The msaABCR operon is required for maintaining the cell wall integrity via affecting peptidoglycan cross-linking. The msaABCR operon also plays a role in oxidative stress defense mechanism, which is required to facilitate persistent and recurrent staphylococcal infections. Staphylococcus aureus is the most frequent cause of chronic implant-associated osteomyelitis (OM). The CA-MRSA USA300 strains are predominant in the United States and cause severe infections, including bone and joint infections.The msaABCR deletion mutant was attenuated. We observed minimal bone necrosis, with no evidence of intramedullary abscess and/or fibrosis, along reduced intraosseous bacterial population and significantly less biofilm formation on the K-wires by the msaABCR mutant. microCT analysis of infected bone showed significant bone loss and damage in the USA300 LAC and complemented strain, whereas the msaABCR mutant’s effect was reduced. In addition, we observed increased osteoblasts response and new bone formation around the K-wires in the bone infected by the msaABCR mutant. Whole-cell proteomics analysis of msaABCR mutant cells showed significant downregulation of proteins, cell adhesion factors, and virulence factors that interact with osteoblasts and are associated with chronic OM caused by S. aureus.The USA300 LAC strain caused significant bone damage, as evidenced by the presence of severe bone necrosis with multiple foci of sequestra and large numbers of multinucleated osteoclasts. Intraosseous survival and biofilm formation on the K-wires by USA300 LAC strains was pronounced. However, the msaABCR operon in USA300 LAC strain lead to defective biofilm in K-wire implants, decreased intraosseous survival, and reduced cortical bone destruction. Thus, msaABCR plays a role in implant-associated chronic osteomyelitis by regulating extracellular proteases, cell adhesions factors and virulence factors. However additional studies are required to further define the contribution of msaABCR-regulated molecules in osteomyelitis pathogenesis.This study showed that deletion of  Staphylococcus aureus is the most common pathogen causing chronic infections in humans [S. aureus in immunocompromised patients, severely debilitated patients, and patients with indwelling medical devices are on the rise [S. aureus is the most common causative pathogen, accounting for 20–30% of chronic OM cases associated with fracture fixation and prosthetic joint replacement [S. aureus is the most prevalent pathogen in these infections [S. aureus (CA-MRSA) strains are emerging as leading pathogens causing the severe sepsis associated with acute OM [n humans –3, and on humans –7. Accorn humans  and Weicn humans , approxin humans , 8. Amonn humans , 9. In athe rise –12. The the rise , 13, 14.lacement –18. Accolacement , 600, 00lacement . Furtherfections . The comacute OM –24.S. aureus is complex and involves one or more surgical debridement, implant removal, and prolonged antimicrobial therapy. The ability of S. aureus to form biofilm, small colony variants, and persister cells inside osteoblasts contributes to treatment recalcitrance [Treatment of OM infections by citrance –28. Prevcitrance  and Lee citrance  both foucitrance , 12.msaABCR operon is a four-gene operon that regulates several virulence factors , biofilm formation, persister cell formation, oxidative stress response, and antibiotic susceptibility in S. aureus [msaABCR deletion mutants are not defective in growth [msaABCR mutants are defective in biofilm formation due to increased production of extracellular proteases in vitro [msaABCR operon regulates the expression of 238 genes that are involved in metabolism, pathogenesis, transport, signal transduction, and stress response [msaABCR operon in pathogenesis in vivo using implant-associated OM in a SD rat model. In this model of implant associated osteomyelitis, we used K-wires coated with S. aureus strains to implant transcortically through the tibial metaphysis as previously described to mimic clinical OM infection [S. aureus strain USA300 LAC and its isogenic msaABCR mutant.The . aureus –37. We an growth . Previouin vitro . MsaB isin vitro , 34, 35.in vitro  showed tin vitro . Previouresponse . The prinfection , 40. We msaABCR operon in the pathogenesis of chronic implant-associated OM and intraosseous bacterial survival. Rats were infected with a clinical isolate of USA300 LAC due to its prevalence in community-associated invasive infections. For induction of chronic implant-associated OM, K-wires were first colonized with the test strains  and implanted transcortically through the bone metaphysis  was similar in all test strains and were not statistically different  and on the K-wires (1.45X104 CFUs) after the surgical procedure were not statistically different from each other , microbial counts were obtained from the post-mortem blood and organ samples. Our results showed that the bacterial CFUs in blood, heart, and spleen samples recovered from the animals infected with the ant Fig. : Fig. S1msaABCR operon in the pathogenesis of OM, the infected bones harvested after each infection period were analyzed by microCT.To further delineate the role of the msaABCR mutant strains , a small piece of bone was missing near the infection area, as evident in the 3D image  that have been previously linked to staphylococcal OM infections. We observed a significant decrease in IL-1 serum levels in animals infected with res Fig. a. At thires Fig. b. Howeveres Fig. b. The anods Fig. c.Fig. 9msaABCR mutant relative to the animal group infected with USA300 LAC and complementation strains on day 4 and day 15 post-infection  were affected [msaABCR mutant was performed and compared with the USA300 LAC strain under planktonic and biofilm growth conditions. Whole-cell proteomics of the msaABCR mutant under planktonic growth conditions revealed that a total of 187 proteins were differentially expressed relative to the USA300 LAC strain , clumping factor A (ClfA), clumping factor B (ClfB), serine–aspartate repeat-containing protein D (SdrD), and Extracellular matrix binding protein (Embp), but showed increased production of serine–aspartate repeat-containing protein C (SdrC) (Table Deletion of the em Table . Under pC) Table . Under bC) Table .Table 2msaABCR mutant under planktonic conditions  and uncharacterized leucocidin-like protein 1 (LukL1), which leads to osteoblast death or triggers osteoclastogenesis and promotes persistence of infection, were downregulated significantly in the ns Table . Under bns Table . Extraceel Table .msaABCR proteomics under both planktonic and biofilm growth condition  versus the osteoblast local response in the previous study.Previous studies have shown the role of proinflammatory cytokines and chemokines in maintaining the balance between bone resorption and formation by recruiting and activating infiltrated immune cells during bacterial infection of bone –66. Hostl models , 65, 67.l models . In thiss result  that shomsaABCR mutant. This observation correlates with the microCT data and bone histopathological data concerning the infected bone, in which the animals that received a sham pin and those infected with the msaABCR mutant showed increase osteoblast response during all post-infection periods and evidence of newly formed bones after day 15 post-infection. Whereas the reduced ALP activity in animals infected with the wild type and complementation strains may be the result of increase osteoclast and less osteoblast activity, increased bone loss, and with no evidence of any reactive bone formation even after 15 days post-infection.Bone-specific ALP is synthesized by osteoblasts in response to bone injury and plays a role in bone matrix calcification . In studS. aureus, thus promoting bacterial colonization and biofilm formation [cna, bbp, and/or fnbB genes are efficient in attaching to bone extracellular matrix and concentrating around the osteoblasts. Of all the S. aureus strains that cause OM, 10–60% express cna, bbp, and fnbB, whereas all S. aureus strains express either fnbA or clumping factors A and B [S. aureus also produces several virulence factors that can directly interact with osteoblasts and bone extracellular matrix, thus triggering increased ostecloclastogenesis and causing serious bone damage in the host [S. aureus also overexpress several other virulence factors, such as α-hemolysin (Hla), phenol-soluble modulin (psms), and proteases, which are critical virulence factors in several animal models for brain abscesses, skin and soft-tissue infections, and pneumonia [S. aureus (USA300 LAC), which was partly due to its ability to express high levels of accessory gene regulator (agr), resulting in the production of high levels of extracellular toxins, such as phenol-soluble modulins (PSMs) and alpha toxins, which were shown to be more cytotoxic to osteoblasts in vitro. In this study we showed that several staphylococcal proteins that facilitates S. aureus to attach bone extracellular matrix components, infect bone osteoblasts, and modulates host immune system were significantly affected in the msaABCR mutant , hosts cells invasion, evasion of immune system, and osteolytic factors such as LukA and alpha hemolysins that have been shown to play role in osteomyelitis in previous studies [msaABCR proteomics were performed in in vitro conditions and not from in-vivo samples. Therefore, additional studies are needed to further define the contribution of various msaABCR-regulated molecules to pathogenesis during osteomyelitis infection.Several studies showed that specific staphylococcal proteins (MSCRAMMS and virulence factors) play a role during the initial interaction with bone and the progression of OM infection , 71–73. ormation , 75–79.  