Coxiella burnetii is the Gram-negative bacterium responsible for Q fever in humans and coxiellosis in domesticated agricultural animals. Previous vaccination efforts with whole cell inactivated bacteria or surface isolated proteins confer protection but can produce a reactogenic immune responses. Thereby a protective vaccine that does not cause aberrant immune reactions is required. The critical role of T-cell immunity in control of C. burnetii has been made clear, since either CD8+ or CD4+ T cells can empower clearance. The purpose of this study was to identify C. burnetii proteins bearing epitopes that interact with major histocompatibility complexes (MHC) from multiple host species .Of the annotated 1815 proteins from the Nine Mile Phase I (RSA 493) assembly, 402 proteins were removed from analysis due to a lack of inter-isolate conservation. An additional 391 proteins were eliminated from assessment to avoid potential autoimmune responses due to the presence of host homology. We analyzed the remaining 1022 proteins for their ability to produce peptides that bind MHCI or MHCII. MHCI and MHCII predicted epitopes were filtered and compared between species yielding 777 MHCI epitopes and 453 MHCII epitopes. These epitopes were further examined for presentation by both MHCI and MHCII, and for proteins that contained multiple epitopes. There were 31 epitopes that overlapped positionally between MHCI and MHCII across host species. Of these, there were 9 epitopes represented within proteins containing ≥ 5 total epitopes, where an additional 24 proteins were also epitope dense. In all, 55 proteins were found to contain high scoring T-cell epitopes. Besides the well-studied protein Com1, most identified proteins were novel when compared to previously studied vaccine candidates.C. burnetii peptide epitopes. Furthermore, the inclusion of human, mouse, and bovine data capture a range of hosts for this zoonotic pathogen plus an important model organism. This work provides new vaccine targets for future vaccination efforts and enhances opportunities for selecting multiple T-cell epitope types to include within a vaccine.These data represent the first proteome-wide evaluation of The online version contains supplementary material available at 10.1186/s12859-021-04181-w. Coxiella burnetii is the causative agent of Q fever in humans [C. burnetii may present with a variety of different symptoms, ranging from asymptomatic to acute and further to chronic disease [C. burnetii is endemic worldwide, except for New Zealand, and most human outbreaks are blamed on domestic agricultural animals acting as reservoirs of the bacterium [C. burnetii [C. burnetii is found in large numbers within the placenta of aborted neonates but detection of the bacterium in the urine, milk, uterine fluid, vaginal mucus, and feces of parenteral animals has also occurred [The obligate intracellular bacterium n humans –3. Centen humans , 4, 5. H disease . Acute d disease . Individacterium , 7. Cowsburnetii , 5, 6, 8burnetii . While coccurred , 11.C. burnetii fixed with formalin [The most widely accepted vaccines against Q fever, or coxiellosis, are known as Q-vax and Coxevac, where the vaccine contains either the Henzerling or Nine Mile Phase I (RSA 493) isolate of formalin , 12–14. formalin . Q-vax iformalin . Contrasformalin , 10. Eitformalin , 16. WhiC. burnetii vaccines has begun based on specific epitope definition. Multiple studies have identified small numbers of epitopes used in human or mouse immune responses, and a few studies have produced subunit vaccines [A clear need exists for low cost, broadly applicable vaccines and especially those that can be produced in safer biosafety level 2 conditions. Subunit vaccines can meet this need, and a new generation of work on vaccines , 17–19. vaccines , 19. TheBioinformatic tools have been developed to more quickly and cost effectively assess proteins as host antigens –23. ThisBrucella mellintensis protein Omp31 has been of major study during multi-subunit vaccine development against this bacterial agent [Success in the use of T-cell epitope predictors has been seen in rapidly mutating viruses, like HIV and influenza, and in fastidious bacteria , 20. Moral agent –30. Reseal agent , 29. AddC. burnetii, addition of either CD4+ or CD8+ T lymphocytes alone to infected SCID mice was sufficient to achieve immune control of C. burnetii [C. burnetii clearance by macrophages has been shown to rely on IFN-γ production by T-cells during the adaptive immune response, which requires accurate loading of antigenic peptides into MHCII molecules for T-cell presentation [+ T-cells in controlling bacterial replication and host tissue pathology, suggesting that MHCI peptide loading also plays an important role during C. burnetii infection [C. burnetii infection, the inability to link humoral immune responses to restricted bacterial replication suggests that B-cells are not a major player in the control of disease [C. burnetii epitopes have focused on known type IV secretion system (T4SS) effectors or proteins eliciting antibody response [C. burnetii T-cell epitopes on a proteome-wide scale. This will also be one of the few applications to investigate a bacterial proteome, since most prior work has focused on smaller viral proteomes [C. burnetii isolates to identify conserved epitopes with broad utility and leverage predictions from human, mouse, and ruminant hosts to facilitate development of optimally useful vaccines for this zoonotic pathogen.For burnetii . C. burnentation , 21, 32.nfection , 31. Furnfection . While B disease . Thus, tresponse , 17, 19.roteomes . FurtherC. burnetii isolates are genetically diverse, wherein they secrete different type four secretion system effectors, contain antigenic variation, and form a plethora of genomic groups based on multiple loci variable number of tandem repeats analysis (MVLA) [Coxiella isolates was completed to ensure pursuit of epitopes within conserved proteins. Nine Coxiella burnetii isolates were referenced against Nine Mile Phase I (RSA 493) during proteome-wide comparison. Each strain, with its genomic grouping, tissue of isolation, characteristic of interest, and human virulence, if known, are listed in Table s (MVLA) , 35–37. C. burnetii isolates (Table The tested isolate with the highest percent identity to Nine Mile Phase I (RSA 493) is Ohio 314 RSA 270)  resulted in the identification of 1,413 conserved proteins.A total of 352 proteins were removed upon the basis that the Nine Mile Phase I (RSA 493) proteome lacked a homolog in one of the nine isolates aligned. These predominantly consisted of hypothetical proteins and transposases as opposed to better studied proteins. Overall, proteome-wide comparison between C. burnetii. BlastGrabber analysis determined that 391 of 1,413 C. burnetii conserved proteins shared homology with species of interest [C. burnetii proteins for further analysis consisted of 1022 proteins and an overview of the protein selection process can be seen in Fig. During epitope identification, and future vaccine generation, it is necessary to avoid sensitizing the host’s immune system against itself. Therefore, the resultant protein list was queried using Blastp analysis against the host species of interest  and the murine disease model for interest . Thus, tC. burnetii proteins, which lacked host homology, it was possible to exploit NetMHCIIpan 4.0 to define MHCII epitopes. While every murine allele was tested, there were an abundance of human alleles known. To mitigate the number of human alleles, allelic frequency, geographical abundance, and phylogenetic distance were considered , did not bind all eight murine alleles analyzed.Overall, there were 453 peptides, corresponding to 338 total proteins, determined to bind a high number of human and murine alleles or interact with many of the tested alleles strongly. Peptides within this data set that bound to 100% of the tested alleles or proteins that contained greater than or equal to 3 epitopes were isolated to further consolidate the data. Ten peptides bound all 206 human alleles Table . This isftsA), AAO90965.2, and AAO91357.1 (parC). Furthermore, AAO90965.2, along with AAO90357.1 (parC), encompassed the highest number of epitopes per protein with 5 total epitopes present in either protein.Evaluation for epitope dense proteins consisted of data consolidation through isolation of proteins containing a high number of epitopes , 46. Ana+ T-cells play just as important of a role during resolution of C. burnetii infection as CD4+ T-cells [+ T-cells, there are also MHCI epitope prediction programs available that can help identify antigenic peptides specific for CD8+ T-cell recognition [C. burnetii proteins without host-similarity was tested against human, mouse, and bovine MHCI alleles. Similar to NetMHCIIpan 4.0, NetMHCpan 4.1 has a large number of human alleles available for testing. Therefore, phylogenetic trees and geographical frequency of alleles were exploited to alleviate the total number of human alleles run , CBU_1242, CBU_1489 (lpxH), CBU_1928, and CBU_1978 (ostA).In studying MHCI epitopes for epitope dense proteins, we found a higher number of epitopes per protein (7 in AAO91182.1) was achieved as compared to a maximum of 5 MHCII epitopes Table . There wC. burnetii proteome for both MHCI and MHCII epitopes enables identification of multi-use epitopes and proteins. There were 31 epitopes that had overlapping use by MHCI and MHCII (Table Assessment of the II Table . Of thesthiDE), AAO90155.1 (yaeT), AAO90323.2, AAO90990.2, AAO91128.1 (icmO), AAO91393.1, and AAO91455.1 (hemA)). Additionally, there were 19 proteins absent from the combined epitopes dense protein list that were previously encompassed in either the MHCI or MHCII data. Many of the proteins which were lost in the combined epitope dense protein table represent proteins containing the number of epitopes near the bottom of the previous cut-off values. None of the previously studied proteins in Additional file GenBank IDs from MHCI and MHCII output summary tables, Additional files C. burnetii and host genomic diversity to predict widely useful T-cell epitopes across a range of hosts for this zoonotic pathogen. Epitopes were identified by leveraging an array of MHCII and MHCI alleles for antigen presentation, thereby capturing epitopes incorporated in both MHC systems across multiple host species. The results highlight broadly useful epitopes, including many with minimal prior study, that can be used for future work and vaccine development.We sought to leverage both C. burnetii and focus on proteins that would avoid self-reactive antigens. In particular, we selected at least one sequence from each genomic group  genome lack discovery work and that many underlying functions are suggested by homology to alternate bacterial proteins. This promotes analyzing the bacterial proteome in its entirety, as the importance of many C. burnetii proteins has yet to be determined.Foundational data aimed to capture broad representation of up Table , includiC. burnetii proteins for host cell epitopes, wherein previous work has focused on proteins injected into the host cytoplasm by the type four secretion system (T4SS) or proteins which elicit an antibody response [C. burnetii infection is known to rely on the production of a Th1 type immune response that results in the production of IFN-γ [C. burnetii contained epitopes began with identifying MHC class II interacting peptides (See Repository). The MHC class II analysis herein identified numerous epitopes with relatively high allelic interactions , AAO89682.2 (ftsI), and AAO90095.2 (rodA) [Coxiella burnetii infection in these species causes clear economic losses and requires intervention.Examination of either the MHC class I or II datasets demonstrates the return of proteins which have not previously been studied for T-cell epitopes. As mentioned before, much of the earlier work identifying T-cell epitopes has focused on certain protein subsets , 16, 19.2 (rodA) . The MHC2 (rodA) , 