 bp. Unwanted smaller and larger fragments were removed by size selection using AMPure XP beads (Beckman Coulter). A DNA library was prepared using the NEBNext Ultra DNA library prep kit for Illumina (NEB) and sequenced as paired-end reads using an Illumina MiSeq platform and a MiSeq reagent kit v3 (600 cycles) . Illumina reads for 27 strains (http://hannonlab.cshl.edu/fastx_toolkit/) and, (iii) assembled using the SPAdes software ( strains  were (i) strains , (ii) fisoftware , 67. SISsoftware , 69 werehttps://github.com/transcript/COG) and the COG databases . The difference between COG families was considered significant when P was <0.01. Figures were generated using ggplot package in R software. sRNA genes were predicted after extracting all sRNA sequences available in the SRD database (http://srd.genouest.org/) and were subsequently aligned with each genome using BLAST and a homemade script (https://github.com/mosassi/Staphylococcus_annotation.git). The pangenome is the sum of the core genome  and the dispensable, accessory genome containing genes present in some but not all the strains, as well as strain-specific genes  was performed on all genomes, and the results are presented in http://tree.bio.ed.ac.uk/software/figtree/).A phylogenetic tree was constructed by considering the 26,577 polymorphic sites retained in the core genome. A transversion substitution model was selected on the basis of the Akaike’s information criterion with jModelTest2 . MaximumTo infer homoplastic SNPs that might be driven by convergent and positive selection, we analyzed the global SNP matrix with the HomoplasyFinder algorithm .P < 10−4; best pairwise comparison P value of <0.01) were considered significant. Due to the strong population structure of our complete collection and the dominance of the ST8 type, an in-depth analysis of genetic elements associated with a host or with a severity outcome was also performed using pyseer (v.1.3.6) . A genome-wide association was considered statistically significant if the likelihood ratio test (lrt) P was <0.00001.Two genome-wide association study (GWAS) analyses (Scoary and pyseer) were performed to investigate pangenome associations with host and clinical phenotypes. The input for both approaches were CDS and IGR presence/absence and SNP matrices . Using Sv.1.3.6) . To do tntree = 500) and a number of variables randomly sampled at each split given by srqt(p), where p is the number of explanatory variables in a matrix of predictors (mtry = sqrt(p)).To build a classifier of the infection status of patients with respect to their genome, we used the well-known ensemble learning method called random forest (RF) . RF aimsS. aureus was precultured in tryptone soya broth  diluted 5 times in RPMI 1640 (Life Technologies) under agitation at 37°C. Overnight cultures were washed twice in synthetic nasal medium SNM3 (600) of 0.1. Cells were grown overnight and monitored by measuring the OD600. Appropriate volumes of cells were centrifuged and transferred either to fresh SNM3 or to fresh bacteremia-mimicking medium (BMM) at an OD600 of 0.1. BMM was prepared from 50% human AB serum (Etablissement Français du Sang), 44% RPMI 1640, and 6% packed red blood cells (washed in RPMI 1640 and set to a hematocrit of 50). Then, S. aureus was incubated for 4 h at 37°C under agitation. Cells were harvested by centrifugation at 4,500 rpm for 8 min, and pellets were washed with 5 mL of H2O, allowing lysis of red blood cells, and centrifuged again at 4,500 rpm for 8 min, and pellets were frozen prior to RNA extraction. Dried frozen pellets were resuspended in 500 μL of cold lysis buffer . Total RNAs were extracted by phenol (pH 4) using a FP120 FastPrep cell disruptor , as described previously . rRNAs were depleted using a Ribo-Zero magnetic kit (Epicentre), and the remaining RNAs were purified by ethanol precipitation according to the manufacturer’s recommendations. Efficiency of depletion was estimated on a bioanalyzer (Agilent). Stranded cDNA libraries were prepared using the NEBNext Ultra directional RNA library prep kit for Illumina (New England Biolabs) following an initial fragmentation step of 13 min at 95°C resulting in cDNA libraries of ~330 bp. The concentration, quality, and purity of the libraries were determined using a bioanalyzer, a Qubit fluorometer (Invitrogen), and a NanoDrop spectrophotometer (Thermo Scientific). Indexed libraries were equimolarly mixed in two pools and sequenced on an Illumina HiSeq 1500 system , as described in the manufacturer’s instructions.Up to 10 μg of total RNA was doubled treated with amplification-grade DNase I (Invitrogen) to remove genomic contaminations. The absence of DNA was checked by quantitative PCR (qPCR) with primers targeting http://hannonlab.cshl.edu/fastx_toolkit/) and then mapped onto their respective genomes. SAM files were filtered on bitwise flag values   to search for transcriptomic markers.All genomes from strains used for transcriptome analysis were annotated using Prokka . Genes fg values  to keep g values  for straan > 15)  for SNM3PRJNA273632, PRJNA280933, and PRJNA290551. The RNA sequences have been submitted to the NCBI SRA database under BioProject accession number PRJNA647528.The genomes sequenced during this study are available under the following BioProject accession numbers:


Fusarium graminearum infection, and 2,066 Khib modified sites on 728 proteins were identified in maize, among which 24 Khib sites occurred on core histones. Subcellular localization results showed that these Khib modified proteins were localized in cytoplasm, chloroplast, and nucleus. Then, comparative proteomic analysis of the defense response to F. graminearum infection showed that Khib modification participated in plant resistance to pathogen infection by regulating glycolysis, TCA cycle, protein synthesis, peroxisome, and secondary metabolic processes, such as benzoxazinoid biosynthesis, phenylpropanoid biosynthesis, jasmonic acid synthesis, and tyrosine and tryptophan biosynthesis. In addition, we also demonstrated that lysine 2-hydroxyisobutyrylation sites on histones were involved in the gene expression of pathogenesis-related proteins. Our results provide a new perspective for the study of plant disease resistance, and had directive significance of maize disease resistance for molecular breeding.Proteins post-translational modification (PTMs) is necessary in the whole life process of organisms. Among them, lysine 2-hydroxyisobutyrylation (Khib) plays an important role in protein synthesis, transcriptional regulation, and cell metabolism. Khib is a newly identified PTM in several plant species. However, the function of Khib in maize was unclear. In this study, western blotting results showed that Khib modification level increased significantly after  Drosophila and Saccharomyces cerevisiae  can change the charge, conformation and molecular weight of proteins by adding chemical groups to the amino acid residues of proteins , which previsiae , 2018. Wrevisiae . Due to revisiae . Thereforevisiae . Global revisiae . Althougrevisiae . In whearevisiae . Total orevisiae . Recent  reduced . HoweverFusarium graminearum infection. In this study, we used high-throughput TMT-based technology for proteomics comparison to explore protein lysine 2-hydroxyisobutyrylation modification and their biological processes in maize stems, which showed moderate resistance to F. gramincarum infection. Totally, 2,066 Khib-modified peptides on 728 proteins were identified in our proteomics data. Functional analysis results showed that proteins with different Khib modification were involved in many important biological processes. In addition, we found that Khib modification on PR proteins was involved in disease resistance in maize. These results provided a new understand to Khib modification and explores the function of the identified proteins in plant disease resistance.Maize is an important food and economic crops and susceptible to a variety of pathogens during growth and development . HoweverFusarium graminearum was grown on PDA plates at 28°C for 6 days. Conidial suspensions were harvested by adding sterile distilled water containing 0.05% (v/v) Triton X-100 and scraping the plates using a glass spreader. The concentration