A and B , 80, 81.the host , 77. Créthe host  showed tneumonia , 82, 83.R mutant , 32. In  et al.,  showed rR mutant . They alproteins . Findingproteins  suggest  studies , 54–56. msaABCR mutant is defective in several cell-surface adhesion protein factors that are important for interacting with and colonizing host cells, such as osteoblasts, and host cell matrix, including bone extracellular matrix. In addition, the msaABCR mutant is also defective in some important virulence factors, such as hemolysin and leukocidins, which can kill host cells, such as osteoblasts, and their dissemination that can lead to infection progression. Thus, the msaABCR operon mutant is defective in implant-associated in vivo biofilm formation on K-wires, and decreased intraosseous survivability, that led to reduced bone damage and therefore it plays a role in implant-associated chronic OM in rat model. The findings from this study provide additional insights into the pathogenesis of implant-associated chronic OM infections by USA300 LAC and explore the potential for targeting msaABCR for therapy. It should be however noted that while we observed reactive bone formation in the msaABCR mutant similarly to the sham controls, the msaABCR mutant still caused some degree of bone destruction. This implicates other virulence factors, whose production were not impacted by the deletion of msaABCR operon and should be investigated further to fully understand osteomyelitis pathogenesis caused by S. aureus.In this study we showed that the msaABCR operon deletion mutant (msaABCR mutant) strain [7 CFUs per ml) in prewarmed TSB for planktonic growth conditions and in prewarmed biofilm media for biofilm growth conditions.Overnight bacterial cultures  strain , and a c) strain  were pren = 24) USA300 LAC, msaABCR mutant, complementation, and sham. All animals underwent a surgical procedure to place a K-wire with or without bacteria, and three animals per group were sacrificed immediately following the surgical procedure to assess bacterial load. The remaining 21 animals per group  were euthanized on days 4, 8, and 15 by an overdose of 5% isoflurane (2-chloro-2-(difluoromethoxy)-1,1,1-trifluoro-ethane) followed by bilateral pneumothorax prior to collection of blood, vital organs, and bone. Post-mortem blood samples were processed to obtain plasma and serum, aliquoted into storage tubes, and stored at − 80 °C. Five legs were placed on ice and processed for bacterial counts, while 2 legs were processed for microCT analysis of bone followed by histopathological analysis. Vital organs  were collected and immediately frozen at − 80 °C for analysis of bacterial load resulting from dissemination from the infection site. Organ samples for histopathological studies were placed in buffered formalin.This study included 96 animals randomly divided using a random number generator into 4 groups  pre-normalized to an OD600 of 0.05 (2X107 CFUs per ml). The biofilm-coated K-wires were stored at − 80 °C until use and for not more than 7 days. Six biofilm-coated K-wires were used to enumerate the bacterial CFUs prior to the surgical procedure to evaluate the number of initial bacterial CFUs. Sterile K-wires coated with SD-Rat plasma were used as negative control (sham infection). The K-wires colonized with test strains were inserted into the bones of Sprague Dawley rats (SD-Rat) to induce OM.The surgical procedure was performed as previously described for an animal model of chronic OM, with slight modifications , 40. In n = 24), and a K-wire was inserted surgically. Isoflurane (2%) was used for anesthesia induction followed by intraperitoneal injection of ketamine (75 mg/kg) and xylazine (5 mg/kg) for surgical anesthesia, and a presurgical dose of Buprenorphine  (0.05 mg/Kg) for analgesia. The skin above the bone was disinfected using 70% ethanol, providone iodine scrub, providone, followed by 70% ethanol. Following the aseptic surgical preparation, the position of the bone metaphysis was determined by palpation, and the K-wire pin was implanted transcortically through the metaphysis. Afterwards, the pin was bent on both ends and cut adjacent to the skin to secure it in the bone. Six hours post-surgery, the animals received a second subcutaneous injection of 0.10 mg/kg buprenorphine . Following surgery, the animals were housed individually for the first 3 days post-surgery, provided Buprenorphine (0.1 mg/kg) for pain every 12 h during the first 2 days and then assessed for pain daily, and analgesics were provided as needed. Wounds were evaluated for infection daily for the duration of the experiment. Animal cages were cleaned daily for the first 3 days, then every other day for the remainder of the experiment. The bedding was collected into biohazard bags, and the cages disinfected before entering the cage wash area.Following approval from the University of Mississippi Medical Center institutional Animal Care and Use Committee (IACUC), a total of 96 SD-rats weighing 250–300 g were purchased from Charles River Labs. The animals were housed under a 12-h day/night light cycle and allowed access to food and water ad libitum. The animals were acclimated for 1 week prior to surgical manipulation. The animals were randomly divided using a random number generator into into 3 sets of 4 groups  prior to infection, the K wires coated with each bacterial strain  were inserted in rat bone. All the animals were euthanized immediately, and K-wires and bone samples were harvested to enumerate the bacterial CFUs retained in the bone and on the K-wires after surgery.Infected bone samples (5 per group) with K-wire pins were carefully excised from the bone, and the bone shaft was removed using a scalpel. The bone was cut into smaller sections, homogenized in the presence of liquid nitrogen using mortar and pestle. The samples were suspended in 5 ml of 1X PBS, and plated in tryptic soy agar (TSA) agar plates after serial dilutions in sterile 1x PBS to determine the viable bacterial count, as previously described .The K-wires (5 per group) were carefully removed from the infected bone samples and rinsed twice with sterile 1x PBS. The K-wires were suspended in 5 ml of sterile 1x PBS and sonicated for 30 min in a 2.8-L ultrasonic bath (Fischer Scientific) to release the bacteria from the biofilms that had formed on the pins. Appropriate serial dilutions were performed and plated on TSA media to count the total number of bacterial CFUs present on the K-wire samples.The blood samples were collected from all animals for enumeration of bacterial CFUs . One hundred microliters of blood samples were serially diluted in 1 ml of 1X PBS and plated on TSA media to count the total number of bacterial CFUs present in the blood samples after overnight incubation at 37 °C.Six organ samples per group  were collected in a sterile plastic container and immediately stored at –80 °C until use. They were later retrieved from the − 80 °C freezer and rinsed with sterile 1x PBS thrice. The organ samples were cut into small pieces using a sterile surgical blade and transferred to a 15-ml FastPrep tube containing 5 ml of an equal mixture of 0.5-mm and 1-mm zirconia/silica beads (BioSpec Products). Five milliliters of sterile 1x PBS was added, and the beads beating was performed in FastPrep-24 instrument . The cell debris was removed