6, 7. FC. burnetii defined T-cell epitopes have used methodologies that measure the ability to achieve host T-cell activation in response to epitopes of interest; including EliSpot, ELISA, flow cytometry, and peptide loading into MHCs [A potential pitfall of bioinformatic analysis of T-cell epitopes is the possibility of false positives , 21, 52.nto MHCs , 18, 19.C. burnetii protein of interest, was represented within this output. Importantly, former analysis of Com1 as a vaccine candidate against C. burnetii has demonstrated a decent amount of promise [Once data had been acquired for both MHC class I and II alleles, it became possible to cross-analyze outputs. Investigation into overlapping MHC class II and I epitopes defined 31 peptides of interest Table . Com1, a promise , 18, 19. promise , 18, 36. promise , 53. WhithiDE), AAO90155.1 (yaeT), AAO90323.2, AAO90990.2, AAO91128.1 (icmO), AAO91393.1, and AAO91455.1 (hemA), which represent epitope rich proteins that have a balanced MHC class I and II coverage. Three of the previously mentioned proteins are designated as secreted or membrane exposed proteins by Inmembrane, AAO90155.1 (yaeT), AAO91128.1 (icmO), and AAO91393.1. Therefore, these proteins are suggested to more readily interact with the immune system upon arrival of the bacterium within host tissues. IcmO and YaeT are significant proteins in regards to host:pathogen interaction as IcmO is part of the multi-subunit T4SS and YaeT is responsible for assembly of beta-barrel surface proteins [Com1 did not remain in the MHC class I and II cross-analysis when assessing for epitope dense proteins Table . Likewisproteins –56.Coxiella burnetii to move forward without restrictions on the approach to be used.Cross-analysis between MHC class I and II data allows for future vaccination efforts to cover both classes of T-cell epitopes. Furthermore, the investigation herein also aids in epitope decision with regards to alternate vaccine types. For instance, identified epitope dense proteins provide a source of epitopes which can partake in a vectored vaccine , 34. On C. burnetii. The use of multiple divergent C. burnetii isolates enabled the identification of widely conserved proteins and epitopes to empower future work. Furthermore, the use of multiple host species for antigen presentation analyses supports the existence of widely conserved epitopes that can be broadly useful across many host species for this zoonotic pathogen. The specific results highlight many proteins and epitopes not previously described in regards to host immune recognition, and in so doing provide useful direction for future work in developing epitope-rich vaccines.These data represent the first comprehensive, proteome-wide examination of T-cell epitopes for C. burnetii isolates (https://www.patricbrc.org/) [−8 was used, where proteins were considered homologs if the percent identity was 90% or above [The PATRIC database (Pathosystems Resource Integration Center) was exploited to run proteome-wide comparisons between rc.org/) , 58. Bacrc.org/) , 59–62. rc.org/) , 39. An or above , 63.C. burnetii isolates were entered as a multi-FASTA file onto the Blastp server and analyzed for homologs present in host species. The host species tested and their taxonomic Id’s are as follows human (txid 9606), mouse , cow (txid 9913), goat (txid 9925), and sheep (txid 9940). BlastGrabber was exploited to analyze results obtained from NCBI’s basic local alignment search tool (BLASTp) [−2) and a percent identity greater than 35% was set based on previous experimental methods used to remove host homologs from analysis [Nine Mile Phase I (RSA 493) proteins found to be conserved between (BLASTp) . An E-vaanalysis , 63, 64.http://www.allelefrequencies.net/default.asp) [https://www.ebi.ac.uk/ipd/imgt/hla/) [The top ten most common MHCI alleles for eleven global regions were determined using the Allele Frequency Net Database (AFND) (ult.asp) , 66. Dupgt/hla/) . Of the gt/hla/) . Afterwagt/hla/) . Proteingt/hla/) . The remhttps://services.healthtech.dtu.dk/service.php?NetMHCpan-4.1) and (http://www.cbs.dtu.dk/services/NetMHCpan/) [Conserved Nine Mile Phase I (RSA 493) proteins lacking homology to host species were loaded onto the NetMHCpan 4.1 database for analysis across multiple host species (MHCpan/) , 47, 69 https://services.healthtech.dtu.dk/service.php?NetMHCIIpan-4.0) [NetMHCIIpan 4.0 was exploited to study peptides that can bind human or murine MHCII alleles (pan-4.0) , 23, 70.The multi-FASTA file that contained conserved bacterial and nonhomologous host proteins was run through Inmembrane to determine each protein’s localization within the bacterium . The proAdditional file 1. C. burnetiiproteins lacking host homologs and containing inter-isolate conservation. A FASTA format list of the C. burnetii proteins studied for T-cell epitopes. Additional file 2. Allelic phylogenetic analysis. Phylogenetic trees containing MHCII (A and B) or MHCI (C and D) alleles from human species. MHCII alleles were included based on geographical representation for the DRB1 locus or an allelic frequency of 0.05 or greater for the remaining loci. MHCI alleles were included based on geographical representation as denoted by AFND. 999 bootstraps were run during neighbor-joining tree generation for MHCII alleles (A), while 1,000 bootstraps were completed when producing the maximum likelihood tree for MHCII alleles (B). MHCI allelic comparison using either the neighbor-joining (C) or the maximum likelihood method (D) using 1,000 bootstraps. Trees were condensed to only show branching when bootstrap values were 80 or above.Additional file 3. Isolation of quality controlled MHCII and MHCI epitopes. Contains the methodology used to select output epitopes of interest. Additional file 4. Previously studiedCoxiella burnetii epitopes. Locus tag and gene name based upon genomic assembly annotations for Nine Mile Phase I (RSA 493) on National Center for Biotechnology and Information (NCBI). Species indicates which host or model organism the epitope was analyzed for. The epitope type column describes if the peptide studied was in regards to a B-cell or T-cell (MHCI or MHCII) epitope. If more than one epitope was isolated, then the epitope types are separated by backslashes to indicate the order of MHC epitopes or an ampersand to indicate B-cell production of antibodies. Locus tag superscripts denote protein subcellular location and if the protein was disqualified from NetMHCpan due to previous analysis. 1 is for membrane associated, 2 is for cytoplasmic location, 3 is for unknown location, and an asterisk indicates removal. Epitope amino acid positions are annotated to represent the pre-processed forms of the proteinsAdditional file 5. MHCII epitopes with high scoring allelic interactions. MHCII epitopes found to bind either 186  human alleles or 8  murine alleles. Data also includes MHCII epitopes found to interact strongly with 90  human alleles or 5  murine alleles. NCBI retrieved information includes the GenBank ID, gene name, and locus tag. Location of given proteins was determined through use of the program Inmembrane, where PSE represents a potential surface exposed protein. The peptide column contains the 15mer peptides generated by NetMHCIIpan 4.0 for MHCII binding assessment, position of peptide start within a protein is indicated within the pos (position) column. NB, WB, and SB represent peptide and MHCII allele interactions, where NB is the total number of alleles bound, WB signifies weak binders, and SB represents strong binders. Species dictates the animal in which the alleles tested originated from.Additional file 6. Condensed MHCII epitopes. Pos indicates the position within the protein in which the NetMHCIIpan 4.0 15mer generated peptide begins. GenBank ID, gene name, and locus tag are protein specific information originating from NCBI. Location of proteins was annotated using the Inmembrane program, where PSE stands for potentially surface exposed protein.Additional file 7. Murine MHCII epitopes with exceptional allelic coverage. Rows with asterisks present next to the position number indicate a peptide shift from epitopes defined in Additional Table 4, where peptides bound more murine alleles when the 15mer was shifted one to two amino acids over. Pos indicates the starting position of the peptide of interest. Protein identification is determined by NCBI annotated information given by the GenBank ID, gene name, or locus tag. NB, WB, and SB described the character of peptide:MHCII allele interaction, where total alleles bound, weak binding, and strong binding are respectively defined. Inmembrane was used to define protein location within the bacteriumAdditional file 8. MHCI epitopes with high allelic interactions. NetMHCpan 4.1 designated MHCI epitopes within human, murine, and bovine species, wherein epitopes bound 60% of alleles tested or interacted strongly with 45% of alleles tested for each species. Position of peptide start is defined in the pos column. GenBank ID, gene name, and locus tag were defined by Nine Mile Phase I (RSA 493) assembly on NCBI. NB, WB, and SB represent number of alleles bound, weak binders, and strong binders respectively. The species defines what animal the alleles were being tested for and location was designated by Inmembrane.Additional file 9. Condensed MHCI epitopes. Manually annotated high binding MHCI epitopes that are present in all three species, human, murine, and bovine. Pos indicates the peptide of interest start site within the NCBI cited protein . Location was derived through use of the program Inmembrane, wherein PSE defines a potentially surface exposed protein.Additional file 10. Murine MHCI epitopes with exceptional allelic binding. MHCI epitopes that bound 8  alleles in the murine species. Rows with positions labeled by asterisks and bolded text represent epitopes that are different in position as compared to human MHCI epitopes that bind 74 to 76 alleles; while rows with asterisks and underlined text represent peptides that vary in position between murine and bovine epitopes that bind 98% of tested alleles. Pos describes the starting position of the peptide within the protein. Identification of the protein is given through the NCBI obtained GenBank ID, gene name, and locus tag. Location was defined by the Inmembrane program, where PSE is an acronym for potentially surface exposed. The number of alleles bound by a peptide are indicated in the NB column. If the peptide:MHCI interaction was weak it was quantified as WB and if the peptide:MHCI interaction was strong it was quantified as SB.Additional file 11. MHCI and MHCII tested alleles. (A) MHCI alleles tested during the use of NetMHCpan 4.1. Human, murine, and bovine alleles are notated HLA, H-2, and BoLA respectively. (B) MHCII alleles tested during exploitation of NetMHCIIpan 4.0. Murine and human alleles are designated by H-2 or HLA respectively. Notably, the human DRA1 locus is not highly variable, therefore only the DRB1 allele for this pairing changed. Otherwise, each of the DPA1 and DQA1 loci were paired and tested with each of their respective DPB1 and DQB1 loci.Additional file 12. Exclusion of Dugway 5J108-111. (A) Protein GenBank IDs returned to analysis when Dugway 5J108-111 was removed from inter-isolate comparison. Homology to host species is noted in the second column, where a yes indicates removal of the protein before T-cell epitope analysis. (B) C. burnetii defined MHCI T-cell epitopes represented within human, murine, and bovine species during Dugway 5J108-111 exclusion. Pos indicates the position at which the peptide begins within the protein of interest. GenBank ID, gene name, and locus tag provide protein identification parameters present in assembly ASM776v2. Protein localization was defined through the use of Inmembrane. Program updates labeled the location of AAO91013.1 as IM+peri (inner membrane plus the periplasmic space), this was altered to Membrane (non-PSE) to keep with location labels in the remainder of the manuscript.


