Colorectal cancer (CRC) is a common cancer worldwide. The main cause of death in CRC includes tumor progression and metastasis. At molecular level, these processes may be triggered by epithelial-mesenchymal transition (EMT) and necessitates specific alterations in cell metabolism. Although several EMT-related metabolic changes have been described in CRC, the mechanism is still poorly understood.HK3. The mRNA level of these genes was measured in 78 paired colorectal cancer samples by quantitative polymerase chain reaction (qPCR). Upregulation of HK3 and deregulation of 11 genes  involved in EMT were found. The results of correlation study showed that the expression of HK3 demonstrated a strong correlation with 7 of the 21 examined genes  in CRC.Using CrossHub software, we analyzed RNA-Seq expression profile data of CRC derived from The Cancer Genome Atlas (TCGA) project. Correlation analysis between the change in the expression of genes involved in glycolysis and EMT was performed. We obtained the set of genes with significant correlation coefficients, which included 21 EMT-related genes and a single glycolytic gene, HK3 is associated with EMT in CRC and may be a crucial metabolic adaptation for rapid proliferation, survival, and metastases of CRC cells.Upregulation of The online version of this article (10.1186/s12864-018-4477-4) contains supplementary material, which is available to authorized users. In 2012, an estimated 1.36 million people were diagnosed with colorectal cancer (CRC), including 746,300 men and 614,300 women. The mortality rate of CRC is reported to be approximately half of its global incidence. In the same year, 694,000 people died from CRC ), and chromosomal instability (CIN), which are associated with the development of the disease and its aggressive behavior [The stage of CRC plays a significant role in survival of patients. More than 60% of patients are diagnosed at late stage of CRC (III-IV) and approximately 25% of them display metastatic disease , 4. Devebehavior , 9. Thusc-Myc [CCND1 [MMP7 [FN [CD44 [UPAR [A number of studies have demonstrated that epithelial-mesenchymal transition (EMT) is involved in CRC carcinogenesis and metastasis , 11. EMTc-Myc  and CCNDc [CCND1 . MoreoveD1 [MMP7 , 27, FN MMP7 [FN , CD44 [2FN [CD44 , and UPA44 [UPAR  involved44 [UPAR .Metabolic reprogramming characterized by upregulated glycolysis is another crucial feature of cancer. It provides cancer cells with energy and metabolites essential for active cell division, large-scale biosynthesis, invasion, and metastasis –34. IncrHK3 gene expression in CRC samples and its association with EMT.In this work, using CrossHub software we analyzed RNA-Seq data from The Cancer Genome Atlas (TCGA) project to estimate the association between the expression of genes participating in EMT and glycolysis in CRC. Hexokinase 3 (HK3) was found to exhibit significant correlation with a set of genes involved in EMT. Thus, we focused on the investigation of https://cancergenome.nih.gov) project RNA-Seq dataset derived from CRC. The dataset contained 287 tumor samples including 26 paired ones. The selection of genes participating in glycolysis and EMT was performed using Gene Ontology database and “glycolytic process” and “epithelial to mesenchymal transition” as keywords. A set of 132 genes was selected for further analysis.Using CrossHub software , we havers). We carried out two tests: one for paired samples and other for pool of tumor samples. Spearman’s rank correlation coefficients were calculated between (1) fold change values, the ratio of CPM (counts per million) in tumor sample to CPM in conditional normal tissue, (paired samples) and (2) CPM values (pool of tumor samples). Before calculating correlation coefficients, dropping points (no more than 5%) were eliminated using approximate generalized linear model. We focused on the genes with concordant results in paired and pooled tests.The correlation analysis between the expressions of target genes was performed using Spearman’s rank correlation coefficients , were collected from patients in Herzen Moscow Cancer Research Institute - branch of National Medical Research Radiological Center, Ministry of Health of Russian Federation . The sample information is presented in Table Nitrogen-frozen tissues were homogenized using a Mikro-Dismembrator S . Total RNA was isolated using the RNeasy Mini Kit  according to manufacturer’s instructions. Purified RNA was quantified using NanoDrop 1000  and the RNA quality measured with the RNA Integrity Number (RIN) method on Agilent RNA Bioanalyzer 2100 . All RNA samples were treated with RNase-free DNase I . Reverse transcription was performed from 1 μg of total RNA using M-MuLV reverse transcriptase (Thermo Fisher Scientific) and random hexamer primers . Negative control samples were included in each set of reactions. Reactions were heated at 70 °C for 5 min to melt the secondary structure of mRNA, followed by immediate cooling on ice. Samples were further incubated at 25 °C for 5 min, followed by treatment with M-MuLV reverse transcriptase at 25 °C for 10 min. Then the reaction tubes were incubated at 42 °C for 1 h. Following the incubation, samples were diluted to 48 μL in nuclease-free water and stored at − 20 °C.HK3: Hs01092850_m1, VASN: Hs01936449_s1, GREM1: Hs01879841_s1, TGFB3: Hs01086000_m1, TGFB1: Hs00998133_m1, LOXL3: Hs01046941_g1, HGF: Hs00300159_m1, SNAI2: Hs00161904_m1, FAM101B: Hs00823804_m1, SFRP2: Hs00293258_m1, FLNA: Hs00924645_m1, WWTR1: Hs00210007_m1, SFRP1: Hs00610060_m1, GLIPR2: Hs01555479_m1, NFATC1: Hs00542675_m1, TWIST1: Hs01675818_s1, COL1A1: Hs00164004_m1, ZEB2: Hs00207691_m1, VIM: Hs00958111_m1, TP53: Hs01034249_m1, SPP1: Hs00959010_m1, RARRES1: Hs00894859_m1) from TaqMan Gene Expression Assays (Thermo Fisher Scientific). Primers and probes for reference genes, GUSB and RPN1, were designed earlier [Quantitative polymerase chain reaction was performed using commercial primer-probe sets for target genes  was used for revealing correlations. A value of p ≤ 0.05 was considered statistically significant.Statistical analysis was performed using SPSS 10 software . The Wilcoxon/Mann-Whitney and Kruskal-Wallis tests were applied to analyze differences in mRNA expression of target genes in CRC samples. Spearman’s rank correlation coefficient , both in paired as well as pool of tumor samples. In addition, literature data analysis revealed several genes that are involved in EMT process. Preliminary calculation of correlation coefficients between the expression of these genes and HK3 was carried out using CrossHub software and RNA-Seq data from TCGA project as described above. The results obtained are shown in Additional file HK3 gene and five additional genes  involved in EMT. Thus, HK3 and a set of 21 EMT-related genes were selected for further assessment by qPCR.We identified a number of genes that showed high correlations between each other in CRC with the false discovery rate (FDR) ≤ 0.05. In the heat maps, these genes formed a significant correlation module  CRC samples as compared with that in conditional normal tissue  of cases. The mean value of relative mRNA level was 1.7. HK3 overexpression was not correlated with CRC progression stage.Using qPCR, we quantified the expression of COL1A1, TWIST1, NFATC1, GLIPR2, SFPR1, FLNA, GREM1, SFRP2, ZEB2, SPP1, and RARRES1) was detected in CRC (Table p < 0.05) in the mRNA level of COL1A1, TWIST1, CREM1, and SPP1 genes was observed in most examined samples. Moreover, NFATC1, GLIPR2, SFPR1, FLNA, SFRP2, ZEB2, and RARRES1 genes were downregulated by over two-fold (p < 0.05) in more than 40% of CRC cases.In addition, we verified the expression of all selected EMT-related genes using qPCR. Deregulation in the expression of 11 genes  between HK3 mRNA level and expression of seven of 21 examined genes  in CRC. Generally, HK3 gene expression was positively correlated with almost all tested genes at moderate or weak levels. A negative correlation was detected between HK3 and TP53 genes only.In order to determine the correlation between , HK1, HK2, HK3, and HK4 (or GCK) [HK1 and HK2 genes was reported in many tumors including colorectal, prostate, breast, lung, gastrointestinal, and pancreatic cancers [HK3 upregulation in CRC and its potential association with EMT.In the 1920s, Otto Warburg and co-workers observed the altered energy metabolism in proliferating tumor cells. These cells were shown to exhibit increased glycolysis with lactate secretion even in the presence of oxygen — a phenomenon known as the “Warburg effect” . Increas(or GCK) . HKs pho(or GCK) . HKs are cancers , 57–61.  cancers . Neverth cancers . Associa cancers , 65. A r cancers , 67. No TWIST1 gene in CRC, as reported in a previous study [SNAI2 mRNA level was observed in this work, SNAI2 protein expression was earlier shown to be upregulated in colorectal tumors [ZEB2 gene in more than half of CRC samples, while its overexpression was observed at the invasion front of CRC in another study [ZEB2 protein promotes EMT progression through direct suppression of the transcription of genes involved in epithelial dedifferentiation [ZEB2 overexpression may not be generally observed after EMT and may not always correlate with the mesenchymal phenotype [HK3 and TWIST1, SNAI2, and ZEB2 genes in CRC. The association of HK3 with important EMT inducers may indicate its involvement in EMT in colorectal cancer.The loss of E-cadherin expression during EMT is a major event in cancer. Downregulation of E-cadherin is mediated by a series of transcription repressors, including proteins of the SNAIL superfamily  and ZEB family (ZEB1 and ZEB2), forkhead-box protein FOXC2, factors E47 and KLF8, and TWIST bHLH proteins (TWIST1 and TWIST2) . Upregulus study . Furtherl tumors . Shioiril tumors . We alsoer study . Howeverntiation , 82. Thuhenotype . In addiTGFB1 and TGFB3 encode signaling proteins, transforming growth factor-beta 1 and 3 (TGFβ1 and TGFβ3). These are secreted ligands of specific TGFβ membrane receptors that play an essential role during differentiation, proliferation, and embryonic development in normal tissues. In general, the binding of the ligand to its receptor initiates phosphorylation of proteins belonging to the SMAD family. Activated SMAD proteins assemble into complexes with transcription factors to directly regulate gene expression [TGFB1 and TGFB3 genes in CRC was observed in our study. However, these genes demonstrated a strong positive correlation with HK3 mRNA level.The genes pression . Many stpression –88. Altepression , 90. Altpression . Moreovepression . No signGREM1 and COL1A1 gene expression in CRC. A positive correlation was observed between these genes and HK3. GREM1 gene encodes a member of the BMP antagonist family and is known as a mediator of EMT in non-cancerous pathologies and cancer [COL1A1 gene encodes for alpha 1 type I collagen, a target protein for TGFβ1 transcription factor. Type I collagen is associated with EMT in normal and cancer tissues [COL1A1 gene was observed in tissues and serum samples of patients with CRC. The serum level of COL1A1 correlated with the staging and poor survival rate of CRC [We observed a significant upregulation in d cancer –96. GREMd cancer . Howeverd cancer , 99. COL tissues –102. It  tissues . In the e of CRC .TP53 gene encodes a tumor suppressor protein, p53. One of the most important functions of p53 is the activation of apoptosis. TP53 is frequently mutated in human cancers, and over 50% of all tumors display somatic mutations in TP53 gene [HK3 and TP53 genes. Thus, HK3 gene could be a potential p53 target and suppression of p53 may not only contribute to increased glycolysis but also provide some advantages for EMT in CRC cells. It is interesting that the upregulation of HK3 expression was observed in breast cancer samples with TP53 mutations, as per TCGA data [TP53 mutations, but could also be positive for ones.It should to be noted that P53 gene . Loss ofP53 gene . In addiP53 gene , 108. p5P53 gene –111. We CGA data . In thisHK3 gene in CRC and confirmed its involvement in tumorigenesis. Expression of HK3 gene significantly correlated with mRNA levels of important EMT transcriptional factors  and components (GREM1 and COL1A1). Thus, our study suggests that HK3 may participate in EMT process and that its upregulation could be one of the crucial changes for adaptation of glucose metabolism to EMT in CRC.We showed the upregulation of Additional file 1: Table S1.(XLS 393 kb)Additional file 2: Table S2.(XLS 389 kb)Additional file 3: Table S3.(XLS 34 kb)