of F. gramincarum conidia was quantified using a hemocytometer and diluted to 1 × 106 spores/ml for inoculation. A micropipette was used to inoculate the second or third internodes of maize stems above the soil surface, and then 30 μl conidial suspension was injected. Wounds are covered with sterile gauze to retain moisture and avoid contamination by other organisms. The histone deacetylase inhibitor Trichostatin A  was treated at a concentration of 15 μmol/L, and then spraying on maize leaves at V6 stage. After treatment for 2 days, samples were collected into liquid nitrogen and placed in −80°C refrigerator for western blotting.The maize inbred line B73 line was grown in the greenhouse in Hebei Agricultural University . g), the upper phenol phase was transferred to a new centrifuge tube. Proteins were precipitated by adding at least four volumes of ammonium sulfate-saturated methanol and incubated at −20°C for at least 6 h. After centrifugation at 4°C for 10 min, the supernatant was discarded. The remaining precipitate was washed with ice-cold methanol once, followed by ice-cold acetone for three times. The protein was resuspended in 8 M urea (Sigma), and then the redissolved protein concentration was detected with BCA kit according to the manufacturer’s instructions. Protein solution was digested with 5 mM dithiothreitol (Sigma) for 30 min at 56°C and alkylated with 11 mM iodoacetamide (Sigma) for 15 min in darkness at room temperature. Then, the protein samples were diluted by adding 100 mM TEAB to lower than 2 M urea. Trypsin (Promega) was added at the mass ratio of trypsin to protein of 1:50 for the first digestion overnight, and the mass ratio of trypsin to protein of 1:100 was added for the second digestion for 4 h.The sample was grinded into powder by liquid nitrogen and then transferred to 10 ml centrifuge tube. Then, lysis buffer  was added to the powder, followed by sonication three times on ice using a high intensity ultrasonic processor (Scientz). After an equal volume of Tris-saturated phenol (pH 8.0) was added, the mixture was further vortexed for 5 min. After centrifugation  and vacuum freeze-dried. The peptides were dissolved by 0.5 M TEAB and labeled according to TMT kit (Thermo Fisher Scientific) according to the manufacturer’s introduction. The labeled reagent was thawed and dissolved in acetonitrile. After mixing with the peptide, it was incubated at room temperature for 2 h. After mixing with the labeled peptide, it was desalted and vacuum freeze-dried. The tryptic peptides were fractionated into fractions by high pH reverse-phase HPLC using Agilent 300 Extend C18 column . Then, the peptides were combined into 18 components with a gradient of 8%–32% acetonitrile (pH 9.0) and dried by vacuum centrifugation.The peptides were dissolved in IP buffer solution . The supernatant was transferred to the washed 2-hydroxyisobutyryl resin  and placed on a rotary shaker at 4°C, gently shaking and overnight incubation. After incubation, the resin was washed four times with IP buffer solution and washed twice with deionized water. Finally, 0.1% trifluoroacetic acid eluent was used to elute the resin-binding peptide, and a total of three elutions were performed. The eluent was collected and dried by vacuum freezing. After drying, desalted according to the C18 ZipTips (Millipore) instructions, vacuum freeze-dried for liquid chromatography-mass spectrometry analysis.Trypsin peptides are dissolved in 0.1% formic acid and directly loaded on a self-made reversed-phase analytical column . The gradient of solvent B (0.1% formic acid in 98% acetonitrile) was gradually increased from 6% to 23% through 26 min, increased from 23% to 35% over 8 min, climbing to 80% in 3 min and then maintained at 80% for 3 min, all at a constant flow rate of 400 nl/min on an EASY-nLC 1200 HPLC systems. The peptides were subjected to NSI source followed by tandem mass spectrometry (MS/MS) in Q Exactive™ Plus (Thermo) coupled online to the UPLC. 2.0 kV electrospray voltage was applied. The intact peptides were detected in the Orbitrap, with 350–1,600 m/z full scan range at 50,000 resolution. By using NCE setting as 28, peptides were selected for MS/MS, and the fragments were detected in the Orbitrap at a resolution 15,000. The data correlation process alternates between one MS scan and the subsequent 20 MS/MS scans with an interval of 15.0 s. Automatic gain control was set at 5E4. Fixed first mass was set as 100 m/z.p-Value < 0.05 (Fisher’s exact test) was used as the threshold to determine the significant enrichments of GO and KEGG pathways.Maxquant (v.1.5.2.8) software were used to process the MS/MS data. Tandem mass spectra were searched against MaizeGDB (V4) database concatenated with reverse decoy database. Trypsin/P is designated as a cleavage enzyme allowing up to two deletions. The mass tolerance of the parent ion is set to 20 ppm in the first search, 5 ppm in the main search and 0.02 Da in the fragment ion. The aminoformylmethyl on Cys was designated as fixed modification, and the oxidation on Met was designated as variable modification. The lowest fraction of peptide was set as >40, and the FDR was adjusted to <1%. WoLF PSORT was used to predicate subcellular localizations of identified proteins . AgriGOvChIP was performed as described previously with minor modification . 10 g ofTotal proteins were extracted as described previously with minor modification according to F. graminearum infection as the template. According to the manufacturer’s instructions, the TransStart Tip Green qPCR SuperMix kit was used for qRT-PCR experiment. UBQ1 gene was used as an internal reference to normalize by qRT-PCR. Then, comparative Ct analysis (2–ΔΔCt) of each gene was employed and its relative expression levels at different time points, and quantitative data were expressed as mean ± standard error of mean (SEM). Primers used in this study were designed and listed in The same samples used in proteomics were frozen in liquid nitrogen for RNA extraction. The total RNA of the sample was separated by TRIZOL (Invitrogen) and purified by Qiagen RNA purification kit (Qiagen). Quantitative real-time PCR (qRT-PCR) used the cDNA reversely transcribed from the samples collected at 0 and 2 days after Cochliobolus carbonum infection  plays a key role in the regulation of protein function in maize, such as protein acetylation is involved in the biological process of responding to nfection . To invenfection . The immnfection , Kcr  on histones is widespread in plants, such as nd wheat . Howevernd wheat . Comparind wheat . In ordend wheat . These rIn order to explore the conserve amino acid sequence around the 2-hydroxyisobutyrylation modification site, we analyzed the conserved motifs in the upstream and downstream 10 amino acid residues with lysine as the center by motif-x algorithm. Totally, we identified 9 conserved motifs around Khib modified sites in maize, included KxxxxxKhib, KxxxxxxKhib, KxxxxxxxxxKhib, and a non-polar amino acid preference motif KxxxxKhibA (x represented a random amino acid residue) . Then, hF. graminearum to infect maize stems, and statistically analyzed the differences of 2-hydroxyisobutyrylation modified proteins and modified sites on day 0 and day 2 after F. graminearum infection. The results showed that, the Khib modification levels of 453 proteins were upregulated, including 972 Khib modification sites   was performed. The results showed that in the biological process, proteins with different 2-hydroxyisobutyrylation level were mainly involved in protein translation, protein folding, glycolysis, nucleosome assembly, TCA cycle, and response to biotic stress  . Only a c stress . GO enric stress . In the c stress . Then, Ktabolism , 6. In atabolism , 6. We atabolism . InteresF. graminearum infection, except PR2 and PR10  are a class of proteins that play important roles after pathogen infection in plants . Khib moand PR10 . Our prodetected . In Arabrt sites . Thus, Cnfection ,D, whichArabidopsis, rice, Physcomitrella patens, rhubarb, and wheat  can maintain chromatin structure and be considered to be associated with gene expression in many important biological processes in plant, such as flower transformation, cell differentiation, gametogenesis and response to abiotic/biotic stresses . MoreoveOverall, 2-hydroxyisobutyrylation, as a new type of protein modification, participates in the process of maize resistance to pathogen infection, which was of great significance to the study of plant disease resistance.https://www.ebi.ac.uk/pride/archive/, PXD030131.