TERT promoter (TERTp) mutations are important factors in papillary thyroid carcinomas (PTCs). They are associated with tumor aggressiveness, recurrence, and disease-specific mortality and their use in risk stratification of PTC patients has been proposed. In this study we investigated the prevalence of TERTp mutations in a cohort of Polish patients with PTCs and the association of these mutations with histopathological factors, particularly in coexistence with the BRAF V600E mutation. A total of 189 consecutive PTC specimens with known BRAF mutational status were evaluated. TERTp mutations were detected in 8.5% of cases (16/189) with the C228T mutation being the most frequent. In six of the PTC specimens (3.2%), four additional TERTp alterations were found, which included one known polymorphism (rs2735943) and three previously unreported alterations. The association analysis revealed that the TERTp hotspot mutations were highly correlated with the presence of the BRAF V600E mutation and their coexistence was significantly associated with gender, advanced patient age, advanced disease stage, presence of lymph node metastases, larger tumor size, and tumor-capsule infiltration. While correlations were identified, the possibility of TERTp mutations being key molecular modulators responsible for PTC aggressiveness requires further studies. BRAF V600E mutation occurs in about 50% of PTC cases and has been identified in a number of studies to be related to an advanced disease stage, extrathyroidal invasion, or the presence of LNM [BRAF mutation is associated with a decreased expression of thyroid specific genes like NIS and TPO, what results in the loss of sensitivity to radioiodine [BRAF V600E and poor prognosis is demonstrated [Despite excellent overall survival in most cases of papillary thyroid carcinoma (PTC), recurrent disease is diagnosed during the 10-year period following initial treatment in 15%, 22%, 46.4%, and 66.7% of patients with TNM stage I, II, III, and IV, respectively. PTC may be a cause of death, even in patients with clinically indolent tumors . Howevernstrated .TERTp) have been detected in a number of solid cancers, including thyroid cancers [TERT encodes the catalytic subunit of telomerase, the enzyme responsible for extending telomeres and thereby preventing replicative senescence. TERT, together with its RNA partner telomerase TERT component (TERC), demonstrates telomerase reverse transcriptase activity and adds tandem repeats of TTAGGG sequence to the end of chromosomes [TERT is involved, it has been proposed to treat TERT as an oncogene that is able to act in a telomere-independent manner [Recent reports provide new data on the molecular background of PTC, especially as it relates to its aggressive phenotype. Mutations in the telomerase reverse transcriptase promoter , and chr5, 1,295,250 C->T , which correspond to positions −124 and −146 bp, respectively, and are located upstream from the ATG start codon of TERT [TERTp mutations is observed in particular histopathological PTC variants with the highest prevalence being seen in tall-cell PTC variant known for its aggressive phenotype [TERTp mutations were widely reported as associated with the presence of the BRAF V600E mutation in human cancers, mainly in TC and melanoma [BRAF V600E and TERTp mutations is associated with a higher cancer aggressiveness, which is reflected by lymph node metastases, distant metastases, advanced tumor stage, recurrence, and even disease-specific mortality of PTC patients [BRAF V600E and TERTp mutations is 7.7% (145/1892) [BRAF V600E and TERTp mutations as an oncogene tandem underlying progression and aggressiveness of thyroid cancers. Similar clinical consequences are observed regarding the coexistence of RAS mutations and TERTp mutations [TERT a potential prognostic marker that may be beneficial for risk stratification of patients with PTC. However, several studies investigating the prevalence of TERTp mutations in TCs have shown significant differences in the frequency of TERTp alterations in different countries with rates of 7.5–25.5% and 13.8–36.4% for PTC and FTC, respectively [In numerous malignancies, including follicular cell derived thyroid cancers (TCs), two hotspot mutations within  of TERT . The prehenotype . Interesmelanoma ,12,18,19patients ,21. AccoTERTp mutations as well as their potential associations with the BRAF V600E alteration and histopathological features in selected series of Polish PTC patients. Moreover, the differences between the presence of a single mutation and the coexistence of TERTp and BRAF 600 codon alterations and their relation to particular histopathological PTC features were analyzed. In the current study we analyzed the prevalence of TERTp in 189 consecutive PTC tumor specimens for the presence of two hotspot mutations, C228T and C250T. In total, 16 of these 189 PTC specimens (8.5%) were positive for the C228T or C250T TERTp mutations. Of the specimens analyzed, 13 harbored the C228T mutation  positive for the BRAF V600E mutation but no TERTp alterations; (3) positive for the BRAF V600E mutation and one of the hotspot TERTp mutations; and (4) a TERTp alterations present other than C228T or C250T. Subgroups 2–4 were compared to subgroup 1 to determine how the presence of the analyzed alterations was related to particular histopathological PTC features, both individually or together  no detected mutation within the together .BRAF V600E mutation alone was significantly associated with more advanced age of the patients (p = 0.024), larger tumor size (p < 0.0001), more advanced disease stage (p = 0.002), and infiltration of the tumor capsule (p = 0.0007). The TERTp hotspot mutations alone were significantly associated only with advanced age of patients (p = 0.005) and histopathological PTC variant (p = 0.041); however, the number of cases in the cohort was too small to interpret these results with a high degree of confidence. Coexistence of the BRAF V600E mutation and TERTp hotspot mutations, demonstrated an association with gender (p = 0.006), advanced age of the patients (p < 0.0001), advanced disease stage (p < 0.0001), the presence of lymph nodes metastases (p < 0.0001), larger tumor size (p < 0.0001), and infiltration of the tumor capsule (p = 0.0007). The results are summarized in The TERTp alterations other than the hotspot mutations, no association with any of the analyzed factors was identified. However, the small number of samples in that group (n = 6) does not justify drawing any unequivocal conclusions. Also, it was not possible to determine the association of the studied alterations with distant metastases since none of PTC cases in the cohort demonstrated distant metastases at the time of diagnosis. With respect to the subgroup of PTC specimens with TERTp hotspot mutation C228T, while two other specimens displayed unknown TERTp alterations −77 C->T and −162 C->T, with the latter one also being positive for the BRAF V600E mutation. In contrast to PTMC specimens containing a single alteration, which was consistent with classical PTC, the single case that harbored the BRAF mutation and the −162 C->T TERTp alteration was diagnosed as a follicular PTC variant. None of the three TERTp-positive PTMC specimens demonstrated angioinvasion, infiltration of tumor capsule, multifocality, or LNM. Twenty of 80 PTMC specimens contained the BRAF V600E mutation. However, infiltration of the tumor capsule was present in only 20% (4/20) of the BRAF-positive cases. These specimens were taken from the oldest patients in the PTMC subgroup. No other histopathological factors related to poor prognosis were observed.Among 80 PTMC specimens, only one tumor harbored the TERTp mutations have been extensively studied in a number of cancers, including melanomas, glioblastomas, clear cell renal carcinomas and nonclear cell renal carcinomas, non-small cell lung cancer, and TCs [TERTp mutations is higher in more aggressive types of TC, as well as in histopathological PTC variants, and the presence of the mutations correlate with a poorer outcome [Since their discovery, somatic  and TCs ,23,24,25 outcome ,12. More outcome ,27.TERTp mutations in PTC between different countries and scarce data concerning the impact of TERTp mutations alone on PTC histopathological features we decided to investigate Polish PTC patients. Regarding the well-documented association between the presence of TERTp and BRAF V600E mutations we analyzed TERTp in PTCs with known BRAF 600 codon status. We also performed a number of comparative analyses to identify a potential role of these two molecular markers alone or their coexistence in PTC carcinogenesis. The TERTp hotspot mutations were detected in 16 of the 189 consecutive analyzed PTC specimens (8.5%). However, if we excluded PTMCs and focused only on PTCs, the frequency of TERTp mutations increased up to 13.8% (15/109). Such observation is in concordance with previous studies [TERTp hotspot mutation C228T but not the BRAF V600E mutation. Consistent with the findings of de Biase and coauthors [TERTp mutations were present, but relatively uncommon in PTMC specimens at a frequency of 4.7%, and did not correlate with unfavorable clinical features. Similarly, our only case of PTMC with the C228T TERTp mutation did not demonstrate extrathyroidal invasion, angioinvasion, or lymph node metastases. In addition to the known hotspot mutations in TERTp, we also detected another known alteration, previously reported in dbSNP (rs2735943). Because its frequency is not known it was treated in our study like the three new TERTp mutations we identified, since there was no certainty whether it was a polymorphism or mutation. The analyses revealed that these four TERTp alterations  were not associated with any of analyzed clinical factors. However, we cannot exclude their impact on PTC carcinogenesis and further studies are needed to determine