Here, we compared the differences in the gut microbiota of male and female healthy controls (HC) and patients with end-stage renal disease (ESRD) caused by immunoglobulin A (IgA) nephropathy (ESRD-IgAN) or type-2 diabetes mellitus (ESRD-T2DM) using high-throughput sequencing of the 16S rRNA gene. We also analyzed the correlation between gut microbiota and clinical immune indicators. We assigned 8, 10, 5, 7, 11, and 20 volunteers to female HC, ESRD-IgAN, and ESRD-T2DM, and male HC, ESRD-IgAN, and ESRD-T2DM, respectively. The results showed sex-specific differences in both physiological and biochemical indices and intestinal microbiota composition, as well as the correlation between them. The correlations between physiological and biochemical indices in men were significantly lower than those in women, especially for indices related to immunity, blood glucose, and cardiac color sonography. Urine output, lymphocyte ratio, serum albumin, blood calcium, dialysis status, serum urea nitrogen, urine protein, and diabetes significantly correlated with male fecal microbiota composition, whereas only creatinine and 2-h post-prandial blood glucose significantly correlated with female fecal microbiota composition. The top 50 dominant operational taxonomic units showed a stronger correlation with physiological and biochemical indices in samples obtained from females than from males. These differences highlight sex-specific differences in the effectiveness of ESRD prevention and treatments  Chronic kidney disease (CKD) is a global public health problem that affects 10–16% of the adult population worldwide . End-staGut microbiota is crucial for metabolic, nutritional, physiological, and immunological processes . The gutDiabetes is the leading cause of CKD in developed countries . Type-2 The analysis of gender differences in intestinal microbiota in patients with CKD and ESRD is great significance to determine whether gender influence needs to be considered in the treatment of CKD and ESRD, because CKD, ESRD, and intestinal microbiota are closely related to the host immune processes. However, although several studies have demonstrated that intestinal microbiota is associated with diabetes, CKD, and ESRD, it is still not clear whether there are gender differences between these effects. The correlation between intestinal microbiota and other diseases, such as hepatocellular carcinoma , shows g2 or required dialysis caused by primary IgA nephropathy according to the CKD Epidemiology Collaboration equations  and was performed in accordance with the Clinical Ethics Guidelines and the Declaration of Helsinki and Rules of Good Clinical Practice. Volunteers who agreed to participate in the research project were recruited from inpatients with ESRD and physical examination subjects in the Second Affiliated Hospital of Guangzhou Medical University from October 2020 to March 2021. The inclusion criteria for ESRD-IgAN patients were defined as glomerular filtration rate of less than 20 mL/min/1.73 mquations , with thBlood and urine samples were collected according to the clinical diagnostic criteria to determine the physicochemical and immunological indices . Fecal sThe clinical data of the patients were collected, and biochemical or cytological tests of blood and urine specimens were performed as previously described . FollowiMicrobial DNA was extracted from approximately 0.2 g of fresh fecal sample using a DNeasyPowerSoil Kit . The V4–V5 hypervariable region of the 16S rRNA gene was amplified using the universal primers 515F and 909R, as previously described . PolymerRaw reads were merged using FLASH 1.2.8  and procPRJNA743980.All DNA datasets were deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive database under the accession number P < 0.05.Principal coordinate analysis (PCoA) was conducted using the QIIME pipeline . PermutaThe number of volunteers assigned to the female HC, ESRD-IgAN, and ESRD-T2DM groups, and the male HC, ESRD-IgAN, and ESRD-T2DM groups were 8, 10, 5, 7, 11, and 20, respectively. In the analysis by group, age, and body mass index (BMI) did not present significant differences, indicating that the study groups were homogeneous . In geneP < 0.05, and Pearson correlation coefficient > 0.6, the physiological and biochemical indices with significant correlation were identified. Our results showed that the correlations between physiological and biochemical indices in men were significantly lower than those in women, especially for indices related to immunity, blood glucose, and cardiac color sonography (P < 0.05). However, natural killer cells were negatively correlated with CD3+, and the proportion of lobulated cells was negatively correlated with the LR  high-quality sequences were obtained from 61 samples. To exclude the influence of sequencing depth on subsequent analysis, 24,573 sequences were randomly selected from each sample for subsequent analysis. Based on 97% similarity, 5,274 OTUs were identified. The sequences (99.995 ± 0.001%) were classified into 18 phyla, in which Firmicutes (48.500 ± 2.533%), Bacteroidetes (31.976 ± 2.183%), Proteobacteria (15.365 ± 2.414%), Fusobacteria (2.946 ± 0.581%), Actinobacteria (0.811 ± 0.239%), Synergistetes (0.215 ± 0.184%), Tenericutes (0.120 ± 0.051%), and Verrucomicrobia (0.050 ± 0.031%) dominated the microbiota . There wpost hoc test to select 53 significantly different OTUs from the 268 dominant OTUs. The obtained OTUs were concentrated in four phyla: Actinobacteria, Bacteroidetes, Firmicutes, and Fusobacteria  and Pearson correlation analysis. The results showed that urine output (UO), LR, serum albumin, BCa, dialysis, urea nitrogen (UN), urine protein (UP), and diabetes were significantly correlated with the male fecal microbiota composition , whereasEscherichia coli was significantly positively correlated with age, troponin, and IgG. Albumin, UO, BCa, and the top 50 dominant OTUs in the fecal samples presented similar trends, as they were significantly negatively correlated with R. gnavus, and significantly positively correlated with P. copri and Lachnospira sp. However, UO was also significantly negatively correlated with Dorea sp., and significantly positively correlated with Megamonas sp. , and urine leukocytes (UL). Klebsiella sp. showed a similar correlation with an unidentified Enterobacteriaceae OTU, which was positively correlated with red blood cell count, blood phosphorus, and ALT, but negatively correlated with creatinine , GH, proportion of lobulated cells, and CD3+, but negatively correlated with IgA, IgG, and IgM, whereas Bacteroides caccae, Bacteroides sp., and Megamonas sp. were significantly negatively correlated with WBC count. These results suggest that Bacteroides may have a complex function in human immune regulation. B. ovatus was positively correlated with UL, and R. faecis was positively correlated with complement 3 (C3). The correlation between EF and FS with the top 50 dominant OTUs showed similar trends, that is, they showed significant positive correlation with Fusobacterium sp., and significant negative correlation with R. gnavus and Blautia sp. . Furthermore, the observed bacteria mainly included Ruminococcus, Eubacterium, Bacteroides, Clostridium, Dorea, Roseburia, Lachnospiraceae, Phascolarctobacterium, and Megamonas, which are closely related to human metabolism and immunity, and demonstrate differences between the host sex , serum creatinine (SCr), and serum uric acid (SUA), are commonly used to discriminate between CKD and control cohorts . ESRD paP. distasonis, whereas those significantly negatively correlated were Megamonas sp., Fusobacterium sp., and Lachnospira sp. In contrast, the bacteria significantly positively correlated with female serum urea nitrogen were Dorea sp., Ruminococcus sp., Phascolarctobacterium sp., P. distasonis, B. caccae, R. gnavus, Oscillospira sp., and Blautia sp., and the significantly negatively correlated bacteria were R. gnavus, F. prausnitzii, and an unidentified bacterium in Erysipelotrichaceae  can be found below: The studies involving human participants were reviewed and approved by the Clinical Research and Application Ethics Committee of the Second Affiliated Hospital of Guangzhou Medical University . The patients/participants provided their written informed consent to participate in this study.PZ: conception and design. ZC, JM, and WZ: administrative support. ZC, JF, LZ, and GL: provision of study materials or patients. XL, LX, LuL, YX, LiL, TZ, JW, HX, and RC: collection and assembly of data. PZ and ZC: data analysis and interpretation. PZ: manuscript writing. All authors: final approval of manuscript.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.