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: KZ, JX, and JD designed the experiments. KZ, HC, HS, and JZ analyzed the proteomic data. KZ, YM, HB, LY, XP, and FZ performed maize planting experiment and, ChIP-qPCR and western blotting assays. KZ, JX, and JD organized the results and wrote the paper. All authors contributed to the article and approved the submitted version.This work was supported by the National Natural Science Foundation of China (31901864), State Key Laboratory of North China Crop Improvement and Regulation (NCCIR2020ZZ-9), Natural Science Foundation of Hebei Province (C2019204141), Research Project of Science and Technology in Universities of Hebei Province (BJK2022006), China Agriculture Research System (CARS-02), Key Research and Development Projects of Hebei (19226503D), and Central Government Guides Local Science and Technology Development Projects (216Z6501G and 216Z6502G).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.JHD declared a shared affiliation with the authors at the time of the review.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Breast cancer (BC) is a highly heterogeneous disease with high morbidity and mortality. Its subtypes may have distinctly different biological behaviors, clinical outcomes, and therapeutic responses. The metabolic status of BC tissue is closely related to its progress. Therefore, we comprehensively characterized the function of metabolic genes in BC and identified new biomarkers to predict BC patients' prognoses.  Metabolic genes were identified by intersecting genes obtained from two published pieces of literature. The function of metabolic genes in BC was determined by extracting differentially expressed genes (DEGs), performing functional enrichment analyses, analyzing the infiltrating proportion of immune cells, and conducting metabolic subgroup analyses. A risk score model was constructed to assess the prognoses of BC patients by performing the univariate Cox regression, LASSO algorithm, multivariate Cox regression, Kaplan-Meier survival analyses, and ROC curve analyses in the training set. The prognostic model was then validated on the testing dataset, external dataset, the whole TCGA-BC database, and our clinical specimens. Finally, a nomogram was constructed for clinical prognostic prediction based on the risk score model and other clinicopathological parameters. + T cells, while 49 genes were positively correlated with CD8+ T cells. Furthermore, 5 metabolic subgroups with varying proportions of PAM50 subtypes, TNM classification, and immune cell infiltration were obtained. Finally, a risk score model was constructed to predict the prognoses of BC patients, and a nomogram incorporating the risk score model was established for clinical application.  955 metabolic genes were obtained. Among these, 157 metabolic DEGs were identified between BC and normal tissues for subsequent GO and KEGG pathway enrichment analyses. 5 metabolic genes were negatively correlated with CD8 In this study, we elucidated tumor heterogeneity from metabolite profiling of BC. The roles of metabolic genes in the occurrence of BC were comprehensively characterized, clarifying the relationship between the tumor microenvironment (TME) and metabolic genes. Meanwhile, a concise prediction model was also constructed based on metabolic genes, providing a convenient and precise method for the individualized diagnosis and treatment of BC patients. The latest data shows that breast cancer (BC) was the most common type of cancer worldwide in 2020, with an estimated 2.3 million new cases. Although comprehensive efforts have been made in BC treatment, including surgery, chemotherapy, hormonal therapy, and radiation therapy, the outcome of BC patients is still poor. Moreover, BC is the fifth leading cause of cancer mortality worldwide, with 685,000 deaths , 2. MeanMetabolism is an indispensable biochemical process in living organisms, providing energy and building blocks for macromolecules . Throughα in adjacent fibroblasts, resulting in mitophagy and increased aerobic glycolysis [The metabolic status of BC tissue is also closely related to the progress of BC. Previous studies demonstrated that BC cells transitioning to the metastatic state displayed increased oxidative phosphorylation (OXPHOS) and glycolysis , 13. Furycolysis . Tumor cycolysis , 15. Forycolysis . Cell-inycolysis . In receycolysis , 19. Witycolysis –22.In this study, we elucidated tumor heterogeneity through metabolite profiling of BC. The roles of metabolic genes in the occurrence of BC were comprehensively characterized, clarifying the relationship between the tumor microenvironment (TME) and metabolic genes. Furthermore, a concise prediction model was also constructed based on metabolic genes, providing a convenient and precise method for the individualized diagnosis and treatment of BC patients.https://portal.gdc.cancer.gov/repository). The gene expression data were converted into transcripts per kilobase million (TPM) normalized counts [https://www.ncbi.nlm.nih.gov/geo/) and was used as the external dataset for validation. Furthermore, metabolic genes were retrieved from the published literature [The gene expression profiles (fragments per kilobase of exon per million (FPKM)) and corresponding clinical data of BC patients were obtained from The Cancer Genome Atlas (TCGA) database | > 1 were enrolled in. A volcano map was drawn with the “ggplot2” package to display DEGs.The “limma” R package was used to identify the differentially expressed genes (DEGs) . Genes wTo investigate the biological functions and characterize the metabolic genes, the R package “clusterProfiler” was used to perform the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses , 28. A c+ T cells were identified. The above estimations were carried out by the R packages “GSVA,” “CIBERSORT,” “MCP-Counter,” and “xCell”, respectively. The Spearman correlation of the CD8+ T cell fraction among four algorithms was calculated and visualized by the “ggcor” package.To further elucidate the relationship between metabolic genes and different immune cells, we analyzed the gene transcriptome data of BC patients to estimate the proportion of immune cells . ThroughWe applied the “ConsensusClusterPlus” package to identify BC molecular subtypes. This algorithm, one of the unsupervised class discovery algorithms, defined “consensus” clustering by applying a specific clustering means to the random data subsets , 35. TheP < 0.05) was performed to identify candidate metabolic genes related to overall survival (OS) in the training cohort. The least absolute shrinkage and selection operator (LASSO) algorithm was used to further filter prognosis-specific metabolic genes [β1 × mRNA level of  gene1 + β2 × mRNA level of  gene2 + ⋯+βn × mRNA level of genen, where β corresponded to the correlation coefficient. The risk score (RS) of every patient was calculated based on the prognostic model. The univariate and multivariate Cox regression analyses were carried out by using “survival” R packages, and the “glmnet” package was used for the LASSO regression analysis [The impact of metabolic genes on the prognoses of BC patients was explored. First, the BC patients from the TCGA database were randomly divided into training cohorts and testing cohorts in a 1 : 1 ratio. Next, the univariate regression analysis  of the time-dependent receiver operating characteristic (ROC) curve. The different expression of genes between tumor and normal groups was judged by paired T-test, and the correlation of gene expression in clinical specimens was evaluated by Pearson's correlation coefficient using GraphPad Prism 7 software . The survival curve was drawn with the Kaplan-Meier method, and the log-rank test was used to evaluate the difference between survival curves. The R package “rms” was used to construct a nomogram.Statistical analyses were performed with R (version 3.6.1). P value in our clinical specimens, and the