Measures to improve bird performance have been sought due to the imminent phase out of in-feed antibiotics in poultry and continued demand for higher poultry feeding efficiency. Increasing grain particle size and dietary fibre may improve gizzard function, digestive efficiency and nutrient absorption. This study was conducted to evaluate the effect increased particle size of corn and inclusion of sugarcane bagasse (SB) on mRNA expression of genes encoding digestive enzymes and nutrient transporters in broilers.P < 0.01), where birds fed coarsely ground corn (CC) with 2% SB had lower FCR than those fed CC without SB. A particle size × SB interaction was observed for both expression of pepsinogen A and C (P < 0.01) which were negatively correlated with FCR on d 24. Addition of 2% SB upregulated pepsinogen A and C only in CC fed birds. Further, 2% SB also upregulated pancreatic amylase (AMY2A) and intestinal cationic amino acid transporter-1 (CAT1). Inclusion of dietary CC upregulated duodenal amino peptidase N (APN), jejunal alanine, serine, cysteine and threonine transporter-1 (ASCT1), and ileal peptide transporter-2 (PepT2).A total of 336 day-old Ross 308 males were assigned in a 2 × 2 factorial arrangement of treatments with corn particle size - coarse 3576 μm or fine 1113 μm geometric mean diameter, and SB - 0 or 2% inclusion. Feed conversion ratio (FCR), weight gain and feed intake were measured from d 0–10 and d 10–24. The relative gizzard weight and mRNA expression of genes encoding digestive enzymes and intestinal nutrient transporters were measured on d 24. During d 10–24, a particle size × SB interaction was observed for FCR (These results suggest that both SB and coarse particle size modulate expression of genes encoding important digestive enzymes and nutrient transporters and thus are directly related to bird performance. These findings provide insights into the combination effects of dietary fiber and particle size in the future management of broiler feeding. Consumer pressure and legislation to ban the use of in-feed antibiotics in the EU and voluntarily removal of antibiotics from animal feed in other countries have supressed performance and profitability in broiler chickens due to enteric disorders , 2. The oAT and ASCT1, cationic amino acid transporters, such as CAT1 and CAT2, and Na + −dependent neutral/cationic amino acid exchanger, such as y+ L amino acid transporter-1 and y+ L amino acid transporter-2, and peptide transporters, such as PepT1 and PepT2, in the small intestinal epithelium are closely associated with nutrient absorption capacity [The physical structure of feed ingredients and inclusion of dietary fiber may improve nutrient digestion and absorption as a result of increased gizzard size , 5 and ecapacity , 13. Altcapacity –17, therThis study investigated the influence of fiber supplementation and increased corn particle size on broiler performance at the gene expression level. It was hypothesized that fiber and coarse particle size would stimulate secretion of digestive enzymes and nutrient transporters that would then accelerate the digestive activity in the intestine and improve feed conversion efficiency.A total of 336 d-old male Ross 308 chicks were obtained on the hatching day from a local hatchery . The chicks were assigned in a 2 × 2 factorial arrangement of treatments with 2 particle sizes  and 2 levels of sugarcane bagasse . The geometric mean diameter of corn particle size was determined according to the American Society of Agricultural Engineers (2003). The birds were randomly allocated to 4 treatments with 6 replicate pens each stocked with 14 birds. The broiler chicks were reared in pens measuring 75 cm × 120 cm to 24 d. Hardwood shavings were used as bedding with an initial depth of 7 cm. Each pen was equipped with a single tube feeder and 2 nipple drinkers. Feed and water were provided ad libitum. The lighting, relative humidity and temperature followed Ross 308 strain guidelines .Table The SB was provided by FCR Consulting Group, Brisbane. The composition of SB was determined (“as is” basis) for total non-starch polysaccharides (NSP) and lignin following the method described by Englyst et al.  and Kirkn = 6/treatment) were weighed and the average weight gain, feed intake, and FCR were calculated based on the measurements at the end of the starter, d 10, and grower, d 24 phases. On d 24, empty gizzards without proventriculi from 3 birds per pen were weighed and recorded along with corresponding bird weights. The relative gizzard weight was calculated as mass per unit of live body weight (g/100 g of live body weight).Birds and leftover feed in each pen  following the manufacturer’s instructions. Total RNA quantity and purity was determined using a NanoDrop ND-8000 spectrophotometer . An RNA 6000 Nano kit was used to measure RNA integrity  using the Agilent 2100 Bioanalyzer . The RNA samples were considered of high integrity if the RIN was higher than 7.5 [On d 24, one bird was randomly selected from each pen and killed by cervical vertebrae dislocation. Around 2 cm from each duodenum, jejunum, ileum, proventriculus and pancreas was excised and flushed with 4 °C PBS and collected into a 2 mL Eppendorf cap lock tube, snap-frozen in liquid NThe isolated RNA of each sample was reverse-transcribed with the QuantiTect Reverse Transcription Kit  following manufacturer’s instructions (gDNA elimination step performed). The Rotorgene 6000 real-time PCR machine  was employed to convert the RNA into cDNA. The cDNA was diluted three times with nuclease-free water and stored at − 20 °C until required.https://www.ncbi.nlm.nih.gov/). Table In the current study, the primers were either sourced from previously published studies in chickens or designed using NCBI primer tool  with Rotorgene 6000 real-time PCR machine . The PCR reaction was performed in a volume of 10 μL containing 5 μL of 2× SensiFAST, 400 mM of each primer and 2 μL of cDNA template. After thermal cycling, amplification cycle (Cq) values for all genes were collected and imported into qBase+ version 3.0  software and analyzed against two optimized reference genes, HPRT1 and TBP, in the current study. Both reference genes were applied to the samples of each tissue. The qBase+ applied an arithmetic mean method to transform logarithmic Cq value to linear relative quantity using exponential function for relative quantification of genes , 23 and oAT, CAT1, CAT2, CCK1R, CCK, EAAT3, bo,+AT, GLUT1, GLUT2, LAT1, PepT1, PepT2, SI, y+LAT1, y+LAT2, and rBAT in the intestine.The genes analyzed in the tissues are listed as follow: PGA5, PGC and CCK, in proventriculus; AMY2A, ATP1A1, CCK1R, CCK, CELA1, CELA2A, PNLIP, in pancreas; and APN, ASCT1, ATP1A1, BP < 0.05. Correlations between FCR of the grower phase, d 10–24, and the expression levels of the genes were conducted using the procedures of SPSS statistics version 22 . For correlation analysis, FCR was calculated in each pen (n = 14 birds) and the mRNA expression of the proventricular pepsinogens was measured from a randomly selected bird in each pen (n = 1 bird).The data were analyzed using the General Linear Models (GLM) procedure of SPSS statistics version 22  for the main effect of particle sizes, and SB supplementation along with their interactions. The main effect was analyzed with a treatment with pooled values against the other treatment. Differences between mean values were determined using LSD test at the level of P < 0.05). FCR was impaired in birds fed the diet containing 2% SB compared to those fed the diet without SB treatment during d 0–10 (P < 0.05). No interaction was observed between particle size and SB in early age (P > 0.05). During d 10–24, significant particle size × SB interactions were observed for FCR (P < 0.01). The birds fed CC with 2% SB had lower FCR than those fed CC without SB. However, this was not the case when fine corn was fed. Similarly, CC reduced FCR compared to FC only when 2% SB was included in the diet. During d 10–24, birds fed SB were heavier than those fed without SB (P < 0.05).The effect of corn particle size and SB on broiler performance is presented in Table The effect of corn particle size and SB on relative gizzard weight is presented in Table P < 0.01). The combination of SB and CC significantly upregulated PGA5 and PGC compared to the expression of genes in the other three groups. Both pepsinogen A (P < 0.01 and R = − 0.53) and C (P < 0.01 and R = − 0.59) were negatively correlated to FCR on d 24.The mRNA expression of three genes was investigated in response to SB addition and corn particle size in the proventriculus as presented in Table P < 0.05), while no response to SB was observed in other genes investigated, namely, ATP1A1, CCK1R, CCK, CELA2A, and PNLIP. Corn particle size did not affect the expression of pancreatic genes either as a main effect and no interactions were observed (P > 0.05).The mRNA expression of seven pancreatic genes in response to feed SB addition and corn particle size was examined Table . Genes AoAT, CAT1, CAT2, CCK1R, CCK, bo,+AT, EAAT3, GLUT1, GLUT2, LAT1, PepT1, PepT2, SI, y+LAT2, and rBAT. Addition of 2% SB upregulated CAT1 in the duodenum compared to the expression in the birds without SB (P < 0.05). However, 2% SB did not affect the expression of the genes APN, ASCT1, ATP1A1, BoAT, CAT2, CCK1R, CCK, bo,+AT, EAAT3, GLUT1, GLUT2, LAT1, PepT1, PepT2, SI, y+LAT2, and rBAT in the duodenum (P > 0.05). A particle size × SB interaction was observed for expression of gene y+LAT1 (P < 0.05), where y+LAT1 was downregulated by CC only in the birds fed diet without SB supplementation, but no significant difference was observed for the expression of the gene between FC and CC groups when 2% SB was added.The mRNA