by a quick spin at 2000 x g for 2 min, and the supernatant was transferred to another 15-ml tube. The samples were serially diluted and plated on TSA media to count the total number of bacterial CFUs present in the organ samples after overnight incubation at 37 °C.P-value of < 0.05 was considered statistically significant.High-resolution analysis of cortical thickness, cortical bone destruction, and underlying trabecular bone in the sham group and the bacteria-treated groups was determined by microCT imaging using a 1172 μCT SkyScan scanner with a 10-megapixel camera  and the manufacturer’s analytical software. The SkyScan scanning parameters were set up as previously described by Tucci et al. . StructuFor histopathological analysis, samples of the proximal half of the bone were fixed in neutral-buffered formalin solution, decalcified using Cal-Ex™ solution, embedded in OCT for frozen sectioning, and cut into 10-μm sections. The sections were stained with hematoxylin and eosin (H & E). Histopathological scores were determined for intraosseous acute inflammation (IAI), intraosseous chronic inflammation (ICI), periosteal inflammation (PI), and bone necrosis (BN) using the system previously described by Smeltzer et al. . Each paCommercially available colorimetric sandwich enzyme-linked immunosorbent (ELISA) assays were used to obtain quantitative measurements of IL-1, IL-17, and IL-6 (R&D Systems). The manufacturer’s protocols were followed to determine the concentrations of cytokines in the sample. Briefly, either 25 μL of standard or plasma sample was dispensed in duplicate into microtiter plate wells coated with IL-6, IL-17, or IL-1. The plate was incubated for 2 h to allow the cytokine present in the sample or standard to bind to the immobilized antibody. The plates were washed with 100 μL of the wash buffer supplied by the manufacturer three times, and then 25 μL of a second biotinylated cytokine antibody was added and the plate incubated overnight at 4 °C. Following the overnight incubation, the plate was washed three additional times with 100 μL of wash buffer per wash. After washing, the unbound biotinylated antibody, which was conjugated to HRP with streptavidin, was pipetted into the wells, and incubated for 1 h. The wells were washed again in a similar manner, and 50 μL of TMB substrate solution were added to the wells for 20 min followed by the addition of 50 μL of stop solution. The intensity of the color in the samples was compared with a standard curve generated by the color intensity of the concentration standards at 405 nm, and the amount of cytokine in the samples was thereby determined.The activity of ALP in serum was determined by measuring the rate of hydrolysis of various phosphate esters under specified conditions. In the Pointe Scientific  kinetic assay, p-nitrophenyl phosphate is hydrolyzed to p-nitrophenol and inorganic phosphate and detected at 405 nm, and the rate of hydrolysis is directly proportional to ALP activity. Working reagents were prepared according to the manual instructions. One milliliter of reagent was pipetted into a spectrophotometer cuvette and warmed to 37 °C for 5 min. The reference cuvette contained water and was used as the blank. A plasma aliquot of 0.25 mL was added to warm reagent and allowed to incubate at 37 °C for an additional minute. The absorbance was determined at 405 nm, and the cuvette was then placed back in the 37 °C heat block and measured again after 5 and 10 min. The average absorbance difference per minute was calculated and multiplied by a factor of 2187 to convert the results to IU/L.For whole cell proteomics analysis, the cells were grown to OD600 of 1.5 in TSB broth for planktonic growth condition. The cells were collected, washed twice with ice cold 1X PBS and were used for whole cell proteomics analysis. For biofilm growth condition, the biofilm was grown in flow cells . The flow cells were pre-coated with human plasma. One hundred microliter of starter bacterial suspension were inoculated into the flow cells by injection and allowed to incubate at 37 °C for 1 h. Biofilm media was then pumped through the flow cells at a flow rate of 0.5 ml/min for 24 h. The biofilm cells were collected, washed twice with ice cold 1X PBS and were used for whole cell proteomics analysis.Protein extraction was performed using urea buffer  and mechanical disruption with 1.0-mm silica beads in a Bullet Blender (Next Advance). Samples were centrifuged at 10,000 x g for 10 min and the supernatant removed. The protein concentration was determined using a Qubit fluorometry assay (Invitrogen). Protein concentrations were normalized with lysis buffer. Five hundred micrograms of protein from each sample was reduced with 10 mM dithiothretiol for 30 min at room temperature and alkylated with 10 mM iodoacetamide for 45 min at room temperature. Trypsin was added at a 1:30 enzyme-to-substrate ratio, and urea was diluted to 1.6 M with 25 mM ammonium bicarbonate. The reaction was terminated with 0.1% trifluoroacetic acid (TFA) and the suspension clarified by centrifugation at 10,000 x g for 10 min.Cleared peptide solution was processed by SPE on a Waters HLB PRiME with 30-mg-capacity cartridges using 5 psi. In brief, samples were loaded directly onto the cartridge per the vendor protocol and washed with 2 × 1 mL of 0.1% TFA. Peptides were eluted with 2 × 500 μL of 90% acetonitrile with 0.1% TFA, and the peptide concentrations were determined by UV absorbance at 280 nm.Two micrograms of each sample were analyzed by nano-LC-MS/MS  with a Waters NanoAcquity HPLC system interfaced to a ThermoFisher Q Exactive mass spectometer using a 4-h reverse-phase gradient. Peptides (30 μL) were loaded on a trapping column and eluted over a 75-μm analytical column at 350 nL/min, and both columns were packed with Jupiter Proteo resin . The mass spectrometer was operated, and the data was obtained in data dependent mode with the Orbitrap operating settings as previously described by Franco M et al. .S. aureus strains was created. The strains were 04–02981, bovine RF122 (ET3–1), COL, ED98, JH1, JH9, JKD6159, MRSA ST398 (isolate S0385), MRSA252, MSSA476, Mu3 (ATCC 700698), Mu50 (ATCC 700699), MW2, N315, NCTC 8325, Newman, TW20 (0582), USA300 (TCH1516), and ED133. Data were analyzed using Mascot software with the parameter settings as previously described by Franco M et al. [A composite database comprising the 20 sequenced M et al. . Mascot As a part of the functional genomic analysis of our whole-cell proteomics data, we performed gene ontology (GO) classification of differentially expressed (≥3-fold) proteins, as previously described . We usedAll statistical analyses to test for significance in this study were done using OriginPro software . A significance level of 0.05 was set as the cutoff value while performing statistical analyses. All the data obtained from CFU experiments were analyzed using one-way ANOVA followed by a post-hoc Tukey test.Additional file 1: Fig. S1. Microbial counts from vital organ samples to study staphylococcal dissemination. The organ samples (n = 6): liver (a), kidney (b), and lung (c) from sham and infected animals were harvested after each infection period  and were processed to enumerate the bacterial CFUs, as described in methods. The difference in microbial count between the USA300 LAC and msaABCR mutant strains was analyzed using one-way ANOVA followed by a post-hoc Tukey test. Error bars indicate standard errors of the means. A P-value of < 0.05 was considered statistically significant.Additional file 2: Fig. S2. Structural indices of sham and infected bones after day 4 post-infection. Analysis of bone trabecular microarchitecture  were performed using Nrecon and BoneJ software. All values were analyzed using one-way ANOVA followed by a post-hoc Tukey test. Error bars indicate standard errors of the means. A P-value of < 0.05 was considered statistically significant .Additional file 3: Fig. S3. Structural indices of sham and infected bones after day 8 post-infection. Analysis of trabecular bone microarchitecture  were performed Nrecon and BoneJ software. All values were analyzed using one-way ANOVA followed by a post-hoc Tukey test. Error bars indicate standard errors of the means. A P-value of < 0.05 was considered statistically significant .Additional file 4: Table S1. Comparative Gene Enrichment analysis of msaABCR proteomics under planktonic conditions.Additional file 5: Table S2. Comparative gene enrichment analysis of msaABCR proteomes under biofilm growth condition.


