Zea mays L.) is an important food and feed crop worldwide and serves as a a vital source of biological trace elements, which are important breeding targets. In this study, 170 maize materials were used to detect QTNs related to the content of Mn, Fe and Mo in maize grains through two GWAS models, namely MLM_Q + K and MLM_PCA + K. The results identified 87 (Mn), 205 (Fe), and 310 (Mo) QTNs using both methods in the three environments. Considering comprehensive factors such as co-location across multiple environments, strict significance threshold, and phenotypic value in multiple environments, 8 QTNs related to Mn, 10 QTNs related to Fe, and 26 QTNs related to Mo were used to identify 44 superior alleles. Consequently, three cross combinations with higher Mn element, two combinations with higher Fe element, six combinations with higher Mo element, and two combinations with multiple element (Mn/Fe/Mo) were predicted to yield offspring with higher numbers of superior alleles, thereby increasing the likelihood of enriching the corresponding elements. Additionally, the candidate genes identified 100 kb downstream and upstream the QTNs featured function and pathways related to maize elemental transport and accumulation. These results are expected to facilitate the screening and development of high-quality maize varieties enriched with trace elements, establish an important theoretical foundation for molecular marker assisted breeding and contribute to a better understanding of the regulatory network governing trace elements in maize.Maize (The online version contains supplementary material available at 10.1186/s12870-023-04643-8. Zea mays L.), recognized as a vital staple crop for both human consumption and animal feed, is known as a “longevity food” because of its rich nutrient content comprising essential vitamins, protein and beneficial trace elements [Maize  related to these elements and breeding varieties that are enriched with beneficial elements without toxic elements is an important means to improve the overall quality of human life.MOT1 gene responsible for molybdenum (Mo) [HMA3 gene associated with cadmium (Cd) [HAC1 gene linked to arsenic (As) [Arabidopsis. Similarly, in rice, previous researchers have determined a significant number of QTLs for specific elements using bi-parental populations and association mapping panels [OsHMA3 for Cd [OsHMA4 for copper (Cu) [Currently, numerous studies have been conducted to identify QTLs and candidate genes involved in the production of different elements in plants. In particular, the num (Mo) , 9, the ium (Cd) , the HACnic (As)  have beeg panels –18. Furt3 for Cd , 20, andper (Cu) . In maizper (Cu) –27. AlthMaize, is one of the top three major food crops in the world, especially the largest cultivated crop grown in China, and it is important to map novel and stable loci that control the accumulation of different elements to achieve high-quality maize cultivation. However, although previous studies have mostly used a limited number of molecular markers, such as SSR or InDel, to probe loci associated with target traits, compared with SNP, these traditional methods with less marker density and lower localization accuracy have limited efficiency. Currently, an efficient GWAS method utilizes more recombination events and abundant SNP markers. This methodology enables a higher resolution and facilitates the discovery of more comprehensive genetic information regarding traits of interest. For example, using SNP markers, 23 and 38 QTNs related to shoot and blossom blight resistance, respectively were detected in 273 apple accessions . SimilarAdditionally, soil in many parts of the world lacks trace elements. For example, approximately 30% of the soils in China are deficient in manganese (Mn) and (Mo) molybdenum, while 40% lack iron (Fe) [The statistical analysis aimed to assess elemental traits in different environments, especially Mn, Fe and Mo Table . The meaPopulation structure and principal component analysis were performed to obtain the.P < 4.31E-6). Similarly, the model found significant associations of 29 (E1), 92 (E2) and 197 (E3) SNPs with Fe, and 276 (E1), 132 (E2) and 132 (E3) SNPs with Mo , Chr5 (qFe/Mo-5), Chr7 (qFe/Mo-7), Chr8 , Chr9 (qFe/Mo-9), and Chr10 .Significant SNPs detected through two different methods and across multiple environments were regarded as reliable QTNs., Our analysis identified 15 SNPs related to Fe element Fig.  in two Gent Fig.  were detPs Table  that areP-value smaller than 0.05/116,011 = 4.31E-7 as more reliable peaks. From this, 8 SNPs  Chr3 , Chr4 (qMn-4) and Chr7 (qMn-7).In addition, although Mn element related SNPs were not co-located in multiple environments, we aimed to enhance the selection of high-quality candidate materials related to trace elements. To achieve this, we applied a stricter screening threshold of Mn related SNPs by considering only those with a NPs Fig.  were assThe detected of QTNs during breeding is the goal of molecular marker assisted breeding. In this research, the evaluation of superior alleles in reliable QTNs in different environments was undertaken using a cohort of 130 materials with 0 phenotypic deletion rate in three environments. These materials served as the basis for the evaluation and subsequent selection. Subsequently, 8, 15 and 46 SNPs related to Mn, Fe, and Mo element, respectively, were identified. Here, the phenotypic effect value was calculated for QTN considered to be reliable, in order to understand the effect of a single QTN on the corresponding phenotype.qMn-1a  in environmental E2 and E3, and the accumulation of Mn among materials with different alleles of qMn-3c , qMn-3b (AA), and qMn-4 (AA) revealed a significant positive effect on Mn accumulation across all three environments, with synergistic levels ranging from 3.2689 ~ 3.8682 µg/g, 3.1708 ~ 3.9973 µg/g and 1.7293 ~ 2.2727 µg/g, respectively. While the alternative allele of AA, CC and CC exhibited a negative effect with a reduction level ranging from − 0.3576 ~ -0.3022 µg/g, -0.3331 ~ -0.2642 µg/g and − 0.3791~-0.2602 µg/g, respectively  in all three environments, the corresponding superior allele was TT, and the synergistic level was 4.1660 ~ 98.5975 µg/g. Conversely, the alternative allele (AA), resulted in a reduction ranging from − 6.4654~-0.2187 µg/g. However, the remaining nine QTNs   in E2 and E3, but not in E1 , and both superior alleles are CC  in the same group  ~ 23 (17.69%) materials including superior alleles of each of the eight stable QTNs associated with Mn element. Conversely, the majority of the material, 107 (82.31%) ~ 123 (94.62%), exhibited alternative alleles of each of these eight QTN (unfavorable for element accumulation). Among these, 20 materials containing 1 (15%) ~ 7 (5%) superior alleles have a high accumulation of Mn in three environments Table , of whicSeven (5.38%) ~ 13 (10%) materials have superior alleles of the 10 stable QTNs associated with Fe element, and 117 (90%) ~ 123 (94.62%) materials have alternative alleles. Among these, 19 materials containing 1 (42.11%) ~ 9 (5.26%) superior alleles have higher Fe accumulation in three environments Table . MateriaFive (3.85%) ~ 16 12.31%) materials have superior alleles of the 26 stable QTNs associated with Mo element, and 114 (87.69%) ~ 125 (96.15%) materials have alternative alleles. Among these, 17 materials containing 1 (11.76%) ~ 13 (5.88%) superior alleles were identified to have higher Mo accumulation in three environments Table . CAU254 .31% mateTo obtain materials with high levels of trace elements, several cross combinations of materials can be leveraged to select offspring Table . Three hqFe/Mo-5  and Mo, with 3 and 12 superior alleles, respectively and contains -5 Table . Moreove-5 Table , which cP-value < 0.05), and three candidate genes  found that 1 candidate gene involved 1 KEGG pathway (zma00960), which could be associated with the accumulation of Mn element , Fe (10) and Mo (26) and identified their corresponding superior alleles. This information facilitates the breeding of maize varieties with high Mn, Fe, and Mo contents through molecular breeding. We also used these superior alleles to predict the best cross combinations for producing maize hybrids with high concentration of trace elements in the three environments, following the single parent participated in multiple crosses approach described in previous studies –36. For We also examined CAU425, which holds superior alleles related to Mo than the recommended candidates . Since CAU425 did not show high accumulation Mo at E1, suggesting a relative instability, it was not selected as a preferable candidate. Further verification across multiple environments would provide more reliable locus information.Arabidopsis thaliana. For example, the homologous gene AT5G35080 of Zm00001eb028460 encodes a glycoprotein located in the endoplasmic reticulum, involved in the process of endoplasmic reticulum inward transport and endoplasmic reticulum protein degradation. Loss of function leads to salt sensitivity in plants [2+ and participate in the oxidative respiration function of mitochondria [The detection of most micro-effect genes is challenging due to their low single effect ability, making it even more difficult to locate stable QTNs in various environments. Here, we integrated multiple factors, focusing on the candidate genes near eight QTNs related to Mn, ten QTNs related to Fe, and twenty-six QTNs related to Mo. Through genome comparison, literature search, GO and KEGG analyses, we identified 13 candidate genes that may directly or indirectly participate in the transport process of Mn (4), Mo (7), and Fe (2), of which, 10 have homologous genes in n plants . The homn plants . The homn plants . The homn plants . The homn plants . AT1G184n plants . The homchondria . AT3G015chondria . AT4G079chondria . We antiThe association panel included 170 maize accessions from Dr. Lai’s laboratory at China Agricultural University . These 1Well pollinated ears were harvested from each accession in the different environments. After manual threshing, representative mature grains from each accession were dried at 80℃ for 3 days and pulverized for measuring elements, including iron (Fe), manganese (Mn), molybdenum (Mo). Each sample weighing 5 g was digested in MARS6 microwave (CEM) with 65% nitric acid (superior purity) and covered and left for 1 h. Subsequently, the lid was screwed on the jar with a gradient of temperatures ranging from 120℃ to 180℃ for 45 min followed by a 30 min cooling period. The lid was slowly opened to vent the air, then the inner lid was rinsed with a small amount of water, and the digestion jar was placed on a temperature-controlled hot plate and heated at 100℃ for 30 min. After dilution with deionized water and fixed to 10 mL, the mixture was thoroughly mixed and set aside. A blank test sample was prepared the, samples were diluted in deionized water and the concentration of 3 elements (ug/g) were measured using inductively coupled plasma mass spectrometry (Agilent 7700 series). Finally, the mean of each element was calculated based on three replications.2) was calculated for elemental traits according Knapp [2 = δ2g / (δ2g + δ2e / E + δ2g×e / RE ) where δ2g, δ2e, δ2g×e, E, and RE is genetic variance, residual variance, g×e variance, environment number, and product of the repeat number × the environment number, respectively.The mean, range, standard deviation (SD), skewness, kurtosis, and coefficient of variation (CV) were calculated for the element concentration of the 170 maize accessions in different environments using the PYTHON software. The correlation analysis of each pair elements was performed using “corr” function, and we drew a heat-map correlogram. Analysis Of Variance (ANOVA) was performed to understand the interactions between genes and the environment (g×e) using the “Agricolae” package of R software. The Broad-sense heritability  as described in our previous works . The pop2 ≥ 0.2, window size = 50, and step size = 50, 116,011 independent SNPs were obtained.GWAS was performed to identify peak SNPs using the emmax software with Multiple Loci Linear Mixed (MLM_Q + Kinship and MLM_PCA + Kinship) Model using the mean of the phenotype. A threshold value of P-value < 0.5/116,011 = 4.31E-6 (Bonferroni correction) was used to detect significant SNPs.A total of 3 million SNPs  with a minor allele frequency of ≥ 0.05 were used to perform GWAS via emmax software. Finally, using plink software with the parameters r Haploview 4.2 software  was usedThe phenotypic effect value was calculated according to the method of . The spehttp://www.maizegdb.org) and NCBI (https://www.ncbi.nlm.nih.gov/), based on the peak SNPs. Next, a BLAST analysis (http://plants.ensembl.org/index.html) was performed for all identified candidate genes to search homologous genes and annotate the function compared with Arabidopsis. Gene Ontology (GO) and KEGG analysis was conducted to explain the functional categories using KOBAS v3.0 software (http://kobas.cbi.pku.edu.cn/).The analysis was performed using KOBAS v3.0, in which were observed significant enrichment of the zma00960 KEGG pathway, which plays a pivotal role in the biosynthesis of secondary metabolites in maize.Potential candidate genes were searched on MaizeGDB (Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Supplementary Material 3Supplementary Material 4Supplementary Material 5Supplementary Material 6Supplementary Material 7Supplementary Material 8


