==== Front PLoS One PLoS One plos plosone PLoS ONE 1932-6203 Public Library of Science San Francisco, CA USA 10.1371/journal.pone.0243205 PONE-D-19-35919 Research Article Biology and Life Sciences Physiology Physiological Parameters Body Weight Obesity Biology and Life Sciences Physiology Physiological Parameters Body Weight Body Mass Index Biology and Life Sciences Biochemistry Neurochemistry Neurochemicals Neuropeptides Biology and Life Sciences Neuroscience Neurochemistry Neurochemicals Neuropeptides Biology and Life Sciences Biochemistry Hormones Peptide Hormones Neuropeptides Biology and Life Sciences Genetics Heredity Genetic Mapping Variant Genotypes Biology and Life Sciences Genetics Single Nucleotide Polymorphisms Medicine and Health Sciences Epidemiology Medical Risk Factors Biology and Life Sciences Biochemistry Biochemical Simulations Biology and Life Sciences Computational Biology Biochemical Simulations Physical Sciences Chemistry Chemical Physics Molecular Structure Physical Sciences Physics Chemical Physics Molecular Structure Neuropeptide S receptor gene Asn107 polymorphism in obese male individuals in Pakistan Neuropeptide S receptor gene Asn107 polymorphism in obese maleAhmad Aftab Data curationFormal analysisInvestigationMethodologySoftwareValidationWriting – original draftWriting – review & editing1 Almsned Fahad MethodologySoftwareVisualizationWriting – review & editing23 Ghazal Pasha ConceptualizationProject administrationResourcesSupervision1 Ahmed Malik Waqar Formal analysisSoftware1 https://orcid.org/0000-0003-3174-1007Jafri M. Saleet SoftwareSupervisionValidationVisualizationWriting – review & editing24 https://orcid.org/0000-0002-2171-6062Bokhari Habib ConceptualizationResourcesSupervision14* 1 Department of Biosciences, COMSATS University Islamabad, Chak Shahzad, Islamabad, Pakistan 2 School of Systems Biology and Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia, United States of America 3 King Fahad Specialist Hospital– Dammam, Dammam, Saudi Arabia 4 Center for Biomedical Engineering and Technology, University of Maryland School of Medicine, Baltimore, Maryland, United States of America Li Zezhi Editor National Institutes of Health, UNITED STATES Competing Interests: The authors declare no conflict of interest. * E-mail: habib@comsats.edu.pk 17 12 2020 2020 15 12 e024320529 12 2019 17 11 2020 This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.Neuropeptide S (NPS) is a naturally occurring appetite stimulant, associated with anxiety, stress, and excitement regulation. Neuropeptide S serves as a hypothalamic energy regulator that enhances food intake with a reduced level of satiety. NPS activates fat angiogenesis and the proliferation of new adipocytes in obesity. NPS has an established role in energy regulation by many pre-clinical investigations; however we have limited data available to support this notion in humans. We found significant association of Neuropeptide S receptor (NPSR1) Asn107Ile (rs324981, A>T) polymorphism with obese male participants. The current investigation carried out genotype screening of NPSR1 allele to assess the spectrum of the Asn107Ile polymorphism in obese and healthy Pakistani individuals. We revealed a significant (p = 0.04) difference between AA vs TT + AT genotype distribution of NPSR1 (SNP rs324981,) between obese and healthy individuals (p = 0.04). In this genotype analysis of (SNP rs324981) of the NPSR1 gene, T allele was marked as risk allele with higher frequency in the obese (38%) compared to its frequency in the controls (25%). Single Nucleotide Polymorphism (SNP, rs324981) Asn107Ile of NPSR1gene, that switches an amino acid from Asn to Ile, has been found associated with increased susceptibility to obesity in Pakistani individuals. Furthermore, molecular simulation studies predicted a lower binding affinity of NPSR1 Asn107Ile variant to NPS than the wild-type consistent with the genotype studies. These molecular simulation studies predict a possible molecular mechanism of this interaction by defining the key amino acid residues. However, a significantly (p<0.0001) lower concentration of NPS was recorded independent of genotype frequencies in obese subjects compared to healthy controls. We believe that large scale polymorphism data of population for important gene players including NPSR1 will be more useful to understand obesity and its associated risk factors. http://dx.doi.org/10.13039/501100010221Higher Education Commision, PakistanGrant # IRSIP36- BMS71)Ahmad Aftab The study was supported by the Higher Education Commision, Pakistan (Grant # IRSIP36- BMS71) received by Aftab Ahmad. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data AvailabilityAll relevant data are within the manuscript and its Supporting information files.Data Availability All relevant data are within the manuscript and its Supporting information files. ==== Body Introduction Obesity, defined as an unnecessary accumulation of body fat that puts an individual’s health to risk, is now invading the world populations as an epidemic disease that counts for 2.8 million deaths every year [1]. Traditionally obesity thought to be associated with the developed part of the world; however, now increased prevalence has also been observed in developing and low-income countries. The prevalence of obesity has increased almost three-fold between 1975 to 2016 [1]. Obesity is measured by body mass index (BMI). An individual with a BMI ≥ 30 is considered obese and at higher risk concerning their susceptibility to diseases and other associated risk factors. Furthermore, obesity is a multifactorial disease. Therefore, it is important to identify all possible risk factors including genetic factors, and in particular, Neuropeptide S (NPS), which is associated with energy regulation and homeostasis in humans [2]. NPS binds with the Neuropeptide S receptor (NPSR1) and modulates various cellular functions through its signaling cascade. The NPSR1 gene located on chromosome 7p14.3 encodes the G protein-coupled receptors (GPCRs) vasopressin/oxytocin receptor subfamily. Furthermore, the NPS-NPSR1 interaction regulates the downstream Mitogen-Activated Protein Kinase (MAPK) system and accelerates fatty acid oxidation to protect