their role. It is noteworthy that one of the novel nucleotide substitutions (−77 C->T) created a putative consensus binding site (GGAA) for ETS (E26 transformation-specific) transcription factors; the same as that created by C228T and C250T hotspot mutations. It is believed that creating such additional binding sites for transcription factors is a mechanism of action for C228T and C250T, related to increased TERT expression [Taking into consideration the divergence observed in the frequency of  studies . In our oauthors , TERTp mTERTp hotspot mutations were more prevalent in the BRAF V600E-positive specimens compared to BRAF V600E-negative specimens  and the BRAF V600E mutation was less frequent in TERTp-negative PTC specimens. Our findings are consistent with a previous report [TERTp mutations were less common in well-differentiated PTC specimens harboring BRAF or RAS mutations. However, in PDTC specimens or ATC specimens, BRAF-positive or RAS-positive cases are enriched by TERTp alterations. This observation supports the belief that TERTp mutations may be useful as potential prognostic markers for TC.In the cohort of Polish patients with PTC, analyzed in the current study, the s report . Howevers report . Contrars report , who obsTERTp hotspot mutations were related to any poor prognostic factors in Polish patients with PTC. We determined that these mutations alone were associated only with advanced age at the time of PTC diagnosis and with a histopathological PTC variant (oxyphilic). However, the number of BRAF(−)TERTp(+) samples was small so these data are limited for their interpretative value. On the other hand, similar results were obtained by Gandolfi et al. [TERTp mutations and advanced age, but only in PTC cases with distant metastases. In a study by Shen et al. [TERTp mutations analyzed individually failed to show any significant association with clinicopathological outcomes. In contrast, the coexistence of the BRAF V600E mutation and TERTp hotspot mutations is reported to be strongly associated with a number of histopathological factors related to more aggressive clinical course of the disease [BRAF and TERTp mutations with gender, advanced age of patients, T3 and T4 stage of disease, the presence of lymph node metastases, larger tumor size, and infiltration of the tumor capsule. The role of these two genetic alterations in promoting PTC aggressiveness and poor clinical outcome may be explained by the mechanisms observed at the molecular level. Previous studies indicated a possible molecular interaction between BRAF V600E and TERTp mutations via BRAF V600E-dependent constitutive activation of MAPK pathway leading to activation of ETS transcription factors and their binding to binding motifs, created de novo by the hotspot mutations in TERTp, what in consequence would enhance TERT activity. However, Liu et al. study from 2018 showed the exact role of BRAF and TERTp mutations in the expression of TERT through the BRAF V600E-> MAPK pathway->FOS-> GABP-> TERT axis [BRAF V600E/MAPK pathway also promoted TERT expression via MYC in TERTp mutation-independent manner, but in a much less degree.We analyzed whether i et al.  who descn et al. , TERTp m disease ,31. The ERT axis . MoreoveTERTp hotspot mutations were found in more aggressive histopathological PTC variants, like tall cell variant [BRAF V600E mutation and TERTp hotspot alterations. According to the previous studies,  variant . Our PTCTERTp mutations and more aggressive PTC subtypes, our data did show a strong effect of coexistence of TERTp hotspot mutations and the BRAF V600E mutation on the development of PTCs with histopathological factors related to poorer outcome. In order to determine the independent effects of TERTp mutations on PTC, an analysis of a larger cohort is needed. There is a single study in which only TERTp mutations, but not the BRAF V600E mutation, were associated with poorer histopathological factors [TERTp mutations alone and PTCs with double mutations (TERTp and BRAF mutations) was only four in each group. Thus, the data from such limited sample size should be interpreted with caution. The major limitation of the current study was a small number of cases with more aggressive clinical course of the disease, including no PTC cases with distant metastases. However, while we were not able to identify a possible relation between  factors . HoweverBRAF V600E and TERTp mutations in TCs is not a routine diagnostic procedure. These alterations are mainly analyzed in fine needle aspiration biopsy material in order to support the cytological examination. According to the ATA recommendations [TERTp mutations were shown to be a helpful tool in PTC risk stratification, however, no standardized therapeutic strategies, based on these two molecular markers, were established.So far, the detection of ndations  moleculaBRAF 600 codon. The study cohort included 166 women and 23 men with the median age at the time of PTC diagnosis of 53 years (range 12 to 83 years). The clinical data are summarized in TERTp mutations. The use of human tissue was approved by the institutional Bioethics Committee. Informed written consent was obtained for the use of their tissue for analysis from all patients or their caregivers. All the clinical data were anonymized and de-identified prior to the analysis.The analyzed group of specimens were collected from 189 consecutive Polish patients with PTC treated at the Maria Sklodowska-Curie Institute—Oncology Center, Gliwice Branch in Gliwice, Poland. Among them, 80  of patients were diagnosed with papillary microcarcinoma. The selection of the patients was based on the known mutation status in Total DNA was extracted from 10 μm-thick sections of formalin-fixed and paraffin-embedded (FFPE) tumor specimen tissues (5 sections per sample). The DNA isolation was performed using a QIAamp DNA FFPE Tissue Kit  according to the manufacturer’s protocol after deparaffinization using single xylene extraction and rinsing with ethanol 98%. DNA concentrations were evaluated using a Nanodrop ND-1000 microspectrophotometer .TERTp mutations was performed by direct sequencing using the Sanger method. Approximately 30 ng of genomic DNA was used as template for polymerase chain reaction (PCR) amplification of 157-bp length fragment of TERTp containing the analyzed hotspot sites (−124 and −146 upstream of the ATG start codon). Primers used in the PCR were F-5′-CCCCTTCACCTTCCAGCTC-3′ and R-5′-CAGCGCTGCCTGAAACTC-3′. The reaction was carried out with an initial polymerase activation step at 95 °C for 15 min, followed by 34 consecutive cycles of denaturation at 95 °C for 30 s, primers annealing at 59.7 °C for 30 s, extension at 72 °C for 30 s, and final elongation at 72 °C for 5 min. The PCR product was analyzed for quality by gel electrophoresis and then sequenced on a 3130xl Genetic Analyzer Applied Biosystems . Briefly, PCR products were enzyme purified with alkaline phosphatase (SAP) and exonuclease I (Life Technologies) and then subjected to standard sequencing using an ABI PRISM™ 1.1 BigDye Terminator Cycle Sequencing Ready Reaction Kit (Life Technologies).Detection of BRAF 600 codon status was determined using the following methodology. A fragment of exon 15 of BRAF containing codon 600 was amplified by PCR using primers F-5′-TGTTTTCCTTTACTTACTACACCTCA-3′ and R-5′-GCCTCAATTCTTACCATCCA-3′ resulting in amplification of a 160-bp product. The PCR cycling conditions included an initial polymerase activation step at 95 °C for 15 min; 34 cycles of denaturation at 95 °C for 30 s, primer annealing at 56.5 °C for 30 s, extension at 72 °C for 30 s; and the final elongation step at 72 °C for 5 min. After quality confirmation by gel electrophoresis, the PCR products were sequenced as described for detection of the TERTp mutations.Information regarding the The association analysis was performed on categorical data that were summarized as numbers and percentages. Continuous data were summarized as medians and interquartile ranges.p-values were two sided, and a p-value of 0.05 was considered as statistically significant.In order to test, whether there were significant differences between analyzed groups of tumors, the Fisher’s exact test and the Mann–Whitney U test were applied. The Fisher’s exact test was used for comparisons in case of categorical variables, whereas the Mann–Whitney U test was used in case of continuous variables. All Statistical analyses were performed using the R software version 3.4.1 and Gmisc package version 1.4.1 ,35.TERTp mutations are the key to PTC aggressiveness and dedifferentiation. However, more and more recently published data, including our current study, suggest their association with a poor prognosis. Despite the small number of analyzed PTC samples the frequency of TERTp hotspot mutations as well as the association between the BRAF V600E mutation and some PTC histopathological factors, observed in this preliminary study, were in concordance with the results of previous reports. Moreover, we identified three novel TERTp alterations, which may play a role in PTC aggressiveness. Further functional studies are needed to determine their impact. Although relatively rare, it is clear that TERTp mutations in PTCs are an important molecular event and may influence risk stratification and the clinical management.Although most PTCs represent relatively indolent tumors, some cases develop distant metastases or recurrent disease, or may dedifferentiate into PDTCs or ATCs. There is no definitive answer regarding whether