Objective: Adalimumab (ADA) is an effective treatment for inflammatory bowel disease (IBD), both ulcerative colitis (UC) and Crohn’s disease (CD). The equal effect between the original ADA and biosimilars from Europe and the United States has been shown. However, the biosimilar of ADA is different in China. The effectiveness and safety data of ADA biosimilar (HS016) in China have yet to be discovered.Patients and methods: 91 patients  received HS016 treatment and were enrolled in this study. Therapeutic response and safety profiles were analyzed. Therapeutic drug monitoring (TDM) was also carried out among nonresponse patients. After being considered as “nonresponse” (after three or 6 months of treatment), 20 patients’ serum TNFα concentrations were measured and correlated to their disease severity.Results: Among active CD patients (n = 61), 75.4% (46/61) at 12 w, 73.8% (45/61) at 26 w, 50.8% (31/61) at 52 w achieved the clinical response, respectively; 55.7% (34/61) at 12 w, 65.6% (40/61) at 26 w, and 45.9% (28/61) at 52 w achieved clinical remission. The maintained remission rates of CD (n = 14) in clinical remission were 100% (14/14) at 12 w, 78.6% (11/14) at 26 w, and 63.6% (7/11) at 52 w, respectively. Among active UC patients, 37.5% (6/16) at 12 w and 50% (8/16) at 26 w achieved clinical response. Total adverse event rates were 5.5% (5/91) during 52-week visits. Due to the inadequate serum drug concentration, 30.4% (7/23) of patients had poor clinical responses. Elevations of serum anti-drug antibodies occurred in one additional patient (4.3%).Conclusion: ADA biosimilar HS016 had good efficacy and safety in Chinese IBD patients. Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn’s disease (CD), is a chronic inflammatory disorder of the intestine. The incidence of IBD continually increases worldwide, inducing a tremendous economic burden on patients and society . AlthougDuring the last 40 years, the cellular and molecular mechanisms of the inflammatory diseases’ pathogenesis have been continuously revealed, including UC, CD, ankylosing spondylitis (AS), rheumatoid arthritis (RA), and psoriasis. Among all inflammatory cytokines and chemokines, TNF-α is the first identified vital factor in developing the inflammatory responding process. Thus anti-TNFα therapy emerged and began to use in RA treatment in the mid-1990 s, and therapeutic goals started to evolve and move forward. Due to its potential effect on disease-modifying and mucosal healing, anti-TNFα therapy is associated with a reduced risk of hospitalization, colectomy, and colorectal cancer, especially among IBD patients. Anti-TNFα acts mainly by binding TNF-α in serum (sTNFα) or on immune cell membranes (mTNFα) , and espConsequently, HS016 became China’s first ADA biosimilar approved for CD indications. Several randomized clinical trials, including CLASSIC-I, CHARM, ULTRA 1, and ULTRA 2 , demonstAlthough anti-TNFα unveils a new treatment for IBD, up to 30% of patients show primary nonresponse (PNR), and another 40% lose response over time, i.e., secondary loss of response (SLR) and need to switch therapy . So far,In this study, we retrospectively analyzed the real-world data of HS016 in IBD treatment from one of the biggest IBD centers in China. In addition, we further discussed the factor (TDM) on the efficacy of HS016, providing more evidence and suggestions for clinical decision-making.This study was approved by the Medical Ethics Committee of the First Affiliated Hospital of Fourth Military Medical University, Xi’an, China (KY20222333-C-1). It was performed according to the ethical principles for medical research of the World Medical Association Declaration of Helsinki. All participants signed and informed consent.One hundred three patients were considered for IBD treated with HS016 at Xijing Hospital  from October 2020 to April 2022. ADA is prescribed for patients  failed tn = 4) and had had IBD surgery (n = 8). The remaining 91 patients (75 CD and 14 UC) met the study criteria and were enrolled in this retrospective analysis  and clinical data  were collected from patients.Patients with IBD were treated with ADA biosimilar HS016 , a 160 mg dose at the baseline, an 80 mg dose at week 2, then a 40 mg dose every other week. In case of insufficient efficacy during treatment, the dose would be increased to 40 mg per week after the physician’s judgment.The HS016 efficacy assessment of CD patients by Harvey-Bradshaw Index (HBI) and Simplified Endoscopic Score for Crohn’s Disease (SES-CD) and UC by partial Mayo score (pMS). Patients were evaluated at weeks 0, 12, 26, and 52. An HBI score of ≤4 points was defined as remission, 5–7 points mildly active, 8–16 points moderately active, and >16 points severely active. Clinical response was defined as a decrease in HBI score ≥3 points after treatment; clinical remission was defined as an HBI score ≤4 points, and maintenance of clinical remission was defined as a sustained HBI score ≤4 points before and after treatment with ADA biosimilar . In UC, Serum TNF-α, drug concentration, and anti-ADA biosimilar antibody were measured by immunochromatography in patients with persistent non-benefit under HS016 therapy, either clinical or endoscopic. Suzhou HeRui Biotechnology Company carries out serum TNF-α and TDM. The reference range of serum TNF-α is less than 8.1 pg/mL, the reference range of therapeutic drug concentration is ≥5.0 μg/mL, and the reference range of anti-ADA biosimilar antibody is under 4 ng/mL .2 test or Fisher’s exact probability method. Comparing the means of two or more groups (independent variables) on a single dependent variable using one-way ANOVA. Pearson correlation analysis was performed to analyze the correlation between the serum TNF-α concentrations and disease severity. We used the SES-CD score to represent disease severity. All studies used 2-sided tests. The significant differences were documented as p < 0.05.Data were presented as mean ± SD. Statistical analysis was performed by using the Students t-test  with IBM SPSS (v.26.0) and GraphPad Prism (v.8.3.0). Rates were compared by χn = 2), adverse events (n = 2), and surgery (n = 1). 6 CD patients discontinued treatment before 52-week treatment, with reasons including medication ineffectiveness (n = 3) and surgery (n = 3). According to the HBI score at baseline, CD patients were divided into a clinical remission group and an activity group to assess. Among these 75 patients, 14 were in clinical remission, which showed differences in disease sites from active patients at baseline (p = 0.013). Of all CD patients, twenty-two had extraintestinal manifestations, including seven arthritis, eleven oral ulcers, five perianal abscesses, one anal fistula, one iritis, and one erythema nodosum. Among eight patients exposed previously to TNF-antagonist, three discontinued due to secondary loss of response, one due to high antibody concentration, and four due to allergy. These patients had received treatment with Infliximab before using HS016. Infliximab is a human-murine chimeric antibody, while HS016 is a fully humanized antibody, patients who experienced allergy with Infliximab were advised to switch to the safer HS016 for treatment. Of the 16 UC patients with a median age of 40.5 years, 11 (68.8%) were male, and 11 manifested extensive colitis. Four patients (25%) were mildly active, three patients (18.8%) were moderately active, and nine patients (56.2%) were severely active according to the partial Mayo score (pMS) at baseline. Previously 75% of patients were on 5-ASA, 50% on steroids, and 31.2% on infliximab, as shown in This study has 91 patients with IBD  . A totalWe first assessed the distribution of baseline levels in all CD patients according to Harvey-Bradshaw Index (HBI) scores. 81.3% of CD patients were in the active phase, and most were mild to moderately active . During Additionally, CD patients’ change of every index was analyzed at baseline, week 12, week 26, and week 52.  However,As for the UC patients, 37.5% (6/16) gained a clinical response at a 12-week follow-up. The clinical response and remission at 26 w were 56.3% (9/16) and 31.3% (5/16). At 12 w, the endoscopic response was 50% (4/8), and remission was 37.5% (3/8). The endoscopic remission rate was 42.8% (3/7) with 26-week treatment. During the follow-up, two patients had surgeries.n = 23), including the 9 patients with primary non-response, 9 patients who did not show response under endoscopy at the 26-week follow-up, and 5 patients who did not achieve clinical remission at the 26-week follow-up. 7 (30.4%) patients had drug concentrations less than 5 μg/mL, 5 (21.74%) patients between 5 and 10 μg/mL, and 11 (47.8%) patients above 10 μg/mL . (p = 0.0655)  were also recorded of these patients. And then, we performed a Pearson correlation analysis of two targets. The results showed that there was only a weak correlation between serum TNF-α and SES-CD . . Additio 0.0655) .There were 3 cases of psoriasis-like rash. Of these, one developed a large rash on the forehead and back after the initial treatment, and the other 2 underwent scattered rashes on the extremities at 12 and 52 weeks. In addition, there were two cases of fungal otitis externa. Other serious adverse events were not identified in our study.The primary outcomes of this study show that HS016 has a significant therapeutic effect on IBD patients, and the most frequent factor for the ineffective HS016 treatment is insufficient serum concentration. Our research proves to physicians that HS016 is a practical and safe treatment option for IBD.Compared to IFX, ADA is a fully human-originated antibody with lower immunogenicity and higher safety. The subcutaneous injection mode of administration makes its application more and more widespread. A large number of studies have demonstrated the efficacy of ADA for IBD. For example, CLASSIC- I  reportedOn the one hand, the efficacy of HS016 is confirmed. On the other hand, 98% of CD patients in our study were mild to moderate. It also shows that early application of biologics can improve the cure rates of IBD patients. All findings above suggested that ADA biosimilar HS016 showed similar efficacy as the original ADA.Moens et al.  also revRegarding safety, PYRAMID  with up Anti-TNFα biologics mainly treat IBD by inactivating inflammatory factors, thus inhibiting inflammation . Our stuAlthough a wide range of studies have demonstrated that TNF-α inhibitors are effective therapies for IBD, there are still approximately 40% of patients will experience primary nonresponse (PNR) or secondary loss of response (SLR) . In our On the other hand, 7 (30.4%) patients may have had inadequate drug concentrations (the drug concentration < 5 μg/mL), resulting in loss of response. However, different studies have inconsistent recommendations for the recommended concentration of ADA. One study suggests that ADA concentration >4.9 μg/mL is beneficial for mucosal healing in CD patients . AnotherIn conclusion, we have confirmed the efficacy and safety of ADA biosimilar HS016 among Chinese IBD patients. In particular, the real-world data is equal to the previously reported RCT studies on the effectiveness of HS016 with Chinese CD patients. As research continues, we are confident that increasing biological agents will appear for IBD therapy. Additionally, biosimilars will stimulate competition in the market and have incredible potential to expand patient exposure to biologics in the context of treatment recommendations. Appropriate use of TDM will provide physicians with a more significant basis for selecting and optimizing treatment.Herein, we demonstrate that HS016 may be an effective and safe option for Chinese IBD patients.