expression of nineteen genes was investigated in response to feed SB addition and corn particle size in the duodenum as presented in Tables P < 0.01) and y+LAT2 (P < 0.05) in jejunum were upregulated in birds fed CC diet compared with those fed FC diet. Corn particle size did not affect other genes investigated: APN, ATP1A1, BoAT, CAT1, CAT2, CCK1R, CCK, bo,+AT, EAAT3, GLUT1, GLUT2, LAT1, PepT1, PepT2, SI, y+LAT1, and rBAT, in jejunum (P > 0.05). Birds fed 2% SB had upregulated jejunal CAT1 and downregulated GLUT2 compared to birds fed diet without SB (P < 0.05). No responses to 2% SB were observed in other investigated genes: APN, ATP1A1, BoAT, CAT2, CCK1R, CCK, bo,+AT, EAAT3, GLUT1, LAT1, PepT1, PepT2, SI, y+LAT1, and rBAT, in jejunum (P > 0.05). No interactions were observed between particle size and SB on expression of other investigated genes in the jejunum. However, a tendency for a particle size × SB interaction was observed for expression of CAT1 (P = 0.077) in the jejunum, where the birds fed CC diet with 2% SB inclusion tended to upregulate CAT1.The mRNA expression of nineteen jejunal genes in response to feed SB addition and corn particle size was examined Tables  and 10. P < 0.05). The CC diet tended to upregulate CCK1R (P = 0.056) and y+LAT1 (P = 0.097) in the ileum. Corn particle size did not affect the expression of: APN, ASCT1, ATP1A1, BoAT, CAT1, CAT2, bo,+AT, EAAT3, GLUT1, GLUT2, LAT1, PepT1, SI, y+LAT2 and rBAT, in the ileum. Inclusion of 2% SB upregulated expression of BoAT and CAT1 in the ileum compared to birds given no SB (P < 0.05). Inclusion of 2% SB had no effect (P > 0.05) on other genes: APN, ASCT1, CAT2, CCK1R, bo,+AT, EAAT3, GLUT1, GLUT2, LAT1, PepT1, PepT2, SI, y+LAT1, y+LAT2, and rBAT in ileum. A particle size × SB interaction was observed for expression of ATP1A1 in the ileum (P < 0.05), where 2% SB inclusion increased mRNA expression of ATP1A1 in the birds fed the CC diet but not in the birds fed the FC diet.The mRNA expression of eighteen ileal genes in response to feed SB addition and corn particle size was examined Tables  and 12. This study investigated responses of mRNA expression of genes encoding digestive enzymes and nutrient transporters in birds fed diets with SB and/or increased corn particle size. Gene expression in the proventriculus, pancreas and intestine of broiler chickens was increased in line with bird performance and gizzard development. Addition of 2% SB and inclusion of CC particle in feed upregulated some genes encoding digestive enzymes, and nutrient transporters together with improved gizzard function and performance in birds up to d 24.The combination of CC and SB improved broiler performance as demonstrated by lower FCR in birds fed CC together with SB. Coarse particle size and structural fiber may have: 1) extended digesta retention time, leading to prolonged exposure of nutrients to endogenous enzymes; 2) promoted gut reflux, re-exposing undigested nutrients to digestive enzymes for better digestion; 3) created a better microenvironment for enzyme activity around coarse corn particles of SB that has a strong water-holding capacity ; 4) enha®) increased the activity of pancreatic enzymes such as chymotrypsin [In recent years, the beneficial effects of fiber on gastrointestinal tract development and nutrient utilization in poultry have been discussed frequently. The source, physical structure and amount of fiber in the diet determine the effectiveness of fiber on birds , 28. Forotrypsin , 30. Furotrypsin . The sigotrypsin . In the otrypsin , 29, 31.otrypsin . It has otrypsin . Furtherotrypsin . Therefo0AT, the neutral amino acid transporter located at the brush border membrane, in ileum and CAT1, a transporter mediating the bidirectional transport of cationic amino acids, in duodenum, jejunum and ileum were upregulated by SB. Furthermore, duodenal APN, responsible for final digestion of peptides by N terminus cleavage, jejunal ASCT1, responsible for Na + −dependent neutral amino acid transporter, and jejunal y+LAT2, responsible for Na + −dependent neutral/cationic amino acid exchanger, and ileal PepT2, which has a minor contribution in transporting di- and tri-peptides, were upregulated by CC. In fact, the upregulated nutrient transporters will not only improve nutrient absorption but also play a vital role in the maintenance of intestinal barrier integrity and immune response. A deficiency of amino acids, such as alanine, cysteine, serine, threonine, arginine, and lysine, has long been known to impair immune function and increase the susceptibility of animals to infectious disease [Several studies investigating structural components of diets, such as coarse particle size or fiber, have shown improved nutrient digestibility –17, 34 v disease . Differe disease . In the  disease : 1) gast disease . It has  disease , 39. TheIn conclusion, the inclusion of either SB or CC in a pelleted diet is beneficial to the birds by improving performance likely through the upregulation of genes encoding digestive enzymes and nutrient transporters. The combination of CC and SB was more beneficial for the upregulation of some genes such as PGA5 and PGC. The results suggest enhanced gizzard development as a mode of action for higher production of digestive enzymes and nutrient transporters. These findings provide insights on how dietary fiber and particle size independently or in combination can improve bird performance based on the analysis of gene expression. The knowledge obtained herein will be useful to understand the underlying mechanisms of how feed additives can improve nutrient digestibility and thus feed efficiency. Further, the outcomes lay a foundation for future research to elucidate the usefulness of fiber supplementation and coarse particle inclusion in feed in a nutrigenomic way.


SOD1) gene has been identified in both familial ALS (FALS) and sporadic ALS (SALS), it has rarely been studied in Chinese patients with ALS, and there are few studies with large samples. This study sought to assess the prevalence of SOD1 mutations in Chinese ALS patients. We screened a cohort of 499 ALS patients (487 SALS and 12 FALS) from the Department of Neurology at the West China Hospital of Sichuan University and analyzed all coding exons of SOD1 by Sanger sequencing. In addition, we reviewed the mutation frequencies of common ALS causative genes in Chinese populations. Eight missense mutations in SOD1 were found in 8 ALS individuals: two novel mutations (p.G73D and p.V120F) and six previously reported mutations. The frequencies of SOD1 mutations were 1.03% (5/487) in SALS and 25% (3/12) in FALS from Southwest China. A literature review indicated that the mutation rates of major ALS causative genes were 53.55% in FALS and 6.29% in SALS. In Chinese SALS and FALS, the highest mutation frequency was in the SOD1 gene. Our results suggest that SOD1 mutation is the most common cause of ALS in Chinese populations and that the mutation spectrum of ALS varies among different ethnic populations.Although the copper/zinc superoxide dismutase-1 ( Amyotrophic lateral sclerosis (ALS) is a genetically heterogeneous neurodegenerative disorder that is clinically characterized by progressive neurological deterioration, the coexistence of upper and lower motor neuron signs, and death from respiratory failure, typically 3–5 years after the onset of symptomscopper/zinc superoxide dismutase-1 (SOD1) gene is composed of 5 exons coding for a homodimeric enzyme in which each subunit is composed of 154 evolutionarily conserved amino acids and binds with a catalytic Cu+ ion and a stabilizing Zn2+ ion. The SOD1 gene was first found to be a causative gene for FALS in 1993SOD1 gene account for 12–23.5% of FALS and ~7.3% of apparent SALS, on the basis of multiple large population studies in CaucasiansSOD1 was reported to be the most commonly mutated ALS gene until a large hexanucleotide (GGGGCC) repeat expansion (HRE) in the chromosome 9 open reading frame 72 (C9orf72) gene was identified in 2011. This HRE is found in ~46.4% of FALS cases and ~21.1% of SALS cases, and it is the most common cause of ALS among Caucasian populationsTARDBP), Fused in Sarcoma (FUS), Angiogenin (ANG), Valosin-containing protein (VCP), Sequestosome 1 (SQSTM1), and Profilin 1 (PFN1). However, these genes rarely account for FALS or SALS7TARDBP is 0–0.93%910111213FUS is 1.55–1.85%9ANG is 0.31%VCP is 0%SQSTM1 is 0.98–1.38%15PFN1 is 0–0.19%9MATR3) is 0.20%TBK1) is 0.57%CHCHD10) is 0–0.41%20C9orf72 HRE, five independent studies have revealed that its frequency in SALS is 0–1.53%9112223SOD1, the first identified causative gene, only two studies from northern and central-southern China with a total of 482 SALS patients have investigated the mutation frequency (6/482) of SOD1 in mainland China9SOD1 mutations in southwest Chinese ALS patients by screening for mutations of the SOD1 gene in 499 ALS patients and reviewing the mutation frequencies of common ALS causative genes in Chinese populations.The This study included a total of 499 Chinese patients diagnosed with ALS (487 with SALS and 12 with FALS) according to the EI Escorial revised criteriaSOD1 gene (NM_000454.4)28SOD1 gene were screened for mutations of other common ALS causative genes, including C9orf72, TARDBP, FUS, PFN1 and SQSTM1 , Medline , and China National Knowledge Internet (www.cnki.net) with the individual search terms “SOD1”, “superoxide dismutase 1 gene”, “FUS”, “fused in sarcoma”, “TARDBP”, “TAR DNA binding protein”, “ANG”, “angiogenin”, “VCP”, “valosin-containing protein”, “PFN1”, “profilin 1”, “C9orf72”, “CHCHD10”, “TBK1”, and “TANK-binding kinase 1”, each combined with “amyotrophic lateral sclerosis” or “ALS” or “motor neuron disease” or “MND” and “Chinese”. To eliminate repeat data, when 2 or more simultaneously published studies had the same first author’s name and similar participant characteristics and data, we included the study with the most comprehensive description of the data. Single case reports were excluded; only case series and case-control studies were included in the review.A comprehensive literature review was performed using PubMed .Dichotomous variables such as sex and site of onset were analyzed using a standard chi-square test or Fisher’s exact test. Continuous data were compared using Student’s t-test. The results of all continuous data are presented as the mean ± standard deviation (SD), and a two-tailed SOD1 heterozygous point mutations in 8 ALS patients , excluding the three patients with SOD1 mutations who were still alive. The eight patients carrying the mutations of the SOD1 gene in the current study had no mutations in the C9orf72, TARDBP, FUS, PFN1 and SQSTM1 genes. The clinical features of patients carrying mutations of the SOD1 gene are summarized in The age of onset in ALS patients carrying http://www.ncbi.nlm.nih.gov/SNP/). In addition, previous studies have reported that p.P67S (P66S) and p.C112Y (C111Y) are likely to be pathogenic33The pathogenic roles of p.L39V (L38V), p.G42D (G41D), p.H44R (H43R) and p.I114T (I113T) have been confirmed in ALS with reference to the Single Nucleotide Polymorphism database  in FALS and 1.03% (5/487) in