Salmonella enterica is one of the most prevalent foodborne pathogens. The large quantity of serovar types results in the colonization of a large spectrum of hosts, with different environmental conditions and hazards. The aim of this study was to evaluate the differences in gene expression (bcsA and csgD) of Salmonella enterica serovars Heidelberg, Kentucky, and Enteritidis during biofilm formation using quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR). Overall, there appeared to be differences in expression between the different serovars with high variation between strains. These data are important as they demonstrate considerable variability in gene expression between serovars and strains of poultry isolates of Salmonella enterica. Salmonella enterica is one of the most commonly encountered bacterial foodborne pathogens, with over 2500 serovars currently documented with several associated with produce, meat, and poultry products [Salmonella serovars Enteritidis, Heidelberg, and Kentucky are among the five most identified serovars from poultry and poultry products [Salmonella Enteritidis is considered the most frequently associated with the consumption of undercooked poultry and egg products [Salmonella Heidelberg is also associated with eggs. However, it is more typically identified with outbreaks of contaminated poultry meat [Salmonella Kentucky is commonly found, it is uncommonly associated with foodborne disease [Salmonella Enteritidis, versus host-restricted serovars that cause significant pathology in closely related host species [Salmonella Kentucky is a rare foodborne disease, the ability to survive in the environment and persist is intriguing.Non-typhoidal products ,4,5,6. Sproducts ,8. Salmoproducts ,10. Salmtry meat ,12,13,14 disease . Therefo species ,16,17,18Salmonella expresses a variety of survival mechanisms to the environment, one of which is the ability to form biofilms. Biofilms enable Salmonella to resist antimicrobials and thrive in a variety of habitats [Salmonella can colonize a food processing area and form a biofilm. Any Salmonella strains allowed to persist may form stronger biofilms in the future and become more resistant to removal than freshly introduced strains [Outside of the host, habitats . Biofilmhabitats . Because strains . This prSalmonella, the principle polysaccharide structural component of the EPS matrices is cellulose, chiefly regulated by the bcs  operon, without which, Salmonella is unable to form strong biofilms [csg operons (curli specific gene) which interact with cellulose to start the formation of biofilms and enhance the sequential survival [Salmonella and are critical in the ability to form a biofilm.Comprising up to 90% of the total matter of a biofilm, EPS plays a pivotal role in functions related to structural support, nutrient transport, and protection . In Salmbiofilms . The othsurvival ,25. ThesSalmonella biofilm structural genes across several serovars. Salmonella Enteritidis and Heidelberg are important foodborne pathogens, whereas Kentucky is pervasive yet mostly benign and serves as an interesting juxtaposition. Expression levels of the bcsA and csgD genes were observed in strains of Salmonella serovars Enteritidis, Heidelberg, and Kentucky over the course of the development of bacterial pellicles across a 96-h time period. The authors hypothesized that the different serovars would exhibit contrasting expression levels of the two genes as assessed using quantitative reverse-transcriptase polymerase chain reaction assays (qRT-PCR).The objective of the current study was to examine the differences in the expression of critical Salmonella consisting of three unique strains from the University of Arkansas Center for Food Safety Culture Collection were used in this study. This included three strains of S. Kentucky , three strains of S. Enteritidis , and three strains of S. Heidelberg . Quadrant streaks of the isolates from frozen stocks were prepared on Luria–Bertani (LB)  agar plates and incubated for 24 h at 37 °C. After incubation, single colonies were selected and grown in 5 mL of LB broth overnight in a 37 °C shaking incubator for 18 h. Overnight cultures of each of the Salmonella strains were diluted 1:10 and inoculated into 125 mL flasks containing 50 mL of LB broth without salt (10 g tryptone and 5 g yeast extract per L) . Flasks were placed at room temperature for 96 h with the planktonic cultures developing pellicles by the end of this period. At the 0 h, 24 h, 48 h, 72 h, and 96 h time points, 1 mL of culture was collected directly below the meniscus and total RNA was extracted using a Qiagen RNeasy kit . RNA was stored at −80 °C until qRT-PCR was performed. Two independent trials were performed for this study.Three serovars of TM Mastercycler Epigradient Thermocycler . To remove any DNA remaining after RNA extraction, the RNA samples were treated with DNase I  before each assay. Primer pairs for the csgD, bcsA, and the rRNA housekeeping gene rsmC were synthesized by Integrated DNA Technologies  and confirmed using the National Center for Biotechnology Information (NCBI) BLAST. Primer sets yielded amplicons that were 156, 136, and 190 bp for the putative bcsA, csgD, and rsmC genes. A mastermix was prepared to ensure that each 25 µL reaction contained the following: 12.5 µL of 2× 1-Step qPCR SYBR Mix , 1.25 µL RT Enhancer, 0.25 µL Verso Enzyme mix, 500 nM of each forward and reverse primer, 100 ng of total RNA template, and nuclease-free water . The qRT-PCR conditions consisted of a 5-minute cDNA synthesis step 50 °C step followed by a 15 m Hot Start at 95 °C. Then, 40 cycles consisting of 15 s for denaturation at 95 °C, 15 s for primer annealing at 55 °C, and 20 s for amplicon extension at 68 °C was performed with melt curves. The melt curves were produced by cycling form of 95 °C for 15 s and then by 60 °C for 20 m with a 0.5 °C increase in temperature per minute until a final temperature of 95 °C was reached. Each assay was performed in triplicate.The qRT-PCR assays were performed using the Verso 1-Step RT-qPCR Kit  and optimized using an Eppendorf RealPlexSalmonella serovars, we observed the RNA transcript levels of the pellicle structural genes csgD and bcsA as determined by qRT-PCR. The CT value was taken from 40, which is the maximum number of cycles, to give the relative abundance of CT (nCT). Therefore, an increase in CT means there is an increase in gene expression, a decrease in CT means there is a decrease in gene expression. The differences in CT values were compared with values exhibited by the rRNA housekeeping gene rsmC and analyzed using the JMP® 14.0  software suite. Data were analyzed using n-way ANOVA. Means were separated using Tukey’s protected HSD with a significance level of p ≤ 0.05. The SEM, or standard error of measurement, was included as it represents the variation of the data across the average.In order to study the differences in gene expression among the three sgD expression throughout the study was demonstrated, with the peak of gene expression at day 3. , Enteritidis  and Kentucky  over time were evaluated. The interaction between strain and serovar was statistically significant, with a demonstrated variation between strains within serovar , an allosteric activator of cellulose synthase encoded by the bcs operon [csgD not only produces curli proteins, but activates the bcsA operon. Because of the critical role these two genes provide in Salmonella biofilm formation, they were chosen for this study. Because the temporal activation of this machinery seemed to be different between serovars, as evidence from our study indicates, there may be serovar-specific mechanisms of biofilm formation. Whether this means that biofilms form sooner or later or that the machinery that drives the formation of the biofilm is executed differently, this could explain differences in recoverable Salmonella from biofilms in the poultry industry. The potential difference between serovars may also be exploitable. More evidence is needed to determine the full impact of our findings, including transcriptomic and proteomic studies evaluating biofilm formation differences between serovars. For instance, if Kentucky somehow does not produce biofilms as rapidly, maybe it is cleared better in specific environmental or physiological conditions or it is more susceptible to antimicrobials.Extracellular biofilm formation in ructures . Togetheructures . The traof curli . In addis operon . TherefocsgD expression would lead to increases in cellulose production and require the expression of the bcsA gene. In our current preliminary experiment, we found that each serovar exhibited a unique pattern of gene expression across time, with csgD a being much more volatile than bcsA. Furthermore, the changes of expression varied considerably over the days between all of the individual strains within serovar. Considering the ratio of csgD to bcsA, there were also minor differences between each serovar. Despite differences in the magnitude and direction of gene expression between different strains of Salmonella, the ratios were not significantly different in this pilot study. Additional data are required, including more strains, more replications, and additional gene expression analyses. By expanding on this study, we may potentially find differences between serovars that can serve as targets in the future for the development of new antimicrobials. Additionally, if one serovar expresses cellulose greater than the other, that particular gene cassette may be used for the production of cellulose.As such, it would be expected that increases in Our study found there to be differences in the expression of biofilm-forming genes between serovars and large variations in gene expression between strains within each serovar. Future assessments should include a broader variety of serovars and additional replications to validate the qPCR data found in this paper. The replication and expansion of this data set must include additional common poultry isolates that are important to human health. However, the data presented herein are promising because if proven true, could potentially lead to novel understandings of individual serovar gene expression differences in biofilm formation.