findings were consistent with the above results , indicating a relatively strong consistency . There were 5 metabolic genes negatively correlated with CD8+ T cells, while 49 genes were positively correlated with CD8+ T cells  with the most significant P value in our clinical specimens and have found the consistent results , phosphoglycerate kinase 1 (PGK1), iodotyrosine deiodinase (IYD), quinolinate phosphoribosyltransferase (QPRT), and solute carrier family 27 member 2 (SLC27A2) were chosen for the construction of the risk characteristic formula , TNM stage , age , pathological M stage , pathological N stage , and pathological T stage  could predict the prognosis of BC patients (P < 0.0001) and age  could serve as independent prognostic biomarkers for BC patients . Edwards et al. had reported germline gain-of-function (GOF) mutations in PIK3CD aberrantly induced exhaustion and impaired cytotoxicity of CD8+ T cells [+ T effector cells [+ T cells [+ T cells were closely related to metabolic processes, especially in lipase activity and platelet activation. In contrast, genes that were negatively correlated with CD8+ T cells were focused on nucleotide diphosphatase activity. This section may provide potential principles for screening the immunotherapy-adapted population.CD8rognosis , 47. CD8or cells , 49. The T cells . Severalor cells . We dedu T cells , 53. Bio+ T cells and less fibroblast infiltration were associated with better outcomes. These findings have also been reported by previous studies. BC patients with more CD8+ T cell infiltration [We conducted consensus clustering analysis by employing metabolic genes and obtained 5 metabolic subgroups. PAM50 classification is a method characterized by 5 intrinsic molecular subtypes according to the microarray-based gene expression profiling of BC patients, including the luminal A (LumA), luminal B (LumB), HER2-enriched (HER2-E), basal-like, and normal-like breast cancer subtypes , 54. Sigltration , 49, 52 ltration  had bettThere are many well-established prediction models based on public transcriptomic databases. For example, Liu Y et al. identified a 22-autophagy gene signature based on TCGA and GEO lung cancer cohorts. They also confirmed that gene signature was an independent predictor of prognosis . ThroughIn summary, this study comprehensively investigated the effects of metabolite profiles in BC patients. A 5-gene metabolic prognostic signature was constructed for precise diagnostic assessment and individualized treatment of BC patients.


Prostate cancer (PC) aetiology is unclear. PC risk was examined in relation to several factors in a large population-based prospective study.Male participants were from Sax Institute’s 45 and Up Study  recruited between 2006 and 2009. Questionnaire and linked administrative health data from the Centre for Health Record Linkage and Services Australia were used to identify incident PC, healthcare utilisations, Prostate Specific Antigen (PSA) testing reimbursements and dispensing of metformin and benign prostatic hyperplasia (BPH) prescriptions. Multivariable Cox and Joint Cox regression analyses were used to examine associations by cancer spread, adjusting for various confounders.adjusted = 1.36; 95% CI:1.21–1.52); father and brother(s) diagnosed with PC ; severe lower-urinary-tract symptoms  and vasectomy . PC risk decreased with dispensed prescriptions (versus none) for BPH  and metformin . Advanced PC risk increased with vasectomy  and being obese .Of 107,706 eligible men, 4257 developed incident PC up to end 2013. Risk of PC diagnosis increased with: PC family history (versus no family history of cancer; HRVasectomy and obesity are associated with an increased risk of advanced PC. The reduced risk of localised and advanced PC associated with BPH, and diabetes prescriptions warrants investigation. Prostate cancer (PC) is the second most common cancer diagnosed in men worldwide and the most common cancer diagnosed in men from developed countries . AustralDespite its high incidence, there is limited evidence on risk factors for PC. Established risk factors include advancing age, African ancestry and family history of PC –6. All tSome urological factors are associated with PC risk including prescriptions for benign prostatic hyperplasia (BPH), lower-urinary-tract symptoms (LUTS) and vasectomy. These associations, especially with BPH prescriptions, vasectomy and LUTs may be at least partly attributable to increased medical surveillance –21. The The current study prospectively examined potential risk factors for PC diagnosis in the 45 and Up Study, a large Australian population-based study of 267,153 male and female participants recruited in New South Wales (NSW). The study’s aim was to assess whether selected sociodemographic, behavioural and health-related factors were associated with a higher or lower risk of PC diagnosis, while accounting for possible confounding by PSA testing.The Sax Institute’s 45 and Up Study is a large NSW population-based cohort study of male and female participants aged 45 years and above . All parThe 45 and Up Study was approved by the University of NSW Human Research Ethics Committee, while approval for this analysis of PC risk factors was given by the NSW Population and Health Services Research Ethics Committee (HREC/14/CIPHS/54). The use of Services Australia’s Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) data was approved by the Australian Department of Health’s Departmental Ethics Committee.In 2017, the Centre for Health Record Linkage (CHeReL) linked records of all participants with selected administrative health records. These records were NSW Cancer Registry (NSWCR: January 1994–December 2013), Cause of Death Unit Record File (CODURF: February 2006–December 2015), Registry of Births, Deaths and Marriages records (RBDM: February 2006–December 2016) and NSW Admitted Patient Data Collection (APDC: July 2001–June 2016). All pathology laboratories, hospitals and radiotherapy and medical oncology departments are required by the Public Health Act 2010 to report all newly diagnosed cancers to the NSWCR. Information on the degree of spread at diagnosis is assigned within 4 months of PC diagnosis. However, if not all clinical information is received within this period of data acquisition, then these cases are classified as ‘Unknown’ spread , 25. ProPC diagnosis prior to recruitment to the 45 and Up Study  cardholders were excluded because a proportion of their medical and pharmaceutical benefits are funded by the Department of Veterans Affairs and these data were not available. DVA clients were identified via self-report or any mention of DVA coverage in hospital or emergency department records.The following PC risk factors of primary interest to this analysis were derived using self-reported information at recruitment:2) based on height (m) and weight (kg) and categorised according to the World Health Organisation classification for normal (<24.9 kg/m2), overweight (25.0–29.9 kg/m2) or obese (≥30 kg/m2); [family history of cancer  and no family history of cancer [reference]); family history of PC ; body mass index (BMI in kg/m kg/m2);  height d kg/m2);  LUTS mea kg/m2);  erectileThe following variables were obtained by linkage with PBS, MBS and APDC records up to 3 years before PC diagnosis or the censoring date: dispensing of medications to treat BPH  code C02CA01, C02CA04, D11AX10 and G04C), to treat diabetes using metformin (PBS ATC code A10BA02) or non-metformin medications (PBS ATC code beginning with A10 but excluding A10BA02) with subsidised measurement of glycosylated haemoglobin (MBS 66551) or APDC code indicating diabetes contributed to need for a hospitalisation. Those with missing information for a single variable were included in the analysis as a “missing” category.PC cases were defined as men with a record for ICD-10 code C61 cancer registered with the NSWCR after enrolment in the 45 and Up Study and before the end of follow-up on 31 December 2013 .Potential confounders available in the current cohort study based on previous publications were identified and referenced , 5, 6.All participants’ and, separately, PC cases’ sociodemographic, lifestyle and behavioural characteristics, as self-reported at the time of