Stephanitis chinensis Drake (Hemiptera: Tingidae), is a pest which feeds on the undersides of tea leaves by piercing the epidermis and sucking the sap, and causes great harm to plant growth and tea production. We have obtained the whole mitochondrial genome of S. chinensis (GenBank accession No. MF498769). The entire mt genome is 16,667 bp in size with an A + T content of 78.41%. The tea lace bug mt genome encodes all 37 genes that are typically found in animal mt genomes, consists of 13 protein-coding genes (PCGs), 2 ribosomal RNA, and 22 transfer RNA genes. The gene order is consistent with other sequenced mt genome of lace bugs. The A + T-rich region of this genome is 2215 bp long with the A + T content of 82.58%, and located between the rrnS and trnI genes. Phylogenetic analysis performed using 13 PCGs with 14 heteropteran insects showed that S. chinensis clusters with other Tingidae species.The tea lace bug,  Stephanitis chinensis Drake (Hemiptera: Tingidae), is a pest which feeds on the undersides of tea leaves by piercing the epidermis and sucking the sap, and causes great harm to plant growth and tea production. In this study, adults of S. chinensis were collected from a tea plantation of Chongqing province of China in May 2016, and stored in the insect specimen room of Tea Research Institute of Chongqing Academy of Agricultural Science with an accession number CQNKY-HE-01-01-01.The tea lace bug, S. chinensis is a typical closed-circular DNA molecule with a full length of 16,667 bp, and the annotated sequence file was submitted to NCBI (GenBank accession No. MF498769). This genome is the largest mt genome among all sequenced Tingidae species: Corythucha ciliata  . The total nucleotide composition of the major strand of the mt genome is as follows: A = 43.88% (7314), C = 11.95% (1991), G = 9.65% (1608), and T = 34.52% (5754), with a total A + T content of 78.41% that is heavily biased toward A and T nucleotides. AT-skew and GC-skew of the whole J-strand of S. chinensis are 0.119 and −0.106, respectively.The complete mitochondrial genome of S. chinensis encodes all 37 genes typically existing in animal mt genomes, consisting of 13 protein-coding genes (PCG), two ribosomal RNA (rRNA) genes and 22 transfer RNA (tRNA) genes. The mitochondrial gene arrangement of S. chinensis is identical to all other lace bugs. Although the mt genome of S. chinensis is largest of Tingidae, it is extremely compact. In the mt genome of S. chinensis, a total of 31 bp overlaps have been found at 15 gene junctions. The overlaps are ranging from 1 to 8 bp, the longest one locates between trnW and trnC. However, the mt genome just has 13 bp intergenic sequence without the putative A + T-rich region. The intergenic sequences are at six locations ranging from 1 to 5 bp, the longest one located between trnP and nad6. The A + T-rich region of this genome is 2215 bp long with the A + T content of 82.58%, and located between the rrnS and trnI genes. The large length of the A + T-rich region is a principal cause of huge mt genome size.The mt genome of S. chinensis, and 14 tRNAs are encoded by the J-strand and the remainder encoded by the N-strand. The gene size of tRNAs ranges from 63 bp  to 70 bp (trnV), and A + T content is ranging from 71.83% (trnK) to 91.18% (trnE). Like most insect, 21 tRNA genes have typical cloverleaf shaped secondary structure and 1trnS lacks the dihydrouridine (DHU) arm  to 84.91% (atp8). All of the 13 PCGs initiate translation using ATN codons . Four PCGs  have incomplete terminal codons consisting of single T nucleotide, and the other PCGs stop with TAA or TAG. The incomplete stop codon T is commonly reported and could produce functional stop codons in polycistronic transcription cleavage and polyadenylation mechanisms (Ojala et al. S. chinensis with other true bugs. In the phylogenetic tree, the three lace bugs were clustered into a branch (S. chinensis is closely related to species of Tingidae. The families Tingidae and Miridae belong to superfamily Miroidea, and Tingidea formed a sister clade to Miridae in the tree.In the tea lace bug mt genome, the total length of all 13 PCGs is 10,984 bp, which is accounting for 65.90% of the total genome. The A + T content of the 13 PCGs ranges from 69.27% (a branch , it infe


N = 800) longitudinal cohort of community-dwelling adults of the Whitehall II imaging sub-study with no obvious structural brain abnormalities, who had Framingham stroke risk measured five times between 1991 and 2013 and MRI measures of structural integrity, and cognitive function performed between 2012 and 2016 . Unadjusted linear associations were assessed between the Framingham stroke-risk score in each wave and voxelwise grey matter density, fractional anisotropy and mean diffusivity at follow-up. These analyses were repeated including socio-demographic confounders as well as stroke risk in previous waves to examine the effect of residual risk acquired between waves. Finally, we used structural equation modelling to assess whether stroke risk negatively affects cognitive performance via specific brain measures. Higher unadjusted stroke risk measured at each of the five waves over 20 years prior to the MRI scan was associated with lower voxelwise grey and white matter measures. After adjusting for socio-demographic variables, higher stroke risk from 1991 to 2009 was associated with lower grey matter volume in the medial temporal lobe. Higher stroke risk from 1997 to 2013 was associated with lower fractional anisotropy along the corpus callosum. In addition, higher stroke risk from 2012 to 2013, sequentially adjusted for risk measured in 1991–94, 1997–98 and 2002–04 , was associated with widespread lower fractional anisotropy, and lower grey matter volume in sub-neocortical structures. Structural equation modelling suggested that such reductions in brain integrity were associated with cognitive impairment. These findings highlight the importance of considering cerebrovascular health in midlife as important for brain integrity and cognitive function later in life .Cardiovascular health in midlife is an established risk factor for cognitive function later in life. Knowing mechanisms of this association may allow preventative steps to be taken to preserve brain health and cognitive performance in older age. In this study, we investigated the association of the Framingham stroke-risk score, a validated multifactorial predictor of 10-year risk of stroke, with brain measures and cognitive performance in stroke-free individuals. We used a large ( Higher stroke-risk score over 20 years before the magnetic resonance imaging scan was associated with whole-brain grey matter volume and white matter microstructure in community-dwelling older adults. Structural equation models suggest that brain integrity was associated with cognitive function. Results highlight the need for targeting modifiable risk factors in midlife.  It has been recommended that interventions to limit cognitive decline should target multiple vascular risk factors rather than manage individual risk (The Framingham stroke-risk score (FSRS) is a multifactorial predictor of 10-year risk of stroke (The aim of this study was to assess whether Framingham stroke risk over a 20-year period before the magnetic resonance imaging (MRI) scan was associated with structural brain integrity measures, even after accounting for the effects of confounding variables, such as age  and diffusion-weighted MRI images were acquired at the Oxford Centre for Functional MRI of the Brain, Wellcome Centre for Integrative Neuroimaging. The first 550 participants were scanned on a 3-T Siemens MAGNETOM Verio  scanner with a 32-channel receive head coil (between April 2012 and December 2014), and due to a scanner upgrade, the last 250 participants were scanned on a 3-T Siemens MAGNETOM Prisma scanner with 64-channel receive head–neck coil (between July 2015 and December 2016)  values for the total intracranial volume (GM + WM + CSF) resulting in percentage total GM volume. Hippocampal volume was also estimated. WM lesions appear brighter (hyperintense) on TThe FSRS is a stroke-risk appraisal function that empirically relates cardiovascular risk factors to the probability of a stroke within 10 years  at the time of scan, sex, ethnicity, education, employment grade and scanner type were used as confounding variables. Ethnicity was limited to white versus non-white. Education years were calculated as the difference between the age at which the participant commenced primary school and the age at which they first left full-time education. Socio-economic status was classified according to the occupation grade between 1985 and 1988: senior managers and administrators (highest grade), professionals and executives (middle grade) and clerical and support staff (lowest grade). Scanner model was defined as Siemens 3-T Verio versus Prisma.P < 0.05, using threshold-free cluster enhancement  or WMH volume .IBM SPSS Amos version 25 structural equation modelling software was employed to build additional models to test the hypothesis that stroke risk negatively affects cognitive performance via specific brain measures. Models were optimized by backwards removal of non-significant effects until all effects were significant. With the removal of each variable, the measures of goodness of fit improved. All available data in the covariance matrix were used.https://www.mrc.ac.uk/research/policies-and-guidance-for-researchers/data-sharing/, 20 March 2020, date last accessed). Data will be accessible from the authors after 2019.The study follows Medical Research Council data-sharing policies .VBM analysis was based on a final available sample of M = 27 (4.7 SD) out of 36 on total Hopkins Verbal Learning Test memory recall and M = 9 (2.7 SD) out of 12 on delayed memory recall  = 3.27, P < 0.001; 2002–04: t (563) = 3.24, P < 0.001; 2007–09: t (563) = 3.38, P < 0.001], right caudate  and left central opercular cortex . After removing the contribution of FSRS during the penultimate study wave at mean age 64 (5.2 SD) years, FSRS between 2012 and 2013 [at mean age 68.1 (5.2 SD) years] did ‘not’ predict lower GM density.Model III: after removing the contribution of FSRS at mean ages 47.9, 53.6 and 59.1 (5.2 SD) years, FSRS between 2012 and 2013 [at mean age 68.1 (5.2 SD) years] predicted lower GM density in the MTLs. New FSRS risk acquired