Wood vinegar can be produced from many types of raw materials using different pyrolysis methods resulting in potentially different antioxidant activity. Therefore, this study developed a rapid method to predict the antioxidant activity of wood vinegar based on color using the CIELAB system and spectrophotometric parameters. The 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity and ferric reducing antioxidant power (FRAP) exhibited significant correlations with the L* and b* values of the color parameters and the UV absorbance polyphenol index (I280), European Brewing Convention (EBC) and Institute of Brewing (IOB) color units, color intensity and Linner Hue index of the spectrophotometric parameters. As a result, DPPH radical scavenging activity and FRAP could be predicted by measuring color and spectrophotometric parameters. Overall, this study provide a simple, rapid and no chemical reaction involved method to predict antioxidant activity. Furthermore, models with a set of spectrophotometric parameters could be used to predict antioxidant activities.•Whole new method for predicting wood vinegar antioxidant activity was developed.•The method was models developed by using color and spectrophotometric parameters used in beverage industry.•The models were simple, rapid and involved no chemical reaction. Specification TableWood vinegar or pyroligneous acid is one of the major by-products of charcoal production. It contains several types of compounds, including alcohols, aldehydes, esters, furans, ketones, organic acids, phenols and pyrans . A numbeThe color of wood vinegar ranges from reddish- or yellowish-brown to deep dark brown. Its color range is comparable to some types of beer, caramel, tea and wine and colorimetric and spectrophotometric properties have been used for a long time to determine and classify these beverage products. In 1883, a series of 52 color discs was proposed by Lovibond as a visual method for measuring the color in beer. Later, in 1950, the method was revised by Bishop . AfterwaTo our knowledge, there has been no reported investigation to date on the estimation of antioxidant activity using the color and spectrophotometric parameters of wood vinegar. The objective of this study was to develop a rapid method to predict antioxidant activity using both the color based on the CIELAB system and the spectrophotometric parameters of wood vinegar.The 88 wood vinegar samples analyzed in this study were collected in 2017 and 2018 from several provinces in Thailand including Bangkok, Nakhon Ratchasima, Ratchaburi, Tak, Trad and Ubon Ratchathani.65, 10° observation and a 10 mm path length cell. L* defines lightness, where a low L* value (minimum 0) indicates dark and a high L* value (maximum 100) indicates light. Red/green and yellow/blue colors are denoted by a* and b* values, respectively. An increase in the a* value depicts a shift toward red while a decrease in a* value depicts a shift toward green. Similarly increasing and decreasing the b* value relates to increasing yellowness and blueness, respectively.The colors of the wood vinegar samples were determined in terms of the CIELAB parameters  using a colorimeter  with illuminant DAppropriately diluted wood vinegar which resulted in range of 0.1–1.0 absorbance units at wavelength of 280, 420, 430, 510, 520, 530, 610 and 620 nm was measured by a UV–vis spectrophotometer with a 10 mm path length cell .According to the EBC Recommended Method, absorbance at 430 nm was measured and multiplied by 25 and a dilution factor, to give a color value in terms of the EBC color units .IOB color units was determined from the absorbance at 530 nm multiplied by 1,000 and a dilution factor .According to the wine color intensity method, the color intensity (CI) was calculated as the sum of absorbance multiplied with dilution factor at 420 nm, 520 nm and 620 nm .L) of wood vinegar was determined as 10log(A510/A610) where A510 and A610 are the absorbance multiplied with dilution factor at 510 and 610 nm, respectively [In consonance with caramel color tone measuring methods, the Linner Hue index (Hectively .The UV absorbance polyphenol index (I280) was calculated from the absorbance at 280 nm multiplied by a dilution factor .DPPH radical scavenging activity was determined as described previously . A aliqus-triazine, 1 part of 20 mmol/L ferric chloride and 10 parts of 300 mmol/L sodium acetate buffer (pH 3.6). Then, 100 μL of each appropriately diluted sample was added to 3 mL of FRAP reagent. The absorbance at 593 nm was recorded after incubation at 37 °C for 4 min. Results were calculated in terms of milligrams of trolox equivalent (TE)/L.The ferric reducing antioxidant power of samples was measured using the method described by Benzie and Strain  with sliAll samples were analyzed in triplicate. Data were presented as mean ± standard deviation (SD). Correlation analysis among color, spectrophotometric and antioxidant activity was performed based on Pearson's correlation test. Linear regression analysis using the enter method was performed to obtain equations for estimating antioxidant activity. A p-value less than 0.05 was considered significant. All statistical tests were carried out using SPSS for Windows.The color, spectrophotometric and antioxidant properties of 88 wood vinegar samples are shown in Significant correlations were observed between the DPPH radical scavenging activity and all parameters, though a* showed a weak correlation. Beside a*, the FRAP was also significantly correlated with all parameters. Increasing values of I280, EBC, IOB and CI exhibited higher DPPH radical scavenging activity and FRAP. On the other hand, higher values of L*, b* and HL resulted in lower DPPH radical scavenging activity and FRAP as shown in 2) and root mean square error (RMSE) for the predictions.The regression equations for prediction of the antioxidant from wood vinegar are presented in 2 values for the three-color parameter regression equation for DPPH radical scavenging activity and FRAP were 0.570 and 0.760, respectively. The R2 values of the equations with the five spectrophotometric parameters were higher than for the equations with three color parameters. In spite of that, the predictive performance was better when using a combination of all eight of the color and spectrophotometric parameters. However, the power of prediction for FRAP was slightly lower than for DPPH radical scavenging activity.The RThe predictions of antioxidant activity were compared with the measurement . Solid, Therefore, the proposed predicting models for DPPH radical scavenging activity and FRAP are Eqs. This study also proposed the alternative predicting models based on only a set of spectrophotometric parameters as Eqs. This study showed that lightness, yellowness, UV absorbance polyphenol index, EBC and IOB color units and the Linner Hue index were significantly correlated with the DPPH radical scavenging activity and FRAP of wood vinegar. Wood vinegar that was darker and less yellow was likely to have higher antioxidant activity. Furthermore, the study showed that wood vinegar antioxidant activity could be predicted by measuring the color and spectrophotometric parameters used in the beverages industry. The developed models of prediction from the study were inexpensive, simple and rapid and involved no chemical reaction. It is also environmentally friendly method and minimum waste generation. Despite not being as good as models involving both sets of parameters, the model based on the spectrophotometric parameters alone could be used for estimating antioxidant activity as well.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


IGF1R; the immune genes IL1R1, IL1RL1, DUSP10, RAC3 and SWAP70; the meat quality-related gene SNORA50 and the olfactory gene OR1F1 were identified as candidate differentiated targets. Further, along a principal component separating Pudong White pigs from others, a potential causal gene for coat colour (EDNRB) was discovered. In addition, the divergent signatures evaluated by Fst within Chinese pig breeds found genes associated with the phenotypic features of coat colour, meat quality and feed efficiency among these indigenous pigs. Second, admixture and genomic introgression analysis were performed. Shan pigs have introgressed genes from Berkshire, Yorkshire and Hongdenglong pigs. The results of introgression mapping showed that this introgression conferred adaption to the local environment and coat colour of Chinese pigs and the superior productivity of western pigs.The differences in artificial and natural selection have been some of the factors contributing to phenotypic diversity between Chinese and western pigs. Here, 830 individuals from western and Chinese pig breeds were genotyped using the reduced-representation genotyping method. First, we identified the selection signatures for different pig breeds. By comparing Chinese pigs and western pigs along the first principal component, the growth gene  Since then, various pig breeds have been subjected to different forces of natural and artificial selection, which have been some of the contributory factors to the distinct phenotypes of different pig breeds5. Chinese pig breeds are famous for high prolificacy6, good adaptability to local environment7, high resistance to disease10 and desirable meat quality12. However, there is still variation in these characteristics among different Chinese pig breeds. Compared with Chinese local breeds, European pig breeds are renowned for their fast growth rate13, high feed efficiency14 and superior meat yield15.Pigs were independently domesticated in Europe and China approximately 9000 years agoet al.16 discovered ADAMTS12, SIM1 and NOS1 as candidate genes contributing to high-altitude adaption and EDNRB as a gene affecting coat colour16. Based on a comparison between Chinese and European pigs, Yang et al.17 found that the JAK2 gene was associated with immune response in Chinese pigs and that the IGF1R gene was associated with growth in European pigs17.Therefore, it is possible to use selection signature detection methods to elucidate the genetic background of the distinct phenotypes of pig breeds, which were influenced by different selection pressures. Moreover, the availability of genomic data facilitates the identification of the genomic regions affecting the specific characteristics among different pig breeds. For instance, by comparing the genomes of Tibetan pigs with low-land pigs, belted and non-belted pigs, Ai 18 aiming to improve the productivity of local breeds. A study has shown that Asian pig haplotypes have been introgressed into European pig breeds to improve traits of commercial interest19. For example, reproduction20 and carcass and meat quality traits21 in European pigs have been improved by the introduction of Asian haplotypes. Conversely, European haplotypes might also have been introgressed into Chinese pig breeds. To obtain the superior characteristics of western pigs, human-mediated hybridization and introgression were performed to improve the productivity and environmental adaptability of Chinese pig breeds. This is similar to the formation of the hybrid nature of Chinese Sutai pigs cultivated from Chinese Erhualian and Duroc pigs22. However, except the hybrid breed, Sutai, introgression from western pigs into other Asian pigs has not been reported so far.In addition, there has been gene flow from Chinese pigs to European pig breeds since the nineteenth centuryIn this study, we collected samples from western pig breeds and different Chinese pig breeds with distinct phenotypic characteristics in the Yangtze River Delta (YRD) area in China. By comparing the genomes between Chinese and western pig breeds and among Chinese pig breeds, we were able to identify genes associated with the distinct phenotypes in these pig breeds. Because