recruitment, and relevant health-related factors obtained by linkage to administrative records are listed in Table Hazard ratios (HR) and 95% confidence intervals (CI) were estimated using Cox Proportional Hazards regression, with age as the underlying time variable, using SAS version 9 . In these analyses, the endpoint was PC diagnosis or with censoring on the earlier of December 31, 2013, date death or the diagnosis date of cancer other than PC. Joint Cox Proportional Hazards regression, with age as the underlying time variable, was used to distinguish time to diagnosis based on the spread of disease defined as localised or advanced . Men with missing information to determine disease spread are classified as ‘unknown’ were excluded from the main Joint Cox regression analysis. The regression analyses were, at a minimum, adjusted for age, region of birth, health cover, income, qualifications, place of residence, marital status, Charlson comorbidity index, frequency of PSA testing, frequency of primary healthcare visits and fully adjusted for family history of cancer, BMI, smoking alcohol, physical activity, lower-urinary-tract symptoms, vasectomy, erectile dysfunction, medications for BPH and medications for diabetes (as listed in footnotes of each table).Sensitivity analysis excluding the first year of follow-up for all participants after the date of enrolment in the 45 and Up Study was carried out to reduce the possible impact of reverse causation. Although our analysis examined incident PC in an era where PSA testing was widespread, we conducted a second sensitivity analysis to determine the impact of excluding a highly screened group. National Australian clinical practice guidelines for PSA testing and early management of test-detected PC, recommend men who have been informed of the benefits and harms of testing and who decide to undergo regular testing for prostate cancer, should be offered PSA testing every 2 years from age 50–69 . Based on = 268), were participants from the pilot study (n = 175), were aged below 45 years (n = 5) or were female . Of the remaining 123,732 male participants, there were 107,706 eligible men for our analysis . The risk of PC diagnosis was slightly lower in ever versus never smokers , while height, BMI, alcohol consumption and physical activity showed no association with PC diagnosis.Multivariable regression analyses in a fully adjusted model showed that the overall risk of PC diagnosis was highest in men with a family history of PC , and a weak association with vasectomy (HRadjusted = 1.08 95% CI: 1.00–1.16 for yes versus no); there was no association with previously reporting having emergency department records. The risk of PC diagnosis was lower in men with a record for PBS subsidised prescription for BPH treatment , and prescription for metformin  but no association with non-metformin medications for diabetes—Fig. Analysis of urological factors showed an increased risk of PC diagnosis associated with increased severity of LUTS  Specifically, men who had a vasectomy had a higher relative risk of advanced PC (HR = 1.28 95% CI: 1.06–1.55) than localised (HR = 1.04 95% CI: 0.95–1.14). Similarly, obese men  had a higher relative risk of advanced PC (HR = 1.31 95% CI: 1.01–1.69) than localised PC (HR = 0.98 95% CI: 0.87–1.12). There was no evidence of variation by stage of disease for all other exposures examined  for all exposures examined  than the relative the relative risk of localised PC . This finding is consistent with a 24-year follow-up of the Health Professionals Follow-Up Study, which reported no association between vasectomy and localised or low-grade disease, but an increased risk of high-grade  and advanced-stage prostate cancer  [Our analysis of urological factors showed some evidence that for men who reported having a vasectomy, the relative risk of advanced PC was higher (HR03–1.40) . A 15-ye03–1.40) . A meta-03–1.40) . The bioThe risk of localised and advanced PC was increased with increasing severity of LUTS but decreased in men with the prescription record(s) for BPH treatment. The role of LUTS in the etiologic development of PC and BPH share much in common and thus estimating the true association between LUTS and PC risk is complicated. LUTS is a self-reported measure, defined by increased frequency of urination, incomplete emptying of the bladder or waking during the night to void. It may be caused by BPH through direct obstruction of the bladder outlet or, may be associated with PC when the developing neoplasm exerts pressure on the bladder outlet or the urethra . ResultsBPH is a non-malignant enlargement of the prostate that is clinically diagnosed. Severe BPH symptoms are commonly treated with minimally invasive surgery while mild to moderate symptoms are treated with medications that can shrink the prostate and ease urination. A systematic review of 8 trials showed that 5-alpha-reductase inhibitors (5-ARIs), a class of pharmaceutical prescriptions used in the treatment of BPH reduced the risk of PC diagnosis among men who regularly screened for PC. The Prostate Cancer Prevention Trial (PCPT) and the Reduction by Dutasteride of Prostate Cancer Event trial (REDUCE) which were specifically designed to examine the impact of 5-ARIs (finasteride and dutasteride) on PC risk, reported an overall 22.5–24.8% decreased risk of low-risk PC, but an increased incidence of high-risk disease –44. SimiMetformin, commonly used as first-line therapy for diabetes which is the fastest growing chronic condition in Australia, may have a role in PC prevention due to its glucose-lowering and anti-proliferative properties , 46, 47.p = 0.05 for difference between HRs) and this is consistent with previous reports [Obese men in our study had a higher relative risk of advanced PC diagnosis  than localised PC  ( reports , 8. We h reports , 53. The reports . AnalysiThe study sample comprises approximately 10% of the total population of NSW aged 45 and above and, consistent with most cohort studies, has a relatively modest response rate of 18% . HoweverSince the early 1990s, PSA testing in Australia increased substantially with corresponding increases in the diagnosis of localised cancers and a fall in the rate of advanced cancers. Increased PSA testing has been documented in higher socio-economic status groups and in those with ‘healthier’ behaviours . TherefoSupplementary Figures 1,2,3, 4Supplementary Tables 1,2,3,5,6Legends for Supplementary Figures and TablesReproducibility checklist


Studies have indicated that the ethanol exposure impairs the gut microbiota, At the same time, high levels of alcohol exposure damage sperm in mice. However, whether the gut microbiota is involved in mediating the effects of alcohol on sperm quality remains unclear. This study aimed to assess the effect of chronic alcohol consumption on intestinal microbiota in mice and analyze the potential pathophysiological effect of altered intestinal microbiota on sperm quality. We established a mouse model of chronic alcohol consumption by allowing male C57 mice to freely ingest 10% ethanol for 10 weeks, and collected the fecal microbiota of the male mice in the chronic drinking group  and the control group (control) and transplanted the specimens into the transplant groups . Sperm quality was significantly decreased in the alcohol-FMT group compared with the control-FMT group. Gut microbiota analysis revealed that the abundance of 11 operational taxonomic units (OTUs) was altered in the alcohol-FMT group. Nontargeted metabolomics identified 105 differentially altered metabolites, which were mainly annotated to amino acids, lipids, glycerophosphoethanolamine, organic oxygenic compounds, organic acids and their derivatives, steroids, and flavonoids. In particular, the oxidative phosphorylation pathway, which is the key to spermatogenesis, was significantly enriched in the alcohol-FMT group. Moreover, compared with the control-FMT group, the alcohol-FMT group presented significantly higher serum endotoxin and inflammatory cytokine levels, with more pronounced T cell and macrophage infiltration in the intestinal lamina propria and elevated levels of testicular inflammatory cytokines. In addition, RNA sequencing showed significant differences in the