Aquaculture plays a vital role in food security and economic stability worldwide . SelectiHemibagrus wyckioides), belonging to the family Bagridae, initially possess a white caudal fin that becomes bright red when it reaches approximately 15 cm. The red-tail catfish is the largest Bagridae fish in body size and weight, reaching 130 cm and 80 kg, respectively  is an order of major aquaculture species worldwide, especially in China, the United States, and Vietnam . Red-taiectively . Red-taiectively . Therefoectively . H. wyck catfish . Thus, iH. wyckioides using k-mer analysis. Based on 26, 507, 871, 386 17-mers and a peak 17-mer depth of 34, the estimated heterozygosity rate was ∼0.3%, and the estimated genome size of H. wyckioides was ∼779 Mb  long reads and 6.7 Gb of PacBio HiFi reads. We used these data to construct our initial assembly. We obtained a 789.8 Mb genomic DNA sequence  22.1 Mb . The lon 22.1 Mb . BUSCO  formed a branch. Nine species of Siluriformes formed a monophyletic group. Siluriformes and Gymnotiformes formed sister groups. Otophysa fish were grouped together. Asian species  clustered into one group and North American species (I. punctatus and A. melas) clustered into sister groups. This result is consistent with the “Big Asia” branch views suggested by Sullivan . The fin, blood, brain, gills, heart, head kidney, liver, muscle, and spleen were collected and immediately frozen in liquid nitrogen. Blood samples were collected and DNA was prepared using the QIAGEN® Genomic kit . The qualified libraries (200–400 bp) were sequenced using MGISEQ 2000. Raw reads were first filtered using a fastp (https://www.pacb.com/support/software-downloads/).Samples for genome sequencing of female  a fastp  preproce a fastp . The rawThe RNA analyses involved nine tissues  that were extracted using an RNeasy Plus Mini Kit (Qiagen). The Illumina paired-end sequencing  involved preparing a complementary DNA (cDNA) library using a TruSeq Sample Preparation Kit (Illumina). The qualified RNA from the nine tissues was mixed in equal amounts and reverse-transcribed using SQK-PCS109 (Oxford Nanopore Technologies) for the ONT library preparations and sequencing (Nanopore PromethION). The raw data were filtered in the same manner as DNA.mer frequency distribution analysis using the Jellyfish program . To improve the accuracy of the assembly, the contigs were refined with default parameters in Racon for the long reads and Nextpolish using Illumina for the short reads.Quality-filtered reads were subjected to 17- program . We analsoftware . The siming pIRS  to estimH. wyckioides for the Hi-C library, and sequencing (Illumina MGI-2000) was performed. The scaffolds were further clustered, ordered, and oriented onto chromosomes with LACHESIS  was applied to search for known and novel TEs by mapping sequences against the de novo repeat library and repbase TE library.We first annotated the tandem repeats using the software GMATA  and TRF E-hunter  and RepeE-hunter  with defE-hunter , LTRharvE-hunter , and LTRE-hunter  were als repbase  using TE repbase  to classDanio rerio, Oryzias latipes, Takifugu rubripes, Homo sapiens, P. fulvidraco, Glyptosternon maculatum, Bagarius yarrelli, and Ictalurus punctatus) to the assembly to obtain the gene structure information using homolog prediction. The RNA-seq-based gene prediction involved filtered RNA-seq short reads being aligned to the reference genome using STAR (https://github.com/nanoporetech/pychopper) with default parameters. Full-length reads were aligned to the H. wyckioides reference genome using minimap2 (https://github.com/nanoporetech/pinfish) following Nanopore’s Official recommendation. Redundancy was removed using cDNA_Cupcake software (https://github.com/Magdoll/cDNA_Cupcake) and polished using a reference genome sequence. The assembled transcripts based on full-length and short reads were merged with the open reading frames (ORFs) and were predicted using PASA  and the miscoded genes were further filtered. BUSCO was used to assess the accuracy of gene prediction by searching for the single-copy genes conserved across Actinopterygii among the predicted genes in the assembly.GeMoMa  was useding STAR  with defing STAR . Full-leminimap2  with “-aing PASA . The de tringTie  and analtringTie  to produtringTie  with defeModeler  was usedhttps://www.ncbi.nlm.nih.gov/refseq/about/nonredundantproteins/), KEGG , KEGG , KOG (Ta/), KEGG . The put/), KEGG  program /), KEGG  was usedH. wyckioides and other species  by downloading their protein sequences and aligned them using OrthoMCL (We identified homologous relationships among OrthoMCL .http://molevol.cmima.csic.es/castresana/Gblocks.html), and the GTRGAMMA substitution model in RAxML (http://www.timetree.org/), the control time with the divergence times are provided in Based on the orthologous gene sets identified with OrthoMCL , moleculin RAxML  were use


Here, we focused on the culturable fraction of the fungal community that resides on grapes and determine wine quality, the so-called wine yeast populations. We aimed to analyze local-scale yeast community assemblages and to test whether the hypothesis of biogeographical patterns also applies to wine yeasts in particular. Surveying 34 vineyards across four main viticultural zones in Greece showed significant trends in vineyard-specific patterns. At a local scale, viticultural regions were also linked to distinct yeast community compositions. Importantly, major yeast populations directly related to wine fermentation contributed significantly to the delimitation of regions, highlighting their potential influence on the regionality of wine characteristics. In terms of the microbial terroir influence, yeast communities within an area were temporarily stable, which is critical for the regional character of the wine. Community structure could be explained only partially by environmental features. Maximum temperature, elevation, and net precipitation were the highest correlated variables with the yeast community biogeographic patterns. Finally, we also showed that certain environmental factors may drive the population size of specific yeast populations. The present results indicate that the wine yeast community has a geographical character at local scale, which is an important feature of the microbial terroir concept and thus for the wine industry.Recent research has expanded our understanding on vineyard-associated fungal community assembly, suggesting non-random distribution and implicating regional differences in the wine  Cryptococcus spp., Rhodotorula spp., Sporobolomyces spp., and the yeast-like fungus Aureobasidium pullulans, while thereafter they are gradually occupied by oxidative or weakly fermentative ascomycetous species, such as Hanseniaspora spp., Metschnikowia spp., Pichia spp., and Candida spp.  and present the most heterogeneous landscapes among the areas surveyed. Nemea is divided into three altitude zones, the lowland (at elevation of 260–350 m), the semi-mountainous (350–600 m), and the mountainous (600–800 m). In Peza, the vines are spread amphitheatrically on low hills with varying slope up to an altitude of 700 m (mountainous and semi-mountainous vineyards). Santorini has 15 km2 of vineyards, located in the central and the southern parts of the island. Mantineia is the smallest viticultural region (10 km2), which presents a uniform landscape, located on a mountainous plateau at an altitude of 650 m. Two to nine sampling plots per vineyard were surveyed as previously described , Mantineia plateau in Peloponnese peninsula , Santorini island , and Peza in Crete island , while negative values of R show higher intra-group than inter-group diversity. Values of p < 0.05 indicate significantly different level between groups. When the null hypothesis was rejected, pairwise values of R were examined to reveal major differences between regions. Permutational multivariate analysis of variance (PERMANOVA) tests with 999 permutations was also applied to test significant differences between sample groups using Bray–Curtis similarity measure, where higher values of F statistic indicate greater between-groups variations. Cluster analysis dendrograms were constructed based on Bray–Curtis similarity matrices to test clustering of yeast communities according to the vineyard or the region of origin. Principal coordinate analysis (PCoA) based on Bray–Curtis distances was applied, as an unconstrained method, to depict resemblances of samples in a two-dimensional space. Following PCoA, canonical discriminant analysis (CDA), a constrained ordination method where the groups are defined a priori, was used to produce an ordination plot and compare samples of different regions against species abundances, according to Differences in yeast community composition between regions, vineyards, or vintages were tested with Analysis of Similarity (ANOSIM) on square-root transformed species incidences using the Bray–Curtis distance matrices. ANOSIM R statistic close to the maximum limit of one implies robust group separation rejecting the null hypothesis p < 0.001). Samples from the Santorini island exhibited the highest yeast counts (5.85 ± 1.06 Log CFU/ml) followed by Mantineia (5.49 ± 0.30 Log CFU/ml), Peza (4.54 ± 0.98 Log CFU/ml), and Nemea (3.66 ± 1.10 Log CFU/ml). Species richness was also significantly higher (p < 0.05) in Santorini (16 species) compared to the other regions.A total of 156 grape samples derived from 34 vineyards covering highly important viticultural regions across Greece were analyzed . Yeast cAureobasidium pullulans, Candida diversa, C. glabrata, C. membranifaciens, C. tropicalis, Cryptococcus diffluens, H. uvarum, H. guilliermondii, H. opuntiae, Hyphopichia pseudoburtonii, Lachancea thermotolerans, Meyerozyma caribbica, M. pulcherrima, Papiliotrema laurentii, Pichia anomala, S. cerevisiae, Starmerella bacillaris, and Torulaspora delbrueckii. Except for P. laurentii, the majority of the species belonged to Ascomycota, including species that significantly contribute to the alcoholic fermentation of grape must. M. pulcherrima was the most frequently isolated yeast (M. pulcherrima was highly encountered in Peza (70%), followed by Nemea (50%). It was moderately abundant in Mantineia (27%), but scarcely detected in Santorini (1%). H. uvarum was the second most encountered species across vineyards. It was highly abundant in Mantineia (74%) and moderately found in other regions, i.e., 25% in Santorini, 17% in Nemea, and 15% in Peza.Yeast isolates were analyzed by PCR-RFLP analysis of the 5.8S-ITS rDNA region. Restriction enzyme banding profile comparisons between isolates and published strains combined with sequence analysis of the 5.8S-ITS region assigned groups of isolates to 18 different yeast species, namely, ed yeast ; 37%. M.S. bacillaris, H. guilliermondii, H. uvarum, M. caribbica, and M. pulcherrima. Several species were associated exclusively with Santorini, namely, C. diversa, C. glabrata, C. membranifaciens, P. laurentii, C. tropicalis, S. cerevisiae, and T. delbrueckii, while H. pseudoburtonii and P. anomala were solely encountered in Peza.Only five out of a total 18 species were inter-regionally encountered, i.e., R = 0.401, p = 0.001; vs. Santorini , A. pullulans (Nemea), H. uvarum (Mantineia), and H. opuntiae and H. guilliermondii (Santorini).A Bray–Curtis similarity dendrogram was constructed (Cophenetic correlation: 0.93) to compare yeast communities of the four regions . Most saR = 0.508, p = 0.001; PERMANOVA: R = 12.525, p = 0.001; R = 0.471, p = 0.001; R = 0.388, p = 0.001; M. pulcherrima, while the northern