of the history and location of the YRD, it plays an important role in international communication, as well as in gene flow. Some Chinese pig breeds in this region might have gene flow from western breeds.Therefore, the objective of the study is to (1) identify genes associated with the distinct phenotypic characteristics between western and Chinese pig breeds and within different Chinese pig breeds and (2) characterize the introgression from western into Chinese pig breeds.All experimental procedures were approved by the Institutional Animal Care and Use Committee of Shanghai Jiao Tong University, and all methods involving pigs were in accordance with the agreement of the Institutional Animal Care and Use Committee of Shanghai Jiao Tong University (contract no. 2011-0033).26, except for that from the Pietrain and Berkshire populations.A total of 830 pigs were used in this study, including 156 western pigs, i.e., Duroc (D), Landrace (L), Yorkshire (Y), Berkshire (B) and Pietrain (P) pig breeds, and 674 Chinese indigenous pigs within the YRD region . Figure 27. In the process of GGRS, genomic DNA was extracted from ear tissue using a commercial kit . After the DNA samples were digested with AvaII enzyme and ligated with a unique adapter barcode, the samples were pooled and enriched to construct a sequencing library. DNA libraries (fragment lengths ranging from 300 to 400 bp) were sequenced using an Illumina HiSeq4000 platform according to the manufacturer’s protocol. Quality control of sequences was performed using NGS QC Toolkit28 v2.3, and the parameters were set according to the report from Chen et al.27. Sequencing reads were aligned to the Sscrofa10.2 pig reference genome using BWA29.The individuals of the Pietrain and Berkshire populations were genotyped according to the protocol of GGRS (genotyping by genome reducing and sequencing)30 v0.1.9 (set 1) and GATK UnifiedGenotyper31 with “hard filters” (QD > 20.0 && FS < 60.0 && MQRankSum > −12.5 && ReadPosRankSum > −8.0) by the VariantFiltration tool (set 2) simultaneously. The SNPs found in both set 1 and set 2 were retained for further steps. Beagle32 v4.1 was utilized to impute the missing genotypes in the present study with default parameters. After imputation, SNPs were filtered out if their minor allele frequencies (MAFs) were less than 0.05. Non-autosomal SNPs were also discarded because the demographic patterns of sex chromosomes are different, which may cause distortion in the subsequent analysis33. PLINK34 v1.07 was used to filter the SNPs with extreme deviations  from Hardy-Weinberg equilibrium proportions for each population, and the union set of SNPs that failed to pass the test within at least one population were also discarded. In total, 129,882 high-confidence SNPs were retained for further analysis. Generally, these SNPs were roughly distributed uniformly across the genome, which can represent the information of the whole genome  by singular value decomposition. (2) A total of 41,118 SNPs, which discarded ones that were in LD (linkage disequilibrium) larger than 0.5 across these populations (command: PLINK–indep 50 5 2), were kept for population structure analysis using ADMIXTURE36 v1.3.0. The number of ancestral clusters (K) was set from 2 to 40, and five-fold cross-validation was run to determine the K value with the lowest cross-validation error. The result was shown by DISTRUCT37 v1.1. (3) The allelic richness was calculated by ADZE38 v1.0, which can correct for unequal sample size using a rarefaction procedure41.To illustrate the population structure and evaluate the genetic diversity within and between these populations, the following steps were performed: (1) principal component analysis (PCA) was conducted using SMARTPCA integrated in EIGENSOFTNe) was estimated based on the SNP data used in admixture analysis by the software SNeP42 v1.1, which can estimate Ne at different t generations based on LD between SNPs with the distance of c, where c is the distance measured in Morgan43 (assuming 100 Mb = 1Morgan). Some options were also used for SNeP software: (1) sample size correction for unphased genotypes; (2) correction to account for mutation; (3) Sved & Feldman’s recombination rate modifier44.The historical effective population size  showed, the Pudong White pig population (PD) was obviously separated from the other populations on this axis. Given that PD is a unique Chinese indigenous pig breed covered by a wholly white coat, PC3 was also considered as a phenotype for EigenGWAS analysis. This method corrected for genetic drift by using a genomic inflation factor46. After this correction, the p-values of SNPs were further corrected by Bonferroni correction, and the cut-off was 0.05/129882. Moreover, in order to validate whether the EigenGWAS analysis with PC1 as the phenotype could identify differentiation between Chinese and western pigs, Weir and Cockerham’s Fst47 between them was calculated using VCFtools48. Finally, the Spearman’s rank correlation coefficient between Weir and Cockerham’s Fst and the negative logarithm of p-values of the EigenGWAS with PC1 as the phenotype was calculated in R49.Inspired by the PCA result that the first principal component (PC1) clearly separated the Chinese and western pigs, a method called EigenGWASFst outlier approach implemented in the R package OutFLANK50 was used to find significant differentiation loci among these pig breeds. Firstly, the near-independent SNPs were identified using R package bigsnpr51 according to the tutorial of OutFLANK. Based on this near-independent SNP set, OutFLANK fit a chi-square distribution to the core distribution of Fst  to estimate the mean and degree of freedom, so that this core distribution would not be affected by strong balancing and diversifying selection. Then the p-values of all the SNPs with heterozygosity greater than 0.1 were calculated based on the core distribution of Fst. OutFLANK adjusted multiple p-values to q-values52, and the threshold of 0.01 was used53.To identify highly differentiated genomic regions among Chinese indigenous pig breeds, an 54 software was used to perform the three-population (f3) test55. In the f3 test with the form of f3 , an extreme negative f3 statistic indicates that significant gene flow to population A from populations B and C exists. All 24 populations were included in the f3 test, and this would generate 56 implemented in TreeMix with a window of 200 SNPs that excluded the dependence between different windows was used to calculate the standard deviation of the test. Then, the Z scores were calculated, and combinations that had Z scores less than −2 were regarded as significant.To investigate the statistical significance of admixture among these pig populations, TreeMixf3 test only showed that the Shan pig (SZ) was significantly admixed by Hongdenglong (HD), B and Y. PCAdmix57 v1.0 was used to identify probable significant admixed fragments due to genomic introgression from the three ancestral populations into SZ. Based on PCA, PCAdmix can infer the ancestry of admixed genomes from ancestral individuals using a sliding window along the genome. Then, the posterior probability of ancestry affiliation for each window can be determined by a hidden Markov model. PCAdmix requires phased genotypes, therefore the genotype data used in the ADMIXTURE analysis was phased by fastPHASE58 v1.2 with default parameters. According to Barbato et al.59, the window size was set to be a fixed value of 5 SNPs due to no available linkage map and a low density of markers59. PCAdmix inferred the posterior probability (PP) of ancestry from the HD, B and Y populations for each individual haploid genome of SZ for each window. Then, the PP for each reference population was added up across all the haploid genomes of SZ to calculate the scores of affiliation of different ancestry for each window. The windows with the top 1% of scores for each ancestry affiliation were selected to be candidate genomic introgression regions, and genes within these regions were extracted using the biomaRt60 package.The results of the 61 was used to identify candidate SusScr3 Ensembl genes near the significant SNPs based on EigenGWAS and Fst (within 150 kb). Functional gene set enrichment analysis was then performed for these gene sets and candidate genes within the top genomic introgression regions from the PCAdmix analysis. The R package org.Ss.eg.db was used to annotate the pig genes. Enriched Gene Ontology62 (GO) terms were then identified using the R package GOstats63. Then, the p-value of each GO term was calculated by GOstats using a hypergeometric test. The results with a p-value ≤ 0.05 were reported to identify potential biological processes influenced by these genes.ANNOVARAn overview of the relationships among these populations is presented in Fig. 22 which exhibited approximately 20% Chinese admixture in most European breeds (with K = 2) using 60 K porcine SNP array, no such much admixture could be observed in this study. This might be due to ascertainment bias resulting from the development of the SNP array mainly based on the polymorphisms distributed in western pig breeds, which would overestimate the shared ancestry between Chinese and western pig breeds. When K was 25, all of the populations roughly had their own ancestry, except for Small Meishan (SMS), Chunan (CA) and SZ. SMS and CA contained new ancestry, which might not be part of the populations included in this study. More pig populations are needed to identify the distinct genetic components not shared with other breeds in the study. Compared with Chinese pigs, the extents of admixture within western pig populations across different K values were low and stable.The estimated K for the ADMIXTURE analysis with the lowest cross-validation error was 25, nearly the same as the number of actual populations in this study. When two ancestors were assumed, Chinese indigenous pigs and western pigs were clearly distinguished Fig. , but somNe of each pig breed across the generations is shown in Supplementary Fig. Ne was reflected by LD over shorter recombination distances, and the longer distances provided recent Ne43. From 900 generations to approximately 50 generations ago, all the breeds exhibited a decrease in Ne estimates over time. However, between 900 and 1000 generations ago, there were some obvious inflection points in several lines. The nearest anti-climax point indicated the nearest starting point of artificial selection, which caused the bottleneck in the population64. In general, the Ne of western pig breeds was smaller than that of Chinese pig populations, which was due to the higher LD of western pig breeds65. Admixture is a potential confounding factor for the estimation of Ne that can cause bias66. Therefore, among Chinese indigenous pigs, the populations with a low extent of admixture tend to have a smaller Ne67. The Ne estimates did not get stable even in recent generations. This sort of trend could also be found in another study for Landrace and Yorkshire pigs68. For western pigs, the reason of this phenomenon might be the ongoing strong selection on production traits5, whereas for Chinese local pig breeds, it might be due to the inbreeding caused by small population size69.The estimation of Fst and the negative logarithm of p-values of the EigenGWAS with PC1 as the phenotype was 0.969. Some studies have suggested that individual-level eigenvectors are measures of population differentiation reflecting Fst among subpopulations71. Therefore, the high value of this correlation coefficient validated that the EigenGWAS with PC1 as the phenotype could reflect the differentiation between Chinese and western pigs. In this study, it is difficult to determine whether these differentiated signals were formed in the pre-domestication or during the divergent post-domestication