P = 0.03) and other variables showed no difference according to the tertile of CSI and N6/N3. Participants with the C allele of MC4R rs17782313 had lower height (P < 0.001) and higher HOMA index (P = 0.01). We found that the CC genotype of MC4R interacts with the N6/N3 ratio on the metabolically unhealthy phenotype in the crude model  and even after adjustment for all confounders . The data of this study can justify one inconsistency observed in society, regarding dietary recommendations about metabolic health status. Those with CC genotype, are more likely to have an unhealthy phenotype with an increase in N6/N3 as one fat quality indices than those who do not have CC genotype. We found the interaction of dietary fat quality indices such as N6/N3 and the MC4R gene in metabolically unhealthy overweight and obese women.Obesity has become a common global problem. Some obese people can be metabolically healthy. Gene-environment interaction can be important in this context. This study aimed to assess the interaction between dietary fat quality indices and the Melanocortin 4 receptor (MC4R) gene in metabolically healthy and unhealthy overweight and obese women. This cross-sectional study was conducted on 279 women with overweight and obesity. The definition of metabolically healthy and unhealthy phenotypes was done according to Karelis criteria. Dietary assessment was done using a 147-item validated semi-quantitative food frequency questionnaire and dietary fat quality was assessed by cholesterol-saturated fat index (CSI) and the ratio of omega-6/omega-3 (N6/N3) essential fatty acids. MC4R was genotyped by polymerase chain reaction-restriction fragment length polymorphism technique. A generalized linear model was used to evaluate the interaction between dietary fat quality indices and the MC4R gene in both crude and adjusted models. Study subjects with higher ratio of N6/N3 had higher homeostatic model assessment for insulin resistance (HOMA IR) index ( Complications of obesity, such as cardiovascular diseases, diabetes, and cancer can cause serious health problems for people and high costs to the health care system4. Obese people have different phenotypes with different metabolic risks5. A group of obese people who do not have metabolic complications is considered metabolically healthy obese (MHO) people7. Meanwhile, obese individuals with obesity-related metabolic complications are known as metabolically unhealthy obese (MUO)8. Obesity is a multifactorial phenomenon in which genetic and environmental factors (such as diet) and their interaction can affect its occurrence1. So far, many genes have been associated with the risk of obesity such as Iroquois homeobox protein 3 (IRX3) and retinitis pigmentosa GTPase regulator-interacting protein 1 like (RPGRIP1L)9. Therefore, gene-environment interaction can be considered one of the most important determinants of obesity risk11.Obesity has become a very important health issue worldwide and its prevalence has increased dramatically, becoming an uncontrollable epidemic13. Variation in the melanocortin-4 receptor (MC4R) gene is known as the most common genetic cause of obesity14. Variants of MC4R-rs17782313 can affect food intake, total energy intake, fat intake, appetite, and consequently the occurrence of obesity16. On the other hand, protective effects on obesity have also been seen in some variants in the MC4R gene17. MC4R rs17782313 has three types of genotypes: CC, TC, and TT. The relationship between these genotypes and obesity and related factors has been investigated in some studies. Findings of a cross-sectional study showed interactions between CC genotype and high stress, high appetite, and high energy and fat intake are likely associated with higher BMI. As a result, it seems that people with C allele of MC4R rs17782313 are more susceptible to overweight or obesity18. A study conducted in Asia showed that the minor allele C can be associated with a 1.55 times increase in the risk of obesity, while the homozygous CC genotype can have a stronger effect and is associated with a 2.43 times increase in the risk of obesity in women19.The existence of diverse findings can probably be attributed to changes in the genetic background of individuals and gene-diet interactions20. In addition, the result of a previous study showed that the probability of metabolically healthy obesity was higher in patients with the T/C genotype of MC4R rs1778231321. Therefore, genetic variants in MC4R rs17782313 are considered as an important factor to understand the cause and type of obesity phenotypes22.Results of a cross-sectional study showed that participants with low-frequency alleles of MC4R rs17782313 had a greater risk of metabolically unhealthy obesity24. However, today there is more focus on the quality of consumed fat than its quantity25. To investigate the effect of type of fat intake, previous studies have suggested dietary fat quality indices, such as cholesterol-saturated fat index (CSI) and the ratio of omega-6 to omega-3 (N6/N3). CSI was introduced in 1986 by Connor et al.26 and N6/N3 was proposed by Simopoulos et al.27. Findings regarding the relationship between the type of dietary fat and the risk of obesity are inconsistent. Several studies have shown a positive association between saturated fat intake and obesity29, while the opposite finding has been observed in studies31. Also, regarding the consumption of polyunsaturated fatty acid (PUFA), positive23, negative32, and null33 associations with obesity have been observed. In a cross-sectional study, an inverse relationship between N6/N3 ratio and general and abdominal obesity was seen34. Also, results of a study showed that subjects with metabolically unhealthy obesity had a higher intake of n-6/n-3 PUFA ratio than metabolically healthy obese person35. In addition, the results of a study showed a positive association between CSI and one of the risk markers of cardiovascular diseases in overweight and obese people36.Studies have shown that total dietary fat intake is related to the risk of obesityDespite the importance of investigating the interaction of genes and diet, which can help make dietary recommendations more specific to people, human studies in this field are limited and inconclusive. In this cross-sectional study, we aimed to assess the interaction between dietary fat quality indices and the MC4R gene in metabolically healthy and unhealthy overweight and obese women.2 and age range of 18–68 were the inclusion criteria. We excluded participants who had the following conditions: history of malignancies, acute or chronic diseases, cardiovascular disease, all types of diabetes, thyroid disease, renal or hepatic disease, taking drugs to lower blood pressure, sugar and blood lipids, taking weight loss supplements or using a specific diet in the past year, smoking and presence of pregnancy, lactation or menopause. All methods were carried out in accordance with relevant guidelines and regulations. All participants completed written informed consent. Also, the ethics committee of the TUMS approved the study protocol. (Ethics number: IR.TUMS.MEDICINE.REC.1401.498).The participants of this cross-sectional study were overweight and obese women. 279 women from the health centers of Tehran University of Medical Sciences were included according to the inclusion criteria. Body mass index (BMI) of 25–40 kg/m37. Participants had to take off their shoes, sweaters, and coats and not have metal objects such as earrings, rings, and watches with them. Height was measured in a standing position without shoes using a non-elastic tape with an accuracy of 0.5 cm. Regarding the waist circumference (WC) and hip circumference (HR) of participants, the narrowest part of the waist (after expiration) and the largest part of the hip were measured respectively using an elastic tape with an accuracy of 0.5 cm. Then waist to hip ratio (WHR) was calculated by the formula.Weight, body mass index, fat-free mass (FFM), and body fat (BF) percentage were measured by a bioelectrical impedance analyzer (BIA) ) according to the manufacturer’s protocols38 were used to classify participants in terms of metabolic health. In this way, 5 items were examined: (1) Triglycerides ⩽ 1.7 mmol/L, (2) Low-density lipoprotein (LDL) ⩽ 2.6 mmol/L and no treatment, (3) High-density lipoprotein (HDL) ⩾ 1.3 mmol/L and no treatment, (4) C-reactive protein (CRP) ⩽ 3.0 mg/L and 5) homeostatic model assessment for insulin resistance (HOMA-IR) ⩽ 2.7. If ⩾ 4 items were present, the individual was considered metabolically healthy.Karelis criteria39.The physical activity (PA) of the participants in the last week was assessed by the reliable and validated International Physical Activity Questionnaire-short form (IPAQ) and measured in terms of metabolic equivalent hours per week (METs-h/week)40. Also, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterol were evaluated by direct enzymatic clearance assay41. High sensitive C reactive protein (hs-CRP) level was assessed by immunoturbidimetric assay. Randox Laboratories (Hitachi 902) kits were used for all assessments. The level of insulin was measured and HOMA-IR was calculated according to this formula: HOMA-IR = insulin (Mu/mL) × fasting glucose (mmol/L)/22.542.Biochemical evaluations were carried out in the Nutrition and Biochemistry Laboratory of the School of Nutritional and Dietetics, TUMS. After 10–12 h of fasting, serum samples were collected. First samples were centrifuged, stored at − 80 °C, and analyzed using a single assay technique. Triglyceride (TG) was assayed using glycerol-3-phosphate oxidase–phenol 4-amino antipyrine peroxidase (GPOPAP) enzymatic endpoint43. Individuals were asked to report the consumption frequency of each food item on a daily, weekly, monthly, or yearly basis and the questionnaire was completed by an expert dietician. The intake of macronutrients, micronutrients, and total energy were analyzed by the NUTRITIONIST 4  food analyzer44.Assessment of the participants’ dietary intake in the last year was done using a 147-item validated semi-quantitative standard food frequency questionnaire (FFQ)26. N6/N3 was calculated by dividing N6/N3 contents of foods27.Dietary fat quality was assessed by two indices: (1) cholesterol-saturated fat index (CSI) and (2) the ratio of N6/N3 essential fatty acids. Food intakes extracted from FFQ were used to calculate both of these indices. CSI was calculated according to this formula CSI = (1.01 × g saturated fat) + (0.05 × mg cholesterol)The polymerase chain reaction-restriction fragment length polymorphism (PCR–RFLP) technique was used for genotyping MC4R rs17782313 and rs1333048 single-nucleotide polymorphisms (SNPs) (genotypes C&T).45, and as a result, fragments containing 3 genotypes, CC, CT, and TT, were distinguished.To extract MC4R Genomic deoxyribonucleic acid (DNA), we used 200 mL of whole blood and the GeneAll Mini Columns Type kit . Extracted DNA, was used to evaluation of 2 SNPs reported near the MC4R gene, rs17782313, and rs17700633 SNP, which was performed. Using a specific primer (89 forward primer 5AAGTTCTACCTACCATGTTCTTGG-3 and reverse primer 5-90TTCCCCCTGAAGCTTTTCTTGTCATTTTGAT-3), polymerase chain reaction (PCR) was performed on these SNPs. PCR was performed using 50 ng/ml DNA, 10 mmol of each primer, and 1 M dimethyl sulfide . The amplification steps included the following items, respectively: primary denaturation at 94 °C (5 min), 35 cycles of denaturation at 60 °C (1 min), annealing at 94 °C (45 s), extension at 72 °C (1 min), and final extension at 72 °C (10 min). The SNPs rs7041  and rs4588  were auscultated by StyI and HaeIII enzymes according to the following procedure: The StyI enzymes (1μL) and HaeIII enzymes (1μL) each one separately added to the PCR product (5μL), distilled water (D.W.) (8μL) and Buffer Y. Tango (10 × 1μL). Products obtained from the digestion process were stained (with ethidium bromide) on a 2% agarose gel and imaging was performed. To confirm PCRFLP results, 10% of the sample were sequenced directly. Sequencing was performed using an ABI PRISM 3730 automated sequencer P-value < 0.05 was considered statistically significant and interaction P-value < 0.1 was determined as mariginally significant.The normality distribution of data was checked by Kolmogorov–Smirnov test. Demographic