expression of testis-related genes between the alcohol-FMT group and the control-FMT group. In particular, the expression of genes involved in gamete meiosis, testicular mitochondrial function, and the cell division cycle was significantly reduced in alcohol-FMT mice. In conclusion, these findings indicated that intestinal dysbiosis induced by chronic alcohol consumption may be an important factor contributing to impaired sperm quality. Chronic alcohol consumption induces intestinal dysbiosis, which then leads to metabolic disorders, elevated serum endotoxin and inflammatory cytokine levels, testicular inflammation, abnormal expression of related genes, and ultimately, impaired sperm quality. These findings are potentially useful for the treatment of male infertility. Infertility has become a global problem, with an incidence of approximately 15% worldwide, as the third most common condition after cancer and cardiovascular and cerebrovascular diseases . ApproxiStudies have demonstrated that chronic alcohol consumption is an important factor modulating male spermatogenesis and motility . In ChinIn recent years, the gut microbiota has become a topic of major interest in the field of biomedicine. Numerous studies have found that the gut microbiota is closely related to the status of all major organs and systems in the body . The gutThe factors affecting sperm quality are complex and varied, Researchers are discovering the role of the microbiota in male reproduction, including the gut microbiota and the reproductive tract microbiota . This stAlcohol affects spermatogenesis and sperm quality. However, researchers have not clearly determined whether alcohol affects sperm quality through the gut microbiota. We hypothesized that chronic alcohol consumption causes intestinal dysbiosis, leading to the production of a large amount of toxic and harmful substances that damage the intestinal mucosa, increase intestinal permeability, and then enter the blood circulation and reach different sites, resulting in elevated levels inflammatory cytokines, an imbalance of inflammatory cytokines, the inflammatory response, and testicular damage. Varying degrees of pathological changes in testicular tissue ultimately lead to spermatocyte apoptosis, impaired sperm quality, and a low sperm count. In this study, we established a mouse model of chronic alcohol consumption and tested our hypothesis with fecal microbiota transplantation to investigate the effect of chronic drinking-induced intestinal dysbiosis on sperm quality.Three-week-old male C57 mice from the Experimental Animal Centre of Army Medical University were housed in a specific pathogen-free (SPF) environment at 18°C–26°C and 50%–60% humidity, with a 12-h-on/12-h-off light cycle. The mice had free access to feed and distilled water.After 2 weeks of co-feeding (keep in the same cage), the mice were randomly divided into two groups with12 mice each for the alcohol treatment experiment: the control group , and the chronic drinking group . The average weekly alcohol intake of per mouse in the alcohol group is shown in Fecal microbiota transplantation (FMT) experiment: Three-week-old male C57 mice were co-fed (in the same cage) and then randomly divided into two groups with five mice each 2 weeks later: the alcohol-fecal microbiota transplantation (FMT) group , and the control-FMT group . All mice were sacrificed after anesthesia induction with 1% pentobarbital sodium solution by intraperitoneal injection.Five hundred milligrams of fresh donor stool samples were continuously collected from the alcohol (five mice) and control (five mice) groups immediately upon defecation, resuspended in 4 ml of saline, vortexed for 5 min, and filtered with a 360-mesh sterile strainer. Transplantation into the recipient mice was achieved by gavage with 200 μl of the supernatant from the fecal sample every Monday, Wednesday, and Friday for 10 weeks . Fecal s2 incubator to release sperm. Sperm were observed under a Nikon N200 microscope and measured with a Suiplus semen analysis (SSA) system.The cauda epididymis was minced in 100 ml of phosphate-buffered saline  and incubated for 20 min at 37°C in a 5% COMouse testes and small intestines were harvested. The entire testis was fixed with a special testis solution (Servicebio Cat. No.: G1121) for 24 h. The small intestine was cut into small sections of approximately 1 cm, which were then cleaned and fixed with paraformaldehyde (Servicebio Cat. No.: G1101) for 24 h. Hematoxylin and eosin (H&E) staining was performed after dehydration in graded ethanol solutions, paraffin embedding, and sectioning (2.5 μm). Three random fields of sections on each slide were observed under an Olympus upright microscope (×200).http://www.imagej.softonic.de).The deparaffinized and rehydrated intestine and testis sections were blocked with 5% bovine serum albumin (BSA) containing 0.1% Triton X-100 for 1 h and incubated with a primary antibody against F4/80  at 4°C overnight. Then, the sections were incubated with the secondary antibody  at room temperature for 1 h. After color development, the sections were observed under an Olympus microscope, and the proportions of positively stained cells were analyzed. Protein expression was quantified using ImageJ software  was an Alexa Fluor 488-conjugated donkey anti-rabbit antibody. Sections were examined under a ZEISS 880 confocal microscope. Protein expression was quantified using ImageJ software .For immunofluorescence staining, paraffin sections of the intestines (2.5 μm thick) were assayed with 4′,6-diamidino-2-phenylindole (DAPI) as a counterstain. The primary antibody used in this experiment was rabbit anti-mouse CD3e . The secondary antibody  according to the manufacturer’s instructions. Sections (4 μm thick) were transferred to polylysine-coated slides and incubated overnight at 60°C. Then, they were deparaffinized in xylene. Sections were rehydrated in a graded series of 75%, 80%, 90%, and 100% ethanol and washed with PBS. Slides immersed in 0.1 M citrate buffer were microwaved at 350 W for 5 min for antigen retrieval. PBS was used for washing. Sections were incubated with the TUNEL reaction mixture  enzyme solution) at 37°C for 1 h in a humidified chamber and washed twice with PBS. After this step, Converter-POD was added, and the slides were incubated for 30 min at 37°C and then rinsed twice with PBS. Next, the sections were treated with 3,3-diaminobenzidine at room temperature and rinsed twice with PBS. Sections were counterstained with Harris’ hematoxylin for 10 min. The sections were dehydrated with a series of graded alcohol concentrations. Xylene was used as a clearing agent, and then, the sections were coverslipped with Entellan for light microscopy examination.DNA fragmentation indicative of apoptosis was examined using the terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) method. The TUNEL assay was performed using the Mouse small intestine was harvested, cut into sections of 1 mm × 1 mm × 2 mm, and placed in a 1.5-ml Eppendorf tube (Servicebio Cat. No. G1101) containing 2.5% glutaraldehyde. The samples were then embedded, sectioned (2.5 μm), stained, and observed and imaged by transmission electron microscopy (HITACHI HT7800/HT7700). Image-Pro Plus 6.0  software was used to measure the intercellular space (nm) at five equal positions with an intact intercellular space on each slide . Correlation analysis was performed by calculating Pearson’s correlation coefficient using the R language mixOmics package to calculate the correlation coefficient r2 and the p value of differentially intestinal flora and differentially metabolites. All 16S rRNA datasets are publicly available .DNA from intestinal contents was prepared from animals in the alcohol, control, alcohol-FMT, and control-FMT groups using a fecal DNA extraction kit ; three biological replicates per group were analyzed. DNA quality was monitored on 1% agarose gels. The hypervariable V3–V4 region of the 16S-rDNA gene was amplified by PCR using the primers 341F: CCTACGGGNGGCWGCAG and 806R: GGACTACHVGGGTATCTAAT, where the barcode was an eight-base sequence unique to each sample. PCR was performed in 30-μL reactions composed of 15 μl of Phusion® High-Fidelity PCR Master Mix (New England Biolabs), 