between the youngest and oldest study waves was associated with most widespread lower GM . The association was similar for higher MD, peak cluster location in left corona radiata .Model I: the FSRS at each of the five waves, at mean ages 47.9, 53.6, 59.1, 64.0 and 68.1 (5.2 SD) years and as early as 20 years preceding the brain scan, was associated with widespread lower FA at follow-up. FSRS measured closest to the time of scan, at mean age 68.1 (5.3 SD) years, was associated with the most widespread effect, including anterior thalamic radiation, cingulum, anterior and superior corona radiata, corpus callosum, corticospinal tract, external capsule, forceps major, forceps minor, internal capsule and superior longitudinal fasciculus. Peak cluster location was in the left corona radiata and forceps minor .Model II: after controlling the association for the confounders, FSRS measured at four time points between 1997 and 2013  remained significantly associated with lower FA and higher MD values, although the number of significant voxels was markedly reduced. Associations were localized to the corpus callosum, longitudinal fasciculus, cingulate gyrus and anterior thalamic radiation . FSRS clt (546) = 2.42, P < 0.001, and MD: t (546) = 3.28, P < 0.001].Model III: after removing the contribution of FSRS at mean ages 47.9, 53.6, 59.1, 64.0 and 68.1 (5.2 SD) years, FSRS between 2012 and 2013 [at mean age 68.1 (5.2 SD) years] predicted lower FA and higher MD in the cingulum, anterior corona radiata, corpus callosum, longitudinal fasciculus and posterior thalamic radiation. The most widespread association between new FSRS risk and lower WM integrity was acquired between the youngest and oldest ages  and after controlling for WMH volume both as percentages of whole-brain volume  (Model IV: the association of FSRS measured at mean age 68.1 (5.2 SD) years and lower FA remained significant after controlling for GM [< 0.001] .Voxelwise analyses were also performed separately on data acquired with the Verio and Prisma scanners, yielding results in the same locations, as previously found in a sample of Verio participants . Sample χ2 = 74.062 . Higher 2007–09 FSRS predicted higher WMH volume, and higher 2012–13 FSRS predicted lower right hippocampal volume—both of which in turn predicted poorer delayed memory recall. Other significant direct effects were as follows: (i) higher age with higher WMH, lower hippocampal volume, FSIQ and delayed recall, (ii) women had higher WMH and delayed memory recall, (iii) participants scanned with the 3-T Prisma had higher WMH and right hippocampal volume, (iv) higher intracranial volume (ICV) with higher WMH and right hippocampal volume, (v) higher employment grade with higher FSIQ and (vi) higher FSIQ with higher delayed recall . WMH volume, right hippocampus, full-scale IQ  and delayed memory recall were entered as observed, endogenous variables (dependent variables). Each endogenous variable had an error term  with mean set to 0, and variance to 1. Covariances between exogenous variables and direct effects between each exogenous and endogenous variables were also modelled. Non-significant direct effects were removed until a suitable model fit of CMIN/DF <3 was achieved. The final model was significant with d recall .P < 0.001) and the consequent different signs of effects, the sum of absolute direct and indirect effects did not add up to the absolute total effect, making interpretations difficult  and estimated direct effects of FSRS on Hopkins Verbal Learning Test as β = −0.226 and indirect effects via hippocampal size as β = −0.036, i.e. as 86% and 14%, respectively. The model retaining 2007–09 FSRS values only achieved a χ2 = 60.804  and estimated direct effects of FSRS on Hopkins Verbal Learning Test as β = −0.002 and indirect effects via volume of WMHs as β = −0.180, i.e. as 2% and 98%, respectively.As we were interested in direct and indirect  effects of FSRS on delayed memory recall, we also evaluated a number of models including the direct effects of FSRS on delayed memory. Presumably, due to the collinearity of the FSRS measured at different time points  years statistically predicted lower Hopkins Verbal Learning Test-measured delayed memory performance. WMH volumes largely (90%) mediated this effect, while hippocampal volume did so only marginally.N = 116) of the Whitehall II imaging sub-study, we found that cardiovascular risk in midlife was significantly associated with lower GM perfusion at older ages, whereas this association was not significant for cardiovascular risk in later life, which lends further support to these results (Our findings suggest that the unique effects of 10-year stroke risk impact GM density in (right) medial temporal structures up to old age. This means that FSRS does not only predict established clinical stroke but also ‘subclinical’ GM atrophy, particularly in MTL structures that may be of relevance in the development of cognitive impairment  and in tFSRS in most data waves was associated with widespread lower microstructural WM integrity. FSRS measured 20 years prior to scan and, after the removal of confounders, was only associated with changes in the body of corpus callosum, superior corona radiata and their associated tracts in the right hemisphere. Change in stroke risk over a 20-year period was associated with lower FA in all major tracts. The findings that the associations were significantly reduced after removing confounding effects suggest that a large amount of variance is shared between WM microstructure and demographic factors. WM changes that are visible in fluid-attenuated inversion recovery images are generally interpreted clinically as microvascular changes . FSRS waStroke and dementia have common risk factors, and there is strong evidence for the link between vascular risk factors and cognitive impairment , two decades prior to the scan, with imaging measures highlighting the potential for targeting modifiable risk factors in midlife.Findings of this study show that the Framingham 10-year probability stroke-risk score may relevant for primary prevention not only for stroke risk itself but also for subclinical GM atrophy in younger ages, and as early as 20 years in advance, and subsequent memory changes in later life. Brain areas such as the MTL are often implicated in cognitive impairment and dementias; thus, preserving them into older age is important. In the future, we may be able to use imaging results to track and target the modification of risk factors throughout adulthood.fcaa026_Supplementary_DataClick here for additional data file.


Gammarus fossarum, a small crustacean used as a sentinel species in ecotoxicology. This approach provides a deep insight into the full range of biomasses and metabolic activities of the holobiont components, and differentiates between the intestine and hepatopancreatic caecum.Metaproteomics of gut microbiomes from animal hosts lacking a reference genome is challenging. Here we describe a strategy combining high-resolution metaproteomics and host RNA sequencing (RNA-seq) with generalist database searching to survey the digestive tract of  Microbiome composition and function are sensitive to a variety of biotic, abiotic, and anthropogenic factors, and dysbiosis can have detrimental consequences for the host3. Among the approaches available for studying microbiomes, metaproteomics—defined as the large-scale identification and quantification of proteins from microbial communities—is rapidly gaining traction as a method to directly observe the protein complement of organisms4. This methodology is based on protein shotgun analysis by liquid chromatography tandem mass spectrometry (LC-MS/MS) and interpretation of the MS/MS spectra with available protein sequence databases. The peptide and protein data obtained by metaproteomics can be used for proteotyping the microbial communities present in the sample5, estimating biomass contributions6, and obtaining a direct understanding of the biochemical functions and interactions of the community7.A great diversity of bacteria, archaea, fungi, and viruses (microbiota) inhabit and form complex associations with their animal hosts. In a healthy state, this ensemble of microorganisms and their collective genomes, termed microbiome, can mediate a variety of functions and biological processes in their hosts such as nutrition, energy metabolism, immune responses, or neurotransmission9 and studies on small invertebrates are scarce11. Ideally, metaproteomic data should be interpreted with metagenomics data acquired on the same sample. Similarly, RNA sequencing (RNA-seq) can be used to sequence extracted mRNAs and translate them into putative protein sequences12. However, this implies higher costs, time and efforts, and may be challenging when dealing with limited animal resources. The 16S (and/or 18S) rRNA genes of the taxa present can be sequenced and, based on these results, a customized protein database specific to the taxa identified can be proposed. Alternatively, metaproteomic data may be assigned using generalist databases encompassing all known genomes13. When the microbial communities of a given host are unknown, assignation of peptide/protein from metaproteomics studies is done only at the highest taxonomical ranks. The absence of a reference genome for the animal host can also hinder the reliable interpretation of the proteomic data, as host peptides that are shared with other taxa can be attributed to non-host proteins present in the database. In any case, the use of metagenomics-derived or multi-organism genome-derived databases enlarges significantly the search space, challenging the search algorithms and compromising the sensitivity of peptide identification14. In this context, the use of multi-step database searches helps improve metaproteomics outputs16.Due to the paucity of molecular information for numerous animal species such as arthropods, microbial communities from these animals remain poorly characterized. Animal metaproteomics have focused on exploring the microbiomes from large animals such as mammals or birdsGammarus fossarum from generalist databases, host MS/MS spectra which represent most experimental spectra in the sample could be wrongly attributed. Therefore, we propose to complement generalist databases with an RNA-seq-based protein database specific for the host. Here, we used a