Serratia marcescens is now an important opportunistic pathogen that can cause serious infections in hospitalized or immunocompromised patients. Here, we used extensive bioinformatic analyses based on reverse vaccinology and subtractive proteomics-based approach to predict potential vaccine candidates against S. marcescens. We analyzed the complete proteome sequence of 49 isolate of Serratia marcescens and identified 5 that were conserved proteins, non-homologous from human and gut flora, extracellular or exported to the outer membrane, and antigenic. The identified proteins were used to select 5 CTL, 12 HTL, and 12 BCL epitopes antigenic, non-allergenic, conserved, hydrophilic, and non-toxic. In addition, HTL epitopes were able to induce interferon-gamma immune response. The selected peptides were used to design 4 multi-epitope vaccines constructs  with immune-modulating adjuvants, PADRE sequence, and linkers. Peptide cleavage analysis showed that antigen vaccines are processed and presented via of MHC class molecule. Several physiochemical and immunological analyses revealed that all multiepitope vaccines were non-allergenic, stable, hydrophilic, and soluble and induced the immunity with high antigenicity. The secondary structure analysis revealed the designed vaccines contain mainly coil structure and alpha helix structures. 3D analyses showed high-quality structure. Molecular docking analyses revealed SMV4 as the best vaccine construct among the four constructed vaccines, demonstrating high affinity with the immune receptor. Molecular dynamics simulation confirmed the low deformability and stability of the vaccine candidate. Discontinuous epitope residues analyses of SMV4 revealed that they are flexible and can interact with antibodies. In silico immune simulation indicated that the designed SMV4 vaccine triggers an effective immune response. In silico codon optimization and cloning in expression vector indicate that SMV4 vaccine can be efficiently expressed in E. coli system. Overall, we showed that SMV4 multi-epitope vaccine successfully elicited antigen-specific humoral and cellular immune responses and may be a potential vaccine candidate against S. marcescens. Further experimental validations could confirm its exact efficacy, the safety and immunogenicity profile. Our findings bring a valuable addition to the development of new strategies to prevent and control the spread of multidrug-resistant Gram-negative bacteria with high clinical relevance. The spread of antimicrobial resistance (AMR) is urgent, especially regarding bacteria . Once reSerratia spp. is within the World Health Organization , Coley’s toxins, and Vaccineurin. This formulation was used to treat sarcoma in many countries until 1990  , subtractive proteomics (SP), and genomics studies have emerged as powerful computational tools that have revolutionized the identification of drug targets and potential vaccine candidates . These morganism . In addiorganism . Since ts (MenB) , this toeumoniae , Pseudomruginosa , Mycobacrculosis , Acinetoaumannii , and Neingitidis .Serratia marcescens, which can be used in further experiments to validate its efficacy, safety, and immunogenic profile.In this study, we have applied RV and SP based computational strategies and selected a new multi epitope-based vaccine candidate against S. marcescens strain. A flowchart summarizing the methodology is shown in Subtractive proteomics and reverse vaccinology approaches were used to identify potential vaccine candidates against the S. marcescens were downloaded from the Genome Project database of the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/genbank/). Out of these proteomic sequences, one corresponded to the representative proteome of Serratia marcescens subsp. marcescens Db11 and 48 sequences were from S. marcescens associated with human infections. Bacterial Pan Genome Analysis (BPGA) tool . BPGA uses USEARCH as a default protein clustering tool with an identity cut off = 50%. Strain names, source of isolation, country, RefSeq assembly accession numbers, assembly levels, and references are shown in The proteome sequences of 49 GA) tool  version -4 and bitscore ≥ 100  (http://www.mgc.ac.cn/VFs/) (http://mvirdb.llnl.gov/) -4 and bitscore ≥ 100. The resistance associated proteins were found through a BLASTp against two databases, ARG-ANNOT (Antibiotic Resistance Gene-ANNOTation), which provides protein sequences associated with antibiotic  providing the essential information of the proteins , 38–42. cn/VFs/)  and Micrtibiotic , and CARtibiotic . The E-vHomo sapiens (taxid:9606), using BLASTp with E-value of ≤ 10−4  that determines different subcellular localization like cytoplasmic membrane, outer membrane, periplasm, extracellular, cytoplasmic, and unknown; and CELLO v2.5 (http://cello.life.nctu.edu.tw)  , a web-bhttps://web.expasy.org/protparam/) and UniProt (https://www.uniprot.org/). Antigenicity of proteins was predicted using two online servers: VaxiJen v2.0 (http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html) which predicts whether a protein could be a protective antigen based on physicochemical properties of amino acid sequence and has a threshold value ≥ 0.5; and AntigenPRO (http://scratch.proteomics.ics.uci.edu/), an alignment-free, sequence-based and pathogen-independent predictor of protein antigenicity with 79% accuracy and an area under curve (AUC) of 0.89  server, which predicted the topology of these proteins by the Markov method , a server based on deep neural network method that predicts signal peptide (SP) sequences and discriminates among three main types of SPs  pathway database. Proteins that play a role in unique and shared pathways in both pathogen and host were enlisted (https://www.ebi.ac.uk/interpro/), a server that provides family classification, biological process and molecular function of the protein (The comparison between metabolic pathways of enlisted Table S2enlisted . Protein protein .http://tools.iedb.org/tepitool/) server , and NetCTLpan 1.1 . In the IEDB server, 27 different alleles that cover more than 97% of the global population were selected for MHC class I predictions  uses Artificial Neural Network (ANN) machine-learning to predict B-cell epitopes and has an accuracy of 65.93%. In this server, parameters were set to default. Bcepred (https://webs.iiitd.edu.in/raghava/bcepred/bcepred_instructions.html) predicted B-cell epitopes based on four amino acid properties . We used a threshold of 2.38 that predicts epitopes with 58.7% accuracyPredictions of MHC class II epitopes or HTL epitopes were made by Tepitool, using the IEDB recommended method. A set of the 26 most frequent human class II alleles from DP, DQ, and DR loci was used. Selection criteria was peptides with binding affinity ≤ 50nM for IC50. Prediction of linear B-cell epitopes or BCL epitopes for proteins was achieved by using IEDB server, ABCpred, and Bcepred. IEDB server predicted epitopes based on antigenicity , accessihttp://tools.immuneepitope.org/immunogenicity/). A high score suggests a higher probability of stimulating an immune response. The epitopes with positive immunogenicity value were selected for further studies.The MHC I immunogenicity prediction were assessed by the IEDB server  (http://http://www.imtech.res.in/raghava/toxinpred/index.html), keeping all the parameters to default. This tool predicts the antigenic behavior of epitopes through their physicochemical properties and confirms that the specific immune responses in the host cell will only target the bacteria rather and not host tissue (https://www.ddg-pharmfac.net/AllerTOP/feedback.py). This is a server based on the main physicochemical properties of proteins , an online tool with 82.10% accuracy. The