selection. However, both of these different kinds of significant signals could help explain the genetic background of distinct phenotypes between Chinese and western pigs. In addition, the genomic inflation factors were 100.81 and 17.75 for the EigenGWAS with PC1 and PC3 as the phenotype, respectively. According to the original paper on EigenGWAS45, the genomic inflation factors are highly correlated to eigenvalues, which were 107.58 and 24.26 for PC1 and PC3, respectively. A large eigenvalue indicates underlying population structure. Therefore, correction for genomic inflation factors will filter out signals due to population stratification, allowing loci under selection to be identified45.To find loci that were related to the pattern of PC1, which separated the western and Chinese pigs, EigenGWAS with PC1 as the phenotype was performed. Further, this association study was also performed to explain why PD was separated from the other populations on PC3. The Manhattan plots for PC1 and PC3 are shown in Fig. IGF1R gene. This gene plays an important role in pig production traits, such as post-natal growth72 and carcass and meat content73. In addition, a previous study verified that different alleles of the gene IGF1R are highly correlated with pig performance based on litter size74, indicating that pleiotropy of the IGF1R gene can be a potential explanation of the genetic relationship between traits of production and reproduction75. Therefore, the differentiated variants in the IGF1R gene may be considered a potential quantitative trait locus (QTL), which can account for the phenotypic differences of growth and reproduction between Chinese and western pig breeds. Besides, Chinese pigs are well adapted to their local environments7 and are known for their desirable meat quality12. These phenotypic characteristics could be explained by genes near significant signals on different chromosomes ” and “pigment cell differentiation (GO:0050931)”), some behaviour- and cognition-related processes (“adult locomotory behavior (GO:0008344)”, “learning (GO:0007612)” and “cognition (GO:0050890)”) and nervous system-related functions (“nervous system process (GO:0050877)”, “neuron apoptotic process (GO:0051402)” and “neuron death (GO:0043524)”)  and , respectively. This result is consistent with the admixture of SZ ” and “regulation of ossification (GO:0030278)”). Several of the top GO processes enriched by genes introgressed from B (Supplementary Table ASIP was found to be the causal gene. This gene was identified to be related to human pigmentation diversity92. Given these results, it can be summarized that the introgression regions from western pigs were mainly related to growth and development. On the other hand, the major introgression regions from Chinese pig breeds were related to immunity and pigmentation. Breeders in China preferred the high adaptability and black coat colour of Chinese pigs, while western pigs were chosen for their high productivity. Given the hypothesis that these introgressions were deliberately human mediated, the breeding goal of making good use of characteristics of Chinese and western pigs helped explain these results. Moreover, these results contributed to explaining the genetic basis of the phenotypic distinctions between Chinese and western pigs and within different Chinese pigs.There were only two extreme Z scores for the  SZ Fig. , the topEDNRB was identified as a candidate gene for the white colour of the PD pig breed. The significant divergent genetic signals among these Chinese pig populations were related to various economically important traits. Based on admixture and genomic introgression analysis, we observed that there was introgression from western pigs and other Chinese pigs into SZ pigs. The mapping of the introgression also helped to elucidate the genetic basis of phenotypic features, namely, that western pigs are good at production traits, while Chinese pigs do well in adaption to their environments.In this study, the genetic basis of phenotypic differences between Chinese and western pig breeds was studied from the viewpoint of selection signal detection. Numerous genes related to growth, immunity, reproduction and meat quality were identified as candidate differentiated genes, which might contribute to the distinct phenotypes of western and Chinese pigs. In addition, the coat colour-related gene Supplementary InformationTable S1Table S2Table S3Table S4Table S5Table S6Table S7Table S8Table S9Table S10


P values less than 0.05 and FDR less than 0.1. Functional annotation of eQTL analysis results showed that the neuroactive ligand receptor interaction pathway and gated channel activity were involved in lung cancer risk. These pathways provide important insights for the etiology of lung cancer.Genome-wide association studies (GWAS) identified the chromosome 15q25.1 locus as a leading susceptibility region for lung cancer. However, the pathogenic pathways, through which susceptibility SNPs within chromosome 15q25.1 affects lung cancer risk, have not been explored. We analyzed three cohorts with GWAS data consisting 42,901 individuals and lung expression quantitative trait loci (eQTL) data on 409 individuals to identify and validate the underlying pathways and to investigate the combined effect of genes from the identified susceptibility pathways. The KEGG neuroactive ligand receptor interaction pathway, two Reactome pathways, and 22 Gene Ontology terms were identified and replicated to be significantly associated with lung cancer risk, with  The chromosome 15q25.1 locus is a leading susceptibility region for lung cancer. Here, the authors interrogate three GWAS cohorts with 42,901 individuals to investigate potential pathological pathways such as gated channel activity and neuroactive ligand receptor interaction in lung cancer etiology. Several genome-wide association studies (GWAS) have been published and identified the chromosome 15q25.1 locus as a susceptibility region for lung cancer4, smoking behavior6, and nicotine addiction4 in Caucasians2, African-Americans7, and Asians8. Epigenetic analyses provided evidence that epigenetic silencing of nAChR-encoding genes clustered at the 15q25.1 locus may contribute to lung cancer risk9. In addition, expression quantitative trait loci (eQTL) studies showed an influence of alleles in this region on the expression of several genes at chromosome 15q25.1, providing a mechanism by which these variations might affect lung cancer risk10. Our previous GWA studies found that variants in chromosome 15q25.1, including single-nucleotide polymorphisms (SNPs) and haplotypes, are involved in the etiology of overall lung cancer susceptibility and by histology and smoking status11. However, lung cancer, being a disease of complex origin, is usually considered to result from complex effects of smoking along with multiple genetic variants affecting a number of pathways or biological process. Common SNPs are not individually known to add greatly to individual risk, unless more complex gene–gene interactions play a crucial role in the genetic architecture of pathogenesis of complex disorders12, such as lung cancer. The pathogenic pathways, through which lung cancer susceptibility SNPs within chromosome 15q25.1 affect disease etiology and development of lung cancer, have not been studied comprehensively, limiting mechanistic understanding.Lung cancer, accounting for 13% of all cancer cases and 23% of all cancer-related deaths worldwide, is a leading cause of cancer death in the US and around the world, and represents a major public health problemThe objective of this study was to explore the underlying pathways that are involved in the molecular mechanisms by which variants at the chromosome 15q25.1 locus modify lung cancer risk and increase lung cancer occurrence and development. We first performed a GWAS analysis with a cohort of 1923 lung cancer cases and 1977 healthy controls of Italian origin combined with a cohort of 2995 lung cases and 3578 controls of European ancestry, and then conducted a meta-analysis to identify the index SNPs within the chromosome 15q25.1 locus that were significantly associated with lung cancer risk. We then investigated the SNP–SNP interaction between the index SNPs within the chromosome 15q25.1 locus and the entire genome to identify the SNPs that interact with the 15q25.1 index SNPs, and are therefore involved in lung cancer etiology through interaction. Furthermore, using the index SNPs and their related SNPs in the whole genome, we explored the pathogenic pathways that may be relevant to lung cancer etiology, and replicated the findings with an independent cohort of 18,439 lung cancer cases and 14,026 healthy controls. We also studied genome-wide gene expression data in human lung tissues and conducted an eQTL analysis to investigate whether the functional annotation of the eQTL results can validate the susceptibility pathways from our GWAS analyses. Finally, we explored whether genes from our susceptibility pathways might jointly affect the process by which the chromosome 15q25.1 locus influences lung cancer risk. Our findings suggest that common genetic variations within chromosome 15q25.1 are likely to affect lung cancer etiology by influencing the expression/structure and thereby the function of genes that comprise the neuroactive ligand receptor interaction pathway or gated channel activity and related terms. Such new biologic insights from pathway analysis will provide a better understanding of the etiology and development of lung cancer, potentially shortening the interval between increasing biologic knowledge and translation to patient care.The study design is presented in Fig. P values of association with lung cancer of less than 5 × 10−8 in the 1st discovery cohort and in the meta-analysis  to reduce the possibility of biased results. We applied mapping rules of SNPs to genes by incorporating a region 20 kb upstream and downstream of each gene . The 22 GO terms included substrate-specific channel activity (i-GSEA P < 0.001 and FDR = 0.005), ion channel activity (i-GSEA P < 0.001 and FDR = 0.005), gated channel activity (i-GSEA P = 0.002 and FDR = 0.006), and several similar terms.In order to identify chromosome 15q25.1-associated pathogenic pathways and biological processes that may be relevant to lung cancer etiology, we then conducted enrichment analyses using i-GSEA4GWASP value of each index SNP of less than 1 × 10−22 . Four transporter activity GO terms, including ion channel activity, cation channel activity, substrate-specific channel activity, and cation transmembrane transporter activity, had borderline significant associations with lung cancer    in the meta-analysis of the discovery cohorts and in the replication cohort were selected to assess the individual and joint effects on lung cancer risk. With the threshold of P value of the reference SNP less than 0.1, the same genes/SNPs were selected in gated channel activity term and the 4 transporter activity terms whose functional annotation for eQTL study were borderline significantly associated with lung cancer. Therefore, we explored the accumulated risk in the neuroactive ligand receptor interaction pathway and the gated channel activity term.In order to explore whether genes from our susceptibility KEGG pathways and GO terms could jointly affect lung cancer risk, we calculated the individual and combined effects of multiple functionally-related genes from our susceptibility pathways and GO terms on lung cancer risk. Supplementary Data CHRNA3 rs1051730 and CHRNB4 rs6495309 reached the criterion and were included for further analysis of the independent association and combined effects of SNPs on lung cancer risk. With respect to the gated channel activity term, CHRNA3 rs1051730, CHRNB4 rs6495309, KCNJ4 rs138396, and SCN2B rs7944321 reached the