animals from dietary obesity. NPSR1 has different physiological roles like modifying eating habits, release of adrenocorticotropic hormone (ACTH), suppressing anxiety, fear, panic, and sleep regulation [3, 4]. Polymorphisms in the NPSR1 gene have been linked with panic disorders, asthma, inflammatory bowel disease (IBD), and rheumatoid arthritis [5–7]. A change of an amino (Asn107Ile) is enforced by single nucleotide polymorphism (SNP) rs324981 A>T. T-allele (Asn107Ile) of the NPSR1 gene has a ten-fold higher risk for associated disorders. Nervousness and anxiety-related disorders are also strongly associated with NPSR1 T-allele [8–10]. NPSR1 gene expression is also dependent on external factors, e.g. individuals with the TT genotype were found to be highly sensitive to the development of depression and anxiety due to their malnourished status during their early phase of life [11]. Moreover, the external environment variably influences the association of NPSR1 Asn107Ile between populations [12]. Earlier investigations have revealed that the NPS-NPSR1 system effects the bioactivity of the NPS peptide due to A/T single nucleotide polymorphism [13, 14]. Moreover, previous studies have not investigated the NPSR1 variants’ interaction with their functional efficiency. The current investigation explores the possible association of NPSR1 Asn107Ile variant with NPS serum levels and the NPS-NPSR1 interaction through molecular simulation analysis in obesity. We used molecular simulation to explore how the Asn107Ile variant structure differs from wild-type to understand how the two forms are functionally different. Material and methods Study population This investigation has been designed to explore the association of NPSR1 gene with obesity in adult (20 to 50 years) Pakistani population. A total of 116 adult subjects with their written consent were included in this investigation. This study was approved by the Departmental Ethical Review Board of University of COMSATS University Islamabad. All adult male participants visiting Social Security Hospital Islamabad from diverse urban settings and communities of Pakistan were enrolled between August, 2017-June, 2018. A homogenous ancestry background of the studied population was assured to limit the influence of population stratification on our investigation. Participants were categorized on the basis of their BMI scores in Kg/m2 presenting Kg as weight and height in meters squared (m2). The participants were weighed in Kg by using standard calibrated weight machine and height was measured in (cm) by using stadiometer both height, and weight was measured without shoes. All participants were divided into groups as per WHO guidelines, those with BMI equivalent to 30 Kg/m2 or more were categorized as obese, and those with BMI equivalent to 29.9 Kg/m2 or less were considered as a control group. Study exclusion criteria were 1) having non-Pakistani lineage; 2) having any infectious disease; 3) having some other medical complication. Participant’s blood samples were collected in serum separating vacutainers. After collection, each blood sample tube was left undisturbed at room temperature for 30 minutes and the resulting top layer of serum was transferred into clean tubes. These serum tubes were kept at -20°C until further analysis. NPS was quantified by using a commercial Human NPS (Neuropeptide S) ELISA kit Catalog No: E6618h (EIAab® USA). Lipid profile Total cholesterol, LDL, HDL, triglycerides levels, and blood glucose level for all participants were determined by utilizing automated enzymatic analyzers (Cobas Integra 700; Hoffman-La Roche, Basel, Switzerland). Genotyping Genotyping of the NPSR1 Asn107Ile polymorphism was performed utilizing the PCR-RFLP technique as reported previously [15]. DNA was isolated from blood samples using the TIAGEN® DNA blood kit (Tiagen China) according to the manufacturer’s protocol. Briefly, 500 μl of each homogenized whole blood was processed for DNA isolation from all samples. DNA quantity and quality was measured by Nanodrop spectrophotometer (Nanodrop Technologies, USA) and visualized by agarose gel electrophoresis. Isolated DNA was utilized for amplification of rs324981 in a 25 μl volume reaction using thermal cycler (Thermo Electron Corporation) and NPSR1 rs324981F Forward 5′-ACC CTG AAT GTA AGC ACT TGT 3′ and NPSR1 rs324981R Reverse 5—TGT CTC ATC ACA TTT GGA AGGT—3′ set of primer was used under the following conditions: 95°C for 5 min, followed by 35 cycles of 94°C for 30 sec (denaturation), 55°C for 30 sec (annealing), 72°C for 1 min (extension), and 10 min at 72°C for a final extension. Gel electrophoresis was executed on 2% agarose gel for 40 min at 90 Volts and 400 mA to visualize the resulting PCR amplicon of 169 bps under UV light utilizing the transilluminator. Finally, the resulting amplicon was treated with restriction endonucleases (BsiEI) from a strain of thermophilic Bacillus circulans under the following set of conditions incubated at 37°C for 3 hours and afterward at 80°C for 20 minutes. Processed amplicons were run in 2.5% agarose gel and visualized in UV illuminator (Alpha Imager Mini Bucher Biotech, Basel). Sanger sequencing Sanger sequencing technique was applied to confirm all PCR-RFLP results using Big Dye Terminator Cycle Sequencing Ready Reaction Kit and the ABI PRISM 3730 DNA analyzer (Applied Biosystems, USA). Enzyme-linked immunosorbent assay (ELISA) Human NPS ELISA kit (EIAab® The Top Notch Elisa Kit Manufacturers USA) was used for the in vitro quantitative determination of human Neuropeptide S levels in serum of both obese and control groups. The samples were added to each microtiter plate for preparation of biotin-conjugated antibody specific for human NPS with Avidin conjugated to Horseradish Peroxidase (HRP) and incubated for three hours, Wells with biotin-conjugated antibody and enzyme-conjugated Avidin exhibited change in color on the addition of a tetramethylbenzidine substrate solution. A change in color was measured at a wavelength of 450 nm ± 2 nm with a spectrophotometer after terminating enzyme-substrate reaction. The concentration of neuropeptide S was determined by comparing the O.D. of each sample to the standard curve. Molecular