SALS patients from southwest China.Although studies on SOD1 gene was the most common causative gene, accounting for 1.45% of SALS and 25.33% of FALS in Chinese patients. However, the proportion was lower than that in Caucasians, especially in SALS C9orf72 was the most common gene in Caucasians TARDBP or FUS  mutation has been reported in some FALS patients and is associated with earlier disease onset (mean onset age: 41.5–44.9 years), a mean duration of 2.8 years and classical ALS symptoms4353637V mutatioAside from these six previously reported mutations, the pathogenic roles of the novel mutations p.G73D and p.V120F were suggested by the following observations. First, the novel variants were absent in our HCs, as assessed using the PCR-RFLP method as well as available repositories including the dbSNP build 146), the 1000 Genomes database and the Exome Aggregation Consortium (ExAC) database. Second, the amino acids “G” of p.G73 and “V” of p.V120 are highly conserved across species . We did , the 1003447SOD1  to date. SOD1 mutations can give rise to almost all described clinical ALS phenotypes, such as progressive muscular atrophy and bulbar palsy, but no clear correlations between the mutated codon and the phenotype have been found. Disease duration may vary among patients harboring the same SOD1 mutationsSOD1 gene presented with clinical manifestations consistent with those of previously reported patientsSOD1 from two SALS patients who presented with relatively slow disease progression. This observation supported the pathogenic roles of these mutations by primary analysis, though these results were not verified. Therefore, our findings expand the mutation spectrum of the SOD1 gene in ALS.Currently, the best-studied mechanism for ALS involves SOD1SOD1 gene appears to be a common cause of ALS in Chinese individuals. Our findings expand the mutation spectrum of the SOD1 gene in ALS. Our findings also support the assumptions that the regions of SOD1 mutations are diverse among different geographical backgrounds and that the mutation spectrum of ALS varies among different ethnic populations.In conclusion, mutation of the How to cite this article: Wei, Q. et al. Analysis of SOD1 mutations in a Chinese population with amyotrophic lateral sclerosis: a case-control study and literature review. Sci. Rep.7, 44606; doi: 10.1038/srep44606 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


EGFR and rs12034979 in HSPG2 significantly correlated to HPV16 infection; rs2575738, rs2575712, rs2575735 in SDC2 and rs6697265 in HSPG2 significantly correlated to HPV18 infection; rs10510097 in FGFR2, rs12718946 in EGFR significantly correlated to HPV52 infection; rs4947972 in EGFR, rs2981451 in FGFR2, rs2575735 in SDC2 significantly correlated to HPV58 infection. And rs3135772, rs1047057 and rs2556537 in FGFR2, rs12034979 in HSPG2, rs16894821 in SDC2 significantly correlated to cervical lesion progression induced by HPV16 infection; rs6697265 and rs6680566 in HSPG2, rs16860426 in ITGA6 by HPV18 infection; rs878949 in HSPG2, rs12718946 and rs12668175 in EGFR by HPV52 infection; no SNP by HPV58 infection. Our findings suggest that HPV receptor and associated gene variants may influence the susceptibilities to HPV type-specific infection and cervical lesion progression, which might have a potential application value in cervical cancer screening and therapy.Human papillomavirus (HPV) infects cervical epithelial cells through cellular membrane receptors, and then induces the initiation and progression of cervical cancer. Single nucleotide polymorphisms (SNPs) may impact the susceptibility and outcome of diseases, but it's still unknown whether variant in HPV receptor and associated genes is associated with type-specific HPV infection and cervical lesion progression. We examined 96 SNPs in 8 genes which may participate in the HPV infection process in 875 samples with HPV negative or single HPV16, 18, 52, 58 positive from 3299 cervical exfoliated cell samples, by Illumina BeadXpress VeraCode platform, and analyzed the correlation between the SNPs and type-specific HPV infection and cervical lesions progression. We found rs28384376 in  Cervical cancer is the second most common cancer among women, with more than 527,624 new cases and 265,672 deaths , in the The differences of HPV genotype distribution are also found in different grades of cervical lesions. A meta-analysis showed that HPV16 positivity was gradually increased from normal/ASCUS/LSIL/CIN1 (20-28%), through CIN2/HSIL (40/47%) to CIN3/ICC 58/63%)[8/63%[5].CD4, as well as CXCR4 and CCR5, for HIV [NTCP) for HBV [FURIN) mediated L2 minor capsid protein cleavage that increases L2 N terminus exposure. Along with binding to HSPGs, virion binds to alpha 6 integrins, which initiate further intracellular signaling events. Following these primary binding events, HPV16 binds to a newly identified L2-specific receptor, the annexin A2 heterotetramer. Therefore, as opposed to a sequential handoff of the virion from one receptor to another, we hypothesize that a receptor complex coalesces and includes HSPGs(HSPG2 and SDC2), CyPB(PPIB), alpha 6 integrin(ITGA6), tetraspanins(TSPAN1), GFR(EGFR and FGFR2) and A2t. Thus, we selected eight genes, which have been identified as mainly molecules involved in HPV infection process, into our study. The eight genes included EGFR, PPIB, HSPG2, FGFR2, FURIN, ITGA6, TSPAN1 and SDC2). Considering the association of individual SNP with pathogen infection and disease outcome, we assume that SNPs in HPV receptor and associated genes may influence the susceptibility to type-specific HPV infection and cervical lesions progression.Previous studies have revealed that viruses infect host cells through the relevant receptors, such as herpes virus entry mediator(HVEM) for Herpes virus,  for HIV , and sod for HBV . Further for HBV , and SNP for HBV . It has  for HBV –16. Acco for HBV –18. GrowThus, we selected 96 single-nucleotide polymorphism (SNP) sites in the eight genes that were reported to be involved in the process of HPV infection using the haploview software, and evaluated the correlation between the distribution frequency of various SNP sites and type-specific HPV infection and cervical lesion progression in four common HPV genotypes (HPV16/18/52/58) in China and East Asia. Our study aimed to find out the genetic susceptibility to type-specific HPV infection and cervical lesion progression and search for a novel strategy for cervical cancer prevention or therapy.The median age of 875 women whose cervical samples were collected was 41 yrs (21-69 yrs), included 40 yrs (22-68 yrs) in 214 controls (HPV negative), 43 yrs(21-69 yrs) in 294 single HPV16 positive, 44 yrs(27-59yrs) in 55 single HPV 18 positive, 40 yrs(21-69 yrs) in 155 single HPV 52 positive, and 41 yrs(23-68 yrs) in 157 single HPV 58 positive. Further, the median age in 502 women with ≤LSIL and 373 with ≥HSIL was 40 yrs(21-63 yrs) and 43 yrs(21-69 yrs), respectively.2 test and fisher's test.All the SNPs in control population of this study were tested by Hardy–Weinberg equilibrium (HWE) as shown in SDC2 gene  and one in EGFR  in HPV16 positive group. There were three significant SNPs in SDC2 gene  and two in HSPG2 gene  and one in TSPAN1 gene  in HPV18 positive group. There was one significant SNP in SDC2 gene , one in HSPG2 gene  and one in PPIB gene  in HPV52 positive group. There were two significant SNPs in EGFR gene  and one in FURIN gene  and one in SDC2 gene  in HPV58 positive group. The detailed data were shown in Compared with HPV negative, there were two significant SNPs in Compared with HPV negative, there was one protective SNP genotype “TA” of rs2651465  in HPV16 positive group. Three protective SNP genotypes “AA” and “AC” of r s2575712  and “AG” of r s3767137  in HPV18 positive group. There was one susceptible SNP genotype “AA” of rs2589205  and one protective SNP genotype “GA” of rs6680566  in HPV52 positive group. There were two susceptible SNP genotypes “AG” of rs11770506  and “GC” of rs4947972 , two protective SNP genotypes “AC” of rs17514846  and “AA” of rs2575712  in HPV58 positive group. The detailed data were shown in HSPG2 gene (Freq (case) =0.057, Freq (control) =0.16, χ2=7.612, Chi square's P value=0.0058, Fisher's P value=0.004508) in HPV18 positive group. There were one protective haplotype “GGG” in HSPG2 (Freq(case)=0.326, Freq (control) =0.403, χ2=4.421, Chi square's P value=0.0355, Fisher's P value =0.036331) and one susceptible haplotype “GGA”  in HSPG2 (Freq (case) =0.295, Freq (control)= 0.228, χ2=4.305, Chi square's P value =0.038, Fisher's P value=0.039358) in HPV52 positive group. There were two protective haplotypes “GG”  in EGFR (Freq (case) =0.578, Freq (control)=0.667, χ2=6.133, Chi square's P value=0.0133, Fisher's P value =0.01617) and “AA”  in FURIN (Freq (case)=0.097, Freq (control)=0.152, χ2=4.724, Chi square's P=0.0297, Fisher's P value=0.043411) in HPV58 positive group. The detailed data were shown in Compared with HPV negative, there was no significant different haplotype in HPV16 positive group. There is one protective haplotype “GGAGA”  in All of the tested SNPs in the table were in Hardy–Weinberg equilibrium (HWE) in the control population from the normal samples (p>0.05), as shown in Table EGFR gene  and one in HSPG2 gene  in HPV16 positive group. There were three SNPs in SDC2 gene  and one in HSPG2 gene  in HPV18 positive group. There was one SNP in FGFR2 gene  and one in EGFR gene  in HPV52 positive group. There was one SNP in EGFR gene  and one in FGFR2 gene  and one in SDC2 gene  in HPV58 positive group. The detailed data were shown in Table Compared with HPV negative in the normal samples, there was one significant SNP in Compared with HPV negative in the normal samples, there were two susceptible SNP genotypes “AA” of 28384376  and “AG” of rs12034979  in HPV16 positive group. Three susceptible SNP genotypes “AA” of rs2575738 , “GG” of rs6697265  and “GC” of rs6697265  in HPV18 positive group. There was one protective genotype “GG” of rs12718946  in HPV52 positive group. There was one protective genotype “AA” of rs2981451  in HPV58 positive group Table .2=4.149, Chi square's P value =0.0417, Fisher's P value =0.059048) and “GGAA” (Freq(case)= 0.037, Freq (control)= 0.006, χ2=4.395, Chi square's P value =0.0361, Fisher's P value =0.096763) in block 10  in FGFR2 gene and one critically protective haplotype “GA” (Freq(case)= 0.161, Freq (control)= 0.287, χ2=3.876, Chi square's P value =0.049, Fisher's P value =0.051813) in block 7  in SDC2 gene in HPV16 positive group. There was one susceptible haplotype “GGG” in block3  (Freq(case)= 0.587, Freq (control)= 0.414, χ2=4.93, Chi square's P value =0.0264, Fisher's P value =0.0386) and two protective haplotypes “GGA” in block2  (Freq(case)= 0.108, Freq (control)= 0.243, χ2=4.209, Chi square's P value =0.0402, Fisher's P value =0.040537) and “GGAGA” in block1  (Freq(case)= 0.043, Freq (control)= 0.157, χ2=4.26, Chi square's P value =0.039, Fisher's P value =0.042397) in HSPG2 gene in HPV18 positive group. There were two susceptible haplotypes “GGGAA” (Freq(case)= 0.225, Freq (control)= 