vineyards showed much higher biodiversity. By applying nMDS and hierarchical clustering dendrograms grouping of samples according to their vineyard of origin was detected in Peza and Santorini, but it was less obvious in Nemea. We further asked whether there was any structure according to the geographical distance of vineyards but Mantel test did not support such a correlation (p ≥ 0.34).We also compared the yeast communities within regions, where multiple vineyards were sampled across vintages, i.e., Nemea and Santorini regions (vintages A and B) and Peza region (vintage A). Overall, significant trends of vineyard-specific patterns were revealed by both ANOSIM and PERMANOVA tests .We further asked whether the environmental conditions (climatic and grape must characteristics) affect regional yeast community delimitation. Significant differences were detected between regions in the majority of the environmental conditions with the exception of the average high and maximum temperatures and the sugar content of grape juice ANOVA; . ConversA BEST was applied to identify the most important environmental factors that drive community differences across regions. BEST showed that among the different environmental factors the maximum temperature could better explain yeast community differentiation . The maxF, each explaining 19–23% of the total yeast diversity. Stepwise sequential tests also highlighted the importance of average wind speed and maximum wind speed, which along with average maximum wind speed and average high temperature cumulatively explained approximately 50% of the variation  was applied to demonstrate the association between the set of environmental variables, and the yeast species in order to identify which climatic factors may drive specific yeast populations across regions and vintages. Among the different yeast populations, 11 species explained more than 10% of the variation in the first two components and were thus retained for the construction of the PLSR model. Projections revealed highly covariable relationships between specific environmental factors and yeast species . In partterroir effect. The geographical variation of wines based on their chemical and sensory profiles has been well documented  was prevailing Santorini island. Differences were also detected in minor populations and only five out of 18 species were inter-regionally encountered. Notably, several species were region-specific. It is worth mentioning that the same amounts of H. uvarum domination (70%) were previously found in Mantineia plateau  radiation, desiccation, and low temperatures and it was recently shown to significantly affect airborne fungal communities (M. pulcherrima, average wind speed of H. guilliermondii, and total acidity of H. opuntiae. Net precipitation was found to be highly correlated with A. pullulans and H. uvarum. Precipitation levels were previously shown to correlate with the abundance of Metschnikowia, Torulaspora, and Saccharomyces yeast species in grapes from different Chilean valleys (The inherent climatic and topographical features of viticultural regions form the basis of the wine ineyards . It is uportance . Here, dmunities . Net premunities . We also valleys . Given tOur results here support the hypothesis that different regions harbor distinct microbial communities, which was shown to apply specifically to the wine-associated yeast populations. We also highlight the implication of climatic features in regional wine yeast biogeography. The fact, however, that major environmental features, like climatic and grape must characteristics, could explain only a part of biogeography points to the complexity of the factors driving wine yeast community composition. Other regional factors, including modes of yeast dispersion parameters, landscape composition or biotic interactions may also interfere in community assembly.The original contributions presented in the study are included in the article/AN designed the study, performed experiments, analyzed data, and wrote the paper. IC conducted experiments and analyzed data. CT contributed to data analysis and writing the paper. GB analyzed data and contributed to writing the paper. All authors contributed to the article and approved the submitted version.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.


Parkinson’s disease (PD) is a progressive neurodegenerative disease with no definite molecular markers for diagnosis. Metal exposure may alter cellular proteins that contribute to PD. Exploring the cross-talk between metal and its binding proteins in PD could reveal a new strategy for PD diagnosis. We performed a meta-analysis from different PD tissue microarray datasets to identify differentially expressed genes (DEGs) common to the blood and brain. Among common DEGs, we extracted 280 metalloprotein-encoding genes to construct protein networks describing the regulation of metalloproteins in the PD blood and brain. From the metalloprotein network, we identified three important functional hubs. Further analysis shows 60S ribosomal protein L6 (RPL6), a novel intermediary molecule connecting the three hubs of the metalloproteins network. Quantitative real-time PCR analysis showed that RPL6 was downregulated in PD peripheral blood mononuclear cell (PBMC) samples. Simultaneously, trace element analysis revealed altered serum zinc and magnesium concentrations in PD samples. The Pearson’s correlation analysis shows that serum zinc and magnesium regulate the RPL6 gene expression in PBMC. Thus, metal-regulating RPL6 acts as an intermediary molecule connecting the three hubs that are functionally associated with PD. Overall our study explores the understanding of metal-mediated pathogenesis in PD, which provides a serum metal environment regulating the cellular gene expression that may light toward metal and gene expression-based biomarkers for PD diagnosis. Parkinson’s disease (PD) is one of the most prevalent neurodegenerative diseases occurring at the substantia nigra (SNc) region of the central nervous system . DegenerAmong biological data, meta-analysis of microarray-based gene expression profiles was extensively investigated to overcome the difficulties in conducting a large cohort study. Meta-analysis is a scientific method that systematically integrates the gene expression data of different populations on a similar entity to analyze and provide more reliable results . AlthougConsidering various environmental factors, metal exposure is one of the most frequently reported causative factors in PD. Several reports suggest altered metal concentration in PD . HoweverThe retrieval, selection, analysis, and reporting of this study follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines .1 and Array Express database2 by inception of database records between January 1990 and November 2018 using the keywords related to “Parkinson’s disease.” Two independent groups  tested the procedure (Homo sapiens), (2) comparative studies between PD and other neurological diseases, (3) brain or whole blood as study specimens, (4) datasets include raw or processed data. Whereas the exclusion criteria include (1) animal model and in vivo studies, (2) secondary gene expression studies, (3) microarray dataset with less than two replicate samples in a group, and (4) studies other than microarray experiments.Gene expression dataset searches were executed in NCBI Gene Expression Omnibus (GEO)rocedure  of datasData extraction was performed independently by two authors (AA and KM) and cross-verified between them to eliminate errors. Any differences in opinion were resolved through group discussion. The information relating to each selected dataset, such as investigator name, year, accession number, sample source, array platform, number of samples (PD and control), expression data, study population, and primary and secondary outcome measures, was recorded. The characteristics of the dataset are included in p-value < 0.05, adjusted by Benjamini–Hochberg’s false discovery rate (FDR).Each dataset was background corrected and normalized using Robust Multi-chip Averaging (RMA) method. Principal component analysis (PCA) plot was visualized to determine the sample distribution based on gene expression before and after normalization to identify outliers. The differentially expressed genes (DEGs) in PD compared to control was computed using the limma method  with p-vp-value < 0.05 from the multiple datasets to determine the significantly differentially expressed meta-analyzed PD genes of the brain and blood.Meta-analysis was performed using MetaDE package in R  by folloHomo sapiens; (2) Experimental method: XRD; (3) Molecule type: Protein; (4) Sequence feature: Wild-type protein; and (5) X-ray resolution: > 3A°. Overall, we retrieved 6,367 proteins that bound with 60 different metals. Each metalloprotein was named using its official gene symbol. Among 6,367 metalloprotein-encoding genes, we only took DEGs that are commonly overexpressed and downregulated in both blood and brain of PD. This exercise yielded 280 DEGs, which served input for metalloprotein network construction.Next, the metal-bound protein  search was executed in Protein Data Bank (PDB) by applying the search filters that include: (1) Organism type: p-value. The top three hubs showing the highest significant p-values were selected and subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) database for pathway analysis. Simultaneously, the hub proteins and their connections were investigated to identify any intermediary molecules connecting the three hubs. The gene expression of intermediary molecule was analyzed in PD. Also, the concentrations of the metals that bound to the hub proteins were examined in PD serum compared to healthy control.Cytoscape v3.1 is a network biology software that enables to construct and investigate the complex interaction of genes and proteins using a BisoGene plug-in. The BisoGene retrieves protein–protein interaction (PPI) data from the databases DIP, BIOGRID, HPRD, BIND, MINT, and INTACT to create the network . The 280 metalloproteins were given as input to create interaction networks connecting each other’s edges. Among 280 metalloproteins, 90 forms the protein network. Whereas the other proteins were noticed to have a maximum of one interacting partner and few were without interaction. The constructed network was visualized using Cytoscape network visualizer, which shows the interconnectivity among dysregulating metalloproteins. Additionally, the topological characteristics of the network were tested using the network analyzer plug-in of Cytoscape. From the network, we extracted the hubs that are highly connected using the Clustering with Overlapping Neighborhood Expansion (ClusterONE) algorithm . Cluster2. Whereas the exclusion criteria include (1) previous history of alcohol abuse; (2) smoker or tobacco user; (3) any severe systemic diseases; (4) participants under minerals and chelating agents as supplements; (5) participants who underwent any surgery in the last 8 months; and (6) presence of any secondary parkinsonism due to accident, trauma, or drugs. Before sample collection, the signed informed consent was obtained from all the participants. Peripheral blood 6 ml (3 ml + 3 ml) was collected in the Vacutainer tube . Then, 3 ml blood was centrifuged immediately for 10 min at 3,500 rpm, and we stored serum at −80°C for metal analysis. We used the remaining 3 ml for the isolation of peripheral blood mononuclear cell (PBMC) and RNA extraction for the gene expression analysis.We obtained samples from PD patients (PD = 33) who underwent