simulation Initial structures NPRS1 sequence was retrieved in the FASTA format form the UniPort database (UniProtKB—Q6W5P4) [16]. The I-TASSER server was utilized to construct the initial structure of NSPR1 [17, 18]. The crystal structure of bovine rhodopsin from the Protein Data Bank (PDB: 1U19:A) has been used as a template for the construction [19]. All default parameters without any constraints were employed. The best-predicted structure (C-score = 0.62) was selected for the simulation. UCSF Chimera (version 1.13.1) was applied to mutate the 107th amino acid in the structure from asparagine to isoleucine (Asn107Ile) [20]. System setup Protein structures were viewed and examined with Visual Molecular Dynamics (VMD) [21]. VMD psfgen plugin was used to generate a dynamics-ready psf and PDB files from a raw PDBs [21]. Both structures have been inserted palmitoyl-oleoyl-phosphatidyl-choline (POPC) with all overlapping lipid molecules removed using the Membrane plugin and solvated in a water box using the Solvate plugin in VMD [21]. Molecular dynamics (MD) simulations Molecular dynamics (MD) simulations were made using NAMD2.9 [22]. The CHARMM27 parameter was used for the protein and the POPC lipids [23]. Simulations of both structures started with equilibration of the lipid tails. With all other atoms fixed, the lipid tails were energy minimized for 1000 steps using the conjugate gradient algorithm for 0.5 ns at 300 K. The systems were further equilibrated at 300 K and constant pressure with harmonic position restraints applied to the protein atoms for 0.5 ns. The systems were further equilibrated at 310 K and constant pressure with all atoms unrestrained for 0.5 ns. At this point, the system volume was found to decrease; suggesting that water molecules, ions, and lipids were well equilibrated surrounding the protein structures. The final production run for both systems was performed at 1 atm pressure and 310 K for 2 ns with a constant ratio constraint applied on the lipid bilayer. Molecular dynamics simulations result analysis The R statistical language and R studio were used for the analysis of results [24, 25]. Bio3D R package was used to calculate and generate RMSD, RMSF graphs [26]. The R package ggplot2 was used to generate histograms [27]. Pocket size calculation The size of the pocket inside NPSR1 for NPS binding was calculated using CASTp software. Statistical analyses Two-way analysis of variance ANOVA was applied to analyze biochemical tests data means of obese and controls. Chi-square test, Z-test, dominance, recessive model and additive tests, odd ratio, Fisher exact were performed to analyze genotype and allele frequencies. The statistical significance level was set at p < 0.05. Andrew F. Hayes Moderation effect analysis was performed using process Version 3.5 to analyze the effect of obesity as independent variable directly on NPS concentration considering genotype as moderator. Results Anthropometric and lipid profile of participants A total 116 participants with similar age and sex were included in this study, out of which 49% were obese, while 51% were healthy individuals (Table 1). This study shows that the mean values of BMI, cholesterol, triglycerides and LDL were significantly (p<0.05) higher in the obese group while NPS concentration were found significantly (P<0.001) lower as compared to control group (Table 1). However, blood glucose (fasting), HDL concentrations were found non-significant (p>0.05) different in obese and control group (Fig 1). 10.1371/journal.pone.0243205.g001Fig 1 Lipid profile and anthropometric parameters vary significantly between obese and control groups. 10.1371/journal.pone.0243205.t001Table 1 Anthropometric and lipid profile of participants. Parameters Obese Controls 95% CI of diff. P value Age 35.8 32.4 - p>0.12 BMI Kg/m2 32.90* 21.83* -20.27 to -1.872 P<0.01 Blood Sugar (mg/dl) 98.08 95.12 -12.16 to 6.239 P > 0.05 Total cholesterol (mg/dl) 196.1** 106.3** -98.95 to -80.55 P<0.001 Triglycerides (mg/dl) 76.79** 51.63** -34.35 to -15.95 P<0.001 HDL (mg/dl) 35.94 42.96 -2.182 to 16.22 P > 0.05 LDL (mg/dl) 97.35** 56.94** -49.60 to -31.20 P<0.001 NPS (pg/ml) 99.58** 195.4** 90.05 to 108.5 P<0.001 Results of Two way Anova (Obese. Ctrl Age, BMI, Glucose, Cholesterol, Triglycerides, HDL, LDL, NPS conf. level = 0.95) * Indicates p < 0.05. Genotyping Genetic models PCR-RFLP results confirmed by Sanger sequencing technique (S1 Fig) revealed, 28 AA and 29 TT+ AT in obese while 40 AA and 19 TT+AT in healthy individuals. The reported genotypes single nucleotide NPSR1 gene rs324981(A>T) polymorphisms were in Hardy-Weinberg equilibrium (HWE) (p2 + 2pq + q2 = 1) and the observed and expected genotype frequencies were found significantly (p = 0.005) different through Chi-square statistic. The current study has shown a significant (p = 0.04) difference of NPSR1 Asn107Ile (rs324981) geneotype frequencies of between obese and controls. The analyses of NPSR1 rs324981 (A>T) polymorphism find a significant association with obesity in Dominant genetic models (AA Genotype vs AT + TT Genotype) (Table 2). However, non-significant results of Recessive genetic model (AA +AT Genotype vs TT Genotype) with their significant (p = 0.04) Chi- Square and Z-test values are shown in Table 2. The ‘T’ allele frequency was high in obese (38%) than in healthy individuals (25%) but insignificant as shown (OR = 1.83, 95%CI, 0.829–4.125, p = 0.06) (Table 2). Moreover, in additive model (A vs T) Z-test and chi-square found a non-significant difference in rs324981 allele frequencies between the obese and the control group (Table 2). 10.1371/journal.pone.0243205.t002Table 2 Genotypic distribution of NPSR1 (rs324981, A>T) in normal controls (n = 59) and obese (n = 57) Pakistani individuals. Model OR(95% CI)P-Value X2(p-value) Z-test(P-value) rs324981 Dominant 2.165(1.019–4.70)0.04 4.131(0.04) -2.041(0.04) Recessive 0.629(0.245–1.571)0.36 4.131(0.04) 2.041(0.04) Additive 1.83(0.829–4.125)0.06 2.31(0.128) -1.52 (0.12) p-Values<0.05 were considered significant. NS: non-significant. Moreover, we applied two way ANOVA model to compare the means of independent variables obesity and genotype against the dependent variables Age, NPS and lipid profile parameters both in obese and control groups. In this study we found the means for NPS and triglycerides were significantly (p = 0.0001) different for different genotypes in obese and control group. Furthermore, means for Cholesterol, BMI, HDL, blood sugar and LDL are significantly different in obese and control group (p<0.01). Interestingly only in the case of triglycerides there is an interaction between the independent variables obesity and genotype (Fig 2, Table 3). 