server constructs overlapping sequences from which the IFN-γ epitopes are predicted. The default prediction method was set as “Motif and Support Vector Machine (SVM) hybrid” and “IFN-gamma vs. Non-IFN-gamma” model to predict IFN-γ-inducing peptides based on score. The higher the score, the higher the chance of inducing IFN-γ , an online tool for generating de novo peptide 3D structure (https://bioinfo3d.cs.tau.ac.il/PatchDock/php.php) tool. The obtained models were refined and re-scored by FireDock server (http://bioinfo3d.cs.tau.ac.il/FireDock/), that ranks the docked models by their global energy, and the lowest global energy represented the best prediction (https://pymol.org/pymol.html?) (The best-selected MHC class I and MHC class II epitopes were submitted to PEP-FOLD3 server (tructure . The docediction . The MHCl.html?) . The epiM. tuberculosis, accession number: AGV15514.1), and HBHA conserved sequence  sequence with CTL epitopes and the CTL epitopes among themselves. GPGPG linkers were used to conjugate the CTL epitopes with HTL epitopes and also the HTL epitopes with the other HTL. KK linkers were used to attach the HTL and BCL epitopes as well as the BCL epitopes among themselves . Adjuvansequence .http://crdd.osdd.net/raghava/algpred/) servers were used to evaluate the allergen potential of the multi-epitope vaccine construct. Allergen prediction is based on similarity of known epitope of any of the known region of the protein. It uses MAST to search MEME/MAST allergen motifs and predict the allergen if it has a motif. AlgPred is an SVM module based program which uses amino acid or dipeptide composition for the prediction of allergen. The parameters (IgE epitope + MAST + SVM + ARPs BLAST) were combined to predict the allergenicity of vaccine constructs , a neural network-based method trained on MHC class I ligands produced by the human proteasomes (http://www.dmbr.ugent.be/prx/bioit2-public/SitePrediction/index.php) server for MHC class II epitopes , a web-based freely accessible online server that also predicts the transmembrane topology, transmembrane helix, fold and domain recognition (https://zhanglab.ccmb.med.umich.edu/I-TASSER/). I-TASSER is an integrated platform for automated protein structure and function prediction based on the sequence-to-structure-to-function paradigm. I-TASSER initial creates three-dimensional (3D) atomic models from several threading alignments and iterative structural assembly simulations starting from an amino acid sequence. In five community wide CASP  experiments, I-TASSER has been ranked best server for protein 3D structure prediction (The secondary structures of the multi-epitope vaccine constructs were generated using online tool PSIPRED 4.0 (ognition . PSIPREDognition . The 3D ediction . Pymol phttp://sysbio.rnet.missouri.edu/3Drefine/). 3Drefine server is based in optimization of the hydrogen bonding network and composite physics and knowledge-based force fields to give atomic-level energy minimization using the MESHI molecular modeling framework (https://servicesn.mbi.ucla.edu/PROCHECK/) (https://prosa.services.came.sbg.ac.at/prosa.php) (http://services.mbi.ucla.edu/ERRAT/)   that anaOCHECK/) ; ProSA (osa.php)  that comosa.php) ; and ERR/ERRAT/)  that ana/ERRAT/) .https://cluspro.bu.edu/login.php) and PatchDock (https://bioinfo3d.cs.tau.ac.il/PatchDock/php.php). ClusPro 2.0 ranks the cluster of docked complexes based on their center and lowest energy scores (https://haddock.science.uu.nl/) and FireDock server (http://bioinfo3d.cs.tau.ac.il/FireDock/php.php), respectively. The PRODIGY server produces binding affinity score (Each vaccine construct was docked against TLR4-MD2 complex (PDB:3FXI). The docking experiments were made using ClusPro 2.0 (y scores . PatchDoy scores . ClusProty score  and the http://imods.chaconlab.org/)  complexed with TLR4-MD2 was subjected to molecular dynamic simulation by the online server iMODS (ab.org/) , using tab.org/) , 97. Morab.org/) .http://tools.iedb.org/ellipro/) that predicts epitopes based upon solvent-accessibility and flexibility  was used for the immune simulation study. It uses position-specific scoring matrix for immune epitope forecast and machine learning techniques to estimate immune interactions (C-ImmSim server (ractions . The thrractions . All parractions .http://www.prodoric.de/JCat)  plasmid vector by adding XhoI and NdeI restriction sites at C and N terminus, respectively. The optimized sequence of the vaccine was inserted into the expression vector [pET-28a (+)] using Benchling webserver (https://www.benchling.com/).Reverse translation and codon optimization were performed using Java Codon Adaptation Tool (JCat) server (de/JCat) . The JCade/JCat) . The idede/JCat) . The E. S. marcescens associated with human infections, and a Serratia marcescens subsp. marcescens Db11 as a reference strain for our vaccine prediction. The proteomes of all S. marcescens strains were retrieved from Genome Project database of the National Center for Biotechnology Information (NCBI). With the help of Bacterial Pan Genome Analysis (BPGA) tool the number of core proteins found from analyzing of the 49 proteomes was 2832 proteins.Primarily, we selected representative proteomes of 48 All the 2832 proteins were subsequently analyzed for essential, virulent and resistance functions. The analyses of the non-redundant proteins resulted in 1815 proteins. Of these proteins, we have found 879 essential proteins, 155 proteins contained virulence property, 98 were resistant proteins, 370 proteins were found to be virulence and essential, 70 were resistant and essential, 42 resistant and virulence, and 201 proteins were related with essential, virulence and resistance functions, 1106 were non-homologous with human proteins. Of these 1106 proteins, 20 were gut flora non-homologous proteins, and were used for subsequent analysisNext, the subcellular localization of 20 gut flora non-homologous proteins revealed that 2 proteins were outer membrane proteins, 6 periplasmic, and 2 extracellular Table 1.Of these 10 proteins selected, analyses of presence of signal peptide/anchor resulted into 3 proteins with secretory signal peptides that are transported by the Sec translocon and cleaved by Signal Peptidase I (Sec/SPI), 2 proteins having lipoprotein signal peptides transported by the Sec translocon and cleaved by Signal Peptidase II (Sec/SPII), and 1 protein with Tat signal peptides transported by the Tat translocon and cleaved by Signal Peptidase I (Tat/SPI). Only 1 protein (MoaF domain-containing protein) contained 1 transmembrane helix Table 1.). We excluded the patatin-like phospholipase family protein because its prediction analysis did not reach all the recommended parameters .The prediction of T-cell epitopes of MHC class-I of the 5 proteins  had the sequence length 9 residues. Among the 284 predicted epitopes, the 123 common epitopes found in three servers were selected for immunogenicity analysis and resulted in 59 epitopes. From these, 31 epitopes were found to be antigenic and we found no epitopes with toxicity. Out of 31, 17 epitopes were non-allergenic. Epitope conservancy analysis found 14 peptides with a score of more than 50%. GRAVY analysis resulted in 7 peptides with negative value score, which suggests hydrophilic nature of peptides. For further analysis, we selected 7 MHC class-I epitopes  that were found to be immunogenic, antigenic, non-allergenic, non-toxic, conserved, and with negative hydropathicity Table 2The MHC-II binding prediction of the 5 proteins  resulted in 415 MHC-II epitopes with higher affinity. From these, 196 were antigenic, and all were subjected to toxicity and allergenicity prediction. According with results, all selected epitopes were