previously RNA-seq data set acquired on G. fossarum. This data set comprises paired-end Illumina sequences of total mRNAs of specific organs from a large number of male and female gammarids. Reads with at least 90% overlap were assembled into contigs. Contigs were then translated into six reading frames, resulting in a customized protein sequence database (GFOSS) for interpreting MS/MS data (details can be found in ref. 17). The workflow was applied for the study of the microbiome of two tissues from the digestive tract of G. fossarum—a key species for aquatic systems (body length 2–15 mm) and an emergent model in environmental toxicology18. Characterization of the microbiomes associated with the intestine (INT) and hepatopancreatic caeca (HC) is of considerable interest, to better understand their role and possible involvement in mediating the impact of toxicants for the host.Here we propose an iterative generalist database search strategy combining high-resolution MS of proteins and RNA-seq of the host to characterize the microbiome of an organism not yet genome-sequenced. In the absence of genomic information on Figure 16, and additional metazoan sequences identified in step1; (ii) food-associated peptides, using DB-streptophyta, which comprises the remaining multicellular eukaryotic proteomes; and (iii) the unicellular microorganism-associated peptides using all eukaryotic and prokaryotic unicellular organisms (DB-unicellular). Most MS/MS signals from the tissues were assigned to the host (38%), the microbiome (3%), and the residual food . Taxon-discriminant peptide sequences highlighted the potential presence of 749 taxa  and unicellular eukaryotes (52%). The most abundant genus was Streptomyces with 6% of the assigned signal. Bacteria were dominated by Proteobacteria (20%), Actinobacteria (16%), Firmicutes (9%), and Bacteroidetes (4%). All four phyla are commonly found in the gut of other aquatic organisms including supralittoral talitrid amphipods19, decapods21, cladocerans22, and fish23. Eukaryotic organisms in the gut were dominated by Ascomycota (21%), followed by Apicomplexa (10%), Basidiomycota (10%), Zoopagomycota (7%), and Bacillariophyta (4%). Figure G. fossarum. Figure Actinobacteria and Apicomplexa were more abundant in the HC, whereas Firmicutes, Bacteroidetes, and Basidiomycota were more abundant in the INT. Eight and three genera were more abundant in the HC and the INT, respectively. Among these was the genus Paenibacillus, which is notable for the production of chitinase24 and could contribute to chitin degradation and recycling during molting of the host. The identification of the genus Eimeria (Apicomplexa), more abundantly present in HC, might indicate that these vertebrate parasites use aquatic invertebrates as an intermediate host before infecting fish25.The microbiome of 27, the functional composition of a microbiome can be more informative than community abundance when characterizing a phenotype. As community abundance in the gut  can occasionally change drastically between individuals and species, examining function can help provide a more comprehensive view of host–microbiome interactions. Functional analyses of protein data are represented in Fig. Streptomyces, Mycobacterium, and Eimeria contributed to 31% of the signal associated with these Gene Ontology (GO) terms  plot, suggesting that some functionalities differ in each tissue. A sparse PLS-DA (sPLS-DA) analysis allowed the identification of the subset of GOslims that best discriminate the two tissues . Sexually mature males in amplexus with females in the last stage of their reproductive cycle were selected for this study (before fertilization occurred). The INTs and the HC of three males  were aseptically dissected and rapidly frozen in liquid nitrogen for storage at −80 °C.Gammarids were collected from the Bourbre River in France, acclimatized, and maintained in the laboratory, as previously described33.Tissues were homogenized in 75 µl of LDS buffer (Invitrogen) containing β-mercaptoethanol. Samples were vortexed, heated for 5 min at 99 °C, incubated for 5 min in an ultrasound bath, and then mechanically homogenized through bead beating in a Precellys® tissue homogenizer (Bertin Technologies). A volume of 30 μL of supernatant was transferred into a new tube and heated for 5 min at 99 °C. Each sample was then subjected to a short SDS-polyacrylamide gel electrophoresis migration at 200 V. The whole protein content from each well was fractionated in three bands per sample, processed, and digested with Trypsin Gold (Promega) using 0.011% ProteaseMAX surfactant (Promega), as described34. Peptides were desalted on a reverse-phase C18 PepMap 100 column. Separation was achieved at a flow rate of 0.2 µL/min and with a 60 min gradient of CH3CN, 0.1% HCOOH going from 4% to 25% for 50 min, and from 25% to 40% for 10 min. The gradient full-scan mass spectra were acquired from m/z 350 to 1800 with an automatic gain control (AGC) target set at 3 × 106 ions and a resolution of 60,000. The top 20 precursor ions in each scan cycle were subjected to fragmentation through high-energy collisional dissociation. MS/MS scanning was initiated when the AGC target reached 105 ions with a threshold intensity of 17,000 and potential charge states of 2+ and 3+ after ion selection performed with a dynamic exclusion of 10 s. The experiment was conducted using three biological replicates for each tissue. Peptide solutions from each biological replicate were injected twice to obtain two analytical replicates.LC-MS/MS analysis was carried out using a data-dependent approach with a Q-Exactive HF mass spectrometer (Thermo Fisher) coupled to an UltiMate 3000 LC system (Dionex-LC Packings) operated as describedMS/MS spectra were assigned to peptide sequences using the MASCOT Daemon 2.6.1 search algorithm (Matrix Science) with the following parameters: trypsin as enzyme, maximum of one missed cleavage, mass tolerances of 5 p.p.m. on the precursor ion and 0.02 Da on the MS/MS, fixed modification of carboxyamidomethylated cysteine (+57.0215), and oxidized methionine (+15.9949) as variable modification. Mascot DAT files were parsed using the ms_peptidesummary function from Matrix Science msparser version 2.5.2. Peptide-to-spectrum matches (PSMs) with the expectation values below 0.05 were validated using the MASCOT homology threshold. Multiple PSMs per MS/MS spectra were allowed in case of ion scores higher than 98% of the top ion score. The peptide sequences for each PSM were mapped to NCBI protein accessions, which were matched to taxa as follows: the files “assembly_summary_refseq.txt” and “assembly_summary_genbank.txt”, downloaded from “ftp://ftp.ncbi.nlm.nih.gov/genomes/ASSEMBLY_REPORTS,” were used to map taxids to RefSeq assemblies (GCF) and GenBank assemblies (GCA). Then, the “GCF/GCA *_assembly_report.txt” files were used to map GCF/GCA to nucleotides and the “*_genomic.gff.gz” files were used to map GCF/GCA to protein accessions. A collation of information was performed following the NCBI taxonomical tree from direct assignation to “canonical” taxonomical levels—species, genus, family, order, class, phylum, and superkingdom. For each taxon at each level, an identification of matching peptide sequences and TSMs was performed, as well as a count of specific or unique peptide sequences, and corresponding specific TSMs. The DB-GFOSS-M, DB-streptophyta, and DB-unicellular databases were built using the proteomes from species identified with (i) more than 9, 7, and 1 specific peptides or (ii) more than 109, 74, and 6 added TSMs for eukaryota, bacteria, and archaea species, respectively. For each species retained, a maximum of ten representative taxa were selected, ordered by the number of TSMs, and with a minimum of two added TSMs.5 with default parameters . The FDR was estimated through comparing the number of taxonomic hits from forward and decoy peptides identified from both databases assuming a threshold of at least three taxon-specific peptides.The FDR for genus identification was calculated after a search against the target database DB-unicellular (#hits_target) and its reversed decoy version (#hits_decoy) and taxonomical assignation of peptide sequence hits. Peptides were assigned to the genus taxonomical rank using the Unipept 4.0 web interfaceFor step 3, spectra were searched against DB_metaP to identify and quantify all peptides present in the sample. PSMs with an FDR < 1% were filtered and selected for peptide and protein inference. Proteins were grouped if they shared at least one peptide and label-free quantification was based on PSM counts for each protein following the principle of parsimony. Spectral counts from all the proteins in a group were summed for assigning each protein group with an abundance value. Host proteins were analyzed individually, while microbial proteins were grouped in GOslims. Briefly, GO annotations of proteins were performed using a DIAMOND search against the UniRef50 database and the homologs found in this search were mapped to GO terms through the GO annotation database. GOslim terms were obtained by using the Map2Slim option in OWLTools and the generic GOslim list of GO entries. The spectrum count values of all proteins associated with a GOslim term were summed, granting each GOslim term with an abundance value.metacoder35 was used for representing taxonomic abundance as a differential heat tree. Univariate differential analysis of taxon and GOslim abundances between conditions was performed through non-parametric Wilcoxon tests corrected for multiple comparisons (Benjamini–Hochberg). The pairwise Bray–Curtis β-diversity indices were calculated using the function vegdist from the vegan package in R. NMDS ordination of Bray–Curtis distances among samples was performed with the function metaMDS. PLS-DA, sPLS-DA, and heatmap clustering were performed with the package mixOmics36. The PLS-DA and NMDS plots were drawn with GraphPad Prism v8.0.1 (GraphPad Software).Quantitative count values from both taxonomic (number of PSMs) and functional data (spectrum counts) were scaled to their total sum in the sample. The R package Further information on research design is available in the Supplementary InformationReporting SummaryDescription of Addtional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7