10.1371/journal.pone.0243205.g002Fig 2 Two-way ANOVA analysis with obesity and genotype as the independent variables and NPS, triglycerides, blood sugar, BMI, HDL, LDL, age, and cholesterol are significantly p = 0.0001 different for different genotypes and for obese vs non-obese subject. 10.1371/journal.pone.0243205.t003Table 3 Comparison of independent variables obesity and genotype against dependent variables (NPS, triglycerides, blood sugar, BMI, HDL, LDL, age and cholesterol) by two-way analysis. Variable p-value Tukey HSD p-value NPS obesity–<2.2×10−16 AT-AA– 0.0 genotype– 5.4×10−10 TT-AA– 0.0 obesity:genotype– 0.61 TT-AT– 0.94 Triglycerides obesity–<2.0×10−16 AT-AA– 0.072 genotype–<2.0×10−16 TT-AA– 0.70 obesity:genotype– 0.01 TT-AT– 0.92 Blood Sugar obesity– 0.004 AT-AA– 0.94 genotype–.019 TT-AA– 0.098 obesity:genotype– 0.19 TT-AT– 0.40 BMI obesity–<2.0×10−16 AT-AA– 0.88 genotype– 0.23 TT-AA– 0.098 obesity:genotype– 0.90 TT-AT– 0.40 HDL obesity– 0.008 AT-AA– 0.28 genotype– 0.49 TT-AA –0.35 obesity:genotype– 0.47 TT-AT– 0.99 LDL obesity–<2.0×10−16 AT-AA– 0.96 genotype– 0.84 TT-AA– 0.97 obesity:genotype– 0.72 TT-AT– 0.91 Age obesity– 0.23 AT-AA– 0.99 genotype– 0.33 TT-AA– 0.53 obesity:genotype– 0.58 TT-AT– 0.64 Cholesterol obesity–<2.0×10−16 AT-AA– 0.99 genotype– 0.99 TT-AA– 0.70 obesity:genotype– 0.79 TT-AT– 0.73 We found a statistically significant difference in average NPS by both SNP (f(2) = 26.0613, p <5.435e-10) and by obesity (f(1) = 186.6127, p<2.2e-16), though the interaction between these terms was not significant. A Tukey post-hoc test revealed significant pairwise differences between genotype “AA” and genotype “AT” (diff = 47.930945), between “AA” and genotype TT” (diff = 47.930945). While no significant difference was found between “AT” and genotype TT” (diff = -2.531281). Moreover, We found a statistically significant difference in average Triglycerides by both SNP (f(2) = 4.7670, p = 0.01034) and by obesity (f(1) = 399.2172, p<2.2e-16); however the interaction between these terms was not significant. NPS is the only dependent variables that show a significant difference in both SNP and obesity. Correlation analysis The data showed a statistically significant (p<0.0001) difference of mean NPS serum level 99.58 ±34.43 pg/mL in obese compared to controls 195.49 ±38.87 pg/ml (Fig 3). There was a statistically significant negative correlation (r2 = 0.73, p = 0.0001) between BMI score of all participants and NPS serum levels (Fig 3). 10.1371/journal.pone.0243205.g003Fig 3 Pearson correlation analysis of NPS serum concentration (pg/ml) versus BMI of all the samples (n = 116). (r2 = 0.73, p = 0.0001). Moderation effect analysis NPS concentrations were significantly (p = 0.000) moderated by NPSR1 gene Asn107Ile polymorphism with increasing obesity (Fig 4). Furthermore moderation effect analyses revealed a more negative effect of obesity on NPS concentration with AT+TT and AA genotype as shown in (S1 Table). 10.1371/journal.pone.0243205.g004Fig 4 Moderation effect of analysis, genotype (AT+TT and AA) negatively moderated the effect of obesity on NPS concentration. Molecular simulation effect Molecular simulations were performed to understand the effect of the mutation on the interaction of NPSR1 and NPS. The WT trajectory is shown in black, and the variant Asn107Ile trajectory is shown in red (Fig 5A and 5B). Fig 5C and 5D showed the WT and variant molecular structure trajectories have difference in the structures as measured by the RMSD (root mean squared deviation) (Fig 5C). The average RMSD is shown by the blue dashed line with the WT above the variant. This distribution of the RMSD of the population of structures obtained during the final 2ns of the simulation is shown for the WT (red) and variant ASN107Ile (blue) in Fig 5C and 5D. The differences in RMSD distributions for the population of structures are more pronounced when isolating residues 100–330. The simulations predict that the binding of NPS to NPSR1 differs in the WT vs. the variant Asn107Ile. It is interesting to note that the difference in RMSF between WT and variant is greatest with residue 198 suggesting its importance in NPS binding. S2 Fig shows the FCC scores for the WT and variant. The higher the score the closer the interaction between NPS and NPSR1. Note that the values in the WT are greater. In fact, in cluster 1 (red—circled) which includes residues (100–330), the WT has a value slightly above 0.6 while the Asn107Ile variant is zero, suggesting a reduced binding affinity of NPS for the Asn107Ile variant of NPSR1. 10.1371/journal.pone.0243205.g005Fig 5 The root means squared difference (RMSD) of both WT and Asn107Ile in (A) all residues, and (B) residues 100–300 for each frame (each frame is 2 ps of a simulation). There is a clear divergence between the two structures when comparing residues 100–330. (C) RMSD Histogram for NPRS1 WT, and NPRS1 Asn107Ile obtained during the final 2 ns of the simulation. (D) The changes in RMSD distributions s more noticeable when isolating residues 100–330. (E) Molecular structure (gray) of E. wild-type and (F) Asn107Ile variant NPSR1 obtained by molecular simulation with the pocket calculated by CASTp (red). The pocket-size is 732.885 A for WT and 449.454 for Asn107Ile. The three-dimensional structure of wild-type and Asn107Ile NPSR1 obtained by molecular simulation from Fig 5A and 5B. The relative “pocket sizes” for binding of NPS to NPRS1 WT and NPRS1 Asn107Ile calculate by CASTp were 732.885A and 449.454A, respectively (Fig 5E and 5F). These predictions suggest that NPS binds the Asn107Ile variant less strongly than NPS binds to the wild-type due to the changes in the NPSR1 structure. Discussion Worldwide over 650 million individuals are obese. The World Health Organization (WHO) has declared obesity as an epidemic, especially in low and middle-income countries that are more vulnerable with the double burden of disease coupled with obesity. i.e., Pakistan consumes 0.71% of total health care expenditure directly and 11.7% indirectly to cure obesity and its co-morbidities. The risk for this non-contagious disease increases with increases in BMI [1, 28]. This study revealed a negative correlation of NPS concentration in serum versus BMI which decreased significantly with increasing BMI scores. This may be because of the underrepresentation of the AA genotype in obese Pakistani participants. Previous studies specify the possible defensive role of AA genotype against obesity with relatively less frequent occurrence in the Asian Population [8]. It has a protective role in asthma, that helped to reduce airway hyper-responsiveness as reported for the Chinese population [29] while the under-representation of AA genotype was found associated with panic disorder in the Japanese population [8]. Additionally, participants with AA genotype have equally good sociability characteristics and better adaptability to the environment with a minimum level of anxiety and depression [8, 30]. Mechanistically, negative correlation of NPS concentration in serum with BMI may be because of reduced neuropeptide binding of A allele (Asn 107) at the receptor site compared to the T allele (107Ile) [13]. The T allele has been reported with increased neural stress in the Estonian males [26]. There was a significant difference between AA and AT+TT genotype frequencies of NPSR1 gene between the obese and control group. However, NPS concentrations in serum varied significantly with genotype between the obese and control group. Furthermore, genotype as moderator in moderation effect analysis, negatively affected the NPS concentration with increasing obesity. These results suggest that presence of T allele is not only the single factor responsible for the increase of NPSR1 sensitivity to NPS as reported earlier [13], besides the fact TT+AT genotype was frequently observed in Pakistani obese male participants, very similar to previous Asian and German population studies [8, 29]. Similarly, Frequency of risk allele T was recorded higher (38%) in obese Pakistani participants as compared to their controls (25%). Furthermore, NPSR1 Asn107Ile variant (AT+TT) genotype distribution was found to be associated with obesity in the Pakistani population. Similarly reduced cognitive stress in healthy Chinese has been found associated with the TT genotype of NPSR1 rs324981 [31–33]. This may because of environment-dependent expression of NPSR1 gene, as revealed earlier in malnourished individuals with TT genotype were found highly sensitive against depression and anxiety [11] and that may be the reason for the association of (rs324981) T allele with anxiety [34] impulsivity [35] sleep disorder [36] and panic disorder [37, 38]. In this study, we revealed a significantly decreased level of NPS concentration in obese individuals dependent of genotype and allele frequency. That may because of reduced signaling transduction of T allele (107Ile) at (rs324981) of NPSR1 gene with its attenuated binding affinity to NPS and altered biological response at receptor site [12, 39, 40]. Therefore, NPS receptor the NPSR1with elevated agonistic response possibly may contribute towards hyper-stimulation and neurotransmission which ultimately may result in the pathophysiology of obesity in the Pakistani population, but still, further investigations with larger data sets are required to evaluate in the context of NPS-NPSR1 interaction. This study is first to report the significant association of NPSR1 (Asn107Ile rs324981) polymorphism with obesity in the Pakistani obese male individuals. Smaller data sets can be a potential limitation of this investigation but its significance in terms of outcomes for the scientific community cannot be undermined. Our results NPSR1 A/T polymorphisms provides further supportive evidence in the association of obesity. However, in this study, we observed that the AA genotype was significantly associated with healthy male individuals in Pakistan. We observed a reduced level of NPS concentration in obese individuals with weaker binding affinity to the NPSR1 Asn107Ile variant. That might have contributed have contributed towards the pathophysiology of obesity In addition, the NPS-NPSR1 system on downstream accelerates MAPK activity and fatty acid oxidation to protect organisms against diet-induced obesity [41]. Moreover, previous experimental studies data suggest the decreased activation of NPSR1-NPS signaling can lead to obesity [42]. Furthermore, NPS-NPSR1 activation results in the increased expression of cholecystokinin (CCK), vasoactive intestinal peptide (VIP), and neuropeptide Y [42]. CKK and VIP are responsible for the pancreatic secretion and nutrient absorption in the intestine region collectively can modulate PYY release [43]. Neuropeptide Y regulates food intake decreasing intestinal motility giving the feeling of satiety [44]. Furthermore, the NPSR1 Asn107Ile variant has been shown to increase cortisol levels in European subjects [32, 45]. Therefore, reduced NPSR1-NPS signaling activates pathways that lead to weight gain. This study also provides insight into the molecular mechanism defining the difference between WT and variant Asn107Ile. The modeling suggests a reduced binding affinity of NPSR1 for NPS in variant Asn107Ile compared to WT similar to experimental data findings [41]. The simulation studies suggest that the amino acid residue 198 is important in the interaction between NPS and NPSR1. The variant Asn107Ile displays increase fluctuation in molecular structure most significantly at residue 198 suggesting its importance in NPS binding to NPSR1. This predicts that the mutation at Asn107 might exert its effect through a distant residue. Experimental findings support the importance of residue 198; the NPSR1 variant Phe197Cys shows no binding of NPS [41]. Further studies are needed to test the predicted critical role of residue 198 and the involvement of the other predicted residues involved in this interaction as these might play an important pharmacological role. Supporting information S1 Fig Verification of the NPSR1 A>T (rs324981) SNP between (A) obese (n = 57) and (B) healthy (n = 59) by Sanger sequencing. (TIF) Click here for additional data file. S2 Fig Shows the FCC scores for the WT and variant. The higher the score the closer the interaction between NPS and NPSR1. In cluster 1 (red—circled) which includes residues (100–330). (TIF) Click here for additional data file. S1 Table Moderation effect analysis, when genotypes act as a moderator while obesity is independent variable and NPS is response variable. (DOCX) Click here for additional data file. S1 File (ZIP) Click here for additional data file. The authors appreciate the technical supports of Alpha Genomics and would like to express thanks. The authors acknowledge the patient recruitment support from doctors of PESSI Hospital Islamabad and sincerely thank the participants for this study. 