clinical examination and MDS-UPDRS grading by a movement disorder specialist at the Chettinad Hospital and Research Institute, Tamil Nadu, India . SimilarAll precautions were made by following the National Committee for Clinical Laboratory Standards (NCCLS) guidelines to avoid contamination throughout the sample processing of metal estimation. The nitric acid-based microwave digestion was carried out to extract the metals from the serum. Flame atomic absorption spectrophotometer (AAS)  was usedCt–ΔΔ method.PBMCs were isolated using Histopaque-1077 (Sigma-Aldrich) following the manufacturer’s protocol. RNA was extracted from the PBMC using TRIzol (Invitrogen) reagent and quantified using Nanodrop 2000 (Thermo Fisher Scientific). The 60S ribosomal protein L6 (RPL6) gene expression was examined in PD and control using quantitative real-time PCR  using forward (GACGGGAATGAGAAAGGCCA) and reverse (AGCCCTGGAGCTGAGGAATA) primers. We used the gene GAPDH as internal control (forward: AAGGTGAAGGTCGGAGTCAA and reverse: ACATGTAAACCATGTAGTTGAGGT). Finally, the relative expression was measured following the 2p < 0.05) between PD and control was assessed using the Student’s t-test method. The interdependency between metal concentration and RPL6 gene expression was calculated by Pearson’s correlation method. All statistical analyses were carried using the SPSS version 21 software.All data were tested for normal distribution. Further, the statistical significance  containing 610 samples (control = 297 and PD = 313) and eight datasets from the blood tissue  containing 852 samples . The detailed characteristics of the datasets included in this study are presented in p < 0.05 as cutoff in Fisher’s exact test. Overall, 1,951 DEGs in the blood and 3,984 DEGs in the brain were obtained. Simultaneously, 3,453 metal-bound proteins were collected from the PDB, as described in the “Materials and Methods” section. We converted each metal-bound protein into the official gene symbol and mapped with 1,951 (blood) and 3,984 (brain) differentially expressed meta-analyzed PD genes. A total of 280 DEGs were identified in PD showing common trends in both tissues. These 280 DEGs were used for the network construction.Prior to the meta-analysis, each dataset was RMA processed and DEGs between PD and control were determined using the limma package. To identify common DEGs in the blood and brain between PD and control samples, the datasets from the blood and brain were meta-analyzed with the p-values. Hub1 contains XPO1 with 22 interacting proteins, hub2 contains CAND1 with 20 connecting proteins, and hub3 contains HSP90AB1 connected with 11 proteins  in PD  in PD . Further5) in PD  based onWe performed a meta-analysis to identify common DEGs in PD brain and blood that allows new biological insights in PD pathogenesis. To our knowledge, this is one of the largest gene expression meta-analyses conducted in PD using 15 microarray gene expression datasets of the brain containing 610 samples and eight datasets in whole blood containing 852 samples to overcome the limitation expressed by In the past decades, progression in PD research has been shown to be exponentially productive; however, identifying the influence of cellular metal on causative genes in PD pathogenesis is needed. Therefore, we attempted to link the serum metal concentration with the DEGs of PD. We identified DEGs by meta-analysis by merging multiple PD-associated gene expression datasets. This resulted in 280 DEGs from a protein network containing 90 proteins with 177 edges contributing to three important PD hubs representing molecular pathways.Insulin signaling pathway is one of the molecular pathways represented by the hubs . SeveralOn the other hand, ErbB signaling plays a vital role in the central nervous system. Dysregulated ErbB signaling was noticed in our pathway analysis of hubs. In PD, Subsequent analysis of hubs showed that most hub proteins were bound with Zn and Mg . Also, eIn conclusion, to our knowledge, this is the first meta-analysis that investigates the association of metalloproteins and metals in PD. Our study confirms the change in serum Zn and Mg concentration in PD compared to control. Also, the altered metals contribute change in gene expression of key linker proteins connecting PD-associated hubs. Together, relating the gene expression with metal concentration can be used as biomarkers for PD diagnosis.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE8397Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc~=~GSE19587https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc~=~GSE20141https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc~=~GSE20146https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc~=~GSE20163https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc~=~GSE20164https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc~=~GSE20168https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc~=~GSE20186https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc~=~GSE20291https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc~=~GSE20292https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc~=~GSE20295https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc~=~GSE28894https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc~=~GSE49036https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc~=~GSE42966https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc~=~GSE6613https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc~=~GSE22491https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc~=~GSE54536https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc~=~GSE72267https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc~=~GSE18838https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc~=~GSE49126https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc~=~GSE57475https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc~=~GSE99039.The studies involving human participants were reviewed and approved by the Institutional Human Ethical Committee, Chettinad Academy of Research and Education. The patients/participants provided their written informed consent to participate in this study.AA and PA-B conducted the experimental work and analysis and interpretation of the data. PP supervised the gene expression analysis. SA and AS designed the study, performed part of the experiment and the analysis and interpretation of the data, and critically revised the manuscript. RM supervised the statistical analysis. SK and KM provided critical suggestion in the design of the experiment and manuscript correction. Particularly, SA, RM, PP, and SK were involved in validating the procedure for dataset collection for meta-analysis. All authors read and approved the current version of the 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.


Triticum aestivum L. Two approaches were used for the genealogical study of hexaploid wheats—the complete sequencing of chloroplast DNA and PCR-based haplotype analysis of the fourth intron of Wknox1d and of the fifth-to-sixth-exon region of Wknox1b. The complete chloroplast DNA sequences of 13 hexaploid wheat samples were determined: Free-threshing—T. aestivum subsp. aestivum, one sample; T. aestivum subsp. compactum, two samples; T. aestivum subsp. sphaerococcum, one sample; T. aestivum subsp. carthlicoides, four samples. Hulled—T. aestivum subsp. spelta, three samples; T. aestivum subsp. vavilovii jakubz., two samples. The comparative analysis of complete cpDNA sequences of 20 hexaploid wheat samples (13 samples in this article plus 7 samples sequenced in this laboratory in 2018) was carried out. PCR-based haplotype analysis of the fourth intron of Wknox1d and of the fifth-to-sixth exon region of Wknox1b of all 20 hexaploid wheat samples was carried out. The 20 hexaploid wheat samples (13 samples in this article plus 7 samples in 2018) can be divided into two groups—T. aestivum subsp. spelta, three samples and T. aestivum subsp. vavilovii collected in Armenia, and the remaining 16 samples, including T. aestivum subsp. vavilovii collected in Europe (Sweden). If we take the cpDNA of Chinese Spring as a reference, 25 SNPs can be identified. Furthermore, 13–14 SNPs can be identified in T. aestivum subsp. spelta and subsp. vavilovii (Vav1). In the other samples up to 11 SNPs were detected. 22 SNPs are found in the intergenic regions, 2 found in introns, and 10 SNPs were found in the genes, of which seven are synonymous. PCR-based haplotype analysis of the fourth intron of Wknox1d and the fifth-to-sixth-exon region of Wknox1b provides an opportunity to make an assumption that hexaploid wheats T. aestivum subsp. macha var. palaeocolchicum and var. letshckumicum differ from other macha samples by the absence of a 42 bp insertion in the fourth intron of Wknox1d. One possible explanation for this observation would be that two Aegilops tauschii Coss. (A) and (B) participated in the formation of hexaploids through the D genome: Ae. tauschii (A)—macha , and Ae. tauschii (B)—macha M6, M9, T. aestivum subsp. aestivum cv. ‘Chinese Spring’ and cv. ‘Red Doly’.The aim of the presented study is a genetic characterization of the hexaploid wheat  T. aestivum, genome BBAuAuDD and Triticum zhukovskyi Menabde & Ericz., GGAuAuAmAm. T. zhukovskyi and its predecessors (Triticum monococcum L. and Triticum timopheevii (Zhuk.) Zhuk.) form a separate lineage irrelevant to the evolution of the principal wheat lineage, which is formed by T. aestivum and its predecessors Aegilops tauschii Coss. And Triticum turgidum L. [Wheat is the leading grain crop in the world. It originated in the Fertile Crescent approximately 10,000 years ago and has since spread worldwide. There are two biological species of hexaploidy wheat—T. aestivum lineage is divided into the domesticated, hulled lineage and the free-threshing lineage. The free-threshing lineage includes T. aestivum subsp. Aestivum, T. aestivum subsp. Compactum (Host) Mackey, T. aestivum subsp. Sphaerococcum  Mackey, and T. aestivum subsp. carthlicoides nom. nud.  