criterion and were included for further analysis. Because the frequency of CHRNA3 rs1051730 T, CHRNB4 rs6495309 C, KCNJ4 rs138396 A, and SCN2B rs7944321 A alleles among the cases were slightly higher than among controls in the discovery cohorts and in the replication cohort, we assumed these alleles may be putative risk alleles in further combined analyses.In total, for the neuroactive ligand receptor interaction pathway, CHRNA3 rs1051730 was the most significantly associated with increased lung cancer risk. With respect to CHRNA3 rs1051730, compared with the CC homozygote, the CT heterozygote was associated with an elevated risk of lung cancer with ORs being 1.32  in meta-analysis of the discovery cohorts and 1.27  in replication, while the TT homozygote was associated with increased lung cancer risk with ORs being 1.89  in meta-analysis of the discovery cohorts and 1.63  in replication. We also found and validated a significant dose–response relationship between the number of CHRNA3 rs1051730 T alleles and lung cancer risk  and replication (adjusted trend test P = 4.80 × 10−50).For the neuroactive ligand receptor interaction pathway, based on the number of risk alleles of the combined P = 2.46 × 10−18) and in replication (adjusted trend test P = 2.09 × 10−37).Based on the number of risk alleles of the combined genotypes in the gated channel activity term, we grouped the individuals into four genotype groups, as follows: zero or one risk allele of either gene; only two or three risk alleles; four or five risk alleles; and six to eight risk alleles Tables  and 6. CP < 0.05, pathway analysis found the same one KEGG pathway and eight GO terms were identified and validated as significantly associated with lung cancer19 risk in the meta-analysis of discovery cohorts and in the replication cohort with statistically significant P values (Supplementary Table P = 0.004 and FDR = 0.017) and in the replication cohort (i-GSEA P < 0.001 and FDR = 0.003). We did not explore chromosome 15q25.1-related for lung cancer in never smokers because the association between SNPs within chromosomes 15q25.1 and lung cancer did not reach genome-wide significance in the discovery cohorts.When we performed the stratified analyses according to smoking status, we found that chromosome 15q25.1 was the most significant susceptibility locus for lung cancer risk among smokers in the 1st and 2nd discovery cohort, and a meta-analysis of discovery cohorts also supported this finding. Eight SNPs within chromosome 15q25.1 were identified and validated as associated with smoking-related lung cancer and were defined as the index SNPs for further selection of the candidate SNPs  participate in the biological process by which smoking induces nicotine dependence. Thus, the association between chromosome 15q25.1, this pathway, lung cancer likely reflects, at least partially, an indirect effect of these genes on lung cancer risk through their effects on smoking behavior. Aside from nicotine dependence and lung cancer risk, this pathway also influences other neurotransmitter-mediated disorders, such as alcohol dependence37, Parkinson’s disease38, schizophrenia drug therapy39, and autism spectrum disorders40. Thus this pathway may have many complex effects on lung cancer risk, either directly by influencing lung tissues or lung cancers as suggested by expression studies, indirectly through smoking behavior or even through effects on other neurotransmitter-related diseases.The neuroactive ligand receptor interaction pathway is also implicated in nicotine dependence, which also contributes to increasing lung cancer risk. Most of the selected genes of this pathway from the current GWAS pathway analyses, including, 42, which belong to the gated channel group or ion channel group. Second, CHRNA5–CHRNA3–CHRNB4 within chromosome 15q25.1 has been documented to modify some pathways of gated channel activity and ion channel activity, and therefore to play a crucial role in leading to and maintaining malignant phenotypes43. Finally, the majority of genes which were chosen as the significant or selected genes in the current GWAS pathway analyses, such as KCNJ444, CACNB245, and SLC14A46, have been reported to be involved in cancer etiology and development. In addition, our finding that genes from our susceptibility transporter activity GO terms jointly affected the chromosome 15q25.1-related lung cancer risk also supported the hypothesis that these pathways were implicated in the mechanisms of lung cancer. Therefore, we speculated that the accumulated effects of multiple functionally-related genes from our susceptibility pathways caused lung cancer occurrence, even though a single gene in any pathway may have only a moderate or weak effect on lung cancer risk. However, more biological mechanism research involving these pathways needs to be carried out in future.Another important finding in our study is that a few transporter activity GO terms, such as gated channel activity, were implicated in the mechanisms of chromosome 15q25.1-modified lung cancer risk. Although we first reported the association between the transporter activity GO terms and lung cancer risk, this finding could be supported by previous studies. First, numerous studies have shown that a few transporter activity GO terms are involved in the processes driving the malignancy, such as calcium channelsCHRNA3, CHRNB4, TGFBR247, RTPRG48, FGFR149, OPCML51, and ROR153, were involved in influencing lung cancer risk. It is thus likely that combined effects and interaction of the genes in the two susceptibility pathways triggered lung cancer pathogenesis. Although we do not know at this stage whether the biological pathways identified in our study have a direct functional role in affecting lung cancer etiology, the susceptibility pathways represent attractive candidates.Our GWAS pathway analyses also suggest that receptor activity GO terms and membrane terms, might play roles in the mechanism via which the chromosome 15q25.1 locus is involved in the pathogenesis of lung cancer, though the involvement exhibits nonsignificant association in the functional annotation of eQTL studies. This finding was supported by the fact that most selected genes in the two pathways from current GWAS pathway analyses, including 12. Therefore, we applied a pathway-based approach to identify the sets of pathways that were significantly associated with cancer risk. To correct for multiple testing associated with pathway analysis we followed a false discovery rate approach. Identification and verification of the susceptibility pathways in both GWAS analysis of discovery and replication and the eQTL study confirmed the reliability of our study. Nevertheless, our results should be confirmed in the future with genome-wide expression data and protein–protein interaction data, and more biological mechanism research involving these pathways needs to be carried out. Finally, we realized that, in the GWAS pathway analyses, all subjects used in both discovery and replication phase are of European ancestry, and that the subjects in the lung eQTL study are French Canadians, which suggest that our findings can be applied to the population of European ancestry.Despite these intriguing findings in this well-characterized pathway study, our investigation still had some limitations. First, we performed the epistasis test between SNPs in the univariate model; this may have led to the omission of the effects of other cofactors, such as age, gender, and smoking status, on the interaction between SNPs. However, not including cofactors typically reduces power rather than false positive findings, so that a model ignoring cofactors seems a reasonable first step to analysis. In addition, we only retained the SNP pairs, which exhibited statistically significant interactions in the 1st discovery cohort and in the meta-analysis results and showed at least a borderline significant interaction in the 2nd discovery cohort, for further analysis, which ensured the reliability of this study. Second, only GWAS data without genome-wide expression data were applied to identify the SNPs and their related genes that interact with the chromosome 15q25.1 locus. However, the susceptibility pathways and GO terms from our GWA study can share pathways with the functional annotation of genes in chromosome 15q25.1 and its related genes identified by eQTL studies, which supports the interpretation of some of our findings. A concern in this study is the large number of tests that were performed to identify epistatically acting SNPs. However, the purpose of conducting SNP–SNP interaction test in the current study is to select a group of candidate SNPs which are the most associated with the index SNPs in chromosome 15q25.1. On the other hand, majority of SNPs/genes in the pathways have weak and minor influence on the pathogenesis of complex disorderMany genetic variants certainly contribute to the large unexplained portion of lung cancer pathogenesis, and it is expected that more mechanisms contributing to increased lung cancer risk will be identified in the future. The data presented here suggest that common genetic variations within chromosome 15q25.1 are likely to affect lung cancer etiology by influencing the expression/structure and thereby the function of genes that comprise the neuroactive ligand receptor interaction pathway or gated channel activity and related terms. To the best of our knowledge, this is the first study to explore the pathogenic pathways related to the mechanisms through which the chromosome 15q25.1 locus modifies lung cancer risk. These pathways provide important leads to a better understanding of the etiology and development of lung cancer, potentially shorten the interval between biologic knowledge and improved patient care, and are beneficial to the design of future functional studies to increase understanding of these mechanisms.54, which was composed of 1923 lung cancer cases and 1977 healthy controls, was used as the 1st discovery cohort. The EAGLE study participants were recruited in Italy between 2002 and 2005 for a population-based case-control study, which included incident primary lung cancer cases of any histologic type and healthy population-based controls, matched by gender, residence, and 5-year age-group. All subjects in the EAGLE study are of Italian nationality and born in Italy.The study design is presented in Fig. 2 and the International Agency for Research on Cancer (IARC) study55 as the second discovery cohort, in total comprising 2995 lung cancer cases and 3578 healthy controls. The MDACC study participants were recruited at the University of Texas MD Anderson Cancer Center between 1997 and 2007 and included 1154 primary lung cancer cases of adenocarcinoma and squamous cell carcinoma and 1136 healthy controls that were matched to cases by smoking behavior, ethnicity, and 5-year age-group. The IARC study was a multicenter study from six countries of central Europe, which recruited newly-diagnosed lung cancer cases of any histologic type and healthy individuals without diagnosed cancers or any family history of cancers, matched to cases by sex, age, and center or region within European countries. The current case-control comparison included 1841 cases and 2442 controls from IARC available data. All subjects used in the current study are of European ancestry.We used the M.D. Anderson Cancer Center (MDACC) study56. The participants who lacked imputed data, disease status, were close relatives (second-degree relatives or closer) or had low-quality DNA, or were non-European, were excluded from the current study. Therefore, a total of 18,439 cases and 14,026 healthy controls were included in the current case-control study.The Oncoarray consortium, which analyzed samples of 18,439 European-descent lung cancer cases and 14,026 European-descent