non-toxic and 114 had non-allergic nature. Conservancy analysis showed that 93 epitopes had score more than 50%, and GRAVY analysis revealed that 70 epitopes had a hydrophilic nature. Additionally, the 31 the best resultant epitopes of all analyses conducted were analyzed for their IFN-γ inducing. A total of 16 epitopes  had a IFN-γ inducing profile and were selected for molecular docking analysis Table 3.The prediction of linear B-cell epitopes for D-alanyl-D-alanine carboxypeptidase, patatin-like phospholipase family protein, Phospholipase C, TonB-dependent receptor and spore coat U domain-containing protein is showed in All the 7 MHC class I and 16 MHC class II T-cell epitopes were subjected to 3D structure generation by the PEP-FOLD3 server, and the predicted 3D structures found were docked with 8 MHC class I alleles and 7 MHC class II alleles, respectively. Among the epitopes, 5 MHC class I and 12 class II epitopes showed the best result with the lowest global energy of -34.89 and -70.54, respectively Table 5 M. tuberculosis, accession number: AGV15514.1) . For vaccine construction, the adjuvant sequence was linked with PADRE sequence by EAAAK linker, GGGS linkers were used to join the PADRE sequence with the CTL epitopes and the CTL epitopes with the other CTL epitopes, GPGPG were used to linked the CTL epitopes with the HTL epitopes and also the HTL epitopes among themselves, and KK linkers were used to conjugate HTL with the BCL epitopes, the BCL with the other BCL epitopes, and BCL with the PADRE sequence. Each vaccine construct was finished by an additional GGGS linker.We combined an adjuvant, PADRE sequence, CTL epitopes (MHC-I epitopes), HTL epitopes (MHC-II epitopes) and BCL epitopes (B-cell epitopes) in a sequential manner, and constructed four vaccines candidates, named SMV1, SMV2, SMV3 and SMV4. All designed vaccine proteins contained 5 CTL epitopes, 12 HTL, and 12 BCL epitopes. The vaccines differed each other only by adjuvant sequence, and the adjuvants used were 50s ribosomal L7/L12 protein, beta defensin, HBHA conserved sequence and HBHA protein (15514.1) Table 6E. coli. Therefore, all of the vaccine constructs showed be antigenic, non-allergenic, hydrophilic, stable and soluble. The sequence of vaccine constructs and their physiochemical properties are showed in Each designed vaccine construct contained 668 (SMV1), 659 (SMV2), 554 (SMV3) and 639 (SMV4) residues long, while the molecular weight of each construction was found to be 70.335, 69.217, 57.867 and 66.147 kDa respectively. The theoretical pI of each construct ranged from 9.85 to 10.36, suggesting that the constructions have a negative charge if the pH is above the isoelectric point and vice versa. The computed instability index of constructions varied from 28.01 to 35.66 representing the stable nature of the vaccine proteins. The high aliphatic index range (66.68 to 74.19) of all vaccine constructs suggest the protein stability in several temperatures. The negative GRAVY value of the vaccine constructs revealed that all of them has a hydrophilic in nature. All four vaccine constructs showed good solubility (>0.873) during its heterologous expression in the We investigated both proteasomal and cathepsin specific peptidase activity on the vaccine constructs. NetChop 3.1 server detected 17 proteasomal sites, which majority of them were close to the linkers. SitePrediction server provided 1 peptidase and 14 peptidase links with 99.9% and 99% specificity for cathepsin B, respectively; 1 peptidase and 2 peptidase links with 99.9% and 99% specificity for cathepsin D, respectively; 8 and 3 peptidase links with 99% specificity for cathepsins E and G, respectively; 2 peptidase links with 99.9% and 4 peptidase links with 99% specificity for cathepsin K, and 1 peptidase link with 99% specificity for cathepsin L. Our results indicates that these multi-epitope vaccine constructs might be processed and presented in context of MHC class molecule.The analyze of the secondary structure of vaccine constructs showed that SMV1 had 48.35% of amino acids in coil structure, 40.12% of amino acids in alpha helix, and the lowest percentage of the amino acids in beta sheet formation (11.23%). SMV2 had 49.75% of amino acids in coil structure, 38.56% in alpha helix region, and 11.69% of the amino acids in the beta sheet formation. SMV3 had the highest percentage of coil structure (55.05%), 27.62% of the amino acids in alpha helix region, and the highest percentage of the amino acids in the beta sheet formation (17.33%). SMV4 presented coil structure in 54.23%, 30.05% of alpha helix region, and 15.72% of the amino acids in the in beta sheet formation Figure 2The 3D structure was obtained by threading using I-TASSER web server. For each vaccine sequence was predicted five 3D models, and the first model of each construction was selected. All the model was ranked on their C-scores values, which measure similarity between the query and template based on the significance of threading template alignment and the query coverage parameters. C-score values ranges between -5 and 2, and a higher value represents a model with a higher confidence and correct topology. SMV1 presented a Z-Score ranging from 0.64 to 2.42 and a C-Score of -2.41. SMV2 showed a Z-Score ranging from 0.65 to 2.39 and a C-Score of -2.41. SMV3 had a C-Score of -1.92 and a Z-Score ranging from 1.08 to 3.43. SMV4 exhibited a Z-Score of 1.06 to 5.61 and the highest C-Score, -1.34 . The ProSA Z-Score for SMV1, SMV2, SMV3 and SMV4 were -4.60, -4.42, -2.02 and -5.16 respectively, indicating models were within the range of scores typically found for the native proteins of similar size , in order to find out the best constructed ). The elastic map  of SMV-4-TLR4 docked complex is showed in ). The amino acid residues present in conformational epitopes, the number of residues, their scores, and the 3D representation of conformational B-cell epitopes are shown in Eight discontinuous B-cell epitopes with scores ranging from 0.713 to 0.872 were predicted by Ellipro online tool at IEDB. Shortest and longest discontinuous B cell epitope ranged from 3 to 63 residues long respectively , showing good probable expression of the vaccine candidate in the E. coli K12; and CAI value 0.958 (0.8–1.0), indicating a high gene expression potential. In the next step, the SMV4 sequence was cloned between XhoI and NdeI restriction sites at the multiple cloning-site of the pET28a(+) vector. The clone had a total length of 7212 bp , a family of proteins inhibited by ß-lactam antibiotics involved in peptidoglycan synthesis and remodeling , transmembrane helix, Signal IP, MHC-I and MHC-II adhesion probability, and essentiality. Some features are required to select a potential vaccine candidate, such as sub-cellular localization; presence of a signal peptide; transmembrane domain; and antigenic epitopes. In addition to recognizing antigenic and virulence factors, one of the main strategies behind identifying potential vaccine candidates is predicting epitopes that are likely to bind to major histocompatibility complex molecules on the antigen presenting cells within the host  epitopes since this cytokine plays a significant role in innate and adaptive immune responses, stimulates macrophages and natural killer cells, and provides an enhanced response to MHC antigens , HBHA conserved sequence, beta-defensin, L7/L12 ribosomal protein  were constructed using five MHC class-I, twelve MHC class-II and twelve B cell epitopes; four different adjuvants HBHA protein . This study was partially financed by the Fundação de Amparo à Pesquisa do Estado de São Paulo-Brazil (FAPESP) as a fellowship to MD (FAPESP fellowship 2018/24213-7).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.