Mackey and T. aestivum subsp. vavilovii Jakubz. In hulled wheats, glumes tenaciously enclose seeds, and strong mechanical force is needed to liberate seeds from glumes during threshing. The hulled lineage includes spelt wheat Triticum abyssinicum Vav., T. sphaerococcum and T. spelta, are absent [According to Dekaprelevich, Georgia is characterized by the largest number of cultivated wheat species in the world; altogether, 12 species of are found here. Only three narrowly endemic species, e absent .Triticum are known. Georgian endemic wheat species include one Triticum species and four subspecies [Triticum turgidum subsp. palaeocolchicum (Menabde) A. LoveTriticum turgidum subsp. carthlicum (Nevski) A. LoveTriticum timopheevii subsp. zhukovskyi (Menabde & Ericzjan) L. B. CaiTriticum zhukovskyi Menabde & EriczjanTriticum aestivum subsp. macha (Dekapr. & Menabde) McKeySouth Caucasus (notably Georgia) and its earlier residents played an important role in wheat formation. In total, 17 domesticated species and subspecies of bspecies ,6,7: Tricarthlicum which has been distributed in East Georgia as well. All these species and subspecies grew in the territory of Georgia until the middle of the last century.All these cultivated species and subspecies have been found only in West Georgia, except subsp. The “Wheat Enigma” was a term for the observation that wild predecessors of five Georgian endemic wheat subspecies are found in Fertile Crescent, quite far from the South Caucasus ,9. One pTriticum were determined in our laboratory [Triticum turgidum and T. aestivum. Plasmon of T. zhukovskyi belong to the G type. The examination of genealogical data provides insights into the evolutionary history of a species. The wide application of gene genealogies for evolutional studies in plants involves identifying DNA sequences with levels of ordered variation within chloroplast, mitochondrial, or nuclear genomes . Traditiboratory ,15,16. PTriticum species is the three homoeologous loci of wheat Wknox1 gene, functioning at shoot apical meristems (SAM) [Wknox1 genomic sequences revealed accumulation of numerous mutations, particularly in the fourth intron and the 5′-upstream region. Later, Takumi a. Morimoto [Wknox1b KNOTTED1-type homeobox gene in a common wheat subspecies (T. aestivum subsp. carthlicoides).Another effective tool for gene genealogy studies for ms (SAM) . A compaMorimoto  reportedT. aestivum subsp. aestivum, two samples; T. aestivum subsp. compactum, two samples; T. aestivum subsp. sphaerococcum, one sample; T. aestivum subsp. carthlikoides, four samples; T. aestivum subsp. spelta, three samples; T. aestivum subsp. macha, six samples; T. aestivum subsp. vavilovii, two samples.The purpose of the present investigation was to carry out the comparative analysis of complete cpDNA sequences of 20 hexaploid wheat accessions  (collected in Armenia)  . In the Triticum aestivum subsp. spelta  and T. aestivum subsp. vavilovii (Vav2) samples (T. aestivum subsp. vavilovii (Vav1) (collected in Armenia) in the position 56,120–56,157 (intergenic rbcL—psaI). One 35bp insertion and three inversions  have been identified in  samples . One 38bThis section may be divided into subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.Wknox1d fourth intron regions were amplified by PCR using the primer pair of Takumi a. Morimoto [T. aestivum subsp. aestivum cv. ‘Chinese Spring’ (CS), and a durum wheat, T. turgidum subsp. durum cv. ‘Langdon’ (Ldn), were used for PCR as control and the amplified DNA fragments were visualized on an agarose gel. In the fourth intron of Wknox1d in common wheat, a 122-bp MITE insertion has been reported [macha (11 samples) the 453 bp band was observed. In the case of two macha samples (M6 and M9) the MITE-containing band typical for other hexaploid subspecies (411 bp) were detected.In the present investigation, the genomic sequences of the Morimoto  (Table 7reported . The MITWknox1d DNA region of hexaploid wheat gives four bands: N1 (280 bp), N2 (375 bp), N3 (411 bp), and N4 (453). Bands N3 and N4 were cut out and sequenced. In the case of band N4 (453), a 42 bp insertion was detected in the position 7009—AGTTTGCACACCTGAACATTTTGCATTATGTTCGGGAGCCTA  is present in T. turgidum subsp. durum cv. ‘Langdon’; CS; Red Doly .PCR-based haplotype analysis of the fifth-to-sixth exon region of Red Doly . This baWknox1d and the fifth-to-sixth-exon region of Wknox1b are given in Hexaploid wheats with matching values of the fourth intron of T. aestivum is in a region between Transcaucasia and southwestern Caspian Iran [uAuBB genome [T. aestivum L. (BBAuAuDD) arose in southwestern Caspian Iran and Transcaucasia by allopolyploidization of the cultivated Emmer wheat Triticum dicoccum Schrank with the Caucasian Ae. tauschii [It is widely believed that the birthplace of ian Iran . The firB genome . Approxitauschii ,22,23,24Ae. tauschii was found in the western Caucasus as well [Ae. squarrosa (Ae. tauschii) grows in lowlands and mountain foothills, rarely in dry and humid silty areas up to the middle of the mountain, mainly in desert, semi-desert and field vegetation, as well as weeds in Abkhazia, Samegrelo, Imereti, Guria, Adjara, Kartli, in outer Kakheti, Gardabani” [Ae. tauschii (A) and (B) participated in the formation of hexaploids through the D genome: Ae. tauschii (A)—macha —and Ae. tauschii (B)—macha M6, M9, CS, RD  and West Georgia. This hexaploid wheat accession showed the subsp. carthlicum-like morphology. Subsp. carthlicum was proposed to have originated from spontaneous hybridization between subsp. carthlicoides and cultivated emmer wheat, T. turgidum subsp. dicoccon (Schrank) Thell. Subsp. carthlicoides should be considered as the original and elder genotype from which genes for this particular morphology of the ear were transferred together with the Q-factor to T. carthlicum [T. dicoccum, T. carthlicum and T. aestivum subsp. macha [carthlicoides took part in the formation of the subsp. macha as an ancestor.caucasia ; In 1967rthlicum ,18. Accop. macha . It can T. aestivum subsp. macha [T. turgidum subsp. palaeocolchicum) [One of the four subspecies of wheat detected in Georgia is hexaploid, domesticated, hulled wheat p. macha . This sup. macha . It is elchicum) .T. aestivum and wild emmer wheat [T. aestivum subsp. aestivum) by hybridization with hulled emmer (T. turgidum). Experimental data suggest that European and Asian spelt may be polyphyletic. Free-threshing, hexaploid wheat seems to precede spelt. At least some European spelt originated from hybridization of club wheat (T. aestivum subsp. compactum) with emmer (domesticated hulled Triticum turgidum). Free-threshing hexaploid wheat was an ancestor of not only European spelt but also of some of the Asian forms of spelt, although the exact role free-threshing wheat has played is debatable [It is proposed that macha and West Georgian wheats are sibling cultivars that arose in a hybrid swarm involving er wheat . It is aebatable .Triticum aestivum subsp. spelta three samples + T. aestivum subsp. vavilovii (Vav1) (collected in Armenia), and the remaining 16 samples including T. aestivum subsp. vavilovii (Vav2) (Collected in Sweden).The comparative analysis of complete nucleotide sequence 20 samples of hexaploid wheats offers the opportunity to draw several conclusions: 20 hexaploid wheat samples are divided into two groups—T. aestivum: Plastogroup I and II. Most subsp. aestivum, compactum, macha, and sphaerococcum accessions belonged to Plastogroup I. Subspecies spelta was split into Plastogroup I (70% of accessions examined) and Plastogroup II (30%). So, given the numbers of subsp. spelta accessions used , the cpDNA trees  appear quite consistent. Plastogroup I (and our major lineage) might represent a major lineage that was transmitted from cultivated T. turgidum via hybrid cross with Ae. tauschii and its subsequent allopolyploidization. Plastogroup II (and our minor lineage) might have resulted from introgression between hexaploid and tetraploid wheats, because subsp. spelta has the emmer wheat ancestry in Europe and Asia [Hirosawa et al.  found twand Asia .Wknox1d and the fifth-to-sixth-exon region of Wknox1b of all 20 hexaploid wheat samples was carried out. PCR-based haplotype analysis of the fourth intron of Wknox1d and the fifth-to-sixth-exon region of Wknox1b provides an opportunity to make an assumption that hexaploid wheats T. aestivum subsp. macha var. palaeocolchicum and var. letshckumicum differ from other macha samples by the absence of a 42 bp insertion in the fourth intron of Wknox1d. It can be assumed that two Ae. tauschii (A) and (B) participated in the formation of hexaploids through the D genome: Ae. tauschii (A)—macha —and Ae. tauschii (B)—macha M6, M9, T. aestivum subsp. aestivum cv. ‘Chinese Spring’ and cv. ‘Red Doly’. Another possible explanation would be that the insertion might have arisen during the diversification process of common wheat after polyploidization. PCR-based haplotype analysis of the fourth intron of In recent years, wheat yields have not increased per hectare, which is important due to the growing world population. Transferring agronomically important genes from wild relatives to common wheat has been shown to be an effective genetic resource for hexaploid wheat improvement. Advances in new technologies have made the complete wheat reference genome available, which offers a promising future for the study of wheat improvement which is essential to meet global food demand .The seeds of hexaploid wheat samples were received from the seed bank of The Agricultural University of Georgia (the late Prof. P. Naskidashvili) and Institute of Botany  (ISU); The U.S. National Plant Germplasm System ; The National BioResource Project—WHEAT Centre ; The Scientific Research Center of Agriculture (SRCA) (Georgia) and Tel Aviv University (Israel). The seeds were germinated in water at room temperature. Total genomic DNA extraction from young leaves, the construction of genomic DNA libraries and assembly of cpDNA have been described earlier ,31. An aConstruction of 13 shotgun genomic libraries and sequencing on the NovaSeq 6000 was carried out at the Roy J. Carver Biotechnology Center, University of Illinois at Urbana-Champaign (UIUC). The shotgun genomic DNA libraries were constructed from 50 ng of DNA after sonication with a Covaris ME220  to an average fragment size of 400 bp with the Hyper Library Preparation Kit from Kapa Biosystems . To prevent index switching, the libraries were constructed using unique dual-indexed adaptors from Illumina . The individually barcoded libraries were amplified with 6 cycles of PCR and run on a Fragment Analyzer  to confirm the absence of free primers and primer dimers and to confirm the presence of DNA of the expected size range. Libraries were pooled in equimolar concentration and the pool was further quantitated by qPCR on a BioRad CFX Connect Real-Time System .The pooled, barcoded, shotgun libraries were loaded on a NovaSeq SP lane for cluster formation and were sequenced for 150 cycles from each side of the DNA fragments. The fastq read files were generated and demultiplexed with the bcl2fastq v2.20 Conversion Software .Wknox1d 4th intron regions were amplified by PCR using the primer pair of Takumi a. Morimoto [Wknox1b 5th-to-6th exon region was amplified by PCR using the primer pair 5′-GCTGAAGCACCATCTCCTGA-3′ and 5′-CATGTAGAAGGCGGCGTTAG-3′. The DNA fragments (PCR products) were excised from the agarose gel. The DNA extraction and purification from the agarose gel was performed by the QIAquick Gel Extraction Kit (QIAGEN). PCR products were purified with GenElute PCR Clean-Up Kit (Sigma-Aldrich), dye labeled using a Big Dye Terminator Kit (Applied Biosystems), and sequenced on Applied Biosystems 3700 genetic analyzer .The genomic sequences of the Morimoto : 5′-AAAAT. aestivum subsp. spelta three samples + T. aestivum subsp. vavilovii collected in Armenia, and the remaining 16 samples, including T. aestivum subsp. vavilovii collected in Europe (Sweden).Based on complete chloroplast DNA sequences, the 20 hexaploid wheat samples can be divided into two groups—Wknox1d and the fifth-to-sixth-exon region of Wknox1b hexaploid wheats, T. aestivum subsp. macha var. palaeocolchicum and var. letshckumicum were found to differ from other macha samples by the absence of a 42 bp insertion in the fourth intron of Wknox1d.Based on the fourth intron of Aegilops tauschii Coss. (A) and (B) participated in the formation of hexaploids through the D genome: Ae. tauschii (A)—macha —and Ae. tauschii (B)—macha M6, M9, T. aestivum subsp. aestivum cv. ‘Chinese Spring’ and cv. ‘Red Doly’.Two