0.2 μM forward and reverse primers, and 10 ng of template DNA. Thermal cycling consisted of an initial denaturation step at 98°C for 1 min, followed by 30 cycles of denaturation at 98°C for 10 s, annealing at 50°C for 30 s, and elongation at 72°C for 30 s. Finally, the reactions were incubated at 72°C for 5 min. PCR products were detected on 2% agarose gels by electrophoresis and then purified using the GeneJET Gel Extraction Kit (Thermo Scientific). Sequencing libraries were generated using the Illumina TruSeq DNA PCR-Free Library Preparation Kit  according to the manufacturer’s recommendations, and index codes were added. Sequencing was performed using the Illumina NovaSeq 6,000 platform . Sequences were analyzed using QIIME software package 2 (version 2020.2). The effective tags were clustered into operational taxonomic units (OTUs) of ≥97% similarity using the MOTHUR pipeline. The tag sequence with the highest abundance was selected as the representative sequence within each cluster. Bacterial alpha diversity was determined by performing a sampling-based OTU analysis and presented as the observed OTUs, Chao index, Shannon index, and Simpson index, which were calculated using the R program package “vegan.” Principal coordinate analysis (pCoA) was conducted using the R packageg for 20 min at 4°C. Some of the supernatant was diluted with liquid chromatography–mass spectrometry (LC–MS)-grade water to a final concentration of 53% methanol. The samples were subsequently transferred to a fresh Eppendorf tube and then centrifuged at 15,000 × g for 20 min at 4°C. Finally, the supernatant was injected into a liquid chromatography tandem mass spectrometry (LC–MS/MS) system. Ultrahigh-performance liquid chromatography (UHPLC)-MS/MS analyses were performed using a Vanquish UHPLC system  coupled with an Orbitrap Q Exactive™ HF-X mass spectrometer  at Novogene Co., Ltd. . The raw data files generated by the UHPLC–MS/MS system were processed using the Compound Discoverer 3.1 platform  to perform peak alignment, peak picking, and quantitation of each metabolite. The normalized data were used to predict the molecular formula based on additive ions, molecular ion peaks, and fragment ions. Then, peaks were matched with the mzCloud,Tissues (100 mg) were individually ground in liquid nitrogen, and the homogenate was resuspended in prechilled 80% methanol by thoroughly vortexing the mixture. The samples were incubated on ice for 5 min and then centrifuged at 15,000 × t test) to calculate statistical significance . Metabolites with a variable importance in projection (VIP) value > 1, p-value < 0.05, and fold change (FC) ≥ 2 or ≤ 0.5 were considered differentially altered metabolites. Volcano plots were constructed to filter the metabolites of interest based on the log2 (FC) and log10  of metabolites. The functions of these metabolites and metabolic pathways were analyzed using the KEGG database. Next, we evaluated the metabolic pathway enrichment of the differentially altered metabolites. When the ratios were satisfied by x/n > y/N, the metabolic pathway was considered enriched, while when the p value of the metabolic pathway was <0.05, the enrichment of the metabolic pathway was considered statistically significant. Correlation analysis was performed by calculating Pearson’s correlation coefficient using the R language mixOmics package to calculate the correlation coefficient r2 and the p-value of differentially expressed genes and differentially expressed metabolites. All differentially expressed genes and differentially altered metabolites obtained in the alcohol-FMT group compared with the control-FMT group were mapped to the KEGG database to obtain their common pathway information and for statistical analysis.These metabolites were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database,Total RNA was extracted from the testicular tissues of animals in the alcohol-FMT and control-FMT groups, with three replicates in each group. RNA integrity was assessed using an RNA Nano 6000 Assay Kit and a Bioanalyzer 2100 system . The cDNA libraries were prepared using an Illumina TruSeq RNA Sample Prep Kit. The average size of the library cDNAs was 150 bp (excluding adapters). The integrity and quality of cDNA libraries were assessed using an Agilent 2100 Bioanalyzer and an ABI StepOne Plus real-time PCR system. RNA sequencing (RNA-seq) was performed by Novogene Company . RNA-seq reads from the FASTQ files were mapped to the mouse reference genome using TopHat2. The accession number for the RNA-seq data is GEO GSE192522.p values were adjusted using the Benjamini–Hochberg approach to control the false discovery rate. Genes with an adjusted p-value < 0.05 identified by DESeq2 were considered differentially expressed. The GO enrichment analysis of differentially expressed genes was performed using the clusterProfiler R package, and gene length bias was corrected. We used the clusterProfiler R package to test the statistical enrichment of differentially expressed genes in KEGG pathways.The original data were filtered to obtain clean data and ensure the quality and reliability of the analysis. Trinity software was used for transcript assembly. Corset hierarchical clustering was performed for splicing. BUSCO software was used to evaluate the splicing quality of Trinity.fasta, unigene.fa, and cluster.fasta files. The gene function was annotated using the following databases: Nr . The differential expression analysis of the two conditions/groups was performed using the DESeq2 R package (1.20.0). DESeq2 provides statistical routines for determining differential expression in digital gene expression data using a model based on a negative binomial distribution. The resulting p < 0.05 was considered statistically significant.The data are expressed as the mean ± standard error of the mean (SEM). Statistical comparisons between two measurements were analyzed by independent-samples t tests using SPSS 26.0 software . When analyzing gut microbiota sequencing data, we performed a two-tailed Wilcoxon rank-sum test by R Project; p = 0.479, p = 0.0104, p = 0.0373, p = 0.0373, p = 0.0249, p = 0.0373, p = 0.0249, and p = 0.0373, respectively). The beta diversity based on weighted PCoA clearly showed a difference in the microbiota composition between the control group and the alcohol group, with a significant difference along the PCO1 axis  to investigate the effect of chronic drinking-induced intestinal dysbiosis on sperm quality. No significant difference was observed in body weight between the alcohol group and the control group , which was consistent with the results described above. A distinct clustering of the microbiota composition for the two groups was observed using Bray–Curtis-based pCoA . Pearson’s correlation analysis of sperm quality and the relative abundance of gut microbiota showed a significant negative correlation between sperm motility and the abundance of Lachnospiraceae_NK4A136_group (p = 0.025)  . These fp = 0.081 and p = 0.73, respectively). The correlation analysis result of differentially enriched KEGG pathways with the species at the genus level showed that differentially abundant microbiota species were enriched in a series of metabolic pathways (p = 0.0150), riboflavin metabolism (p = 0.0287), nicotinate and nicotinamide metabolism (p = 0.1638), morphine addiction (p = 0.0739), lysine degradation (p = 0.1638), glyoxylate and dicarboxylate metabolism (p = 0.0456), cortisol synthesis and secretion (p = 0.1115), citrate cycle (tricarboxylic acid (TCA) cycle) (p = 0.0287), caffeine metabolism (p = 0.0456), alanine, aspartate, and glutamate metabolism (p = 0.1370), and oxidative phosphorylation  and IL-22 (p = 0.0246) levels were significantly higher in the alcohol-FMT group, indicating significantly increased inflammation in this group compared with the control-FMT group. We also analyzed inflammatory cytokine levels in the testes and detected higher levels of the proinflammatory cytokines IL-2 and IL-Ib and lower levels of the anti-inflammatory cytokines TGF β1, IL-12p70, and IL-22 in the alcohol-FMT group than in the control-FMT group, although the differences did not reach statistical significance (p > 0.05).As described above, serum endotoxin levels were elevated