0.148, χ2=4.164, Chi square's P value =0.0413, Fisher's P value =0.043573) in block 1  and “GGA” (Freq(case)= 0.333, Freq (control)= 0.244, χ2=3.958, Chi square's P value =0.0466, Fisher's P value =0.05283) in block2  in HSPG2 gene in HPV52 positive group. There was one susceptible haplotype “GC” (Freq(case)= 0.311, Freq (control)= 0.184, χ2=7.765, Chi square's P value =0.0053, Fisher's P value =0.006766) and one protective haplotype “GA” (Freq(case)= 0.17, Freq (control)= 0.278, χ2=5.001, Chi square's P value =0.0253, Fisher's P value =0.029213) in block 8  in FGFR2 gene in HPV58 positive group = 0.09, Freq (control)= 0.032, χup Table .EGFR and HSPG2 gene to HPV16 infection, SDC2 and HSPG2 to HPV18 infection, EGFR, FGFR2 and HSPG2 to HPV52 infection, and EGFR, FGFR2 and SDC2 to HPV58 infectionTaken above results together, some variants in HPV receptor and associated genes were found to be correlated to type-specific HPV infection, including All of the tested SNPs in the table were in Hardy–Weinberg equilibrium (HWE) in the control population of this study (p>0.05 except for rs878949 and rs12668175 in HPV52), as shown in Table FGFR2 gene  and one  in HSPG2 gene and one  in SDC2 gene in single HPV16 positive with ≥HSIL. There were two significant different SNPs in HSPG2 gene  and one in ITGA6 gene  in HPV18 positive with ≥HSIL. There were two significant different SNPs in EGFR gene  and one in HSPG2 gene  in HPV52 positive with ≥HSIL. There was no significant different SNP site in HPV58 positive with ≥HSIL  and “AG”  of rs3135772; “AG”  of rs12034979 and “GA”  of rs16894821; two susceptible SNP genotypes “GG”  of rs2556537 and “AG”  of rs1047057 in single HPV16 positive with ≥HSIL. There was a susceptible SNP genotype “CC”  of rs6697265 and two protective SNP genotypes “GG”  and “GA”  of rs6680566 in HPV18 positive with ≥HSIL. There were two susceptible SNP genotypes “AG”  of rs878949 and “GG”  of rs12718946 in HPV52 positive with ≥HSIL. There was no significant different SNP genotype in HPV58 positive with ≥HSIL Table .2=5.442, Chi square's P value =0.0197, Fisher's P value =0.022419) in block 6  in SDC2 gene in HPV16 positive with ≥HSIL. There was one protective haplotype “GG” (Freq(case)= 0.333, Freq (control)= 0.594, χ2=6.25, Chi square's P value =0.0124, Fisher's P value =0.021295) in block2  in HSPG2 gene in HPV18 positive with ≥HSIL. There was one susceptible haplotype “CAA” (Freq(case)= 0.271, Freq (control)= 0.131, χ2=7.681, Chi square's P value =0.0056, Fisher's P value =0.00897) in block2  in HSPG2 gene in HPV52 positive with ≥HSIL. There was no significant different haplotype in HPV58 positive with ≥HSIL = 0.209, Freq (control)= 0.319, χIL Table .FGFR2, HSPG2 and SDC2 with HPV16, ITGA6 and HSPG2 with HPV18, and EGFR and HSPG2 with HPV52, but no with HPV58.Taken above results together, some variants in HPV receptor and associated genes were found to be associated with cervical lesion progression induced by type-specific HPV, including It is well known that the occurrence and development of human cervical cancer are related to high-risk HPV infection. HPV 16 binds to heparin sulfate proteoglycans (HSPGs) on either the epithelial cell surface or basement membrane through interactions with its L1major capsid protein, and afterwards HSPG/growth factor/HPV16 complexes activate growth factor receptors which initiate signaling cascades during early virion-host cell interactions . After HEGFR gene and rs12034979 in HSPG2 gene, and two susceptible genotypes “AA” of rs28384376 and “AG” of rs12034979, and two risk haplotypes “GGAA” and “GGGG” in block10  in FGFR2 gene and one protective haplotype “GA” in block7  in SDC2 gene in HPV16 positive group, suggesting the genetic variants in EGFR, HSPG2, FGFR2 and SDC2 genes may be associated with type-specific HPV16 infection. The differences of haplotype frequency distributions in target genes between cases and controls were significant by χ2 test (p<0.05) while not by fisher's test (p>0.05), suggesting that each kind of haplotype may be susceptible to type-specific HPV16 infection and there is no dominant susceptible or protective haplotype. The OR of rs28384376 and rs12034979 are 2.642 and 3.114 respectively, and there was no protective SNP site except for one critical haplotype. All those phenomenons might be employed as an explanation that HPV16 possesses the highest prevalence among all high-risk genotypes in the worldwide. Again we found three susceptible SNP sites rs2575738 and rs2575735 in SDC2 gene and rs6697265 in HSPG2 gene, and one protective SNP site rs2575712 in SDC2 gene, and three susceptible genotypes “AA” of rs2575738 and “GG”, “GC” of rs6697265, and one susceptible haplotype “GGG” in block3  and two protective haplotypes “GGA” in block2 , “GGAGA” in block1  in HSPG2 gene in HPV18 positive group, suggesting that genetic variants in HSPG2 and SDC2 genes are associated with type-specific HPV18 infection. Similarly, there was one susceptible SNP site rs10510097 in FGFR2 gene and one protective SNP site rs12718946 in EGFR gene, and one protective SNP genotype “GG” of rs12718946; and two susceptible haplotypes “GGA” in block2 , “GGGAA” in block1  in HSPG2 gene in HPV52 positive group, suggesting that genetic variants in HSPG2, FGFR2 and EGFR genes are associated with type-specific HPV52 infection. And again, there was two susceptible SNP sites rs4947972 in EGFR gene and rs2575735 in SDC2 gene and one protective SNP site rs2981451 in FGFR2 gene, and one protective SNP genotype “AA” of rs2981451, and one susceptible haplotype “GC” and one protective haplotype “GA” in block8  in FGFR2 gene in HPV58 positive group, suggesting that genetic variants in EGFR, SDC2, and FGFR2 are associated with type-specific HPV58 infection. Thus, variants of HPV receptor and associated genes in SNP sites, genotypes and haplotypes may influence significantly the susceptibility of the individual to type-specific HPV18, 52 and 58 in Chinese women, but seems unobvious to type-specific HPV16 infection.In an analysis on the susceptibility of HPV receptor and associated gene variants to type-specific HPV infection, we found two susceptible SNP sites rs28384376 in FGFR2 gene, and three protective SNP sites rs3135772 in FGFR2 gene, rs12034979 in HSPG2 gene and rs16894821 in SDC2 gene, and two susceptible SNP genotypes “GG” of rs2556537 and “AG” of rs1047057 and four protective SNP genotypes “AA”, “AG” of rs3135772, “AG” of rs12034979, “GA” of rs16894821, and one protective haplotype “GG” in block 6  in SDC2 gene, suggesting that genetic variants of FGFR2, HSPG2, and SDC2 are related to cervical lesion progression induced by HPV16 infection. In HPV18 positive group, there was one susceptible SNP site rs6697265 in HSPG2 gene and two protective SNP sites rs6680566 in HSPG2 gene and rs16860426 in ITGA6 gene, and one susceptible SNP genotype “CC” of rs6697265 and two protective SNP genotypes “GG” and “GA” of rs6680566, and one protective haplotype “GG” in block2 , suggesting that genetic variants in HSPG2 and ITGA6 are related to cervical lesion progression induced by HPV18 infection. In HPV52 positive group, there were three susceptible SNP sites rs878949 in HSPG2 gene, rs12718946 and rs12668175 in EGFR gene, and two susceptible SNP genotype “AG” of rs878949 and “GG” of rs12718946, and one susceptible haplotype “CAA” in block2 , suggesting that genetic variants in HSPG2 and EGFR are related to cervical lesion progression induced by HPV52 infection. In HPV58 positive group, there was no significant different genetic variant related to cervical lesion progression. Thus, HPV receptor and associated gene variants may be associated with cervical lesion progression induced by HPV16, 18, and 52, but not HPV58, infection in Chinese women.Furthermore, we analyze the correlation between HPV receptor and associated gene variants and cervical lesion progression in each single HPV positive group. In HPV16 positive group, there were two susceptible SNP sites rs2556537 and rs1047057 in Totally 3299 residual cervical exfoliated cell samples were primarily collected from women with various gynecologic disorders who underwent primary HPV testing, HC2 or Cervista test, in Clinic of Gynecologic Oncology of Women's Hospital, Medical School, Zhejiang University from October 2012 to May 2015. Of HPV positive samples, those were excluded just as follows: 1) other than A7/9 positive tested by Cervista, 2) previous surgical or physical therapy to cervix, 3) cervical infection by other pathogens, such as HIV, Syphilis, and Candida, 4) incorporative immune disease or use of immunosuppressive agents, 5) incorporative other malignant tumors, 6) incorporative pregnancy. HPV genotyping was performed for the remaining HPV positive samples using HybriBio's proprietary flow-through hybridization technique, and the single HPV16, 18, 52, 58 positive samples were selected for study. All HPV negative samples were identified by no bands on agarose gel electrophoresis after PCR amplification using HPV L1 consensus PCR primers (primers MY09 and MY11). After the samples with not-enough remaining cells, HPV genotype failure, and unqualified DNA were further excluded, 875 samples were finally enrolled into the study, include 214 HPV negative, 294 single HPV16 positive, 55 single HPV18 positive, 155 single HPV52 positive, and 157 single HPV58 positive. All the samples had the histological diagnosis, and the diagnostic criteria were followed by the American Society for Colposcopy and Cervical Pathology (ASCCP) 2006 guidelines . Sample The HPV test was performed by the Hybrid Capture 2 (HC2) test (Qiagen Digene) or Cervista test (Hologic). HC2 test is used for detecting the pool of 13 high-risk HPV genotypes including HPV 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59 and 68. Cervista test is used for detecting 14 genotypes (above 13 types plus 66), with a report of A5/A6, A7, and/or A9  positive or negative. Hybribio Rapid GenoArray test kit (GA) is used for HPV genotyping, including 6 low-risk types , 15 high-risk types . All the tests were performed according to the manufacturer's protocol –26.EGFR, PPIB, HSPG2, FGFR2, FURIN, ITGA6, TSPAN1 and SDC2) were genotyped. Validated tagSNPs were selected with a MAF > 5% in the HapMap Asia population. SNPs that satisfied the following criteria were considered for detection: 1) tagSNPs were preferentially selected, 2) those SNPs were previously reported to be frequent in Chinese population (http://www.ncbi.nlm.nih.gov/snp). The total genomic DNA was extracted from the cervical exfoliated cells using KoningTM Mutisource Genomic DNA Extration Kit-Mini and PureLink® Genomic DNA Kits (invitrogen). The DNA concentration was detected, agarose gel eletrophoresis was run and the final concentration was quantified to 50 ng/μl. All the SNPs were genotyped by Illumina BeadXpress VeraCode platform (USA), according to the manufacturer's protocol.The Haploview software 4.2  was used to analyze the tagSNPs and haplotype block based on the CHB (Chinese Han Beijing) population data of HapMap , a total of 96 SNPs in 8 HPV receptor and associated genes (All statistical analyses were performed using PLINK version 1.07 . All p v