Sarcophaga tsinanensis  (Diptera: Sarcophagidae), a species from subgenus Heteronychia. In this study, we report the complete mitochondrial genome (mitogenome) of S. tsinanensis. The length of this mitogenome was 14,972 bp in total, containing 13 protein-coding genes, 2 ribosomal RNAs, 22 transfer RNAs, and a non-coding control region. The arrangement of genes was the same as that of ancestral metazoan. Sarcophaga tsinanensis has a high nucleotide bias in A/T accounting for 76.80% of total nucleotides . The result of phylogenetic analysis revealed that S. tsinanensis cluster together with species from the same subgenus Heteronychia, showing a clear monophyletic relationship. Sarcophaga tsinanensis is closely related to its sister species Sarcophaga depressifrons, Sarcophaga plotnikovi, and Sarcophaga shnitnikovi. This study provides the mitochondrial data of S. tsinanensis for further research on evolutionary relationships and species identification of flesh flies. Heteronychia was the largest lineage of Sarcophaga (Diptera: Sarcophagidae) in species number , which could further enrich our understanding of the phylogenetic relationship and phylogeography of subgenus Heteronychia.Sarcophagidae is a family of flies with forensic significance, which can provide valuable data for the estimation of minimum postmortem interval (PMImin) and other crime scene related information , China. All specimens were processed by freezing, and then identified based on traditional morphological features (Xu and Zhao 1996). All specimens were stored at −80 °C in Dr. Wang’s Lab  with a unique code (CSU20200713). Total DNA was extracted from thoracic muscle tissues using TIANamp Micro DNA Kit  according to the manufacture’s instruction. The sequencing of S. tsinanensis mitogenome was carried out with an Illumina HiSeq 2500 Platform (paired-end 150 bp) at OE Biotech. Co., Ltd. . de novo assembly was performed using MITObim v1.9 and SOAPdenovo v2.04 (https://github.com/chrishah/MITObim and http://soap.genomics.org.cn/soapdenovo.html)  , containing 13 PCGs, 2 ribosomal RNAs (rRNAs), 22 tRNAs, and a non-coding control region. The arrangement of genes was the same as those of ancestral metazoan  as an outgroup (Oxysarcodexia genera, two species belong to Boettcheria genera, and all other species come from the same subgenus where S. tsinanensis belongs. All these sequences were aligned using MAFFT version 7 software (Katoh and Standley The length of  Cameron . Among t Cameron . Phylogeoutgroup . These 2S. tsinanensis clustered together with species from the same subgenus Heteronychia, showing a clear monophyletic relationship, and S. tsinanensis is closely related to its sister species Sarcophaga depressifrons, Sarcophaga plotnikovi, and Sarcophaga shnitnikovi. Since there are only limited mitogenomes of Heteronychia subgenus currently available, this study provides mitochondrial data of S. tsinanensis for further research on evolutionary relationships and species identification of flesh flies.The result of phylogenetic analysis revealed that


Tropheryma whipplei (T. whipplei). We report herein a rare case of a cutaneous granulo matosis with hypercalcemia as an unusual presenting feature of Whipple’s disease. The diagnosis of the bacteria was obtained from skin and inguinal lymph node biopsy . T. whipplei was also identified on saliva and stool specimens, using specific PCR and colonic biopsies. Treatment with hydroxychloroquine and doxycycline allowed a rapid resolution of symptoms with a complete recovery. Whipple’s Disease is a rare systemic infectious disease caused by the ubiquitous actinomycetes  Tropheryma whipplei (T. whipplei). T. whipplei is a pathogen responsible for a wide range of clinical manifestations in humans, beginning with acute infections, chronic isolated infections, and classic WD [Whipple’s disease (WD) is a rare chronic systemic infection caused by the slow-growing and Gram-positive bacterium assic WD ,2,3. Asyassic WD . In someassic WD . The genassic WD . We report herein a case of WD with cutaneous granulomatosis and bilateral lymphadenopathy in a patient presenting with chronic diarrhea. The WD diagnosis was raised due to the presence of suggestive histological features and confirmed by polymerase chain reaction (PCR) in multiple sites .2, and erythematous scaly plaques in upper limbs  and monocytes  with lymphopenia of 840/mm3 . Platelets were at 282 Giga /L . C-reactive protein was high at 73 mg/L , and liver function was normal. Serum electrolytes were consistent with expected hypercalcemia at 4.15 mmol/L . An acute kidney failure with creatinine at 207 µmol/L  was noted. PTH, PTH-rp and 25–OH-vitamine D levels were normal. Serum protein electrophoresis showed hypoprotidemia at 48.6 g/L  with hypoalbuminemia at 25.6  associated with hypogammaglobulinemia at 6.9 g/L . Lymphocytes immunophenotyping did not show any B or T clonal circulating lymphoma and the percentage of T, B and NK was normal. Subsets of IgG (at 5.70 g/L), IgA (at 1.59 g/L), IgM (0.23 g/L) and IgE were in the range of the matched age. Immunoelectrophoresis did not show monoclonal gammopathy, medullary smear and bone marrow biopsy were normal, which ruled out lymphoma. Supportive treatment began (simultaneous intravenous (IV) administration of fluids associated with biphosphonates and steroids). Hepatitis B and C, HIV and syphilis serologies were negative. The QuantiFERON-TB Gold test was negative. Direct examination and culture of the stool sample were negative. Skin and a right lymph node biopsy were performed. Upon histological examination, light microscopy showed granulomas composed of aggregates of epithelioid histiocytes, with no necrosis and negative Ziehl–Neelsen staining  and the histological examination using periodic acid-Schiff (PAS) staining  symptoms including nausea and vomiting, abdominal pain and intermittent fluid diarrhea that had been occurring for two years. He did not report any recent travel. He did not smoke tobacco, drink alcohol, nor use illicit drugs. At admission, a physical examination was performed that revealed a blood pressure of 132/100 mmHg, a regular heartbeat (112 beats/min), afebrile (36.7 °C) with a body mass index (BMI) of 18 kg/mer limbs . Abdominstaining . Microscstaining . ColonosBrain magnetic resonance imaging (MRI) and lumbar puncture were normal. Cardiac MRI was normal. Whole exome-sequencing was performed, but no mutation in IRF4 was identified. The patient started hydroxychloroquine (400 mg/day) and doxycycline (200 mg/day). The outcome was favorable, with a progressive improvement of his general status. Diarrhea was discontinued, CT control showed reduction of lymph node volume as well as liver enlargement. Furthermore, calcium level was also normalized. The patient was discharged on the same oral treatment for 12 months with a complete recovery.We report herein a new patient with typical WD symptoms of diarrhea, weight loss, granulomatous lymphadenopathy and cutaneous features. The cutaneous lesions in WD are infrequent and unspecific ,8, excepT. whipplei and Mycobacterium spp., as both are positive with PAS staining [T. whipplei DNA and confirmed the diagnosis and thus ruled out systemic sarcoidosis. This case illustrates a granulomatous dermal reaction related to classic WD. In a similar case of a patient with isolated peripheral lymphadenopathy in absence of GI symptoms, Walters et al. were able to diagnose Whipple’s early in the course of clinical disease from specific PCR in lymph node biopsy [T. Whipplei infection [T. whipplei-specific PCR is required and may be essential for the diagnosis. In a large retrospective study including more than 27,000 samples, Edouard et al. [T. whipplei-specific PCR results of extra-duodenal specimens, repeated duodenal biopsies showed WD-like histological changes, and positive T. whipplei-specific PCR results were obtained. In these patients, T. whipplei-specific immunohistochemistry could be an excellent diagnostic test with very high specificity and sensitivity which allows for the identification of T. whipplei in PAS and PCR-negative specimens [In our case, a lymph node biopsy showed granulomatous lymphadenopathy without necrosis. Several etiologies such as tuberculosis, HIV, sarcoidosis and lymphoma were initially suspected . Ziehl–Nstaining , but onle biopsy . The gole biopsy . Thus, 1nfection . Therefod et al.  observedd et al.  concludepecimens ,20; othepecimens ,21.T. whipplei on lymph nodes biopsies. Cutaneous granulomatosis related to WD is rare. The WD assessment should be considered in the case of patients with impaired general conditions, chronic diarrhea and lymph node enlargement. Given the inadequate sensitivity of PAS stain and in order to contribute to earlier diagnosis, we suggest specific PCR for


Abies forrestii is endemic to southwest China and ecologically important as a major component of the cold temperate forests. This study was the first report complete chloroplast (cp) genome of A. forrestii. The complete chloroplast genome was 120,022 bp in size. In total, 114 genes were identified, including 68 peptide-encoding genes, 35 tRNA genes, four rRNA genes, six open reading frames and one pseudogene. Thirteen genes contain introns. In phylogenetic analysis, A. forrestii was found to be closely related with A. nukiangensis, A. fanjingshanensis and A. delavayi subsp. fansipanensis. Our study will provide potential genetic resources for further evolutionary studies of this ecologically important species. Abies Miller is the second largest one in the family Pinaceae, consisting of approximately 48 species . Voucher specimen and DNA sample  were deposited in the herbarium of Institute of Botany, CAS (PE) . Total genome DNA was extracted with the Ezup plant genomic DNA prep kit . Total DNA was used to generate libraries with an average insert size of 350 bp, which were sequenced using the Illumina HiSeq X platform. In total, ca. 10.2 million high-quality clean reads (150 bp PE read length) were generated with adaptors trimmed. The CLC de novo assembler , BLAST, GeSeq (https://chlorobox.mpimp-golm.mpg.de/geseq.html)  and A. neprolepis (KT834974).The plant material of https://www.ncbi.nlm.nih.gov] under the accession no. MH706715. The associated BioProject, SRA, and Bio-Sample numbers are PRJNA747743, SRP328853 and SAMN20294543 respectively. The full length of Abies forrestii chloroplast genome was 120,022 bp. The chloroplast genome showed a typical quadripartite structure that consisted of a pair of IR regions (264 bp) separated by the LSC  and SSC  regions, which was similar to the majority of cp genomes in Pinaceae. The GC content were 38.30%. A total of 114 genes were contained in the cp genome . Fifty-three protein coding, 16 tRNA genes, three open reading frames and one pseudogene are located in the LSC region, while 15 protein-coding, 17 tRNA genes, 4 rRNA and 3 open reading frames are located in the SSC region, respectively. Only one tRNA gene (trnI-CAU) is duplicated and located on the IR regions. All ndh genes have been lost in the genome of A. forrestii like other cp genomes of family Pinaceae. Among the protein-coding genes, two genes (rps12 and ycf3) contained two introns, and other eleven genes  had one intron each. In previous studies, short inverted repeat sequences which consist of trnS-psaM-ycf12-trnG and trnG-ycf12-psaM-trnS (1183 bp) are located in 52-kb inversion points of the cp genome of A. forrestii. Length and sequence of inverted repeats from A. forrestii is identical with those of A. koreana   . And A. panensis .Abies forrestii. Moreover, it would be fundamental to formulate potential conservation and management strategies for this ecologically important species of southwest China.This study provides new insight into the cp genome evolution and phylogenetic relationships of