In 2021 the International Brazilian Journal of Urology received the highest impact factor of his history and this fact was possible because the serious peer review process of our Journal (Thanks a lot!!!!!


Sensor measurements of civil structures provide basic information on their performance. However, it is impossible to install sensors at every location owing to the limited number of sensors available. Therefore, in this study, we propose an optimal sensor placement (OSP) algorithm while reducing the system order by using the constraint condition between the master and slave modes from the target modes. The existing OSP methods are modified in this study, and an OSP approach using a constrained dynamic equation is presented. The validity and comparison of the proposed methods are illustrated by utilizing a numerical example that predicts the OSPs of the truss structure. It is observed that the proposed methods lead to different sensor layouts depending on the algorithm criteria. Thus, it can be concluded that the OSP algorithm meets the measurement requirements for various methods, such as structural damage detection, system identification, and vibration control. In the finite element analysis model, it is impractical to measure the response at a full set of degrees of freedom (DOFs) for electromagnetic induction. Measurements must be undertaken using sensors that are smaller than the system order. Optimal sensor design involves establishing an appropriate number of sensors and sensor positions to investigate the structural performance. Measurements and data analysis are performed for structural health monitoring and damage detection and control, among others. Therefore, sensor locations must be designed in accordance with this purpose. Studies on optimal sensor placement (OSP) have been conducted over the past several decades. With the advent of novel measurement technologies and innovative sensors, OSP techniques have been gradually developed for more accurate evaluation.The OSP algorithms investigate the relative influence of the signal at the sensor locations of a finite element model with a limited number of sensors. Chang and Pakzad  discusseThe effective independence (EI) method is one of the most widely used OSP techniques, which uses the diagonal sum of the Fisher information matrix (FIM) to optimize the linear independence of the mode shape . The EI The modal kinetic energy (MKE) method provides information on the DOFs for each mode to exhibit the maximum responses. Zhou et al.  proposedThe modal assurance criterion (MAC) is a method that determines the OSP from a value by matching the mode shape obtained using the finite element model and the mode shape obtained from the experiment. The MAC-based OSP algorithm captures the maximum value from the elements on the off-diagonal line of the MAC matrix. He et al.  presenteSingular value decomposition (SVD) provides singular values and corresponding orthonormal singular vectors. Kammer  presenteExisting OSP methods based on mode shape and frequency are inefficient because they only use low-frequency regional data. A frequency-effective independence technique based on the frequency domain using principal components evaluated from the frequency response function to obtain data in the low- and middle-frequency domains was presented by Rao et al. . In this study, we propose an optimal sensor design method for an order-reduced system using constraint conditions, constrained dynamic characteristics, and the modal analysis theory. The constraint conditions are extracted by dividing the target modes into master and slave modes and establishing the relationship between them. By utilizing the reduced model of master DOFs, existing OSP methods, such as the modal reduction (MR) method based on the EI, MAC, MKE, and modal strain energy (MSE) methods, are modified. A constrained dynamic equation (CDE) approach using SVD and EI methods is presented, which is derived based on the corresponding OSP criteria. The iteration continues until it matches the initial number of sensors. The final sensor layout is composed of master DOFs with high signal strength. The validity and comparison of the proposed methods are illustrated in a numerical example of the OSP of a truss structure, which shows that the sensors are positioned at different locations because they are sensitive to the OSP algorithm criteria for meeting the objectives of measurement.The OSP algorithms were derived based on the relationship of the modal data. The incomplete target modal data were divided into slave and master modes, and the constraint condition between these two sets was established. Existing OSP algorithms, such as EI, FIM, MKE, MSE, SVD, and MAC techniques, were modified using the order-reduced system under the constraint condition. The modified OSP methods trace potent sensor locations by repeatedly deleting the DOFs corresponding to the lowest signal strength in the master DOFs at each iteration process. M and K denote the u and i-th column of the mode shape matrix. The OSP process begins with a modal analysis of the dynamic system. The dynamic equation to describe the free vibration of an N-DOF undamped dynamic system can be written asu in terms of y is the s and m denote the slave and master modes, respectively. It is difficult to collect a full set of mode-shaped data of the system. Incomplete target modes smaller than the system DOFs were considered to describe the dynamic responses. Assuming the first The method gradually traces the OSP layout at the master DOF iteratively. The reduction process of the master modes continues until the number of candidate sensors corresponds to the initial sensor number, This method is an OSP algorithm that synthesizes an order-reduced system using the EI method. When comparing the signal strength of the order-reduced system expressed by the master DOFs, the candidate DOF with low contribution is relocated to the slave DOF at each iteration. Thus, the candidate sensor locations gradually decrease and coincide with the number of locations in the master mode. Using the generalized inverse in the second equation of Equation (2) and solving it with respect to R, the displacements at the master DOFs can be determined, where R denotes the Boolean matrix to define the master DOFs.The displacements in the slave DOFs are expressed in the master DOFs using the first equation of Equation (4b). The estimated displacement responses containing the external noise can be written asu, of Equation (4a), and The FIM was utilized to obtain the covariance matrix for predicting the maximum expectations as the OSP criterion of the MR approach. The error covariance between the unbiased and biased displacement vectors, The EI method investigates the contribution of candidate sensor locations depending on the spatial independency. Thus, the contribution of each row of the master modes to the total eigenvalues of the FIM is compared, and the row with a low influence index is moved to the slave DOF. In 1990, Kammer presented an effective information distribution (EID) matrix written asj denotes the row of the j-th matrix Using the EI method, the sensor placements are judged by the evaluation of the signal strength, wherein the eigenvalues affect the candidate DOFs. The importance of sensor placement is compared with the elements in the EI matrix. The sum of squares of each row of the The DOF in the candidate sensor locations corresponding to the lowest I denotes the This approach was derived by applying the SVD algorithm to a CDE using modal constraints. The dynamic responses of the order-reduced system can be predicted using incomplete modes as constraint conditions. The dynamic responses were described in accordance with the constraint conditions. From the first equation in Equation (4b), the following relationship between the master and slave displacement DOFs is established:Equation (9) represents the constraints that restrict the dynamic responses of the system. Thus, the system can be described by =Utilizing Equations (1) and (9) in the dynamic equation of motion for a constrained dynamic system derived by Udwadia and Kalaba  yields(Equation (10) is modified by the difference between the constrained and unconstrained acceleration vectors as follows:As V and U are eigenvectors of SVD is related to the eigenvalue decomposition. The non-zero elements of matrix The modified MKE (MMKE) method evaluates the degree of dynamic contribution in an entire finite element model. Using Equation (2), the MMKE matrix is modified in terms of the target modes and the mass matrix as follows.j-th row of the MMKE matrix. Matrix The modified MSE (MMSE) method utilizes the stiffness matrix instead