10.1371/journal.pone.0243205.r001 Decision Letter 0 Li Zezhi Academic Editor © 2020 Zezhi Li2020Zezhi LiThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Submission Version0 20 Mar 2020 PONE-D-19-35919 Neuropeptide S receptor gene Asn107 polymorphism in obese male individuals in Pakistan PLOS ONE Dear Dr. Bokhari, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. 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We look forward to receiving your revised manuscript. Kind regards, Zezhi Li, Ph.D., M.D. Academic Editor PLOS ONE Additional Editor Comments (if provided): The sample size is too small, so the authors should provide the statistical power. In addition, it should be discussed in the limitation. Journal Requirements: When submitting your revision, we need you to address these additional requirements: 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at http://www.plosone.org/attachments/PLOSOne_formatting_sample_main_body.pdf and http://www.plosone.org/attachments/PLOSOne_formatting_sample_title_authors_affiliations.pdf 2. Please upload a copy of Figure 8, to which you refer in your text on page 16. If the figure is no longer to be included as part of the submission please remove all reference to it within the text. 3. Please upload a new copy of Figure S8 as the detail is not clear. Please follow the link for more information: http://blogs.PLOS.org/everyone/2011/05/10/how-to-check-your-manuscript-image-quality-in-editorial-manager/ [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: No Reviewer #2: No ********** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: No ********** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ********** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: No Reviewer #2: Yes ********** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: I read carefully version of the article titled “Neuropeptide S receptor gene Asn107 polymorphism in obese male individuals in Pakistan” I have several major comments listed below --- 1. Neuropeptide S receptor (NPSR1) gene might have many SNPs, the author should describe the possible reason why they choose rs324981 to study. 2. The figures are too obscure to be seen. The authors should provide clearer graph. 3. The language and paper structure is too obscure to understand. Such as “clinical picture “. What is the meaning of clinical picture? 4. The sample size is too small for SNP polymorphism study. Reviewer #2: 1. For a genetic study, the sample size is too small. 2. According to the data given in the manuscript, the result of the chi-square test for genotypes is incorrect. The chi-square value should be 4.251, which does not reach a significant level (p=0.119), and the z-test was not significant either. It is not clear how chi-square=4.131 (p=0.04) is calculated. Therefore, in my opinion, there are no significant results in this study. Here is my chi-square test results: obesity * genotype Crosstabulation Count genotype Total AA TT AT obesity Yes 28a 14a 15a 57 No 40a 10a 9a 59 Total 68 24 24 116 Each subscript letter denotes a subset of genotype categories whose column proportions do not differ significantly from each other at the .05 level. Chi-Square Tests Value df Asymptotic Significance (2-sided) Pearson Chi-Square 4.251a 2 .119 Likelihood Ratio 4.280 2 .118 Linear-by-Linear Association 3.914 1 .048 N of Valid Cases 116 a 0 cells (0.0%) have expected count less than 5. The minimum expected count is 11.79. ********** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #1: No Reviewer #2: Yes: Jiesi Wang [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step. Attachment Submitted filename: review.docx Click here for additional data file. 10.1371/journal.pone.0243205.r002 Author response to Decision Letter 0 Submission Version1 21 May 2020 Response Letter addressing “Revision” Dear Editor PLOS ONE, With Reference ; PONE-D-19-35919 Neuropeptide S receptor gene Asn107 polymorphism in obese male individuals in Pakistan The Manuscript Revision comments & Response here for your consideration.The authors appreciate the time the reviewers have invested in reviewing our manuscript carefully and for their valuable input which has significantly enhanced the impact of this manuscript. Following are the responses to reviewer comments and subsequent changes in the revised manuscript. The specific comments and their responses are mentioned below. Reviewer 1 Comment # 1. Neuropeptide S receptor (NPSR1) gene might have many SNPs, the author should describe the possible reason why they choose rs324981 to study. Response. NPS-NPSR1 interaction have been discussed in discussion section of manscript, this system on downstream accelerates MAPK activity and fatty acid oxidation to protect organisms against diet-induced obesity (Reinscheid et al., 2005; Anedda F., et al 2011). In cell models, the change of Asn(107) to Ile(107) results in 10-fold increase in NPS-mediated intracellular signaling (Bernier et al., 2006; Anedda et al., 2011). A nonsynonymous SNP at rs324981 is more functional gene variation among the well-known NPSR1 SNPs rs324987, rs324957, rs323920, rs324396 and rs323922 (Yan Feng, et al., 2006). GWAS have found an association of rs324981 with major obesity linked comorbidities like IBD, depression, anxiety, panic disorder, sleep and rest in diverse