characteristics of individuals were presented as mean ± standard deviation, minimum and maximum. One-way analysis of variance (ANOVA) was performed to compare anthropometric indices, lipid profile, hs-CRP level, insulin and HOMA-IR between individuals. In order to eliminate the effect of confounding factors, analysis of covariance (ANCOVA) was performed. Post-hoc multiple comparison analysis (Bonferroni corrected) was employed to look into the mean differences between the groups. A generalized linear model (GLM) was used to evaluate the interaction between dietary fat quality indices and MC4R gene in both crude and adjusted models. The results of the analyzes were adjusted for BMI, age, physical activity and energy intake. SPSS version 23.0  was used for data analyzed. This study was supported by grants from the Tehran University of Medical Sciences (TUMS), Tehran, Iran. Each individual was informed completely regarding the study protocol and provided a written and informed consent form before taking part in the study.The current study included 279 women who were either overweight or obese. Individuals' age, weight, BMI, CSI, and N6 per N3 mean and standard deviation (SD) were 36.84 ± 8.45 years, 79.99 ± 10.88 kg, 30.73 ± 3.72 kg/m2, 12.65 ± 5.29, and 12.65 ± 0.10 respectively. Among the genotypes of 279 obese women with the MC4R gene, 40.9% of participants had TT, 26.2% TC and 33% had CC genotypes.P = 0.02) which disappeared after adjustment (P = 0.27). Other variables showed no association with the tertiles of CSI before and after adjustment. No variables had a significant association with N6/N3 tertiles in the crude model, but after controlling for confounding factors, a higher HOMA index was associated with a higher N6/N3 (P = 0.03) (Table Table 3) Table . Figure P =  < 0.001) and higher HOMA index (P = 0.01) after adjustment , linolenic acid, Eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA). Across the higher tertile of N6/N3, we found lower consumption of carbohydrates, monounsaturated fatty acid (MUFA), PUFA, oleic acid, and linoleic acid (Table P = 0.009) and even after adjustment for all confounders , This means that those who homozygously have the risk allele as CC genotype, are more likely to have an unhealthy phenotype with an increase in N6/N3 than those who do not have CC genotype  and all-cause mortality.us or viscerally, is more important46 because visceral adipocytes are more metabolically active and can lead to the development of insulin resistance (IR) and all-cause mortality55. Moreover, higher visceral adipose tissue (VAT)/subcutaneous adipose tissue (SAT) ratios may be associated with increased metabolic and cardiovascular risk, independently of BMI and VAT content56. We found in our study that CC genotype is associated with metabolically unhealthy situations regardless of BMI or visceral fat level, and this may happen because of lower height or higher HOMA index in C allele carriers.A multivariate-adjusted model controlling for covariates found no interaction between the MC4R rs17782313 polymorphism and CSI on the MUH phenotype. But the CC genotype of MC4R rs17782313 interacts with the N6/N3 ratio on the metabolically unhealthy phenotype. Most previous studies support our result. For example, C allele carriers significantly had higher total cholesterol and TG levels in comparison to the TT genotype of MC4R in Brazilian obese children and adolescents, which can cause dyslipidemia58. In addition, an article reported that type 1 diabetes (DT1) was positively correlated with foods rich in PUFAs28 But in contrast to our result, another study found no effect of flax seed on HOMA-IR, although the N6/N3 ratio was lower in this group59. Some other studies examined the benefits of lower intake of N6/N3 ratio, failed to show a significant effect on insulin sensitivity60 the result we found is because of the effect of higher N6/N3 ratio on the expression of inflammatory markers57 which can induce HOMA-insulin resistance61.In our study, a higher intake of the ratio of N6/N3 was associated with a higher HOMA IR index. As a result, two studies expressed that a higher N6/N3 ratio has a worse effect on HOMA-IR and quantitative insulin-sensitivity check index (QUICKI) indices48 another study found that males with CC genotype had higher serum glucose levels compared with the other genotypes (TT and TC), the study expressed that C-allele carriers in the rs17782313 have an increased susceptibility to MUHO compared to the T-allele carriers20. T Schritter et al. demonstrated that in subjects carrying the C allele homozygous or the heterozygous form, the insulin response reduced as compared to TT genotype21. But no statistically significant difference in HOMA index and height status across MC4R rs17782313 genotypes among women and men was seen in some studies62.Participants with the C allele had lower height and higher HOMA index. Our finding in this regard was in line with previous observational studies64 We know that consuming foods that are rich in omega 6 and have higher ratio of N6/N3, accompanied with lower intake of MUFA, omega 3 and oleic acid, and this can induce inflammation65. It is important to mention the strengths of the present study, first, was its novelty, to our knowledge it is the first study that investigated the interaction of dietary fat quality indices and the MC4R gene in metabolically healthy and unhealthy overweight and obese women. Other strengths are that we used validated FFQ questionnaires based on the Iranian population. As our strengths, there are some limitations to our study, first the cross-sectional design of our study, ruling out any causal relationship. The second limitation is the reliance on self-reported dietary data, which can cause information bias. Since our study only included overweight and obese women, we cannot generalize the results of this study to all women in the population.Higher CSI was linked with more consumption of refined grain, vegetable, fish, poultry, egg, red meat, protein, carbohydrate, total fat, cholesterol, PUFA, SFA, linolenic acid, EPA, and DHA, and higher tertile of N6/N3 associate with lower intake of carbohydrate, MUFA, PUFA, oleic acid, and linoleic acid. According to previous studies higher intake of protein especially animal protein associated with higher cholesterol and SFA intake which can cause higher CSI indexThe present study explains the interaction between genetics and the environment. Higher ratio intake of N6/N3 in CC genotype associates with unhealthy phenotype. In addition, C allele carrier of MC4R showed lower height and higher HOMA index. These results can help us to have better dietary recommendations about metabolic health status.


The adaptive acquisition of resistance to BRAF and MEK inhibitor-based therapy is a common feature of melanoma cells and contributes to poor patient treatment outcomes. Leveraging insights from a proteomic study and publicly available transcriptomic data, we evaluated the predictive capacity of a gene panel corresponding to proteins differentially abundant between treatment-sensitive and treatment-resistant cell lines, deciphering predictors of treatment resistance and potential resistance mechanisms to BRAF/MEK inhibitor therapy in patient biopsy samples. From our analysis, a 13-gene signature panel, in both test and validation datasets, could identify treatment-resistant or progressed melanoma cases with an accuracy and sensitivity of over 70%. The dysregulation of HMOX1, ICAM, MMP2, and SPARC defined a BRAF/MEK treatment-resistant landscape, with resistant cases showing a >2-fold risk of expression of these genes. Furthermore, we utilized a combination of functional enrichment- and gene expression-derived scores to model and identify pathways, such as HMOX1-mediated mitochondrial stress response, as potential key drivers of the emergence of a BRAF/MEK inhibitor-resistant state in melanoma cells. Overall, our results highlight the utility of these genes in predicting treatment outcomes and the underlying mechanisms that can be targeted to reduce the development of resistance to BRAF/MEK targeted therapy. V600E and MAPK pathway improves overall survival [V600E or utilization of its splice variants [NRAS, KRAS, MEK1, and AKT1/AKT3 [An estimated 325,000 new cases and 57,000 deaths from melanoma were recorded worldwide in 2020, with significant regional and racial disparities in incidence and mortality rates . Althougsurvival ,9, de nosurvival ,13,14,15variants , activatvariants , and actKT1/AKT3 . Other mKT1/AKT3 , as wellKT1/AKT3 .Targeted therapy reshapes transcriptional programs in cancer cells and the surrounding tumor environment, underpinning tumor phenotypes arising from drug sensitivity or resistance. High throughput gene quantification methods, such as microarrays and RNAseq, allow for unbiased interrogation of the transcriptomic landscape of cancer cells/tissues exposed to targeted therapy . IdentifIn our previous study , we idenp-value < 0.1) odd ratios (OR) in progressed biopsies compared to pre-treatment samples: HMOX1 , ICAM1 , MMP2 , TP53 , and SPARC  . HMOX1, ICAM1, MMP2, and TP53 were upregulated in PROG samples, whereas SPARC was downregulated  A. The Kregulated B.p-value = 7.9 × 10−3). Eight genes actively contributed to the variance observed as their contribution was higher than average  formed a subgroup of genes separate from MMP2 and TP53 (green ellipse). Both gene types have an inverse correlation with VIM and SPARC (orange ellipse). This observation was confirmed via Spearman’s correlation analysis and hierarchical clustering . The significant Spearman’s coefficients were 0.69 for GRN and ICAM1, 0.60 for GRN and ICAM1, 0.60 for ICAM1 and ENG, 0.52 for ICAM1 and HMOX1, and 0.80 for ENG and HMOX1. VIM and SPARC had a significant correlation (rho = 0.36) and were clustered together (cluster 3 in the orange square). Interestingly, cluster 1 was positively correlated with cluster 2 (in green square), with significant correlations between MMP2 and GRN (rho = 0.47), MMP2 and ICAM1 (rho = 0.57), MMP2 and ENG (rho = 0.68), MMP2 and HMOX1 (rho = 0.69), and TP53 and GRN (rho = 0.38). Finally, clusters 1 and 2 were inversely correlated with cluster 3, with significant correlations between MMP2 and VIM (rho = −0.76), TP53 and SPARC (rho = −0.39), TP53 and VIM (rho = −0.38), ENG and VIM (rho = −0.56), HMOX1 and VIM (rho = −0.59), and ICAM1 and SPARC (rho = −0.56). These results suggest that therapeutic resistance is accompanied via increased expression of cluster 1 and 2 genes and decreased expression of cluster 3 genes. This observation is strengthened via the distribution of the 18 genes’ expression between PRE and PROG samples, where genes for clusters 1 and 2 are upregulated, whereas genes from clusters 3 are downregulated in PROG samples compared to PRE samples  explained 40.4% of the variance observed across the samples C. Intere average D. HMOX1,ustering E. HMOX1, samples B.Classifier predictions were performed with a linear discriminant analysis (LDA) regression, followed by the estimation of optimized classifier markers—the AUC of a ROC analysis, the sensitivity, the specificity, the PPV, and the NPV of the 18 genes . The eigp-value of 4.26 × 10−5  , and was highly expressed in PROG samples and significantly dysregulated in the other data sets. In the merged databases, the 13-risk signature had a 2-fold probability of being highly expressed in relapsed/resistant samples   . A linea × 10−6) D. Finall × 10−6) E. These p = 7.87 × 10−3), was observed with a PCA, and differentiated the samples according to the treatment stage , GRN and MMP2 (rho = 0.47), GRN and TP53 (rho = 0.38), and GRN and ENO2 (rho = 0.46). On the other hand, CXCL8, SPARC, and DKK1 were clustered separately, with significant Spearman’s coefficients for DKK1 and CXCL8 (rho = 0.43) . As shown in HMOX1 was significantly more expressed in the samples with a “UP” 13-risk score FC (p = 4.0 × 10−2). The “FiveDatabase” showed similar results, with a highlight of CLXL8 , EGFR (p = 3.9 × 10−2), ENO2 , and TP53  differentiation. The 13-risk score FC-mediated PROG samples stratification could be characterized as a differential expression of BIRC5 (p = 3.1 × 10−2), GRN (p = 8.0 × 10−3), HMOX1 (p = 2.0 × 10−2), MMP2 (p = 2.8 × 10−4), SPARC (p = 1.0 × 10−3), and TP53 (p = 7.0 × 10−3).In the GSE99898 dataset, the expression level of the 13-risk signature explained approximately 40% of the variance  C. The po = 0.43) D–F. In tp = 3.0 × 10−3) (p = 9.4 × 10−4), as well as the pathway PCA index score , with a higher score found in PROG-enriched cluster 1 . A total of 19 pathways were identified as associated with BRAF inhibitor resistance mediated by using the 13-risk gene signature. The top 12 pathways are shown in  × 10−3) B. A hier × 10−3) C. Cluste × 10−3) C. The 13luster 1 D.p = 3.8 × 10−2) (As previously shown in  × 10−2) E—right.  × 10−2) .p = 5.0 × 10−3), “Proteoglycan in cancer” (p = 1.9 × 10−2), “Hippo signaling pathway” (p = 1.9 × 10−2), “Toll-like receptor signaling pathway” (p = 2.8 × 10−2), “Circadian rhythm” , and “Measles” (p = 2.8 × 10−2) , “Estrogen receptor signaling” , “mRNA surveillance pathway” , “Ubiquitin mediated proteolysis” , “Longevity regulating pathway—multiple species” , “Chronic myeloid leukemia” , and “Yersinia infection”   . Similar × 10−2) . Further × 10−2) .BIRC5 and BAK1 , HMOX1 and its regulator PGAM5, and MMP2.The pathways previously selected were strongly correlated with the 13-risk gene panel, as shown in Combining the PPI network and pathway analysis, a model of therapeutic resistance, dependent on the 13-risk panel, was proposed . AlthougOur model suggests a possible role of mitochondrial dysfunction/stress and the rewiring of mitochondrial energy hemostasis   in the eRegulation of HMOX1 abundance and activity is proposed to occur via modulation of NFE2L2, which is a transcription factor that plays a key role in the response of oxidative stress and mitochondrial function and dynamics. However, NFE2L2 protein stability and function are highly regulated through its degradation process. The main mechanism is mediated via KEAP-1 E3 ligase. However, another E3 ligase—β-TrCP-CUL1-based ubiquitin E3 ligase—was also found to target NFE2L2 ,29. ThusTP53, which is a member of the 13-risk gene panel, presented a higher expression in resistant samples and plays the role of the cell cycle guardian. As ATM, the principal activator of p53, is downregulated, p53 overexpression could be the result of the mutations. However, in the context of this project, it was not possible to analyze the mutation status of p53. Interestingly, the overexpression of the Bcl-2 homologous antagonist/killer (BAK) was observed in resistant samples. BAK plays a role in mitochondrial apoptosis through promoting the mitochondrial outer membrane permeabilization, realizing apoptotic factors as cytochrome C [Our model also postulates that the involvement of matrix metalloproteinases and EGFR signaling can contribute to the development of BRAF/MEK inhibitor resistance in melanomas. Studies have shown that MMP-2 overexpression in progressed/resistant stage samples activates EGFR signaling and downstream pathways, including MAPK, MEK/ERK and PI3K/AKT, which are involved in BRAF/MEK inhibitor response. The downstream consequences of these activations include the transcription of anti-apoptotic and pro-proliferation genes, including survivin/BIRC5 . Indeed, the inhibition or activation of MEK was demonstrated to regulate the expression of regulators of cell proliferation and apoptosis, such as BIRC5 . This fichrome C .We employed a hypothesis-driven approach to interrogate the gene expression and predictive potential of a panel of 18 cancer-associated proteins previously detected in a BRAF inhibitor-resistant melanoma cell line . This stSeveral mechanisms for chemotherapy resistance with the critical involvement of HMOX1 were elucidated or suggested. We previously showed that mutant BRAF drives the overexpression of HMOX1, which, in turn, forces the nuclear localization of mutant BRAF and drives proliferation and metastasis in melanomas via reactivation or persistent activation of MAPK signaling . Other rAn additional mechanism in the emergence of resistance involves changes in the expression of SPARC. This gene encodes a cysteine-rich (SPARC) multifunctional matrix-associated protein, which is associated with context- and cell-type dependent effects, ranging from tumor suppression to tumor cell invasion and metastasis . The divIn addition to efforts directed at understanding the relationship between drug sensitivity and the genomic and genetic characteristics of cancer cell lines, major efforts have been invested in relating the findings from in vitro studies of cancer cell lines to in vivo trials.https://www.ncbi.nlm.nih.gov/gds/ (accessed on 22 July 2022)). The accession numbers for these datasets are GSE99898 [Five datasets from microarray or RNA sequencing of patient-derived biopsies (patients were treated with BRAF/MEK inhibitors) were collected from the Gene Expression Omnibus (GEO) datasets repository  . A totalA score, which was named the 13-gene risk score, was calculated according to the following equation: A fold change (FC) of the 13-risk score between PROG and PRE samples was calculated for each sample and stratified as “Down” if FC < 0, “Slightly up” if 0 < FC < z, where z = 0.25 * max (FC), and “Up” if FC > z.p-value <0.1 were selected. Pathways were clustered following a principal component analysis that computed the agglomerate z-scores across the samples analyzed.Pathway enrichment analyses were performed with the pathfinder R package (version 2.0.0)  using thhttps://www.networkanalyst.ca (accessed on 15 January 2023)). Only gene pairs with correlation coefficients >|0.3| were further selected, and a network was built using Cytoscape 3.8 [Protein–Protein interactions were collected using the NetworkAnalyst platform (cape 3.8 .Statistical tests were performed with R software, with Kruskal.test, cor.test, lm, and lda functions for Kruskal–Wallis test, Spearman’s correlation, linear regression analysis, and linear discriminant analysis, respectively.Hierarchical clustering and plotting were performed with the pheatmap  R packagOur approach for predicting melanoma progression leverages both proteomics results and gene expression datasets derived from different populations to yield interesting mechanistic insights on adaptive resistance to BRAF/MEK inhibitors in melanomas. Selecting genes for proteins that are critical in several cancer pathways related to drug sensitivity, our classifier approach rigorously identified patterns that are suggestive of varying influence of genes on the progression of melanomas in gaining resistance to targeted inhibitor therapy. Derived from patient biopsies, these results are clinically relevant and can be interpreted to assist in the treatment and management of melanoma patients in the future. Moreover, based on differentially expressed genes that were predictive of treatment resistance, we proposed potential mechanisms for the development of BRAF/MEK inhibitor resistance, particularly the dysregulation of HMOX1 and SPARC. Lastly, our results allowed the postulation of a biologically informative model that is supported in large part based on evidence in the literature and opens areas requiring further probing. While the utilization of machine learning models and approaches in answering critical questions in cancer biology and clinical care is not without faults, continued research in this area will be beneficial in delivering personalized care that leverages the profiling of genes and proteins to optimize treatment protocols.


Retraction Note: BMC Cancer 19, 400 (2019)https://doi.org/10.1186/s12885-019-5593-5In Fig. 2b, the circRNA overexpression group images appear highly similar to the Control group in Fig. 2d of (, now retFigs. 4c, 4d and 8f appear to contain overlapping panels with Figs. 4d, 4e, 7e and 7f of ;In Fig. 5a, Mock SCC25 and CAL27 appear to contain an overlapping area.In Fig. 5b, Mock SCC25 and CAL27 appear to original from the same sample, with rotation and minor modification.The Editor has retracted this article at the Corresponding Author's request. After publication, concerns were raised regarding highly similar images used in this and other articles, specifically:The authors have stated that these images were misused. The Editor therefore no longer has confidence in the presented data.Wen Su, Feng Wang, Yuehong Shen and Hongyu Yang agree to this retraction. Yufan Wang, Shuai Sun and Minghua Li have not responded to any correspondence from the editor or publisher about this retraction.


Pseudomonas aeruginosa requires all seven genes of the pelABCDEFG operon. The periplasmic modification enzyme PelA contains a C-terminal deacetylase domain that is necessary for Pel-dependent biofilm formation. Herein, we show that extracellular Pel is not produced by a P. aeruginosa PelA deacetylase mutant. This positions PelA deacetylase activity as an attractive target to prevent Pel-dependent biofilm formation. Using a high-throughput screen , we identified 56 compounds that potentially inhibit PelA esterase activity, the first enzymatic step in the deacetylase reaction. A secondary biofilm inhibition assay identified methyl 2-(2-pyridinylmethylene) hydrazinecarbodithioate (SK-017154-O) as a specific Pel-dependent biofilm inhibitor. Structure-activity relationship studies identified the thiocarbazate as a necessary functional group and that the pyridyl ring could be replaced with a phenyl substituent (compound 1). Both SK-017154-O and compound 1 inhibit Pel-dependent biofilm formation in Bacillus cereus ATCC 10987, which has a predicted extracellular PelA deacetylase in its pel operon. Michaelis-Menten kinetics determined SK-017154-O to be a noncompetitive inhibitor of PelA, while compound 1 did not directly inhibit PelA esterase activity. Cytotoxicity assays using human lung fibroblast cells showed that compound 1 is less cytotoxic than SK-017154-O. This work provides proof of concept that biofilm exopolysaccharide modification enzymes are important for biofilm formation and can serve as useful antibiofilm targets.Biosynthesis of the Pel exopolysaccharide in IMPORTANCE Present in more than 500 diverse Gram-negative and 900 Gram-positive organisms, the Pel polysaccharide is one of the most phylogenetically widespread biofilm matrix determinants found to date. Partial de-N-acetylation of this α-1,4 linked N-acetylgalactosamine polymer by the carbohydrate modification enzyme PelA is required for Pel-dependent biofilm formation in Pseudomonas aeruginosa and Bacillus cereus. Given this and our observation that extracellular Pel is not produced by a P. aeruginosa PelA deactylase mutant, we developed an enzyme-based high-throughput screen and identified methyl 2-(2-pyridinylmethylene) hydrazinecarbodithioate (SK-017154-O) and its phenyl derivative as specific Pel-dependent biofilm inhibitors. Michaelis-Menten kinetics revealed SK-017154-O is a noncompetitive inhibitor and that its noncytotoxic, phenyl derivative does not directly inhibit P. aeruginosa PelA esterase activity. We provide proof of concept that exopolysaccharide modification enzymes can be targeted with small molecule inhibitors to block Pel-dependent biofilm development in both Gram-negative and Gram-positive bacteria. Bacterial biofilms are matrix-enclosed microbial communities composed of self-produced extracellular polymeric substances (EPSs) that adhere to each other and/or to biological and nonbiological surfaces. Many human bacterial infections have biofilm etiology. These infections are often highly recalcitrant to antimicrobial treatment. Surgery is often required to resolve infection , 2, resuOne strategy to eliminate biofilms is to inhibit their formation by preventing EPS synthesis. This approach specifically targets the microorganism, as the bacteria will remain in a planktonic state, where they are more susceptible to antimicrobials. Popular approaches to prevent EPS biosynthesis include inhibiting the bacterial secondary messenger bis-(3′–5′)-cyclic diguanylic acid (c-di-GMP) , 7 and qExopolysaccharides are structurally very diverse, with linear and branched homo- and heteropolymers with distinct monosaccharide compositions. These polymers can be further modified post-polymerization through the action of various carbohydrate active enzymes that add or remove functional groups or cleave glycosidic bonds. These modifications impart beneficial characteristics and alter the physicochemical and biological properties of the polymer, which