of the mass matrix in Equation (13) using the MMKE method. The following objective function is evaluated by the sum of squares of the rows of the MMSE matrix: P is moved to the slave DOFs. The remainder of the procedure is similar to that of the MMKE method by iteration.The DOF corresponding to the smallest Modified MAC (MMAC) measures the degree of linearity between two vectors of mode shapes and depends on the angle expressed by the dot product between the two vectors. By substituting Equation (2) into the dynamic equation of Equation (1) and pre-multiplying the result by Utilizing Equation (17) is expressed by The value in the MMAC matrix determinies the correlated mode pairs and ranges between 0 and 1. The MMAC matrix can be written asIf the MMAC value is 1, the two vectors match, and if the value is zero, they are orthogonal. The diagonal elements in the MMAC matrix take a value of 1 because the two mode vectors match and the off-diagonal element values are less than 1. A small MMAC value indicates a clear distinction between the two modes because the relationship between the two modes is small. A large value indicates that the distinction is unclear. The MMAC-based OSP technique optimizes the sensor layout corresponding to the minimum off-diagonal elements to be easily distinguished.The row corresponding to the maximum MMAC value of the off-diagonal in the MMAC matrix is deleted and relocated to the slave DOFs. The objective function to evaluate the MMAC value between the two mode shape vectors can be written asp value is relocated to the slave DOFs until the number of master DOFs coincides with the optimal sensor number.The MMAC values in Equation (18) and their evaluation using Equation (19) are iteratively calculated, and the row with a high The validity of the proposed method was illustrated in the truss structure presented by Sun and Buyukozturk . The truThis example specifies eight sensor locations out of 27 DOFs using the proposed methods and utilizes the lowest six mode shape matrices as the target modes. The initial candidate sensor placements can be selected based on experience and structural topology. In this numerical example, an initial sensor in the slave DOFs was taken as the horizontal-displacement DOF at node 15 by the MMKE method. The sensor location with the lowest MMKE contribution was classified as the slave DOF, and the remainder as master or candidate DOFs. The final sensor layouts predicted by the proposed OSP algorithm are summarized in The sensor layouts did not coincide with the results of Sun and Buyukozturk. The proposed methods also did not provide similar results, except for a few locations, as shown in The proposed OSP algorithms provided different locations depending on the sensor design criteria, as shown in The measurements were performed for a variety of purposes, such as system identification, damage detection, vibration control, and data expansion. Thus, it can be concluded that the OSP design criteria meet the prescribed purpose for subsequent analysis using sensor measurement data.In this study, we proposed OSP algorithms to design a sensor layout by deleting candidate DOFs that deviate from the objective function. The algorithms were derived using an order-reduced model with the constraint conditions of modal coordinates and modal analysis theory. The sensors established by different criteria were positioned at the DOFs to represent the independent signal strength. It was observed from the numerical example of the OSP of the truss structure that the proposed methods provided different locations depending on the contribution degree of the objective function utilized for system identification, damage detection, vibration control, and data expansion. The inconsistency in the OSP design resulted from the different OSP algorithm criteria. Thus, the sensors must be designed such that they meet the prescribed measurement purpose for subsequent analysis using measurement data.


Acinetobacter spp. The latter feature is due to sulbactam’s ability to inhibit certain penicillin-binding proteins, essential enzymes involved in bacterial cell wall synthesis in this pathogen. Because sulbactam is also susceptible to cleavage by numerous β-lactamases, its clinical utility for the treatment of contemporary Acinetobacter infections is quite limited. However, when combined with durlobactam, the activity of sulbactam is effectively restored against these notoriously multidrug-resistant strains. This sulbactam-durlobactam combination is currently in late-stage development for the treatment of Acinectobacter infections, including those caused by carbapenem-resistant isolates, for which there is a high unmet medical need. The following mini-review summarizes the molecular drivers of efficacy of this combination against this troublesome pathogen, with an emphasis on the biochemical features of each partner.Durlobactam is a new member of the diazabicyclooctane class of β-lactamase inhibitors with broad spectrum activity against Ambler class A, C, and D serine β-lactamases. Sulbactam is a first generation β-lactamase inhibitor with activity limited to a subset of class A enzymes that also has direct-acting antibacterial activity against  Acinetobacter species are among the most urgent threats to human health [Acinetobacter infections are chronic, with mortality rates of 40–60%  n health . These pf 40–60% . The incf 40–60%  to 35% of 40–60% . Clinicaf 40–60% . In the orldwide .Acinetobacter infections. Although cefiderocol (Fetroja®), which was recently approved for the treatment of drug-resistant Gram-negative pathogens, has potent in vitro activity against MDR Acinetobacter  β-lactamase inhibitor with broad-spectrum activity against Class A, C, and D β-lactamases  with intrinsic antibacterial activity against ctamases . The keyAcinetobacter and a limited number of other bacterial species  of durlobactam varies between β-lactamases  and the cyclic urea is opened .The covalent bond formed between durlobactam and the active site serine, like that of avibactam , is reve3 moiety, was observed with a subset of β-lactamases . This modification likely leads to an irreversibly inhibited enzyme, since the 197 Da adduct remained in place during acylation exchange experiments.The demonstration that intact durlobactam dissociates, rather than being released as a hydrolytic product, is the ability of the inhibitor to exchange from one enzyme molecule to another (acylation exchange) . Since dThe average number of molecules of durlobactam per molecule of β-lactamase required to achieve full inhibition, which is known as the partition ratio or turnover number, was measured for several enzymes . In mostA. baumannii with a inact/Kik value of 1,800 ± 600 M–1 s–1  concentrations of durlobactam  was solved by A. baumannii isolates are remarkable in the number and diversity of β-lactamase genes each individual strain encodes. A recent analysis of 84 non-clonal, globally diverse clinical isolates from 2006 to 2014 revealed that all strains encoded at least two and up to five distinct β-lactamase genes. These included endogenous class C adc  β-lactamases plus at least one and up to three distinct class D β-lactamases. In addition, over half of the isolates also encoded at least one and sometimes multiple class A β-lactamase genes. This analysis confirmed that any effective Acinetobacter-targeting BLI must demonstrate potent, broad activity against all three classes of serine β-lactamases. The meropenem MIC90 against this collection of strains was ≥128 mg/L whereas the sulbactam-durlobactam MIC90 was 4 mg/L  subsets , to determine its efficacy and safety in patients with bloodstream infections, hospital-acquired or ventilator-associated bacterial pneumonia due to A. baumannii-calcoaceticus complex . If the phase 3 trial confirms clinical efficacy and safety and gains regulatory approval, sulbactam–durlobactam will be an important treatment option for patients with serious and life-threatening infections caused by Acinetobacter species, including carbapenem-resistant strains.Sulbactam-durlobactam was well tolerated in Phase 1 studies in healthy volunteers, and in a Phase 2 study in combination with imipenem in patients with complicated urinary tract infections, including acute pyelonephritis . SulbactAS, SMM, and AM wrote sections of the manuscript. SHM generated the computational model shown in All co-authors are employees of Entasis Therapeutics. The authors declare that this study received funding from Entasis Therapeutics. The funder had the following involvement in the study: the study design, collection, analysis, interpretation of data, the writing of this article and the decision to submit it for publication.