populations but still true causative variations remain to be identified (Gottlieb DJ., et al 2007; Reinscheid, 2008; Domschke et al., 2011; Lennertz L. et al., 2013). Comment # 2. The figures are too obscure to be seen. The authors should provide clearer graph. Response: We have revisited the all figures addressed the issues according to PLOS ONE figures guidelines https://journals.plos.org/plosone/s/figures. Comment # 3. The language and paper structure is too obscure to understand. Such as “clinical picture “. What is the meaning of clinical picture? Response; We have revisited the complete manuscript considering these critical comments and addressed them accordingly. There are number of sentences which have been rephrased for better expression. Moreover, language and grammar was thoroughly checked from abstract to conclusions sections and all the necessary technical expression have been amended accordingly. a) Language and grammar revised by Habib Bokhari, PhD Commonwealth Scholar & Fellow (LSHTM, UK) Fulbright Fellow (Perelman School of Medicine, UPENN, USA) Professor, Department of Biosciences COMSATS University, Islamabad, Pakistan Office: +92-51-250-1223; FAX: +92-51-444-2805 Mobile: +92-300-512-7684 E-mail: habib@comsats.edu.pk b) A copy of manuscript showing track changes has been uploaded. c) A clean copy of the edited manuscript has been uploaded as the new manuscript file. Comment # 4. The sample size is too small for SNP polymorphism study. Response; This study has potential limitations because of limited resources availability, time constraint and stringent inclusion/exclusion criteria for both case & controls. However, the data does have statistical significance as described in the next comment. We have added discussion about this topic to the discussion. Reviewer # 2: Comment # 1. For a genetic study, the sample size is too small. Response; This study has potential limitations because of limited resources availability, time constraint and stringent inclusion/exclusion criteria for both case & controls. However, the data does have statistical significance as described in the next comment. We have added discussion about this topic to the discussion. Comment # 2. According to the data given in the manuscript, the result of the chi-square test for genotypes is incorrect. The chi-square value should be 4.251, which does not reach a significant level (p=0.119), and the z-test was not significant either. It is not clear how chi-square=4.131 (p=0.04) is calculated. Therefore, in my opinion, there are no significant results in this study. Response; Actually we applied the Dominant model considering “T” as a dominant allele (Feifei Zhao et al., 2016). We have reordered data table accordingly. Obese Control Row Total T 29(60.42%) 19(39.58%) 48(100%) A 28(41.18%) 40(58.82%) 68(100%) Column Total 57 58 116 The chi-square statistic is 4.1677. The p-value is .041201. Significant at p < .05. Additional Editor Comments The sample size is too small, so the authors should provide the statistical power. In addition, it should be discussed in the limitation. Response; This study has potential limitations because of limited resources availability, time constraint and stringent inclusion/exclusion criteria for both case & controls. However, the data does have statistical significance as described in the next comment. We have added discussion about this topic to the discussion. Comment # 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. Response; Thanks for your suggestion, We have revised the all file names and style to conform closely to PLOS ONE's requirements. Comment # 2. Please upload a copy of Figure 8, to which you refer in your text on page 16. If the figure is no longer to be included as part of the submission please remove all reference to it within the text. Response. Yes, figure reference has been removed from the text and figure 8 is no longer part of submission. Comment # 3. Please upload a new copy of Figure S8 as the detail is not clear. Response. Thanks for your input, a new copy of Fig S8 labeled as Fig S7 with clear details has been uploaded. Attachment Submitted filename: Response to Reviewers.docx Click here for additional data file. 10.1371/journal.pone.0243205.r003 Decision Letter 1 Li Zezhi Academic Editor © 2020 Zezhi Li2020Zezhi LiThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Submission Version1 24 Jun 2020 PONE-D-19-35919R1 Neuropeptide S receptor gene Asn107 polymorphism in obese male individuals in Pakistan PLOS ONE Dear Dr. Bokhari, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Aug 08 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols We look forward to receiving your revised manuscript. Kind regards, Zezhi Li, Ph.D., M.D. Academic Editor PLOS ONE [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #2: (No Response) ********** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #2: No ********** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #2: No ********** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #2: No ********** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #2: Yes ********** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #2: This revision has some improvement compare to the original one, but the accuracy of the data in this manuscript is still my biggest concern. The following comments are some of the main issues that I think the author should explain or improve. And the corresponding conclusions and discussions also need to be revised, because many conclusions are not supported by data or statistics. Comment1: The genotype of Asn107lle in this manuscript did not conform to the Hardy Weinberg Equation, this should be reported in the article. And the deviation of HWE may due to the small sample size, but for me, it much more like there have genotype errors, especially by using a PCR-RFLP method. The authors said they have confirmed the results by Sanger sequencing, how much samples have been confirmed, especially for Heterozygotes and Rare Homozygotes? Comment2: Some data in the table1 is confusing. For Glucose, obese vs healthy is 94.5 ± 9.1 vs 94.5± 9.2, but p<0.0001, that won’t be possible. The Total cholesterol in control is 105.5 ± 13.6, but the Triglycerides