ROH), excess of homozygosity (FHOM), correlation between uniting gametes (FUNI), and diagonal elements of the genomic relationship matrix (FGRM); 2) to identify frequently occurring ROH (i.e. ROH islands) for our selection signature and gene enrichment studies.Consecutive homozygous fragments of a genome inherited by offspring from a common ancestor are known as runs of homozygosity (ROH). ROH can be used to calculate genomic inbreeding and to identify genomic regions that are potentially under historical selection pressure. The dataset of our study consisted of 254 Azeri (AZ) and 115 Khuzestani (KHZ) river buffalo genotyped for ~ 65,000 SNPs for the following two purposes: 1) to estimate and compare inbreeding calculated using ROH . In the KHZ breed, the genes located in ROH islands were enriched for multiple Gene Ontology (GO) terms (P ≤ 0.05). The genes located in ROH islands were associated with diverse biological functions and traits such as body size and muscle development (BMP2), immune response (CYP27B1), milk production and components , coat colour and pigmentation (PMEL and MYO1A), reproductive traits , and bone development (SUOX).In this study, 9102 ROH were identified, with an average number of 21.2 ± 13.1 and 33.2 ± 15.9 segments per animal in AZ and KHZ breeds, respectively. On average in AZ, 4.35% (108.8 ± 120.3 Mb), and in KHZ, 5.96% (149.1 ± 107.7 Mb) of the genome was autozygous. The estimated inbreeding values based on FROH was in line with expected higher inbreeding in KHZ than in AZ because of the smaller effective population size of KHZ. Thus, we find that FROH can be used as a robust estimate of genomic inbreeding. Further, the majority of ROH peaks were overlapped with or in close proximity to the previously reported genomic regions with signatures of selection. This tells us that it is likely that the genes in the ROH islands have been subject to artificial or natural selection.The calculated F Bubalus bubalis) and the African wild buffalo (Syncerus caffer), the second of which is also known as the cape buffalo [B. bubalis, including of the river  and swamp  subspecies, occurred approximately 3000–6000 years ago [There are two main species of buffalo: the Asian water buffalo  has been the most common parameter for describing the level of inbreeding since Wright [PED depends on the completeness and correctness of pedigree. With the availability of high-density SNP-chip markers, inbreeding can also be defined according to genomic information such as genome-wide autozygosity [ROH) can discriminate between homozygous  and non-autozygous  positions in the genome [ROH. Thus, using genetic markers instead of pedigree information to calculate inbreeding can produce more robust estimates [The inbreeding coefficient measured from pedigree information  in 40 GO terms. These GO terms belonged to 23 biological processes (BP), 12 cellular component (CC) and 5 molecular function (MF) groups  between SNPs with a distance up to 100 Kb , short he) of AZ and KHZ breeds and the consequent increased rate of inbreeding since 20 generations ago [As presented in Table ions ago .The portion of the genome that was autozygote in the AZ and KHZ breeds was lower than the reported ROH coverage in the Marchigiana beef breed (7%) , AustriaHOM, FUNI and FGRM were higher in AZ than they were in KHZ. However, the previously reported Ne for AZ (477) was larger than it was for KHZ (212) [ROH, which showed lower inbreeding for AZ (0.043) than for KHZ (0.059).On average, FHZ (212) . TherefoUNI and FROH (AZ = 0.98 and KHZ = 0.94). Literature has reported different correlation coefficients between FUNI and FROH (0.15–0.80) [HOM and FROH (0.06–0.95) [GRM and FROH (0.17–0.81) [HOM, FUNI and FGRM on allelic frequencies [The highest correlation was observed between F15–0.80) , between06–0.95) , 21, 27,17–0.81) , 38. Thequencies .PED of 0.03 previously reported in Iranian buffalo [ROH in the current study. Given that pedigree data were not available for our study, we could not calculate FPED and compare it with FROH. However, previous studies reported moderate to high (0.47–0.82) and low to moderate (0.12–0.76) correlations between FPED and FROH in cattle and sheep, respectively [PED and FROH was also reported by Peripolli et al. [PED may not accurately capture small IBD segments that result from ancient inbreeding. Further, accurate and in-depth pedigree records are required to measure FPED. Additionally, methods based on allelic frequency have demonstrated considerable variation among different breeds [The F buffalo  was loweectively , 17. A li et al.  in Gyr ct breeds . Given tP > 0.05). In the KHZ breed, however, the genes located in the ROH islands were significantly enriched (P ≤ 0.05) in 40 GO terms  in the BP group. Among these genes, STAT6  has been reported to have large effects on the growth efficiency and the quality of carcass in cattle . AdditioTwelve genes in ROH islands were associated with lipid metabolic process (GO:0006629) in the BP group. Of these genes, BMP2  plays a major role in rebuilding hair follicles in goats . FurtherCYP27B1 (cytochrome P450 family 27 subfamily B member 1) located on BTA5 was also one of the genes enriched in the lipid metabolic process (GO:0006629). This gene is important for making 1-α-hydroxylase, which is required in vitamin D bio-activation, and has been reported to be up-regulated as a result of bacterial infection, suggesting that this gene plays a role in modulating innate immune responses [In ROH islands on BTA13, three genes were associated with the positive regulation of DNA replication (GO:0045740) in the BP group. Proliferating cell nuclear antigen (PCNA) has been reported to be associated with follicular development and growth in buffalo ovaries , and mayP = 0.05) in ROH islands, including MARS , and ADRA1D . MARS has been reported to influence milk and protein production in Chinese [Single-organism cellular process GO:0044763) with 64 genes was significantly enriched  between adjacent SNPs in ROH islands was higher than the r2 of adjacent SNPs located on the same chromosome , which are north-western provinces of Iran. Samples of the KHZ breed were obtained from Kermanshah  and Khuzestan , which are the south and south-western provinces of Iran, respectively  were stored. According to the filtration criteria, samples with more than 5% missing genotype and SNPs with 5% missing rate were eliminated from further analyses. We also filtered out SNPs with unidentified position in the UMD3.1 assembly, positioned on the sex chromosomes, with minor allele frequency of < 2%, and with p-value for the Hardy–Weinberg equilibrium chi-square test < 10− 6. In total, 62,122 SNPs and 369 samples with an average call rate of 99.6% passed the quality-control filters.The SNPs in the 90 K SNPChip were selected using buffalo DNA sequence, but similar to methods used in previous studies , 72–75, http://www.R-project.org/). According to the first and second PCs, we removed four samples: two from each breed that were placed outside their expected breed cluster.Genetic distance, which is based on the IBS matrix, was estimated through the --ibs-matrix command in PLINK v1.9 . PrincipROH can be detected in the genome through two main approaches: 1) genotype-counting algorithms in which the genome is scanned to identify long stretches of consecutive homozygous genotypes like the one implemented in PLINK v1.9 , and 2) n = 252) and KHZ (n = 113) breeds, separately. For each individual, ROH segments with the following attributes were identified: 1) each ROH stretch was at least 1 Mb in length; 2) there was at the most only one heterozygous and one missing SNP in each ROH; 3) there was a minimum number of SNPs that could form ROH in each breed, calculated according to Eq. l is the minimum number of SNPs in ROH, α is the false positive rate of the identified ROH (set at 0.5); na and ns are the number of individuals and the number of SNPs per individual, respectively; and het is the average heterozygosity across individuals. l was calculated to be 40 and 38 in AZ and KHZ breeds, respectively; 4) each ROH contained at least one SNP over 100 Kb; and 5) the maximum gap between two neighbouring SNPs in ROH had to be less than 1 Mb.A genome scan for ROH was conducted for the AZ ( KHZ n = 13 breedshttp://www.R-project.org/).The ROH that had these five attributes were divided into the following five groups: 1–2, 2–4, 4–8, 8–16 and > 16 Mb, as suggested in the literature , 15, 25.ROH), excess of homozygosity (FHOM), correlation between uniting gametes (FUNI) and diagonal elements of the genomic relationship matrix (FGRM).The coefficient of inbreeding was estimated using ROH .FGRM ; and n is the total number of SNPs. The Pearson’s correlation coefficient between FROH and the other genomic inbreeding estimates was also calculated.We also calculated the following three different genomic inbreeding estimations: FGRM Eq. , FHOM  were nominated ROH islands, as suggested in previous studies , 67. Furhttps://www.ncbi.nlm.nih.gov/mapview/). Additionally, to find significantly enriched Gene Ontology (GO) terms (P ≤ 0.05) of the genes located in ROH peaks, we used DAVID v6.8 tool [To identify genes in ROH islands, we used UMD3.1 map viewer from the NCBI website  that had already been published for AZ and KHZ breeds . An iHS Additional file 1. List of the detected runs of homozygosity (ROH) in Azeri (AZ) and Khuzestani (KHZ) breeds.Additional file 2. The estimated inbreeding coefficient using runs of homozygosity (ROH) > 1 Mb (FROH), diagonal elements of genomic relationship matrix (FGRM), excess of homozygosity (FHOM) and correlation between uniting gametes (FUNI) in Azeri (AZ) and Khuzestani (KHZ) breeds.Additional file 3. Frequently occurring runs of homozygosity (ROH) regions (i.e. ROH islands) in Azeri (AZ) and Khuzestani (KHZ) breeds. The last column represents the average r2 of ROH islands divided by the average r2 of each chromosome.Additional file 4. The genes located in detected runs of homozygosity (ROH) islands in the Khuzestani (KHZ) breed that were significantly enriched (P ≤ 0.05) in biological processes (BP), cellular component (CC) and molecular function (MF) Gene Ontology (GO) terms.Additional file 5. List of integrated haplotype homozygosity scores (iHS) for all SNPs in Azeri (AZ) and Khuzestani (KHZ) breeds.Additional file 6. Manhattan plot of integrated haplotype homozygosity score (iHS) across the genome.