Altogether, the results presented in this study suggest that fungal mitogenomes are diverse, contain accessory elements and are absent of a conserved gene that can be used for the taxonomic classification of the Kingdom Fungi.Fungi comprise a great diversity of species with distinct ecological functions and lifestyles. Similar to other eukaryotes, fungi rely on interactions with prokaryotes and one of the most important symbiotic events was the acquisition of mitochondria. Mitochondria are organelles found in eukaryotic cells whose main function is to generate energy through aerobic respiration. Mitogenomes (mtDNAs) are double-stranded circular or linear DNA from mitochondria that may contain core genes and accessory elements that can be replicated, transcribed, and independently translated from the nuclear genome. Despite their importance, investigative studies on the diversity of fungal mitogenomes are scarce. Herein, we have evaluated 788 curated fungal mitogenomes available at NCBI database to assess discrepancies and similarities among them and to better understand the mechanisms involved in fungal mtDNAs variability. From a total of 12 fungal phyla, four do not have any representative with available mitogenomes, which highlights the underrepresentation of some groups in the current available data. We selected representative and non-redundant mitogenomes based on the threshold of 90% similarity, eliminating 81 mtDNAs. Comparative analyses revealed considerable size variability of mtDNAs with a difference of up to 260 kb in length. Furthermore, variation in mitogenome length and genomic composition are generally related to the number and length of accessory elements . We identified an overall average of 8.0 (0–39) introns, 8.0 (0–100) HEGs, and 8.2 (0–102) uORFs per genome, with high variation among phyla. Even though the length of the core protein-coding genes is considerably conserved, approximately 36.3% of the mitogenomes evaluated have at least one of the 14 core coding genes absent. Also, our results revealed that there is not even a single gene shared among all mitogenomes. Other unusual genes in mitogenomes were also detected in many mitogenomes, such as  The Kingdom Fungi is one of the most diverse and globally distributed groups of eukaryotes. Fungi evolved a wide morphological, physiological, and ecological heterogeneity that allowed them to perform vital functions in both terrestrial and aquatic ecosystems . The gloEstimations indicate that only ∼10% of the fungal diversity has been described, with the subkingdom Dikarya containing ∼97% of all known species, further distributed into the phyla Ascomycota, Basidiomycota, and the recently added Entorrhizomycota . AscomycEukaryotes and prokaryotes are in constant interaction, and the most ancient symbiosis we know about led to the emergence of mitochondria . MitochoSaccharomyces cerevisiae (Ascomycota) , the ribosomal genes rnl and rns, and from 20 and 31 tRNA genes  . TypicalNA genes . The gen Candida . Mitogenectively . This plectively .The presence of accessory DNA elements, such as introns, HEGs, and uORFs are directly correlated to the modulation of mtDNA . In fungIn spite of the diversity of size and genes, mitogenomes can be a key tool to investigate phylogenetic relationships by exhibiting a low recombination rate, diverse patterns of inheritance, and by displaying a mutation rate different from the nuclear genome . AdditioConsidering the increasing number of fungal mitogenomes available in genomic databases in the last few years due to exponential growth of high-throughput sequencing (HTS), we observed a discrepancy regarding the diversity of fungal taxa represented. As fungi display a great diversity of species with a broad range of ecological functions, we selected the Kingdom Fungi as a model to better understand the macroevolutionary patterns underlying mitogenome diversity. We characterized the fungal mitogenomes according to genome size, content, and sequence homology to better understand the mechanisms involved in the observed sequence diversity among fungal mtDNAs. We also correlated the genetic code with mitogenome topology, genome size, and gene composition of core and accessory elements. Some species of the 12 fungal phyla are overrepresented; meanwhile, at least four phyla have no representative in genomic databases. In addition, we found a high variation in mitogenome length in fungi. This variation may be due to the variable abundance of accessory  and protein-coding regions. Our analyses showed that no gene is universally conserved in fungal mitogenomes, suggesting that mitochondrial gene content can vary widely without disrupting organelle function. The data explored herein provides a significant addition to our understanding of the diversity and evolution of fungal mitogenomes.A literature review was carried out to obtain information on the number and objective of studies published with fungal mitogenomes until December 2020. The search was performed with the keywords “((fungi[Title/Abstract]) OR ) AND ((mitochondria[Title/Abstract]) OR (mitochondria[Title/Abstract])) AND genomic” in the PubMed database. The abstract of each study was evaluated and classified by year of publication and by topic: characterization of mitogenomes  or biotechnological . The 1 on December 4th, 2020. In our analyses, we selected representative and non-redundant mitogenomes based on the threshold of 90% identity using the CD-HIT software (n = 685) were used in all subsequent analyses. Identification of fungal species, taxonomic classification, and accession numbers for the mitogenomes are provided in A total of 788 mitogenomes were available for download in the NCBI databasesoftware . This thsoftware . Two mit2 to assess the proper mitochondrial genetic code for protein-coding gene annotation. Mitogenomes were annotated using Mfannot3 to standardize the process and to allow a fairer comparative analysis. Mfannot program was selected for its greater specificity in the annotation of fungal mitochondrial genes that has already been used in other studies of mitogenome characterization  were manually curated. A gene was considered absent when it did not show nucleotide similarity above 50% when compared to all mitochondrial genes deposited in the NCBI database4. All annotations are available in We verified each species’ page in NCBI Taxonomy Browserrization . Mitogen5 to identify known protein domains in the unidentified open reading frames.With the annotation files generated by Mfannot, we extracted the sequences annotated as “ORFs” and translated them into amino acids using the corresponding mitochondrial genetic code for each species. These protein sequences were then submitted to the Batch CD-Search interface of NCBI’s Conserved Domain Databasedpo), and RNA-polymerase (rpo), were clustered into different files for further analyses. Furthermore, we estimated the coding potential of all annotated ORFs (with and without known protein domains) using the CPC2 software , tRNA genes and other unusual genes found in mitogenomes (dpo and rpo). The number of features per file was counted to get the total number of features per category. Another Python algorithm that removes overlaps of feature coordinates and calculates final genic lengths was written to properly calculate the length of the intergenic spaces in fungal mitogenomes. These numbers were subtracted from the total fungal mitochondrial genome length, resulting in the total length of intergenic regions. The data obtained from the aforementioned scripts were organized and plotted using the ggplot2 package v.3.3.5 . According to “feature name,” output files (tabular and fasta format) were generated for multiple categories: ORFs, introns, HEGs, core genes . Despite the removal of these sequences, the dataset remained with almost 90% of all mitogenomes, suggesting that the genomic content is quite divergent, which led us to investigate the conservation of protein-coding genes and the presence of accessory elements  in most of the mitogenomes.R. allomycis Chytridiomycota – NC_003061) to 272 kbp (Ophiocordyceps camponoti Ascomycota – CM022976). Since the two mitogenomes are from different and distant phyla, we assessed whether mitogenome length would be related to phyla classification  containing all genes except for nad6. Genes of atp, cox, and cob families are the most frequent among fungal mitogenomes. Homologs of nad, rns and rnl are the least found within Ascomycota. It is worth noting that 149 mitogenomes are from the genus Saccharomyces, which does not have nad homologs  is 43 kb long but does not show any of the core genes. This mitogenome has 47 ORFs, of which 22 have unknown protein domains, and the others have domains from the superfamilies haloacid dehydrogenase (HAD), Glutenin high molecular weight, among others , and lacks nine core genes .As a controversy of the relationship between size and