contribute to improved bacterial persistence and survival . Common N-acetylgalactosamine  polymer that is composed predominantly of GalNAc-GalN repeat units  in Gram-negative organisms, where disruption of a deacetylase also abolishes biofilm formation, suggests two potential mechanisms. Catalytically inactive mutants of the deacetylase PgaB in Escherichia coli and HmsF in Yersinia pestis do not export PNAG, while deletion of BpsB in Bordetella bronchiseptica results in acetylated PNAG being exported and shed from the cell surface  (Wisteria floribunda lectin conjugated to horseradish peroxidase (WFL-HRP), which recognizes terminal GalNAc  . Using Wl GalNAc , we probl GalNAc   and low (1% [wt/vol] sodium dodecyl sulfate) controls, fluorescent  and quenching  compounds were identified . As a result, 694 fluorescent and quenching compounds were omitted from further analyses. For the remaining 68,666 compounds, the percentage of inhibition was calculated and plotted . The meain vitro may not translate into inhibition of Pel-dependent biofilm formation, we next explored whether the 56 hits attenuated Pel biofilm formation using a microplate biofilm assay. The PBADpel strain was used in this secondary in vitro assay as it forms robust Pel-dependent biofilms. We anticipated three potential outcomes for our hit compounds . A compound could have either no effect on biofilm formation, bactericidal activity, or cause a dose-dependent attenuation of biofilm development. Quantitatively, an absorbance at 595 nm (A595), which measures biofilm adherence, of <1 with no titratable response and a lack of turbidity suggests a bactericidal effect. Compounds that had either no effect on biofilm development  or bactericidal activity were not pursued further. Of the 56 hits, 48 had no effect on PBADpel biofilm formation and four compounds, SK-000424, SK-000433, SK-072525, and SK-001263, were found to be bactericidal. Four validated hit compounds, SK-049294, SK-017044, SK-034365, and SK-017154 resulted in titratable inhibition of biofilms produced by the PBADpel strain .As inhibition of PelA esterase activity P. aeruginosa strains. To determine whether the four compounds specifically inhibited Pel-dependent biofilm formation, we next tested their ability to inhibit biofilm formation using (i) an engineered Psl-overproducing strain , (ii) the PBADpel strain, (iii) PA14, which produces Pel-dependent biofilms, and (iv) a hyperadherent strain with elevated pel expression, PA14 ΔwspF  analysis of SK-017154 did not generate the expected characteristic singlet for the thiadiazole structure provided by the supplier at ~6 ppm for the thioaminal and a broad singlet around the same region for the N-H . Instead, a broad singlet at 13.5 ppm and a sharp singlet at 8.25 ppm were observed. The broadness of the singlet at 13.5 suggests that it is the N-H proton. Its presence so far downfield could be rationalized if it is engaged in an intramolecular hydrogen bond with the pyridyl nitrogen . The presence of the benzylic proton at 8.25 can be rationalized if this carbon is sp2 hybridized. This hypothesis is supported by comparing the spectrum of SK-017154 with one reported for the 2-pyridinecarboxaldehyde Schiff base of S-methyldithiocarbazate . Using SK-017154-O, we repeated the secondary in vitro crystal violet assay and found that it robustly inhibited PA14 biofilms with a 50% inhibitory concentration (IC50) of 1.8 ± 1.1 μM . Having established the structure of the compound provided by the supplier and demonstrated that in-house synthesis of this compound produced an inhibitor of PA14 biofilms, SK-017154-O was prioritized for further studies.Prior to further analysis, we sought to verify the structure of SK-017154. arbazate . We syntarbazate  and founvia a known route gave the phenyl derivative, compound 1 (p-bromo substituent to compound 1 or a larger anthracenyl group (compound 8) was not tolerated. Next, we examined whether larger groups could be tolerated in the dithiocarbazate substituent (compounds 9 and 10). Increasing from methyl (compound 1) to ethyl (compound 9) gave a moderate decrease in potency and all inhibition was lost moving to a larger benzyl derivative (compound 10). Finally, we examined variations in the dithiocarbazate linkage. Moving from a dithiocarbazate to carbazate (compound 11), forming sterically similar thiocarbamates (compounds 12 and 13) to compound 1, or moving to a phenyl carbamate (compound 14) resulted in loss of biofilm inhibition potency. From these structure-activity studies, it is evident that the pyridyl nitrogen of SK-017154-O is not required for antibiofilm activity. However, the dithiocarbazate is essential, suggesting a key role in the inhibition of biofilm formation as conservative substitutions on either side of the dithiocarbazate are well tolerated. Given the potency and the expected decrease in metal binding affinity, further biochemical characterization of SK-017154-O and its phenyl derivative compound 1 was carried out. We found that both of these compounds were able to inhibit Pel-dependent biofilms formed by Gram-positive Bacillus cereus ATCC 10987 50 values0 values  and d; hA) and the length-to-width ratio (LWR) of IMR-90 human lung fibroblast cells and calculated a cell rounding Index (CRI), where CRI = A × LWR. CRI has been used previously to quantify death of a target cell . PelAΔ46Chis6 was therefore used in all subsequent assays to determine the inhibitory mode of action of SK-017154-O and its phenyl derivative, compound 1. We first tried to use the pseudosubstrate ACC to kinetically characterize PelAΔ46Chis6. However, we found that this compound was prone to autohydrolysis and that saturation of enzymatic activity could not be achieved due to its solubility limit  , was therefore used as a pseudosubstrate to test the mode of inhibitory action of SK-017154-O and compound 1. AMMU has significantly reduced levels of autohydrolysis and has been used as a suitable pseudosubstrate to characterize the activity of the exopolysaccharide deacetylases PgaB from eumoniae . Using A 1.0 μM) . Unexpec 1.0 μM) , despiteThe requirement of the dithiocarbazate for biofilm inhibition and the potential of these compounds to serve as acylating species, albeit at elevated temperature, spurred us to investigate the reversibility of the enzyme inhibition with SK-017154-O . Using gVmax (mK (F test and compared the fit of both models. The mixed inhibition model did not provide sufficient goodness of fit to justify its higher complexity (P = 0.11), so the simpler noncompetitive inhibitor modality was chosen. By globally fitting the data to a noncompetitive inhibitor model, we determined the inhibition constant as iK = 1.13 ± 0.03 μM  that could then damage the cell  cells . This is90 cells  and d. Pt growth , 47. Bott growth . The bact growth . FurtherPel biosynthetic loci are widespread across eubacteria , 48. TheE. coli and P. aeruginosa strains were routinely grown at 37°C overnight, and broth cultures were shaken at 200 rpm. Lysogeny broth (LB) contained the following per liter of ultrapure water: 10 g tryptone, 10 g NaCl, and 5 g yeast extract. No-salt lysogeny broth (NSLB) was prepared as LB, but excluded NaCl. Jensen’s medium contained the following per liter of ultrapure water: 5 g NaCl, 2.51 g K2HPO4, 15.56 g glutamic acid, 2.81 g l-valine, 1.32 g l-phenylalanine, 0.33 g/L MgSO4•7H2O, 21 mg CaCl2•2H2O, 1.1 mg FeSO4•7H2O, 2.4 mg ZnSO4•7H2O, and 1.25% (wt/vol) d-glucose. Vogel-Bonner minimal medium (VBMM) 3•6H2O, 27.7 × 10−3 g CaCl2, 7.0 × 10−3 g CoSO4•7H2O, 4.3 × 10−3 g CuSO4, 6.0 × 10−3 g NaMoO4•H2O, 123.2 × 10−3 g MgSO4•7H2O, and 32.4 × 10−3 g NiCl2 per 250.0 mL of Milli-Q water, which was filter sterilized and stored at −20°C. To prepare solid medium, 1.5% (wt/vol) agar was added to LB or NSLB or 1.0% (wt/vol) noble agar was added to VBMM. Antibiotics were added to growth medium, where appropriate. For E. coli, 50 μg/mL kanamycin (KAN), 50 μg/mL carbenicillin (CAR), or 10 μg/mL gentamicin (GEN). For P. aeruginosa, 60 μg/mL GEN was used for merodiploid selection, whereas 30 μg/mL GEN was used for mini-Tn7 mutant selection.The bacterial strains and plasmids used in this study are listed in Table S1 and Table S2, respectively, in the supplemental material. m (VBMM)  was prepescribed . The 1,0The ChemBridge, Maybridge, Prestwick, Microsource, and LOPAC chemical libraries were purchased by the SickKids Proteomics, Analytics, Robotics and Chemical Biology Centre (SPARC) BioCentre at SickKids. The 7-acetoxycoumarin-3-carboxylic acid (ACC)  used for1H and 13C NMR analyses of synthesized compounds.Figure S7 shows the S-alkyldithiocarbazates.(i) General procedure for the synthesis of  Potassium hydroxide  was dissolved in 15 mL of a 14:1 solution of ethanol and water and cooled on ice. Hydrazine hydrate  was added slowly. Following this addition, a solution of carbon disulfide  in 2.5 mL ethanol was added dropwise over a 1-h period, maintaining the temperature below 10°C. The resulting biphasic solution was transferred to a separatory funnel, and the pale-yellow top layer was isolated and diluted into a 10-mL solution of 4:6 ethanol and water and then cooled on ice. Alkyl bromide (1 eq) was added to this solution dropwise with vigorous stirring and left for 10 min. At this point, 10 mL of ice-cold water was added and the reaction mixture was left for a further 10 min. The mixture was then filtered, and the solid was washed with water and subsequently dried under vacuum and used immediately and without any further purification.S-alkyldithiocarbazate (1 eq) was dissolved in a 1:1 solution of ethanol and water, to which was added 1 equivalent of aldehyde. The reaction mixture was left for 10 min before the reaction mixture was filtered, and the solid was washed thoroughly with cold ethanol and dried under vacuum.Crude S-methyldithiocarbazate (SK17154-O) are as follows. 1H NMR : δ = 13.47 , 8.65 , 8.29 , 7.97 to 7.87 , 7.47  2.56 . 13C NMR : δ = 199.3, 152.3, 149.8, 146.5, 137.0, 124.9, 120.0, 16.84. HRMS: [M + H]+ calculated, C8H9N3S2 212.03161; found, 212.03105.Analytical data for 2-pyridinecarboxaldehyde S-methyldithiocarbazate (compound 1) are as follows. 1H NMR : δ = 13.29 , 8.23 , 7.70 , 7.44 , 2.51 . 13C NMR : δ = 198.8, 146.8, 133.8, 131.2, 129.4, 127.9, 17.24.Analytical data for benzaldehyde S-ethyldithiocarbazate (compound 9) are as follows. 1H NMR : δ = 13.25 , 8.26 , 7.75 , 7.47 , 3.20 ,1.30 . 13C NMR : δ = 197.3, 146.4, 133.4, 130.7, 129.0, 127.4, 27.34, 13.83.Analytical data for benzaldehyde S-benzyldithiocarbazate (compound 10) are as follows. 1H NMR : δ = 13.38 , 8.27 , 7.71 ,7.49 to 7.41 , 7.35 , 7.29 , 4.51 . 13C NMR : δ = 196.6, 146.6, 136.8, 133.3, 130.8, 129.2, 129.0, 128.5, 127.5, 127.2, 37.60.Analytical data for benzaldehyde (ii) Synthesis of benzaldehyde methylcarbazate (compound 11). Dimethylcarbonate  and hydrazine hydrate  were mixed together and heated at 50°C for 20 min. At this point they were allowed to return to room temperature and left overnight. The crude product was dried under vacuum and used immediately and without any further purification. Analytical data are as follows. 1H NMR : δ = 11.11 , 8.04 , 7.64 , 7.46 to 7.36 , 3.71 . 13C NMR : δ = 134.3, 129.5, 129.7, 126.6, 51.39.Methylcarbazate  was dissolved in 12 mL of a 1:1 mixture of toluene and diethyl ether, to which benzaldehyde  was added. The mixture was left overnight before the reaction mixture was filtered and the solid was washed thoroughly with cold ethanol and dried under vacuum.(iii) Synthesis of methyl 2-phenylethyl carbamodithioate (compound 12). Potassium hydroxide  was dissolved in 2 mL of a 14:1 solution of ethanol and water and cooled on ice. Phenethylamine  was added slowly. Following this addition, a solution of carbon disulfide  in 0.5 mL ethanol was added dropwise. The resulting biphasic solution was transferred to a separatory funnel, and the pale-yellow top layer was isolated and diluted into a 1-mL solution of 1:1 ethanol and water and then cooled on ice. Methyl iodide  was added to this solution dropwise with vigorous stirring and left for 10 min. At this point, 5 mL of ice-cold water was added and the reaction was left for a further 10 min. The mixture was then filtered, and the solid was washed with water and subsequently dried under vacuum. The compound was purified via column chromatography using dichloromethane/MeOH. Analytical data are as follows. 