The potential for using secondary structures of the large and small ribosomal subunits for phylogenetic reconstruction was predicted. The phylogenetic relationships were explored through comparative analyses across more than 30 coccinellid species. We performed phylogenetic analyses with both concatenation methods (Maximum Likelihood and Bayesian Inference) and multispecies coalescent method (ASTRAL). Phylogenetic results strongly supported the monophyly of Coccinellidae. Within Coccinellidae, the Epilachnini and the Coccinellini including Halyziini were monophyletic, while the Scymnini and Coccidulini were non-monophyletic.The Coccinellidae are one of the most familiar beetle families, the ladybirds. Despite the great ecological and economic significance, the phylogenetic relationships of Coccinellidae remain poorly understood. One of the reasons is that the sequenced mitogenomes available for this family are very limited. We sequenced complete or nearly complete mitogenomes from seven species of the tribe Coccinellini with next-generation sequencing. All species have the same gene content and gene order as the putatively ancestral insect mitogenome. A large intergenic spacer region (> 890 bp) was found located between  Coccinella septempunctata Linnaeus. Currently, the family is subdivided into about 360 genera, with approximately 6,000 species worldwide (Bulaea Mulsant and the whole subfamily Epilachninae (Epilachna varivestris Mulsant and Henosepilachna vigintioctopunctata (Fabricius)  .Despite the monophyly of the whole Coccinellidae is well supported , the phyPrevious authors have attempted to reconstruct the phylogenetic relationships among the subfamilies based on morphological  and/or me.g., Galerucinae and Chrysomelidae) with similar divergence time, some authors have successfully used mitogenomes to resolve the subfamily  and small subunit (rrnS) may provide potential information for phylogenetic studies  to add more mitogenome data to uncover the evolutionary relationships of ladybirds.In this study, we sequenced seven complete or nearly complete mitogenomes of the subfamily Coccinellinae, with three main aims: (1) to compare mitogenome organization and gene content across coccinellid lineages; (2) to identify conserved sequence motifs and the associated secondary structure elements in Coccinella lama Kapur, Hippodamia variegata (Goeze), Coccinella transversoguttata Faldermann, Adalia bipunctata (Linnaeus), and Oenopia dracoguttata Jing were collected from Lhasa, Tibet, China in June 28, 2019; Harmonia axyridis  and Harmonia eucharis (Mulsant) were collected from Linzhi, Tibet, China in August 3, 2019. Thoracic muscle was used for DNA extraction, using the TIANamp Genomic DNA Kit , according to the manufacturer’s protocol. Extracted DNA was stored at −20 °C.A total of 46 mitogenomes were analyzed in this study: 40 species of ladybird beetles and other six species of Cucujifamia outgroups . Of whicAbout 500 ng genomic DNA of the individual species were used for library preparation using the Illumina TruSeq TM DNA Sample Prep Kit , according to manufacturer’s instructions. DNA were sheared into 350-bp fragments in a Covaris M220 instrument (Covaris Inc.). Libraries were sequenced on the Illumina HiSeq 2500 platform with 150-bp paired-end reads, at Shanghai OE Biotech. Co., Ltd, China.Cycloneda sanguinea (Linnaeus) (accession number: KU877170) was used as reference for assembly.Statistics for next-generation sequencing are presented in rrnL and rrnS genes were predicted by reference to the darkling beetle Gonocephalum outreyi Chatanay  and Bayesian Inference (BI). PartitionFinder 2  was usedBI analysis was performed with PhyloBayes MPI  implemenWe used ASTRAL v 5.7.1  to estimH. axyridis) and 22.48 Mbp (O. dracoguttata)  . The proO. dracoguttata) to 2.19% (C. magnifica) corresponded to mitochondrial reads. Length of assembled mitogenomes varied between 17,963 bp (C. transversoguttata) and 21,391 bp (H. eucharis) (H. eucharis) to 1,251-fold (C. magnifica). Sequencing depth was not closely correlated with the mitogenome length. The current sequencing depth was sufficient to cover the entire mitogenome.Of the clean reads, 0.75% (ucharis) . The aveAll seven newly-sequenced mitogenomes contained the typical 37 mitochondrial genes and a complete control region . All sevcox1, the putative initiation codon was TCG(Ser) in all newly sequenced mitogenomes. Canonical stop codons (TAG and TAA) were present for 11/12 protein-coding genes depending on species. Incomplete stop codons (T or TA) were inferred for cox2 (H. variegata and H. eucharis), cox3 (C. lama and C. transversoguttata), nad1 (O. dracoguttata and H. eucharis), and nad2 (H. axyridis).Nucleotide composition was strongly biased toward A and T. The average A+T content for the whole mitogenomes was 77.9%, made up of 76.5% in the protein-coding genes, 78.9% in the tRNA genes, 78.1% in the rRNA genes and 82.5% in the control region. Twelve of the 13 protein-coding genes started with the typical codon ATN . However, for trnH, A. bipunctata) to 71 bp  in size. All of mitochondrial tRNA genes had secondary structures commonly seen in other insects, with the exception for trnS1, which lacked a complete dihydrouridine (DHU) arm , and lacked domain III. rrnS genes had three domains  composed of 26 helices.The rrnS and trnI was identified within assembled mitogenomes, demonstrating circulation of the molecule. Besides the control region, a large non-coding region (or intergenic spacer region) was present between trnI and trnQ. The sequence lengths of this region varied between 895 bp (C. transversoguttata) and 2,745 bp (C. lama).The complete control region between rrnL and 31 rrnS secondary structures of coccinellid species, we found that species in the same genus shared conserved motifs. For rrnL domain V, the loop located in the tip of helix 35 had largely identical nucleotide composition within a single genus  between them . A clade comprising a part of Scymnini (Nephaspis) and the species representing Noviini (Rodolia quadrimaculata) emerged as sister to the rest of the family. But these relationships received no statistical supports (BS = 47). Similarly, in the BI analysis, the basal relationships within Coccinellidae were ambiguous. In particular, relationships among Scymnini, Noviini and Coccidulini were unresolved. The Scymnini and Coccidulini were non-monophyletic.Monophyly of the Coccinellidae was strongly supported in both ML and BI analyses . All genera with more than two species included in this study  were supported as monophyletic .The Coccinellini including Halyziini formed a large clade. In both ML and BI analyses, the Halyziini was sister to a clade comprising ASTRAL analysis showed better performance in resolving deeper nodes in the Coccinellidae  with restrnI and trnQ. The position of the intergenic spacers accorded with our prior study (trnQ and nad2 was found in the mitogenome of Manduca sexta (Linnaeus) (Lepidoptera: Sphingidae) in a previous study , also supported the Coccinellinae as a monophyletic group. Our result is consistent with the previous studies. The current mitogenome data supported Epilachnini as monophyletic. This was congruent with Chilocorus (Chilocorini) and the clade Coccinellini + Halyziini was supported by the present mitogenome analyses. This arrangement is congruent with Coelophora and Propylea, between Hippodamia and Harmonia were supported, consistent with the prior studies by Multispecies coalescent analysis with ASTRAL showed improved performance in recovering relationships within Coccinellidae, especially for the basal relationships among the lineages of Scymnini and Coccidulini. In addition, the sister group relationship between Chilocorini and Coccinellini was robustly supported. Within Coccinellini, the sister-group relationships between Exploration of meaningful characters for phylogenetic analysis can be essential to better understand insect evolution. As illustrated for this the secondary structures inferred for this study, two classes of regions are observed in rRNA molecules: the double-stranded stems and single-stranded loops. It has been long debated whether stem characters or loop characters contain useful information for phylogenetic inference . Our anaC. transversoguttata) to 21,391 bp (H. eucharis), due to a large intergenic spacer (>890 bp) located between trnI and trnQ. The mitochondrial rRNA secondary structures were compared to provide a better phylogenetic alignment. Besides the control region between rrnS and trnI often found in insect mitogenomes, the presence of a large intergenic region in other position is interesting and further studies are needed to investigate the underlying mechanisms creating and preserving such mitochondrial arrangements. The newly sequenced mitogenomes also contribute to a better understanding of the phylogenetic relationships and evolutionary history of ladybirds. The monophyly of Coccinellidae and of several genera within it are recovered, the bootstrap support value has reached 100. Despite this, increased taxon sampling from other species of the coccinellid group other than Coccinellini is needed to comprehensively evaluate the phylogenetic relationships in Coccinellidae.In the present study, we utilized NGS data to reconstruct complete or nearly complete mitogenome of the Coccinellidae. The mitogenomes are very large, ranging from 17,963 bp (10.7717/peerj.12169/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.12169/supp-2Supplemental Information 2Red numbers denote the helices and blue roman numbers denote the domains.Click here for additional data file.10.7717/peerj.12169/supp-3Supplemental Information 3Red numbers denote the helices and blue roman numbers denote the domains.Click here for additional data file.10.7717/peerj.12169/supp-4Supplemental Information 4Red numbers denote the helices and blue roman numbers denote the domains.Click here for additional data file.10.7717/peerj.12169/supp-5Supplemental Information 5Red numbers denote the helices and blue roman numbers denote the domains.Click here for additional data file.10.7717/peerj.12169/supp-6Supplemental Information 6Red numbers denote the helices and blue roman numbers denote the domains.Click here for additional data file.10.7717/peerj.12169/supp-7Supplemental Information 7 Red numbers denote the helices and blue roman numbers denote the domains.Click here for additional data file.10.7717/peerj.12169/supp-8Supplemental Information 8Red numbers denote the helices and blue roman numbers denote the domains.Click here for additional data file.10.7717/peerj.12169/supp-9Supplemental Information 9Red numbers denote the helices and blue roman numbers denote the domains.Click here for additional data file.10.7717/peerj.12169/supp-10Supplemental Information 10Red numbers denote the helices and blue roman numbers denote the domains.Click here for additional data file.10.7717/peerj.12169/supp-11Supplemental Information 11Red numbers denote the helices and blue roman numbers denote the domains.Click here for additional data file.10.7717/peerj.12169/supp-12Supplemental Information 12Red numbers denote the helices and blue roman numbers denote the domains.Click here for additional data file.10.7717/peerj.12169/supp-13Supplemental Information 13Red numbers denote the helices and blue roman numbers denote the domains.Click here for additional data file.10.7717/peerj.12169/supp-14Supplemental Information 14Click here for additional data file.10.7717/peerj.12169/supp-15Supplemental Information 15Click here for additional data file.10.7717/peerj.12169/supp-16Supplemental Information 16Click here for additional data file.10.7717/peerj.12169/supp-17Supplemental Information 17Click here for additional data file.10.7717/peerj.12169/supp-18Supplemental Information 18Click here for additional data file.10.7717/peerj.12169/supp-19Supplemental Information 19Click here for additional data file.