Neochanna apoda) living in harsh environments had slower metabolism, and thus higher population densities, compared to a fish species  in physiologically more benign habitats. Interspecific differences in the intercepts for the relationship between body and density disappeared when species mass-specific metabolic rates, rather than body sizes, were used to predict density, implying population energy use was equivalent between mudfish and kōkopu. Nevertheless, despite significant interspecific differences in the slope of the metabolic scaling relationships, mudfish and kōkopu had a common slope for the relationship between body size and population density. These results support underlying logic of energetic equivalence between different size species implicit in metabolic theory. However, the precise slope of metabolic scaling relationships, which is the subject of much debate, may not be a reliable indicator of population density as expected under metabolic theory.Differences in population density between species of varying size are frequently attributed to metabolic rates which are assumed to scale with body size with a slope of 0.75. This assumption is often criticised on the grounds that 0.75 scaling of metabolic rate with body size is not universal and can vary significantly depending on species and life-history. However, few studies have investigated how interspecific variation in metabolic scaling relationships affects population density in different sized species. Here we predict inter-specific differences in metabolism from niche requirements, thereby allowing metabolic predictions of species distribution and abundance at fine spatial scales. Due to the differences in energetic efficiency required along harsh-benign gradients, an extremophile fish (brown mudfish,  Understanding the drivers of species distribution and abundance is an important goal of predictive ecology . DistribWhile this metabolic concept has support at macroecological scales ,8, many One way to improve local predictions from metabolic theory may be to consider how functional traits, such as metabolism, vary with environmental gradients to affect ecological function at finer spatial scales . While tNeochanna apoda) and banded kōkopu  are fish that are allopatrically distributed along an environmental stress gradient involving extreme drought, hypoxia and acidity in forest ponds ) was then calculated from these data. The log10 of the average mass of individuals in each population (log[μMASS]) was also calculated. Thus μMASS and μSMR population-level measures of fish mass and SMR calculated for the average fish in each population, respectively. Finally we calculated the log10 population biomass density for each pool sampled.The SMR scaling relationships determined above for mudfish and kōkopu were used to estimate the metabolic rate of each fish caught in each population based on their mass and species identity and was treated as mass-specific metabolic rate . A one-way ANOVA showed that the average daily maximum temperature in mudfish pools (13.9 ± 0.4°C) was not significantly different to that in kōkopu pools . Finally, we compared the strength of support for the two simplified models using R2, AICc, and Akaike weights (w). All analyses were performed in R 3.0.1 .We were interested in the relative variance in population biomass density explained by log(μSMR) compared to the variance explained by the combination of species identity, log(μMASS) and their interaction. If interspecific variation in metabolism was responsible for interspecific variation in population biomass density, then log(μSMR) should explain the same variation in density as the combination of log(μMASS), species identity and their interaction. In this case, log(μSMR) would be the only variable necessary, and log(μMASS) and species identity would be removed in the process of model simplification. Thus we ran two model simplification analyses starting with two alternative full ANOVA models that depicted two cases. In the body mass model, where log(μSMR) was excluded, the starting full model included species identity and log(μMASS) direct effects and their interaction as predictors of log population biomass density. In the metabolism model we included these effects in addition to the main effect of log(μSMR), and its interaction with species identity. Predictive terms were removed from each model starting with non-significant interaction terms, followed by the least significant main effects based on type II sums of squares ANOVA which accounts for potential co-linearity between terms . All mod2 h-1 and ranged between 0.1–0.8 and 0.1–2.1 mg O2 h-1, respectively and increased with mass for both species. The slope of the relationship between SMR and mass was significantly greater for banded kōkopu than for mudfish  (SMRmudfish= 0.11Mass0.62(±0.03) and SMRkōkopu = 0.17Mass0.72(±0.03), where SMR is in mg O2 h-1. The average mass of all fish measured for SMR was 6.7 g. At this weight, these equations predict kōkopu will consume 1.9 times more oxygen (0.67 mg O2 h-1) than mudfish (0.36 mg O2 h-1) at rest.Mudfish and kōkopu SMR averaged 0.32 (± 0.04) and 0.70 (± 0.04) mg O= 0.038) . Thus fo2 h-1 and ranged between 0.1–1.7 and 0.2–11.9 mg O2 h-1, respectively and increased with mass for both species. The slope of the relationship between MMR and mass was significantly greater for kōkopu than mudfish  (MMRmudfish = 0.16Mass0.77(±0.05) and MMRkōkopu = 0.35Mass1.0(±0.05), where MMR is in mg O2 h-1. Thus at 6.7 g, these equations predict kōkopu will maximally consume 3.4 times more oxygen (2.35 mg O2 h-1) than mudfish (0.69 mg O2 h-1).Mudfish and kōkopu SMR averaged 0.6 (± 0.03) and 3.5 (± 0.85) mg O < 0.01) . Thus foF1,39 = 1.50, P = 0.23) and AS was significantly lower for BM than BK for all sizes  . Thus at 6.7 g, these equations predict that kōkopu can more than triple its SMR (3.5x SMR) if needed, whereas mudfish can only double theirs (2.0 × SMR) if needed, which is a 1.75-fold difference.Mudfish and kōkopu factorial aerobic scope averaged 1.8 (± 0.03) and 3.6 (± 0.29) times SMR and ranged between 1.1–2.4 and 1.8–6.3 times SMR, respectively and increased with mass for both species. There were no differences in the slopes of the relationship between AS and mass for BM or BK (Homogeneity of slopes test: < 0.001) . Thus foMudfish and kōkopu population sizes averaged 8.8 and 6.4 and ranged between 1–42 and 1–20, respectively. Mudfish had a lower mass-specific SMR than kōkopu and thus, according to MTE, they should have a higher mass-specific population density, which would require a species identity term to explain differences in population density (i.e. density = log(μMASS) × species identity). If mass-specific SMR differences were responsible for the mass-specific density difference, then a model that additionally included species mass-specific log(μSMR) would explain the same variation but species identity and mass effects would become redundant.F1,61 = 46.11, P < 0.001), with a slope of 0.96, which did not differ significantly between species    was excluded, the minimal adequate model that explained variation in mudfish and kōkopu population density included both the direct effects of log(μMASS) and species identity . Populat = 0.24) . However< 0.001) . Excludiish mass .F1,62 = 65.17, P < 0.001), with a slope of –3.07, which was identical for both species  and had an R2 of 0.51 . Thus mudfish and kōkopu population densities were similar when comparing populations with similar average individual metabolic rates, but not when comparing populations with similar sized fish  in the metabolic model was virtually identical to that explained by the combination of species identity and log(μMASS) in the body mass model , the minimal adequate model that explained variation in mudfish and kōkopu population density included only the single effect of log(μSMR) . Log [The fact that the slopes of the relationship between body size and population density were equivalent between mudfish and kōkopu, despite significant differences in metabolic scaling exponents, suggests that allometric scaling factors other than metabolism were involved in determining population density. In our case, population density increased with body size more rapidly than expected from the mudfish and kōkopu metabolic scaling exponents. This may occur if food availability also increased with body size due to the reduction in gape size limitation that occurs with mass , which c beetle) . Thus, a-3 (± 0.0005 S.E.) . Although these estimates were made using dry invertebrate body masses, it is likely that additional allochthonous invertebrate inputs would be required to sustain the total mudfish population biomasses reported here, which averaged 246 g m-3 (± 61 S.E.). Even so, low metabolic rates are likely also necessary to sustainably achieve such high biomass. Previous research using the same populations studied herein showed no significant differences in canopy cover between each species suggesting such allochthonous subsidies are likely to be equivalent between species [Regardless of the lack of correspondence between the metabolic and population density scaling exponents, it is interesting that the overall interspecific differences in metabolic rates and population density were directly proportional. Mudfish mass-specific population density was higher compared to that of kōkopu for the range of fish sizes surveyed because mass did not account for inter-specific variation in metabolic rate. Differences in species intercepts for the relationship between mass and density disappeared when species mass-specific metabolic rate was used to predict density. Thus, mudfish and kōkopu population sizes and total energy use were equivalent once their metabolic differences were accounted for. It is possible, however, that higher food availability in mudfish populations could also explain the higher mudfish population densities, as has been shown for mammals . Populat species . Thus alNeochanna burrowsius) by virtue of their scaleless integument, as comes standard with fish in the Galaxiidae family [2 g-1 h-1, brown mudfish routine metabolic rate was consistent with previous studies on brown mudfish SMR [Galaxias brevipinnis (0.102 mg O2 g-1 h-1), Galaxias vulgaris (0.117 mg O2 g-1 h-1) and Galaxias maculatus (0.141 mg O2 g-1 h-1) which, like kōkopu, typically inhabit normoxic flowing streams and rivers and struggle to survive hypoxia and drought [In addition to the potential influence on population density, the interspecific difference in mudfish and kōkopu metabolic rates may play an important role in their driving their allopatric distribution associated with environmental harshness. Brown mudfish are generally only found in lentic pools that may dry up to 20 times a year and are hypoxic and acidic ,38,54. Ae family . With nee family . At apprfish SMR , and appfish SMR . Mudfish drought ,55,56. AProtopterus sp.), also adopt a low metabolic rate during droughts, however, unlike mudfish, lungfish undergo metabolic depression [Species similar to mudfish, such as African lungfish Click here for additional data file.