Breast cancer (BC) is the first cause of cancer morbidity and mortality in women. This disease has been linked to obesity; however, it is not clear how fat accumulation affects women who survive breast cancer. Although the visceral adiposity index (VAI) is a marker of cardiometabolic risk and adipose tissue dysfunction, it is not clear how it changes in breast cancer survivors. The aim of this investigation was to compare VAI in women with and without breast cancer.  A case-control cross-sectional study was conducted on women who were BC survivors and women without the history of BC (control group). Body composition was assessed using electrical bioimpedance while VAI by means of waist circumference (WC), body mass index (BMI), triacylglycerols (TG), and high-density lipoprotein cholesterol (HDL-C). p=0.025); at once, TG and VAI were significantly higher for the survivor group (243.55 ± 199.84 vs. 159.84 ± 75.77) (p=0.007) and (11.03 ± 11.15 vs. 6.41 ± 3.66) (p < 0.005), respectively. Body composition parameters were similar in both groups.  49 women in the BC survivor group and 50 in the control group. WC was wider in the survivor group as regards control (93.65 ± 10.48 vs. 88.52 ± 9.61 cm) ( VAI is higher in women who are BC survivors in comparison with controls matched by age and bodyweight. Breast cancer (BC) is the second most frequent neoplasm among the general population worldwide, only surpassed by lung cancer; however, it is the first cause of cancer morbidity and mortality in women , 2. SmokObesity is the excessive accumulation of fat in adipose tissue, with a positive energy imbalance and weight gain. Types of fat include visceral central and peripheral subcutaneous , 10. TheIn the majority of studies, obesity is ascertained resorting to BMI. This parameter cannot differentiate between visceral and peripheral fat. Interestingly, visceral adiposity index (VAI) is a marker of adipose tissue dysfunction, which comprises physical and biochemical parameters such as WC, BMI, triacylglycerols (TG), and high-density lipoprotein cholesterol (HDL-C) –17. VAI Breast cancer survivors with high fat mass have a higher risk of relapse and developing cancer in other parts of the body as well. Furthermore, these patients experience adverse effects induced by chemotherapy or radiation, including increased fat mass, adding to nausea, vomiting, energy loss, fatigue, and muscle mass loss , 20. It A case-control cross-sectional study was performed. Participants were recruited at the General Regional Hospital 251 of the Mexican Institute of Social Security (IMSS). The investigations included women between 40 and 65 years of age who were categorized in two groups. The first included women with breast cancer diagnostic at the follow-up, with at least one year without any BC-related chemotherapy with cytotoxic drugs, radiotherapy, or surgery, who attended regular oncology appointments; a review of their medical files was carried out in order to obtain general data and define all the inclusion and exclusion criteria. Patients with known type 2 diabetes, hypertension, kidney or hepatic failure, or any other chronic disease, which the research group considered could modify the variables under study, were excluded from participating in the study. The second group (control) comprised women who attended the hospital for annual mammography screening and were matched by age with the survivor group. The absence of BC was determined by including women with BIRADS 1, 2, and 3 only. Patients with diabetes mellitus, hypertension, and kidney or hepatic failure were excluded from either group. Women who met these criteria were contacted and invited to participate, and individual appointments were scheduled.Body composition was assessed by electrical bioimpedance using an InBody® 230 apparatus . This analysis included 10 measurements at 2 different frequencies, 20 kHz and 100 kHz, in 5 segments . Participants were fasting, barefoot, and wearing light clothing when taking these measurements. The data obtained were total body water (TBW), proteins, minerals, body fat mass (BFM), bodyweight (BW), skeletal muscle mass (SMM), BMI, percentage of body fat (PBF), lean segmental mass (LSM), trunk segmental fat (TSF), obesity analysis , fat free mass (FFM), WHiR, visceral fat level (VFL), and segment impedance and frequency. Height was measured using a stadiometer (Seca® 213) with a 20–205 cm range.Blood samples were taken with BD Vacutainer® Tubing SSTTM and EDTA K2 tubes. Once the sample was obtained, measurements were performed on an AU680 Beckman Coulter® device. HDL-C was determined with selective detergent methods; TG was determined with the glycerol phosphate oxidase test; low-density lipoprotein cholesterol (LDL-C) concentrations were calculated with the Martin–Hopkins equation, while glucose was evaluated with the hexokinase-UV/NAD test. HbA1c was measured by HPLC (high-performance liquid chromatography) utilizing a Variant II Turbo® system. All the tests were run by Quest Diagnostics® Clinical Analysis Laboratories (Mexico).VAI was calculated with the following equation: [WC/(36.58+(1.89 × BMI))] × [TG/0.81] × [1.52/HDL − C] .t-test for variables that followed a normal data distribution, while Mann–Whitney U if they did not. A chi-squared test (χ2) was also run to find associations between qualitative variables. The statistical analysis was carried out with IBM® SPSS® Statistics, Mac version 25.A descriptive analysis was carried out for continuous variables through means and standard deviation. The normal distribution of the variables was found using the Kolmogorov–Smirnov test. The comparison of the means between the groups was by means of the A total of 56 women were enrolled in the BC survivor group and 54 women in the matched control group. However, 11 women (7 in the survivor group and 4 in the control) were excluded owing to the presence of diabetes mellitus diagnosed upon the study appointment. The final sample included 49 women in the BC survivor group and 50 controls. The most frequent histological subtype was ductal (83.7%), whereas the molecular subtype was luminal A (49.0%) . 16 out p=0.025), though there were no differences in BMI and BW. With regards to body composition, there were no differences in bioimpedance parameters nor in blood concentrations of glucose, HbA1c, HDL-C, LDL-C, and total cholesterol. However, TG concentrations were significantly higher in the survivor group as compared with controls  for 12–15 months, which do not report differences between the body composition of surviving women and that of the general population nor between the types of therapy they received after the diagnosis of cancer , 26, 35.The concentrations of blood lipids which have been associated with metabolic syndrome, cardiovascular disease, and atherosclerosis , 38 are Another study conducted on women, with either a recent diagnosis of BC or without it , reported no significant differences in lipid concentrations . Even thIn Mexico, where our study was performed, there is a strong correlation between VAI and TG. Noticeably, elevated TG levels have been reported for Mexican population (adolescents and adults) , 43. It In this group of survivors, the most frequent BC subtypes were histologically ductal carcinoma and molecular luminal A type. Other studies have reported an association between obesity and BC with positive estrogen receptors, which was unrelated to other molecular subtypes .No association was found between VAI and obesity with BC with positive estrogen receptors in the group of survivors. To further dissect this possible association, additional studies should be performed on populations in which metabolic syndrome and obesity are less prevalent or the individual components as well as the level of metabolic syndrome and obesity are assessed.This research presents some limitations, for instance, a larger sample size of breast cancer survivors would be needed to identify strong associations between metabolic syndrome and breast cancer and establish thus a clearer relationship between VAI and its components with BC. Another limitation is the cross-sectional design of the study, which does not allow for a follow-up of BC survivors and establish whether there is a risk of either BC relapse or metabolic syndrome when VAI is elevated.However, adhering to a healthy diet with high consumption of vegetables and fruits and reducing overweight and obesity will bring about additional beneficial effects in BC survivors by reducing the risk of relapse, comorbidities, and death . In thisThe conclusion of this study is that VAI increases in BC survivors compared with controls, regardless of BMI. Early identifications of this marker may be useful as a tool in future studies to assess increments in the risk of metabolic syndrome and cardiovascular diseases among this population.