healthy controls, was used for replication. The Oncoarray consortium is a network created to increase understanding of the genetic architecture of common cancers and included GWAS data of a total of 57,776 samples, obtained from 29 studies across North America and Europe, as well as Asiawww.tissuebank.ca)57. Lung tissue samples were obtained in accordance with Institutional Review Board guidelines. Genotype data and a detailed pathology reports were available for all patients.Noncancerous lung tissue from Laval University was obtained from 420 patients undergoing surgical resection for lung cancer. Through quality controls, 409 samples were used for whole-genome gene expression profiling in the lung and eQTL analysis. All patients in the Laval cohort were from a French Canadian population and underwent lung cancer surgery between April 2004 and December 2008. Samples were stored at the Institut universitaire de cardiologie et de pneumologie de Quebec (IUCPQ) site of the Respiratory Health Network Tissue Bank of the Fonds de la recherche en sante du Quebec  at the Center for Inherited Disease Research, part of the Gene Environment Association Studies Initiative (GENEVA) funded through the National Human Genome Research Institute. Genotyping of 317,498 SNPs in MDACC samples was carried out using Illumina 300K HumanHap v1.158 and 1000 Genomes Project Phase 3 (ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/phase3/data). Following imputation, 20,734,083 SNPs in the whole genome were available for further analysis.To effectively replicate the findings in the discovery phase, we imputed additional SNPs in Oncoarray samples to allow us to integrate the data with the common SNPs studied in the discovery cohorts. Imputation was performed with the software package Impute 2 v2.3.2Case-control association tests for genotyped data were conducted using 1-degree-of-freedom Cochran–Mantel–Haenszel tests with the application of PLINK version 1.9. SNPTEST v2.5.2 was used in the analysis of case-control association for each SNP in imputed data. Meta-analysis of the 1st and 2nd discovery cohorts was performed on the results of case-control association analysis using the basic meta-analysis function in PLINK v1.9, which conducted a fixed-effects analysis using inverse variance weighting to combine the studies. In the discovery phase, a total of 310,276 SNPs were the same in both the 1st and 2nd discovery cohorts, passed quality control steps and were retained for association analyses and meta-analyses.P values of less than 5 × 10−8 in the 1st discovery cohort and in the meta-analysis of the 1st and 2nd discovery cohort, were selected as index SNPs. A Bonferroni correction was applied to adjust the association of the index SNP for multiple comparisons with using PLINK version 1.9 and R package of adjust P-values for multiple comparisons. Because 310,276 SNPs in the discovery phase were performed for association analyses and meta-analyses and 3411 SNPs in the replication phase were conducted association analyses, adjustments for 310,276 tests in discovery and 3411 tests in replication were used. After selection, eight SNPs met the criterion for index SNP selection and were used for further selection of the candidate SNPs in the whole genome which had potential functional connections with the index SNPs in the chromosome 15q25.1 locus.SNPs within the 15q25.1 locus spanning 203 kb, which were associated with lung cancer risk with The epistasis test between SNPs in the whole genome and the chromosome 15q25.1 locus was performed separately for the 1st and 2nd discovery cohorts using the application PLINK version 1.9. A total of 2,482,200 SNP × SNP pairs were calculated in both cohorts. We then carried out a meta-analysis to combine the epistasis results in the 1st and 2nd discovery cohorts with the application of the basic meta-analysis function in PLINK v1.9 that conducted fixed-effects analysis using inverse variance weighting.P value of less than 0.05 in the 1st discovery cohort and in the meta-analysis of both discovery cohorts, and less than 0.10 in the 2nd discovery cohort, were selected as the candidate SNPs for further pathway analyses. After selection, 3409 candidate SNPs met the criterion for candidate SNP selection and were identified to have potential connections with the eight index SNPs in the chromosome 15q25.1 locus.The SNPs, which interacted with the index SNPs within the 15q25.1 locus with an epistasis 59, and GO60. The Reactome database is based on reactions between diverse molecular species rather than limiting the pathways to protein–protein interactions. The KEGG database represents experimentally-validated pathways of metabolic processes and gene sets of human diseases. GO is a major framework for the model of biology that defines classes used to describe gene function, and relationships between these concepts.We included curated pathways from the Canonical pathways, Reactome, BioCarta, KEGG databaseshttp://software.broadinstitute.org/gsea/msigdb/collections.jsp), we selected gene sets whose number of genes were between 21 and 200, and without limiting gene sets by keyword (e.g. immune) and without masking the MHC region. In order to reduce the possibility of biased results due to LD patterns from SNP arrays13, we pruned the set of SNPs, including the index SNPs and the candidate SNPs, for LD and only inputted SNPs not in LD (r2 < 0.2) to enrich pathways. After pruning, we performed gene set enrichment analysis with associated P values of the SNPs that using the option of “−logarithm transformation”, as required by the software, in the meta-analysis of discovery cohorts and the replication cohort, respectively.Gene set enrichment analysis was performed by i-GSEA4GWAS. SNPs were retained for analysis that were within 20 kb upstream or downstream of a gene. We used gene set databases of canonical pathways, GO biological process, GO molecular function, and GO cellular component, separately, and applied the standard input gene set file of KEGG, BioCarta and, Reactome, which were downloaded from the Molecular Signatures Database (MSigDB) in GSEA  to yield the significant proportion-based enrichment score (SPES). SPES multiplies by the proportion of significant SNPs in the pathway so that i-GSEA4GWAS identifies pathways/gene sets including a high proportion of significant genes. It is, therefore, more appropriate for study of the combined effects of possibly modest SNPs/genes and gives i-GSEA improved sensitivity for complex diseases13. Pathways/gene sets with FDR < 0.25 were regarded as possibly associated with traits; FDR < 0.05 were regarded as high confidence or with statistical significance.i-GSEA4GWAS performs gene set enrichment to identify pathways that show a higher proportion of statistically significant genes than randomly expected and, with some modifications, is based on the GSEA algorithm15, using same SNPs and P value and to retain SNPs with 20 kb upstream or downstream of a gene. We used gene set databases which were downloaded from the Molecular Signatures Database (MSigDB) in GSEA and selected gene sets whose number of genes was between 21 and 200.Gene set enrichment analysis was also performed by GSA-SNP2, which is a successor of GSA-SNP63 as implemented in the Affymetrix Power Tools software was used to examine expression values. Standard quality control parameters64 were applied to check the quality of the arrays. Through quality controls, 409 patients were available for eQTL analyses with both genotypes and gene expression levels. R statistical software was used to perform tests for robust multichip average expression. Association tests between the expression traits, which were adjusted for age, sex, and smoking status, and the most significant SNPs in each genes within chromosome 15q25.1 associated with lung cancer risk in previous reports, including rs1696996816, rs649530918, and rs803419117, were estimated with the application of quantitative association tests implemented in PLINK. A P value of less than 0.05 was considered to be significant.All lung samples were reviewed by an experienced pathologist for clinical diagnosis and staging. Each lung tissue sample was snap-frozen in liquid nitrogen and stored at −80 °C until further processing. The SV96 Total RNA Isolation System (Promega) was used to extract RNA. Expression profiling was carried out with an Affymetrix custom array (GEO platform GPL10379). The robust multichip average methodP value of less than 0.0005 in the eQTL study were selected as candidate genes for further pathway/gene set analysis. Because the “Functional annotation table” of DAVID may query associated terms for all genes and “Functional annotation clustering” of DAVID can cluster functionally similar genes into groups65, we employed the “Functional annotation table” of DAVID (version 6.8) to perform functional annotation of biological pathways in Reactome, BioCarta, and KEGG and used “Functional annotation clustering” of DAVID (version 6.8) to cluster functionally similar genes into groups of GO terms, with Species and background being set up as Homo sapiens. We used the candidate genes in the whole genome with and without the genes in the chromosome 15q25.1 locus to perform the analyses, by choosing ‘Homo sapiens’ selection to limit annotations by species.The genes in the whole genome with a linear regression http://www.ebi.ac.uk/QuickGO/) and applied Cytoscape (version 3.4.0) with EnrichmentMap plugin (version 2.1). The standard gene sets for EnrichmentMap plugin were downloaded from MSigDB Collections (http://software.broadinstitute.org/gsea/msigdb/collections.jsp). For the selected genes in our susceptibility pathways from current GWAS pathway analyses, we achieved the gene expression level in normal lung tissue from Genecards database (http://www.genecards.org/).We clarified the relationship of sharing pathways identified by GWAS analysis of both discovery and replication phase and validated by eQTL studies with information from the European Bioinformatics Institute (P < 0.1) in the meta-analysis of discovery cohorts and in the replication cohort, were selected to assess the individual effect and joint effects on lung cancer risk. Genotype frequencies between the cases and controls were evaluated using a chi-square test. Univariate and multivariate logistic regression models were used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) of each genotype to estimate its effect on lung cancer risk with or without adjustment for age, sex and smoking status (never and ever). Statistical analyses were performed with Statistical Analysis System (SAS) software  and P value < 0.05 was considered significant.We calculated the individual and combined effect of genes in the pathways/gene set on lung cancer risk using the SNPs that were identified by i-GSEA4GWAS as reference SNPs for the selected genes in each pathway. The genes whose reference SNPs were associated with lung cancer risk with borderline significance . In the subgroups of smokers and non-smokers, we used a similar process to select index SNPs and candidate SNPs, and to carry out pathway analyses. Among smokers, eight index SNPs and the 3401 candidate SNPs were selected for further gene set enrichment analysis in the 1st and 2nd discovery cohort, the meta-analysis of discovery cohorts and the replication cohort. Among non-smokers, no SNPs in chromosome 15q25.1 reached the criteria for index SNP selection, and therefore no subsequent steps for pathway analyses were conducted.http://www.ceph.fr/cancer3.The data that support the findings of this study are available. The access numbers are “phs000336.v1.p1.c1” for EAGLE study, “phs000753.v1.p1” for MDACC study, and “phs001273” for Oncoarray study in dbGAP. The IARC study was made available at Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7