Rosa hybrida) flowers are highly sensitive to dehydration during flower opening and the water recovery process after dehydration induced ethylene production rapidly in flower gynoecia. In addition, this temporal- and spatial-specific ethylene production is attributed to a transient but robust activation of the rose MAP KINASE6-ACC SYNTHASE1 (RhMPK6-RhACS1) cascade in gynoecia. However, the upstream component of RhMPK6-RhACS1 is unknown, although RhMKK9 (MAP KINASE KINASE9), a rose homologue of Arabidopsis MKK9, could activate RhMPK6 in vitro. In this study, we monitored RhMKK2/4/5/9 expression, the potential upstream kinase to RhMPK6, in rose gynoecia during dehydration and rehydration.Flower opening is an important process in the life cycle of flowering plants and is influenced by various endogenous and environmental factors. Our previous work demonstrated that rose  contains supplementary material, which is available to authorized users. Plants are exposed to various abiotic and biotic stresses because of their sessile life style. Therefore, for improved survival, plants develop a system that can rapidly sense signals from a changing environment and respond adaptively and/or defensively by modulating the internal physiological, biochemical, and molecular processes , 2. For Arabidopsis. In addition, ethylene-biosynthetic genes and genes responsive to jasmonic acid, gibberellin, and auxin were activated by rehydration, suggesting hormone balance is involved in the water recovery of plants.Although signal reception and the transduction pathway of dehydration have been extensively reported, they remain largely unknown for the rehydration process. Using a DNA microarray, Oono et al.  found thArabidopsis, several components of the MAPK cascade, such as MPK4 and MPK6, were reported to be activated by drought [Arabidopsis seedlings [In plants, the mitogen-activated protein kinase (MAPK) cascade plays an essential role in the signaling pathway of multiple abiotic and biotic stress cues , 2, 8–11 drought , 14; a reedlings .Rosa hybrida) are an important ornamental crop globally. Dehydration is a considerable postharvest problem for cut roses because their market supply is highly dependent on long-distance transportation, resulting in severe dehydration for this duration. Recently, we reported that in rose (R. hybrida) flowers, rehydration following dehydration triggered rapid, but transient, ethylene production in the gynoecia, namely all the carpels and pistils in a flower [Cut roses , RhMKK4 , RhMKK5 , and RhMKK9 , respectively  and a 333-bp 3′-UTR. For RhMKK2, RhMKK4, and RhMKK5, the cDNA full-length was 1453, 1522, and 1508 bp, respectively  approach. An RhMKK9-specific fragment was chosen to construct the TRV2-MKK9 vector to avoid cross silencing of other MKK genes. We screened positive RhMKK9-silenced flowers by detecting RNA1, RNA2, and RhMKK9 transcription. Both RNA1 and RNA2 transcripts were detected in ~28% flowers, indicating that TRV successfully infected these flowers. RT-PCR analysis revealed that RhMKK9 expression was significantly reduced in gynoecia of RhMKK9-silenced flowers than those of the TRV2 controls, especially at 1 h after rehydration treatment .To further confirm the potential role of ent Fig. . In addiols Fig. , indicatRhMPK6 transcript and protein, as well as the kinase activity of RhMPK6 in RhMKK9-silenced flowers. We tested the RhMPK6 activity and protein level after 30 min of rehydration and RhACS1 protein level after 1 h of rehydration, respectively. This was based on the results from our previous study, which showed that the highest level of RhMPK6 activity and RhACS1 protein appeared at 30 min and 1 h of rehydration, respectively [RhMKK9 silencing. However, phos-tag SDS-PAGE demonstrated that RhMKK9 silencing largely weakened RhMPK6 phosphorylation during rehydration  flowers, removal of gynoecia repressed the production of ethylene and delayed petal senescence [Despite its simple structure, ethylene is functionally complex and plays a crucial role in a broad spectrum of plant developmental and environmental responses , 34. Ethbe block . In Phalnescence , 37. Simnescence .Gossypium hirsutum L.) petioles and bolls [Triticum aestivum L.) leaves [Citrus sinensis Osbeck) flowers [Diospyros kaki Thunb.), and avocado (Persea americana Mill.) fruit, dehydration induces ethylene production [Among the various abiotic stresses, dehydration is known to influence ethylene production in plants, although the underlying regulatory mechanism seems complex. In plants such as cotton  leaves , orange  flowers , as welloduction , 46. In In rose flowers, dehydration gradually increased ethylene production in sepals, whereas rehydration following a period of dehydration triggered rapid ethylene production in gynoecia . MoreoveArabidopsis, MPK6 has been reported to be activated by different MAPK kinases in response to various biotic and abiotic stresses [Arabidopsis to salt stress [In stresses , 48. Colstresses , 50, whestresses , 52. Forstresses , the cort stress .MKK2, MKK4, MKK5, and MKK9 from roses and detected their expression during dehydration and rehydration. It is noteworthy that RhMKK9 expression, which could phosphorylate RhMPK6 in vitro [RhMKK9 led to a significant reduction of rehydration-induced ethylene production in gynoecia. The rehydration-responsive expression pattern and function identification suggested that RhMKK9 is possibly the upstream component regulating the rehydration-activated RhMPK6-RhACS1 cascade and ethylene production in rose gynoecia. Interestingly, RhMKK9 silencing delayed both flower opening and senescence, further supporting that rehydration-caused ethylene production accelerated flower opening and senescence. Detection of gene expression showed that rehydration led to the rapid expression elevation of several ethylene-inducible genes, which were involved in abiotic stress-response and senescence, in TRV control flowers. Consistently, expression of ethylene-inducible genes was significantly inhibited in RhMKK9-silenced flowers. It is worth noting that the expression of two transcription factor genes, RhWRKY40 and RhMYB108, decreased after 12 h of rehydration in TRV controls to a relatively low level, similarly to RhMKK9-silenced flowers. Therefore, RhWRKY40 and RhMYB108 might be closely associated with ethylene production. However, expression of the senescence-associated RhSAG12 gene in the TRV control was significantly higher than that in RhMKK9-silenced flowers at 12 h after rehydration, implying that rehydration-caused ethylene production might also initiate the petal senescence process.Here, we isolated the homologues of in vitro , was relArabidopsis, histone modification of H3K4me3 and H3K9ac were enriched in drought stress-induced genes [In the last decade, increasing evidence has shown epigenetic modification plays crucial roles in tolerance, adaption, and memory of plants to various abiotic stresses –30. In Aed genes , 30, 55.ed genes , 30. In ed genes .ZmMI1 gene expression is associated with DNA hypomethylation in the nucleosome cores [RhMKK9 promoter, whereas elevated methylation on the RhMKK9 gene body. The factors involved in this rapid methylation and de-methylation process should be the subject of further studies.Dynamic DNA methylation and demethylation has also been broadly reported to be involved in plant responses to abiotic stresses –61. In mme cores . Here, wRhMKK9 expression was specifically and rapidly induced by rehydration in gynoecia. RhMKK9 silencing inhibited rehydration-caused ethylene bursts and delayed flower opening and senescence. In addition, we found that changes of DNA methylation status on the RhMKK9 promoter and gene body were associated with RhMKK9 induction by rehydration. These results explained how the flower, the reproductive organ, could quickly recover from dehydration using ethylene as a mediator.In summary, a MAPK KINASE, RhMKK9, is the upstream component responsible for activating the RhMPK6-RhACS1 cascade. R. hybrida ‘Samantha’ were provided by a commercial greenhouse (Sunstone Company) in the Changping District, Beijing. The flowers were harvested at opening stage 2 as described previously [Flowers of eviously . The flo−2 s−1. The dehydration status was defined by fresh weight loss, and the flowers were subjected to rehydration when the flowers lost ~30% fresh weight. After the dehydration treatment, the bottom of flower stems were re-cut to remove about 1 cm under water to prevent air embolisms, and the flowers were placed in deionised water for rehydration. The flower phenotype was observed at different time points: 0, 1, 2, and 3 d after rehydration. After treatment, the flowers were cut open and the gynoecia, including carpels and pistils, were sampled as described previously [Dehydration treatment was conducted as described previously , 16. Floeviously , 16, 20.The gynoecia were weighed and then placed in a 25 ml airtight GC vial, and the vials were kept for 30 min at 25 °C. From each vial, 2 μl headspace gas was withdrawn to measure ethylene concentration using a gas chromatograph equipped with a flame ionisation detector  as described previously . After eAlignment of multiple deduced amino acid sequences was constructed using ClustalW2 software and visualised using the BioEdit program. Phylogenetic analysis was performed using MEGA 5.2.RhUBI, JK622648) was used as the internal control. The primers used in the RT-PCR analysis are listed in Additional file Total RNA of petals was isolated using the hot borate method as described previously , and theRhMKK9 gene silencing was performed as previously described [RhMKK9-specific 3′ end  was used to construct pTRV2-RhMKK9. The resulting constructs pTRV2-RhMKK9, pTRV1, and pTRV2 were transformed into Agrobacterium tumefaciens strain GV3101. Briefly, Agrobacterium was grown in LB broth containing 50 μg ml−1 kanamycin and 50 μg ml−1 gentamycin sulphate at 28 °C with shaking at 200 rpm overnight. These cultures were then diluted 1:50 v/v in fresh LB broth containing 10 mM MES, 20 mM acetosyringone, 50 μg ml−1 kanamycin, and 50 μg ml−1 gentamycin sulphate, and grown overnight as described above. Agrobacterium cells were harvested by centrifugation, and the pellet was suspended in infiltration buffer  to a final A600 of 1.8. A mixture of Agrobacterium cultures containing pTRV1 and pTRV2-RhMKK9 at a ratio of 1:1 (v/v) was used for rose transformation, and a mixture containing pTRV1 and pTRV2 was used as the negative control. The mixtures were stored at room temperature for 4 h in the dark prior to vacuum infiltration.escribed . A 490-bFor vacuum infiltration, the flowers were placed upside down in a container (81.64 L), with the whole flower immersed into the prepared infiltration mixture. The flowers were then infiltrated by vacuum at 30 mmHg twice, each for 2 min. Then they were briefly washed with DW and kept in DW for 3 d at 8 °C before the dehydration treatment .http://www.scbt.com) to detect RhACS1; polyclonal anti-MPK6  to detect RhMPK6. Secondary antibodies  were used as recommended in the manufacturer’s instructions.Gynoecia protein was extracted as described previously . The fol+ was used as described previously [For the kinase activity assay, SDS-PAGE gel was supplied with 25 μM Phos-tag  and 50 μM Zneviously –66. The Alu I, or by the methylated DNA-digesting enzyme McrBC for 3 h. The digested DNA was used as a template to amplify the RhMKK9 promoter and gene body. Undigested genomic DNA was amplified as an internal control.DNA methylation status was analysed by Chop-PCR as described previously . For Cho