presence of core genes, the mitogenome of the yeast g others . The spefections , and thefections . The genfections . AnotherS. cerevisiae, transfer of mtDNA to the nuclear genome has already been observed when the cell had mutations in specific nuclear genes, depending on the structure of the mitogenome and availability of sugars to allow fermentation in the medium, which could lead to the high variation of phenotypes to anaerobic environments . The other phyla exhibited a more homogeneous number of HEGs, with a mean of six HEGs per mitogenome (Acremonium fuci (NC_029851) and Hyaloraphidium curvatum (NC_003048). Currently, six families of HEGs are known, with only two families found in fungal mitogenomes, LAGLIDADG and GIY-YIG  suggesting that the abundance of HEGs length is one of the factors responsible for the observed variation in fungal mitogenome size  with 39 introns, Phycomyces blakesleeanus (Ascomycota- NC_027730) with 38 introns, Parasitella parasítica (Mucoromycota – NC_024944) with 22 introns, and Rhizophydium sp. (Chytridiomycota – NC_003053) with 17 introns. We found that the presence and global length of introns is correlated with the size of each mitogenome   and 1,191 (21.59%) of all introns identified, respectively. Moreover, Ascomycota mitogenomes have an average of nine introns per mitogenome while the other phyla averaged less than five . Mitogen2.2e-16) . Many in2.2e-16) , and foueviously .We assessed the percentage of HEGs within introns as well as the opposite . We founGenerally, IGI is more common in fungi while IGII is more common in plants and most of them are self-splicing . In thisMorchella importuna (NC_012621) had 102 uORF elements and a mitogenome length of 107 kbp   , and a m.2 e-14) . These u.2 e-14) , which s.2 e-14) . Nonethe.2 e-14) , a highedpo (5.60%) and rpo (1.05%). A total of 35.51% of the uORFs lacked any similarity with known protein domains and may represent unidentified protein-coding genes. Nevertheless, by implementing a computational technique that assess coding potential based on sequence intrinsic features, 43.86% of uORFs were predicted to be coding , Basidiomycota (23), and Chytridiomycota (6). In total, amongst all mitogenomes analyzed, it was possible to observe that 10.74% of Ascomycota fungi presented a dpo and/or rpo homolog, in contrast with 57.14% of Basidiomycota species. For the other phyla, the lack of representatives makes difficult to draw meaningful conclusions. For instance, most Mucoromycota species in our dataset, have a dpo homolog; however, considering that we are working with only 24 Mucoromycota species, it is not a fair comparison with the most represented phyla Ascomycota and Basidiomycota.Mitochondrial plasmids are widely found in Fungi and some mitochondrial genes, such as n events . In our ectively . On the dpo sequences  and code 16 6. Overall, 62% of all mitogenomes use genetic code 4 (the UGA codon codes for Tryptophan), belonging to Ascomycota, Basidiomycota, Monoblepharidomycota, Cryptomycota, and Blastocladiomycota , code 3 , code 4  with 68% GC, some species of the genus Candida ranging from 13–53% (Ascomycota – NC_014337 and NC_022174), and the species Glomus cerebriforme with 47% GC (Mucoromycota – NC_022144). Some studies suggest that low GC content restricts mutations in mitochondrial genes  remained after the similarity filter performed, and many do not have the 14 protein-coding and ribosomal core genes (74% of the mitogenomes from the same species lack at least one core gene). These aspects limited the use of a phylogenetic analysis exploring the evolutionary relationships among species, since there were mitogenomes without any gene in common to be aligned. Thus, we performed similarity network analyses to investigate the relationships between the evaluated mitogenomes.Saccharomyces, Fusarium, and Aspergillus. The genus Saccharomyces is one group of fungi that exhibited more than one divergent mitogenome, which after the 90% similarity filter, retained 149 mitogenomes (out of 166 mitogenomes – 10.24% reduction). Saccharomyces can produce energy via the functioning of the mitochondrial organelle or fermentation. Therefore, these organisms can survive only with fermentable carbon sources and even without the presence of a mitogenome. These yeasts present a different phenotype, described as “petit format,” unable to grow on non-fermentable carbon sources , the second one containing mitogenomes only from the species F. oxysporum (orange dots on top right) and the third, containing only the species C. neoformans (orange dots on top left). In this last group, it was possible to observe that some mitogenomes of C. neoformans  were grouped with a closer proximity to mitogenomes of the genus Aspergillus (A. flavus and Aspergillus parasiticus). In general, no significant difference in mitogenome length was observed and all Cryptococcus mitogenomes present all core genes. A mitogenome of F. oxysporum (NC_017930) also grouped with the Aspergillus genus cluster. This mitogenome is the smallest of all F. oxysporum available (34 kbp). Size variation and differences in gene composition in the F. oxysporum species has been described and are associated with the presence of intronic sequences in the nad5, cob and atp6. Indeed, F. oxysporum strains into clades according to the presence of these sequences  was distant from the other mitogenomes of the same phylum. According to our annotation, this species has 23 uORFs and only the ribosomal genes rns and rnl, which may be one of the reasons for the distance among this mitogenome and the others from Kingdom Fungi. Furthermore, Chytridiomycota representatives were not well grouped. The Chytridiomycota are pathogenic and saprotrophic fungi commonly found in freshwater environments. This phylum presents some systematic controversies about the permanence of some species in the group . The size and composition of mitogenomes were variable, mainly explained by the presence of accessory elements. Furthermore, there was no universal gene found in all fungal species. Some studies have already demonstrated the possibility of using mitochondrial genes to identify fungal species . NeverthOur findings clearly show that, despite being considered conserved, fungal mitogenomes have numerous differences that need to be better characterized and understood. Because of this structural diversity, the use of mitogenomes are a promising tool for elucidating evolutionary relationships between species as they have a low rate of recombination, diverse patterns of mitochondrial inheritance, difference in mutation rate in relation to the nuclear genome, and facility to be amplified by PCR to sequence a particular region or the entire mitogenome due to the high number of copies in the cellular cytoplasm .Despite the important role played by mitochondria in the cell, detailed knowledge about the gene regulation of this organelle is still scarce, and very little is known about the control of expansion/contraction of mitochondrial genomes in fungi . The seqIn a more applied approach, the mitochondrial genome may also provide new strategies for controlling fungal pathogens, as well as may help improve the production of secondary metabolites , since fSaccharomyces makes the genus a good model for studies of comparative mitogenomics. Currently, most of the knowledge on comparative genomics in fungi comes from studies within Saccharomycetaceae (We detected a large amount of mitogenomes available for Saccharomycetaceae, showing a representative and non-redundant dataset. This genetic variation in mitogenomes and phenotypic diversity observed especially in cetaceae , but mitdpo, rpo, and mitochondrial genes usually located in the nuclear genome. This gene diversity may be related to HGT between species and even mitogenome length (Fusarium and Saccharomyces species, indicating that fungal mitogenomes have a great plasticity. The implications of the presence of these genes are still poorly understood. Until December 2020, few fungal species had more than one sequenced mitogenome, preventing intraspecific comparisons. This scarcity of genomic data hinders the search for new antifungal agents and the characterization of resistance mutations, as well as the understanding of fungal infections in different hosts. As a summary, the results found in this study show that fungal mitogenomes are highly diverse, without the presence of any universally conserved gene, and vary widely in accessory elements, suggesting that they are in constant and fast change.We also identified many unusual genes in mitogenomes, such as e length . It is egithub.com/LBMCF/fungal_mitogenomes_ frontiers.The original contributions presented in the study are included in the article/PF, RD-P, DA, LT, TM-P, WR, L-ED-B, EA, and AG-N analyzed the data and wrote the manuscript. All authors read and approved the final manuscript.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.