1H NMR : δ = 7.34 , 7.22 , 4.01 , 2.98 , 2.61 . 13C NMR : δ = 138.1, 128.8, 129.7, 126.8, 105.0, 47.96, 34.24, 18.10.Taq DNA polymerase were purchased from New England Biolabs or Thermo Fisher Scientific. Phusion DNA polymerase and BP Clonase II were purchased from Thermo Fisher Scientific. Primers used in this study were obtained from Sigma-Aldrich or Integrated DNA Technologies (Table S3). Site-directed mutagenesis of plasmids was completed using the Agilent QuikChange Lightning site-directed mutagenesis kit. Transformation of P. aeruginosa was performed using standard protocols for electroporation  and then spread on LB agar containing 50 μg/mL CAR and 10 μg/mL GEN. Several colonies were picked, streaked on LB agar containing antibiotic selection, and incubated overnight at 37°C. Subsequently, plasmids were purified from these strains. A plasmid with the desired sequence was identified using Sanger sequencing with the primers oJDR33 to oJDR40 (Table S3). This yielded the plasmid pJDR45, which contains a mutant pelA allele (encoding PelAD528A) fused to araC::PBAD in the Gateway cloning site of pUC18-miniTn7T2.1-Gm-GW (Table S2). This mini-Tn7 transposon was introduced into the chromosome of the target P. aeruginosa JJH879 strain (Table S1) via established methods for coelectroporation with the helper plasmid pTNS2. Insertion of mini-Tn7 at the neutral attTn7 site adjacent to P. aeruginosaglmS was confirmed by PCR using the primers PTn7L, PTn7R, PglmS-up, and PglmS-down (Table S3) as previously described  .In-frame and unmarked exchange . The pEXstructed  and intr PBADpsl  via bipa0 (λpir) . MerodipBADpel-derived strains were grown overnight at 37°C to stationary phase in Jensen’s medium supplemented with 0.5% (wt/vol) l-(+)-arabinose to induce pel expression. Crude cell-associated Pel and secreted Pel were analyzed using dot blots developed with WFL conjugated to horseradish peroxidase (WFL-HRP) as described by Whitfield et al.  of 0.005 in 3 mL of NSLB in borosilicate glass tubes supplemented with 0.5% (wt/vol) l-(+)-arabinose to induce pel expression. The glass tubes were left to grow statically at 25°C for 72 h. The assay was performed in triplicate, with one representative image shown.PBADpel-derived strains were grown to stationary phase in NSLB, diluted to a final OD600 of 0.005 in 1 mL of NSLB, and supplemented with 0.5% (wt/vol) l-(+)-arabinose to induce pel expression. One hundred microliters of the normalized cultures was added to the wells of a Corning CellBind 96-well microtiter plate (n = 5 for each strain examined) and incubated for 20 h at 25°C statically. Nonadherent biomass was removed by washing the wells three times with water, and the remaining adherent biomass was stained with 150 μL of 0.1% (wt/vol) crystal violet for 10 min with agitation at room temperature. To remove excess stain, the wells were washed three times with water and the residual stain was solubilized by adding 200 μL of anhydrous ethanol to each well and left to incubate for 10 min with agitation at room temperature. Forty microliters of the solubilized crystal violet solution was transferred to a plate containing 160 μL of anhydrous ethanol, and the absorbance was measured at 595 nm to quantify the adherent biofilm biomass. B. cereus ATCC 10987 biofilms were grown, stained, and destained as outlined by Whitfield et al.  were performed as described previously  were generated as follows. The nucleotide sequence of pelA from Pseudomonas aeruginosa PAO1 was used to design primers for PCR (Table S3) -RP competent cells, growing in 6 L of LB broth supplemented with 50 μg/mL KAN at 37°C to mid-log phase, whereupon protein expression was induced by the addition of isopropyl β-d-1-thiogalactopyranoside (IPTG) to a final concentration of 1 mM. Following induction, the cells were left to grow overnight at 18°C for 20 h, prior to being harvested via centrifugation at 6,000 × g for 15 min at 4°C. The resulting cell pellets were stored at −20°C until needed for purification.Vectors for expression of C-terminally Hisable S3) . NdeI anChis6 was purified as follows. Cell pellets from a 6-L bacterial culture were thawed and resuspended in 120 mL of buffer A . Cells were then lysed by three passes through a homogenizer at 1,000 lb/in2 while on ice. The resulting lysate was centrifuged at 30,000 × g for 30 min at 4°C to pellet insoluble proteins. The supernatant was run through a Ni2+-nitrilotriacetic acid (Ni-NTA) affinity HisTrap HP  cartridge using a fast protein liquid chromatography (FPLC) device, washed with buffer A until an A280 of <100 milli-absorbance units (mAU) was detected, and then eluted with a 10-column volume linear gradient to 100% buffer B . Samples were taken throughout the purification process and run on a 10% SDS-PAGE gel to assess protein purity. The appropriate elution fractions were concentrated and further purified and buffer exchanged into buffer C  by size exclusion chromatography (SEC) using a HiLoad 16/60 Superdex 200 gel filtration column . Samples from SEC were run on a 10% SDS-PAGE gel to verify homogeneity. The protein concentration was determined using the Pierce bicinchoninic acid (BCA) protein assay kit from Thermo Scientific. PelAΔ46Chis6 was divided into aliquots and stored at −80°C for subsequent use in enzyme kinetics using AMMU.PelAΔ46Nhis6 aliquots were thawed at room temperature for 30 min and diluted with protein buffer  to create solution A. Solution A with reaction buffer (50 mM HEPES [pH 7.0] and 75 mM NaCl) was used to prepare high (solution A and reaction buffer at 4°C) and low (solution A and reaction buffer with 2.5% [wt/vol] SDS) control solutions. To a 96-well polypropylene V-bottom plate , 225 μL of the low and high control solutions was added to wells in column 1 and columns 2 to 12, respectively, to create a PelA master mix plate. From the PelA master mix plate, 10 μL of the low control solution was added to wells in columns 1 and 2 and 10 μL of the high control solution was added to wells in columns 3 to 24 of a 384-well, black, flat-bottom assay plate  .The ACC assay was adapted as a HTS and used as a reconfirmation screen as follows. Frozen PelAΔ46Into a 96-well clear V-bottom polypropylene deep well plate (Axygen), 550 μL of reaction buffer was aliquoted and used to transfer 24.5 μL of reaction buffer to a 384-well, clear, round-bottom polypropylene plate  to generate a drug dilution plate. From chemical libraries in 384-well plates (Echo), 0.5 μL was diluted in the drug dilution plate such that all compounds were at a concentration of 200 μM and 2% (vol/vol) dimethyl sulfoxide (DMSO).Nhis6 statically at room temperature for 30 min. To prepare substrate, 1.2 mL of substrate bulk solution (10 mg of ACC and 6.45 mL of DMSO) was mixed with 28.8 mL of reaction buffer to produce the substrate assay solution. To a 96-well polypropylene V-bottom plate , 250 μL of substrate assay solution was aliquoted, from which 10 μL was added to the entire assay plate to initiate the reaction. Using an EnVision plate reader, the residual fluorescence units (RFU) of each assay plate were measured immediately after addition of substrate (T = 0) and after 3 h of incubation at room temperature using an λem of 355 nm and λex of 460 nm. All liquid handling between plates was performed with an Agilent Bravo liquid handler using 30-μL tips. All reaction mixtures had final concentrations of 0.3 μM PelAΔ46Nhis6, 100 μM ACC, and 2% (vol/vol) DMSO. All drug-treated reaction mixtures had 40 μM compound from a chemical library.To begin the HTS assay, 5 μL of compound in the drug dilution plate was added to the wells in columns 3 to 22 of the 384-well assay plate  and left to preincubate with PelAΔ46T = 0 was used to discern which compounds were fluorescent (RFU greater than 2× the average RFU of the high control wells) and which compounds were quenchers . A total of 694 compounds  were flagged as fluorescent or quenching. These compounds were not included in percentage of inhibition, mean, and standard deviation calculations. The percentage of inhibition was calculated with the measured RFU values as follows:The baseline read at P. aeruginosa grown to stationary phase in NSLB were diluted to a final OD600 of 0.005 in 1 mL of NSLB, where PBADpel was supplemented with 0.5% (wt/vol) l-(+)-arabinose to induce pel expression. Into 96-well polystyrene microtiter plates (Nunc), 99 μL of normalized culture and 1 μL of hit compound solution dissolved in DMSO were aliquoted. Hit compounds were added in duplicate or triplicate to generate 6- to 10-point dose-response curves in 2-fold or 3-fold dilutions starting at 1 mM or below depending on the solubility of the compound . Assays after assessing validation compounds were performed as six independent experiments (n = 3 wells per compound concentration per experiment). Plates were incubated statically for 24 h at 25°C. Bacterial cultures were visually examined for a lack of turbidity to identify bactericidal activity. Bactericidal hit compounds were excluded from further analyses. Nonadherent biomass was removed and stained with 0.1% (wt/vol) crystal violet as outlined above for the crystal violet microtiter plate assay. The absorbance was measured at 595 nm to quantify the adherent biofilm biomass. Half-maximal inhibitory concentrations (IC50) were calculated using nonlinear least-squares fitting to a dose-response model in Prism (GraphPad software). The IC50 was the concentration of hit compound required to reduce the adherent biofilm biomass by half after 24 h of treatment.Overnight cultures of P. aeruginosa PA14 grown to stationary phase in NSLB were diluted to a final OD600 of 0.05 in 1 mL of NSLB. Growth curves were set up and read in 96-well polystyrene microplates (Nunc). Wells treated with compound contained 99 μL of normalized culture and 1 μL of compound solution dissolved in DMSO. Solvent controls contained, 99 μL of normalized culture and 1 μL of DMSO. Untreated controls contained 100 μL of normalized culture. All compounds and controls were tested in triplicate. Plates were sealed with HDClear packaging tape (Duck) and placed inside a Synergy Neo2 (BioTek) plate reader at 25°C with continuous orbital shaking for 18 h, and the OD600 was read every 30 min. Growth rate (r) was estimated as follows, using the exponential growth formula for continuous growth  values plotted against time in Prism v.7.05 (GraphPad Software), thereby providing 95% confidence intervals for r. The coefficient of correlation  was used to guide selection of datum points from exponential growth that best fit the linear relationship. Subsequently, doubling time, dt, was calculated as follows:dt were calculated from the corresponding r values from linear regression.Overnight cultures of s growth :r=ln was calculated as outlined for the ArrayScan high-content phenotypic screen by Tam and Beilhartz et al. , where hCell viability was assessed as described by Zhang et al.  with theNhis6 and PelAΔ46Chis6 as follows. The assay was performed using a 96-well black-bottom plate (Thermo Fisher). Enzyme reaction wells had 5 μL of enzyme and 90 μL of reaction buffer. Enzyme blank reaction wells had 95 μL of reaction buffer. The assay was initiated by the addition of 5 μL ACC solubilized in DMSO to all wells. Using a SpectraMax M2 (Molecular Devices) plate reader, the RFU was measured at 20-s intervals over 5 min at room temperature using an λem of 386 nm and λex of 447 nm. Reaction mixtures were maintained at a final volume of 100 μL with 3.1 μM PelAΔ46Nhis6 or 0.4 μM PelAΔ46Chis6, 2.5 μM ACC, and 5% (vol/vol) DMSO. The background hydrolysis rate was monitored and subtracted from the enzyme-catalyzed reactions. A standard curve was generated using the product of the reaction, 7-hydroxycoumarin-3-carboxylic acid, to calculate the specific activity. Three independent experiments (n = 3 wells per experiment) were performed.ACC was used as a substrate to compare the specific activities of PelAΔ46Chis6 were used as described above and the standard curve was used to calculate the rate. Kinetic parameters were calculated using the general Michaelis-Menten model in Prism .To generate a kinetic curve, various concentrations of ACC and 2 μM PelAΔ4650 curves as follows. Frozen PelAΔ46Chis6 aliquots