in the alcohol-FMT group, which was correlated with certain microbiota, especially the dominant phyla Firmicutes and Bacteroides. We first analyzed intestinal inflammation in the recipient mice. H&E staining showed pronounced lymphocyte infiltration in the lamina propria of the small intestine in the alcohol-FMT group compared with the control-FMT group . ImmunohStudies have demonstrated that chronic inflammation causes sustained activation of endothelial cells, which promotes angiogenesis, increased blood flow, and macrophage aggregation . We obsep ≤ 0.05, or |log2 FC| > 0), including 618 upregulated genes and 407 downregulated genes. We then used the H-cluster method with log2(FPKM + 1) to analyze differential expression and centralization correction to observe clustering (p = 3.14E-12), oxidative phosphorylation (p = 1.16E-11), Wnt signaling pathway (p = 0.0077), synthesis and degradation of ketone bodies (p = 0.0091), proteasome (p = 0.0403), thermogenesis (p = 1.52E-07), and retrograde endocannabinoid signaling (p = 0.0003)  ≥ 1 as the threshold for gene expression. Among the eligible genes, 1,025 genes showed significant differences in expression  , all of  0.0003) .p = 1.16E-11)  . Oxidatiabolites . In summAs demonstrated in the present study, chronic drinking-induced intestinal dysbiosis may be one of the most important mechanisms by which chronic alcohol consumption affects male sperm quality. Specifically, intestinal dysbiosis reduces the amount of bacteria with anti-inflammatory activity; increases the amount of proinflammatory bacteria; increases bacterial endotoxin levels; causes intestinal inflammation, intestinal mucosal injury, intestinal hyperpermeability, and leaky gut; causes a large amount of bacterial metabolites to enter the blood circulation; alters serum cytokine levels; and ultimately results in testicular inflammation and abnormal expression of testis-related genes. To the best of our knowledge, this study is the first to investigate the functional connection between chronic drinking-induced intestinal dysbiosis and impaired male fertility. Further research is needed to investigate the role of the gut-testicular axis in the etiology of male infertility .Allobaculum, Alloprevotella, Adlercreutzia, Gordonibacter, and Mycoplasma decreased significantly, while that of Alistipes, Eubacterium_nodatum_group, Lachnospiraceae_NK4A136_group, Family_XIII_UCG_001, Angelakisella, Ruminiclostridium_5, Ruminiclostridium_6, UBA1819, Moorella, Marvinbryantia, Ruminococcus__gnavus_group, and Ruminococcaceae_UGG_014 increased significantly in the alcohol group. Further analysis revealed that Lachnospiraceae_NK4A136_group, a dominant genus of Firmicutes, and Helicobacter, a dominant genus of Bacteroides, may be the key intestinal bacterial species modulating sperm quality. Further studies are needed to confirm the specific mechanism of their effect on sperm quality. Sperm analysis indicated that the sperm count, sperm concentration, and sperm motility were significantly reduced in the alcohol group compared with the control group. Electron microscopy, H&E staining, and immunohistochemistry showed intestinal hyperpermeability and increased intestinal inflammation in the alcohol group. TUNEL staining revealed more apoptotic spermatocytes in the seminiferous tubules in the alcohol group than in the control group. Based on these findings, chronic drinking induces changes in the gut microbiota and affects sperm quality in mice.Chronic drinking disrupts the intestinal microecosystem . We estaLachnospiraceae_NK4A136_group, a dominant genus of Firmicutes, and Helicobacter, a dominant genus of Bacteroides, exhibited similar trends in both the alcohol-FMT and alcohol groups. In addition, the correlation analysis of sperm quality with the relative abundance of Lachnospiraceae_NK4A136_group and Helicobacter demonstrated strong correlations between decreased sperm motility and these two genera. Even if the Lachnospiraceae_NK4A136_group exerts a direct effect on sperm quality, this result should be considered in the context of chronic drinking-induced alterations in the intestinal ecosystem. Other microbial species may also interact dynamically with these bacteria. Thus, chronic drinking-induced intestinal dysbiosis, particularly alterations in Firmicutes and Bacteroides  may result in impaired sperm quality. KEGG pathway analysis revealed a series of pathways with differential enrichment, such as steroid biosynthesis, Wnt signaling pathway, primary bile acid biosynthesis, secondary bile acid biosynthesis, folate biosynthesis, and oxidative phosphorylation, all of which are all related to sperm quality. We performed a nontargeted metabolomics analysis of intestinal contents to further investigate how intestinal dysbiosis may affect sperm quality.We investigated whether the aforementioned findings were caused by chronic drinking-induced intestinal dysbiosis by collecting fresh feces from the alcohol group and the control group and then transplanting their gut microbiota into normal mice by gavage to establish a mouse model of alcohol-FMT and a mouse model of control-FMT. Analysis of the gut microbiota and sperm of these two groups reproduced the results from the alcohol group, such as changes in the gut microbiota and impaired sperm quality, suggesting that the gut microbiota probably mediates the effect of chronic drinking on sperm quality. Interestingly, Nontargeted metabolomics of intestinal contents identified significantly differentially altered metabolites between the alcohol-FMT group and the control-FMT group. In particular, the oxidative phosphorylation pathway, a pathway closely related to spermatogenesis and sperm quality, overlapped with the 16S sequencing KEGG pathway analysis. The correlation analysis of 16S rDNA sequencing data and nontargeted metabolomics data indicated that changes in the gut microbiota caused extensive changes in metabolites. Interestingly, most of the differentially altered metabolites were lipids and amino acids, and the bile acid metabolic pathway was significantly less enriched in the alcohol-FMT group. These differentially altered metabolites and metabolic pathways may be one cause of differences in sperm quality in mice, although further research is needed to verify the results.Extensive studies conducted over the past few decades have demonstrated that the gut microbiota plays an important role in the metabolism and circulation of nitrogen-containing compounds (including amino acids). The microbiota actively metabolizes a large amount of dietary amino acids, which enter the portal vein for systemic use to improve or prevent metabolic syndrome and regulate various signaling molecules involved in sperm quality, oocyte fertilization, and embryo implantation . A suffiChronic drinking-induced intestinal dysbiosis triggers host immune responses , includiPrevious animal and human studies have confirmed that elevated endotoxin levels cause orchitis and epididymitis, resulting in low sperm motility and spermatogenic dysfunction . An intrSperm quality in mammals is regulated by many factors, and the expression of spermatogenic cells in the testes plays a key role in sperm quality . Chronicvia mitochondrial dysfunction in Leydig cells, decreasing testosterone levels and ultimately affecting sperm quality  in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA009014 and CRA009015) that are publicly accessible at The animal study was reviewed and approved by the Laboratory Animal Welfare and Ethics Committee of the Third Military Medical University.HL, JZZ, NL, and JZ conceived, designed, and directed the study. HL, QL, JZZ, and QZ collected the mouse samples and sperm measurement data. HL and TL performed the FMT experiments. HL and JZZ obtained and analyzed the microbiota data. HL, JZZ, and NL interpreted the results and drafted the manuscript. HL, WC, JY, SY, WF, YZ, GY, JT, JX, and ZL reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version.This study was supported by grants from the National Natural Science Foundation of China (nos. 81900690 and 82102077) and the Natural Science Foundation of Chongqing (nos. cstc2020jcyj-msxmX0065 and cstc2021jcyj-msxmX0056).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.