Mycobacterium tuberculosis is an intracellular pathogen that parasitizes the host macrophage. While approximately two billion people are infected worldwide, only 5 to 10% become diseased with pulmonary tuberculosis, at least in the absence of comorbidities. Tuberculosis control requires development of noninvasive methods probing the host immune status to help distinguish latent infection from active tuberculosis. With such methods, high-risk individuals could be targeted for treatment before disease manifestation. Previous investigations have been based on examination of peripheral blood cells or, more rarely, lung macrophages obtained with invasive procedures, such as bronchoalveolar lavages. Here we show that differences exist in the expression of a surface protein (Toll-like receptor 2) between macrophages recovered from the sputum of individuals in different diagnostic groups: i.e., infection free, latent tuberculosis infection, and active pulmonary tuberculosis. Thus, phenotypic analysis of local macrophages obtained with noninvasive procedures can help distinguish among tuberculosis infection stages. During tuberculosis, macrophages are critical for both pathogen survival and host immune activation. Since expression of particular cell surface markers reflects cell function, we used flow cytometry to measure the abundance of surface markers associated with polarity, lipid uptake, or pattern recognition on macrophages found in induced sputum. Nine macrophage surface markers were examined from three groups of donors: infection-free, latent tuberculosis infection, and active pulmonary tuberculosis. Using a trend test, we found that expression of Toll-like receptor 2 was greater from absence of infection to latent infection and from latent infection to active tuberculosis. The results point to the possibility that innate immune cell phenotypes be used to distinguish among tuberculosis infection stages. Moreover, this study shows that readily accessible sputum macrophages have potential for tuberculosis diagnosis and prognosis.IMPORTANCEMycobacterium tuberculosis is an intracellular pathogen that parasitizes the host macrophage. While approximately two billion people are infected worldwide, only 5 to 10% become diseased with pulmonary tuberculosis, at least in the absence of comorbidities. Tuberculosis control requires development of noninvasive methods probing the host immune status to help distinguish latent infection from active tuberculosis. With such methods, high-risk individuals could be targeted for treatment before disease manifestation. Previous investigations have been based on examination of peripheral blood cells or, more rarely, lung macrophages obtained with invasive procedures, such as bronchoalveolar lavages. Here we show that differences exist in the expression of a surface protein (Toll-like receptor 2) between macrophages recovered from the sputum of individuals in different diagnostic groups: i.e., infection free, latent tuberculosis infection, and active pulmonary tuberculosis. Thus, phenotypic analysis of local macrophages obtained with noninvasive procedures can help distinguish among tuberculosis infection stages. Mycobacterium tuberculosis infection. Consequently, an important public health goal is to identify individuals who are progressing from latent infection to active disease before they become symptomatic and contagious. Existing diagnostics for M. tuberculosis infection, which rely on adaptive immune responses, such as delayed hypersensitivity or cytokine release by antigen-specific T cells  result from activation of latent  T cells . In cont T cells .Among innate immune cells, macrophages are central in tuberculosis pathogenesis: these cells are parasitized by the pathogen, and they participate in establishing and maintaining chronic infection as well as in determining the immunopathology of active disease , 5. The M. tuberculosis , latently infected subjects (LTBI+), and patients diagnosed with active pulmonary tuberculosis (PTB) to determine whether phenotypes of innate immune cells vary with tuberculosis infection stages. We find the abundance of Toll-like receptor 2 (TLR2) on the surface of sputum macrophages varies with tuberculosis infection stage.6 total cells) for flow cytometry. The demographic characteristics of sputum donors (n = 94) are shown in 6 total cells, of which 45% ± 14% were macrophages (CD14 and CD11c double-positive cells).Ninety-four (61%) of 154 sputum samples yielded sufficient cell numbers , we measured the abundance of nine surface protein markers associated with macrophage function by flow cytometry (P < 0.001) (P = 0.06)  changes with tuberculosis infection state but not with tuberculosis disease severity. The results do not indicate that macrophage polarity or the expression of the other selected markers tested distinguishes among tuberculosis infection states.Immunophenotyping of sputum macrophages from three tuberculosis diagnostic groups showed that (i) macrophages in sputum expressed both M1 and M2 polarization markers, thus recapitulating the simultaneous presence of M1 and M2 phenotypes of alveolar and granuloma macrophages in pulmonary tuberculosis –29, and –M. tuberculosis infection  immune responses , with ov17–nfection , were noParticipants were recruited from two regional chest clinics in Essex and Middlesex Counties, New Jersey. All participants provided informed consent as approved by the Rutgers University Institutional Review Board.M. tuberculosis culture-positive sputum, regardless of initial sputum smear findings . Latent M. tuberculosis infection (LTBI+) was defined by lack of symptoms, a negative chest radiograph, and a positive tuberculin skin test (induration of ≥10 mm) or interferon gamma release assay with QuantiFERON-TB Gold (according to the manufacturer’s guidelines). Absence of M. tuberculosis infection (LTBI−) was defined by a negative tuberculin skin test or interferon gamma release assay. Participants with PTB were enrolled within 8 weeks after initiation of antituberculosis treatment. All donors were free from HIV-1 infection.Active pulmonary tuberculosis (PTB) was defined based on symptoms suggestive of active tuberculosis  and/or an abnormal chest radiograph consistent with PTB. Diagnosis was confirmed by PTB disease severity was defined by examining chest radiographs and determining the extent of lung cavitation, which was measured at the point of maximum diameter in 6-ft posteroanterior chest X-ray films. Patients were grouped as follows: 1, absence of cavitation; 2, presence of single or multiple cavities with a diameter of <4 cm in aggregate; or 3, presence of single or multiple cavities with a diameter of ≥4 cm in aggregate.CRP measurements were obtained by using particle-enhanced immunoturbidimetric assays and Roche Cobas c501 analyzers.g at 4°C. Cells were washed twice and resuspended in PBS; the total cell number was determined using a Neubauer hemocytometer. Cells were centrifuged again and resuspended in PBS plus 1% bovine serum albumin at a density of 2.5 × 106 cells/ml for antibody staining. All procedures were approved by the Rutgers University Institutional Biosafety Committee.To minimize contamination from the oral cavity, participants were asked to brush, rinse, and gargle with tap water until the returned fluid was free of debris. An SU99 ultrasonic nebulizer  was used to produce a mist of 3% saline solution that patients were instructed to deeply inhale and exhale through the mouth. Inhalation was interrupted each time the patient expectorated sputum into a sterile container; inhalation was resumed until ~10 ml of sputum was obtained or until a maximum of 20 min of inhalation was reached. Sputum samples were stored at 4°C for ≤2 h, diluted with 1 volume of phosphate-buffered saline (PBS) at pH 7.4 and 0.1 volume of 100 mM dithiothreitol, shaken for 10 min on a platform rocker, gravity filtered twice through a 70-μm-pore nylon strainer, and centrifuged for 10 min at 450 × 6) were incubated for 30 min on ice with specific mouse anti-human monoclonal antibodies or isotype-matched negative-control antibodies. The following antibodies were used: anti-CD14–Alexa 700 clone M5E2, anti-CD11c–phycoerythrin (PE)–CY7 clone B-LY6, anti-TLR2–fluorescein isothiocyanate (FITC) clone TL2.1, anti-CD163–PE clone GHI/61, and anti-TLR4–allophycocyanin (APC) clone HTA125 from Becton, Dickinson, San Jose, CA; anti-CD80–FITC clone 2D10.4, anti-CD86–PE clone IT2.2, anti-CD206–Efluor450 clone 19.2, anti-CD64–Pacific Blue clone 10.1, anti-CD36–APC/CY7 clone 5-271, isotype control mouse IgG2a APC/Cy7 clone MOPC-173, and mouse IgG1 Pacific Blue clone MOPC-21 from BioLegend, San Diego, CA; anti-low-density lipoprotein receptor (LDLR)–APC clone 472413 from R&D, Minneapolis, MN; and isotype control mouse IgG1  clone P3.6.2.8.1 from EBioscience, San Diego, CA. After incubation with antibody, cells were washed twice with phosphate-buffered saline and fixed in 4% paraformaldehyde for 30 min. A total of 100,000 events per sample was recorded with a Becton, Dickinson LSR II flow cytometer, which was calibrated with Calibrite beads . Data were analyzed by FlowJo Software v.7.6.5 .Sputum cells  was tested on the initial ~50 donors. A preliminary analysis identified markers in panel 1 that failed to distinguish among groups; testing of these  was discontinued. Panel 2  was tested on an additional ~40 donors. For each experimental marker, the expression level was calculated as the ratio of the mean fluorescence intensity (MFI) of the marker to the MFI of an isotype-matched control antibody.2 of the marker level (M) versus the infection state (S) coded as 1, 2, and 3 : log2M = β0 + β1S + β2 CRP + β3 age. We assessed the evidence of a trend by testing the null hypothesis β1 = 0, where β1 represents the CRP- and age-adjusted difference in mean log2M, on average, for groups differing by one step in infection state. The outcome of the analysis was left unadjusted or was adjusted for (i) level of C-reactive protein (CRP) in the subject’s blood at the time of sputum collection, which served to control for nonspecific effects related to inflammation, and/or (ii) donor’s age, which controlled for the generally younger age of LTBI− participants compared to the other participants. CRP values and donor’s age are shown in 2M = β0 + β1V + β2 CRP + β3 age, where V is the disease severity category (cavitation grade) coded 1, 2, or 3 . Associations between marker levels and disease severity were adjusted for age and CRP. All statistical tests were Wald tests, and robust standard error estimates were used. The threshold for statistical significance was set at 0.05.Two sets of linear regression analyses were performed. One model was used to estimate the association between macrophage marker levels and infection classification. We performed a linear regression of log10.1128/mSphere.00475-17.1FIG S1 FIG S1, TIF file, 0.7 MB.Distribution of analysis covariates by infection state. The box plots show the distribution of C-reactive protein (CRP) (milligrams per liter [left panel]) and age (years [right panel]) of the study participants grouped by infection state. See the legend to Copyright © 2017 Lakehal et al.2017Lakehal et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the