were thawed on ice for 5 min and diluted in reaction buffer to create a PelA master mix. Hit compounds and AMMU were freshly dissolved in DMSO prior to use. A 25% (vol/vol) DMSO solution was freshly made using reaction buffer. A mixing plate was generated using a 96-well polypropylene plate, wherein each row contained 20 μL of reaction buffer, 20 μL of PelA master mix, serially diluted hit compound in 25% (vol/vol) DMSO, and 10 μL of 4 mM AMMU. To begin the assay, 2.5 μL of serially diluted hit compound was added to the reaction buffer and PelA master mix for the enzyme blank and enzyme reactions, respectively. To initiate the reaction, 2.5 μL of AMMU was added to the enzyme blank and enzyme reaction solutions, for a total reaction volume of 25 μL. From the mixing plate, 10 μL of solution was added to a 384-well, black, flat-bottom assay plate  in duplicate and centrifuged at 3,000 rpm for 30 s. Using a Synergy Neo2 (BioTek) plate reader, the RFU was measured at 30-s intervals over 10 min at room temperature using an λem of 330 nm and λex of 450 nm. Final reaction mixtures were maintained at a final volume of 10 μL with 1 μM PelAΔ46Chis6, 400 μM AMMU, and 12.5% (vol/vol) DMSO. The background hydrolysis rate was monitored and subtracted from the enzyme-catalyzed reactions. The percentage of PelA esterase activity was calculated using the RFU per minute of enzyme reactions with no hit compound present. IC50 values were calculated using nonlinear least-squares fitting to a dose-response model in Prism (GraphPad software).AMMU was used as a substrate to generate ICChis6 and 7.5% (vol/vol) DMSO. Prism (GraphPad Software Inc.) was used for all kinetic analyses and the sum of squares F test. Apparent Vmax and mK parameters were determined by nonlinear regression fitting the initial velocities (iv) to the Michaelis-Menten model:iK, was calculated by globally fitting the initial velocity data to a noncompetitive inhibition model:For kinetic characterization, the assay was performed as described above with the following modifications. The mixing plate contained a row with 10 μL of hit compound at 25% (vol/vol) DMSO and 10 μL of AMMU serially diluted in 50% (vol/vol) DMSO. In the mixing plate, 2.5 μL of hit compound was added to wells with reaction buffer and PelA master mix for enzyme blank and enzyme reactions, respectively, sealed with adhesive foil (VWR), and left to incubate for 2 h at room temperature. The reaction was initiated as indicated above. Final reaction mixtures had 1 μM PelAΔ46Chis6 aliquots, hit compounds, AMMU, and 25% (vol/vol) DMSO were prepared as outlined above for the AMMU assay. A 96-well polypropylene conical-bottom plate (Thermo Scientific) serving as a mixing plate was used to set up three reaction types: enzyme-only, enzyme plus compound control, and reversibility test. To one row of wells in the mixing plate, 40 μL of PelA master mix was added. To three wells for each of the enzyme-only reaction and reversibility test, 5 μL of 25% (vol/vol) DMSO and 5 μL of 0.25 mM hit compound in 25% (vol/vol) DMSO were added, respectively. From wells in the mixing plate, 45 μL corresponding to the enzyme-only reaction and reversibility test and 40 μL of PelA master mix corresponding to the reversibility test were added to the resin beds of a total of nine Zeba spin desalting columns per compound. As per the manufacturer’s instructions, the Zeba spin desalting columns were centrifuged at 1,500 × g for 2 min to perform the gel filtration and collect the eluates. The eluates were transferred back to the mixing plate, across a second row of wells. To the enzyme plus compound control reaction mixtures, 5 μL of 0.25 mM hit compound in 25% (vol/vol) DMSO was added.To assess the reversibility of the compounds, 0.5-mL Zeba spin desalting columns with a 7,000-molecular-weight cutoff (MWCO) (Thermo Scientific) were used as follows. The Zeba spin desalting columns were prepared and buffer exchanged into reaction buffer as per the manufacturer’s instructions. Frozen PelAΔ46in vitro esterase activities of the three reaction mixtures were evaluated using the AMMU assay described above. To a third row of wells in the mixing plate, 10 μL of 4 mM AMMU was added. To initiate the reaction, 5 μL of 4 mM AMMU was added to the eluates in the second row of the mixing plate, from which two aliquots of 10 μL were transferred to a 384-well, black, flat-bottom assay plate . The rest of the AMMU assay was performed as outlined above. Final reaction mixtures were maintained at a final volume of 10 μL with 1 μM PelAΔ46Chis6, 400 μM AMMU, and 12.5% (vol/vol) DMSO. Three independent experiments (n = 2 wells per experiment) were performed.The


Distinct Ca2+ signatures with unique parameters are induced by different stresses and deciphered by various Ca2+ sensors. Recent research on the participation of Ca2+ signaling in regulation of mineral elements has made great progress. In this review, we focus on the impact of Ca2+ signaling on plant mineral uptake and detoxification. Specifically, we emphasize the significance of Ca2+ signaling for regulation of plant mineral nutrition and delve into key points and novel avenues for future investigations, aiming to offer new insights into plant ion homeostasis.Plant mineral nutrition is essential for crop yields and human health. However, the uneven distribution of mineral elements over time and space leads to a lack or excess of available mineral elements in plants. Among the essential nutrients, calcium (Ca 2+ signaling in plant mineral uptake and detoxification. The role of Ca2+ as a second messenger in response to extracellular stimuli and the distinct Ca2+ signatures induced by different stresses are highlighted. The review also offers insights into future investigations of plant ion homeostasis.This review provides a comprehensive overview of Ca Plant growth and development are affected by changing environmental conditions and various stress factors, including soil mineral content and accumulation of harmful elements. The soil provides plants with 14 essential mineral nutrients, which are categorized into macronutrients and micronutrients on the basis of their dry biomass percentage (< or ≥ 0.1%). Macronutrients consist of nitrogen (N), phosphorus (P), potassium (K), sulfur (S), calcium (Ca), and magnesium (Mg), and micronutrients include iron (Fe), manganese, copper, zinc, molybdenum, boron (B), chloride, and nickel . Besides2+ is an essential nutrient and the most prominent second messenger, playing a crucial role in response to extracellular stimuli in all eukaryotes : Ca2+ signals with different parameters such as transient or repetitive oscillation, duration, amplitude, frequency, and spatial distribution , glutamate receptor-like channels, the mechanosensitive channel of small conductance (MscS)-like channels, Mid1-complementing activity channels, reduced hyperosmolality-induced [Ca2+]i increase  channels, two-pore channels, annexins, and MILDEW RESISTANCE LOCUS O proteins (2+ transporters include Ca2+-ATPases and Ca2+/H+ exchangers (CAXs). Ca2+-ATPases are classified into two groups: P2A type (endoplasmic reticulum [ER]-type Ca2+-ATPase: ECA1–ECA4) and P2B type  , calmodulins (CaMs), CaM-like proteins (CMLs), Ca-dependent protein kinases (CPKs), and Ca- and CaM-dependent protein kinases. The roles of Ca2+ sensors and Ca2+ channels in Ca2+ signaling have been studied extensively over the past few decades and have recently been described in detail  . CAX gen7–ACA13) . The pron detail . Here, w3−) serves not only as an N source but also as a signaling molecule that regulates numerous processes, including gene expression, root architecture, shoot development, seed germination, and flowering , a homolog of slow-type anion channel-associated 1 (SLAC1), can be phosphorylated by the CBL1/9–CIPK23 module, improving its NO3− transport function, and CPK21 can also activate SLAH2/3 to promote their NO3− transport  and cytoplasm ensure that N signals are transmitted rapidly and respond in a timely manner to different N concentrations. Expression of the basic region/leucine zipper motif (bZIP) transcription factor family members TGA1 and TGA4 is upregulated in a Ca2+-dependent manner and regulates expression of NRT2;1 and NRT2;2, which mediate NO−3 transport  transcription factor NAC075, regulating expression of the downstream target WRKY53  has been proposed to act as a ligand-dependent transcriptional activator and an intracellular NO− sensor . The sub residue . Two typransport . Under lt WRKY53   is the primary source of N in many species, but excessive NH4+ can lead to NH+4 toxicity  family, AMT1;1 and AMT1;2, are inhibited by the CBL1–CIPK23 complex  in plants, where it plays essential roles in many physiological processes, such as osmotic balance, stomatal regulation, protein biosynthesis, water and nutrient absorption, and enzyme activation  and high-affinity K+ transporter 5 (HAK5), a K+/H+ symporter, are considered to be the major components involved in K+ uptake in Arabidopsis root cells under low-K+ conditions , releasing vacuolar K+ into the cytoplasm  convert inactive FIT into active FIT, further contributing to plant adaptation to Fe deficiency (Fe(III) in the soil is reduced to Fe(II), which can be absorbed and utilized by plants, via Fe(III) chelate reductase (FRO) . Under F to IRT1 . A recening IRT1 . In addiactivity . When Feactivity . CPK21/2ficiency .Figure 2Mn is an important cofactor of more than 30 enzymes, an essential element in the metalloenzyme cluster of the photosystem II oxygen-evolving complex, and a requirement for multiple steps in the biosynthesis of carbohydrates, lipids, and lignin in plants . It is t2+ signaling in Mn uptake and transport in plants. Mn deficiency induces a pattern of long-lasting multicellular Ca2+ oscillations, with maximum concentrations spatially confined to specific cell groups in the root elongation zone. CPK21 and CPK23 interact with and phosphorylate the PM-localized, high-affinity Mn transporter NATURAL RESISTANCE-ASSOCIATED MACROPHAGE PROTEIN 1 (NRAMP1) at the Thr498 residue, enhancing the transport activity of NRAMP1 and facilitating Mn2+ absorption under conditions of Mn depletion , excessive Cu induces Ca2+ release from the ER, and the ryanodine-sensitive and IP3-sensitive Ca channels in the ER are activated in response to excess Cu , As, and Al toxicity .Na in the soil is an important nutrient for plant growth and development. However, at high concentrations, it disturbs and inhibits various physiological processes and plant growth .+ sensor, glycosyl inositol phosphorylceramide senses high salinity and triggers Ca2+ influx, producing a rapid and transient increase in cytosolic Ca2+ levels  such as PM salt overly sensitive 1 (SOS1) and vacuolar NHX1 have been shown to enhance salt tolerance in plants , which binds to and activates the kinase activity of CIPK24 (SOS2), and SOS1 is phosphorylated by CIPK24, which enhances Na+ efflux  by CIPK24 under salt stress, which attenuates the SCaBP8-mediated inhibition of AKT1  rhizoids  at Ser514 and regulates PHT1;1 subcellular localization under As(V) stress  stress , and CPK) stress . In thisbidopsis .2+ signaling in heavy metal regulation, and it is clear that Ca2+ signaling is involved in heavy metal detoxification in plants. Nonetheless, there are still gaps in our understanding of the involvement of Ca2+ signaling in heavy metal toxicity.Significant progress has been made in understanding Ca2+ signaling in regulating the absorption and transport of these elements.S, Zn, Cl, Mo, and Ni are essential mineral nutrients for plants , and man2+ and NO efficiently mitigates Ni toxicity and regulates growth and development of the cyanobacterium Nostoc muscorum ATCC 27893, implying a signaling role for Ca2+ and NO in response to Ni stress .Ca2+ levels in the cytoplasm. These indicators rely on the binding of Ca2+ to specific fluorescent molecules, enabling measurement of Ca2+ concentrations in real time  and the 10.13039/501100002858China Postdoctoral Science Foundation (2023M732883 to C.J.).This work was supported by the All work was performed in collaboration. T.W. and C.W. wrote the original draft and prepared the figures. T.W., X.C., C.J., and C.W. designed and finalized the manuscript. The authors have read and agreed to the published version of the article.