is 104.5± 11.9, it was also impossible, because the Triglycerides is normally about 30% of the Total cholesterol, in this table, it is more than 90%. I would not report the data if I can’t confirm the accuracy. Comment3: According to the date of age, two groups was not age-matched, but the difference of age has not reached a significant level (p>0.12) only. Because the age may have a large influence on BMI, they should consider to be covariates in the correlation analysis or ANOVA analysis. Comment4: If the authors choose the Dominant model in genotype analysis, it also should be used in the whole manuscript. Comment5: Two-way ANOVA used in table1? What is the second independent variable besides obesity? Indeed, a two-way ANOVA with obesity and genotype of Asn107lle should be performed with the dependent variables including BMI, NPS level, and other serum indexes in table1 (if they are corrected). There is no need to compare the NPS level between obesity and control in different genotypes unless there has a significant interaction of obesity and genotype on the NPS level. Comment6: The authors reported rs=0.884, p=0.0004 in the result (what is the meaning of rs?), but in the figure, they show r2=0.1797, p<0.0001? And it also seemed there did not have 116 plots on the figure, please confirmed and provided a more cleared one. Comment7: Molecular simulation can only provide a hypothesis that the SNP may influence the interaction of NPS and Asn107lle. But no real data supported the interaction was affected, and furthermore, this effect has an influence on obesity. Indeed, the authors should perform a moderation effect analysis using the genotype as a moderating variable or perform a mediation effect analysis using the NPS as a mediating variable, these analyses can check if the interaction of genotypes and NPS has an effect on obesity. Comment8: Too many figures. Figure 1 does not need to be provided in the main text; Figure 2 and 3 need to be re-figured according to a new two-way ANOVA analysis; Figure5-7 can be put together into one figure. Comment9: The simulation is just a prediction; I would not write three long paragraphs to report these results, even longer than the results from real data. Comment10: The RESULTS may be better presented with separate paragraphs accompanied by sub-titles. ********** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #2: Yes: Wang Jiesi [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. 10.1371/journal.pone.0243205.r004 Author response to Decision Letter 1 Submission Version2 11 Aug 2020 Response Letter addressing “Revision” Dear Editor PLOS ONE, With Reference ; PONE-D-19-35919 Neuropeptide S receptor gene Asn107 polymorphism in obese male individuals in Pakistan The Manuscript Revision comments & Response here for your consideration. The authors appreciate the time the reviewers have invested in reviewing our manuscript carefully and for their valuable input which has significantly enhanced the impact of this manuscript. Following are the responses to reviewer comments and subsequent changes in the revised manuscript. The specific comments and their responses are mentioned below. Reviewer 2 Comment # 1. Comment1: The genotype of Asn107lle in this manuscript did not conform to the Hardy Weinberg Equation, this should be reported in the article. And the deviation of HWE may due to the small sample size, but for me, it much more like there have genotype errors, especially by using a PCR-RFLP method. The authors said they have confirmed the results by Sanger sequencing, how much samples have been confirmed, especially for Heterozygotes and Rare Homozygotes? Response. Yes, genotype of all 116 samples reported in this manuscript was confirmed by Sanger sequencing rather than relying on PCR-RFLP method, and sequencing results are submitted here as a “supplementary Sanger sequencing file”. Furthermore this genotype data follows the Hardy Weinberg Equation, calculations are shown below. Genotype Allelic Frequency AA TT AT A T 68 24 24 0.69 0.31 f(T)= (AT)+2 X (TT)/2 X (TT) + 2 X (24) + 2 X (AA) =24+ 2(24)/2(24)+2(24)+2(68) = 72/232 = 0.31 f(A)= (AT)+2 X (AA)/2 X (TT) + 2 X (AT) + 2 X (AA) = 24+ 2(68)/ 2(24) + 2(24) + 2(68) = 162/232 = 0.69 Hardy Weinberg Equation p2 + 2pq + q2 = 1 (0.31)2 + 2(0.69)(0.31)+ (0.69)2 = 1 0.0961 + 0.4278 + 0.4761 =1 The sum of the entries is p2 + 2pq + q2 = 1, as the genotype frequencies must sum to one. Hardy Weinberg Equation has been reported in article. Comment # 2. Some data in the table1 is confusing. For Glucose, obese vs healthy is 94.5 ± 9.1 vs 94.5± 9.2, but p<0.0001, that won’t be possible. The Total cholesterol in control is 105.5 ± 13.6, but the Triglycerides is 104.5± 11.9, it was also impossible, because the Triglycerides is normally about 30% of the Total cholesterol, in this table, it is more than 90%. I would not report the data if I can’t confirm the accuracy. Response: Thanks you, yes sure this was serious mistake correction has been made in the table While triglyceroids values has been also corrected by reconsidering units conversion and calculation from initial data sets. Comment # 3. According to the date of age, two groups was not age-matched, but the difference of age has not reached a significant level (p>0.12) only. Because the age may have a large influence on BMI, they should consider to be covariates in the correlation analysis or ANOVA analysis. Response; yes sure age influences BMI of an individual therefore we recruited age, sex matched participants for this study as earlier we mentioned this in the result section but now we have also made it clear in material and methods section. Comment # 4. If the authors choose the Dominant model in genotype analysis, it also should be used in the whole manuscript. Response; Thank you for your valued input we have expanded the applied dominant model to the whole manuscript. Comment # 5. Two-way ANOVA used in table 1? What is the second independent variable besides obesity? Indeed, a two-way ANOVA with obesity and genotype of Asn107lle should be performed with the dependent variables including BMI, NPS level, and other serum indexes in table1 (if they are corrected). There is no need to compare the NPS level between obesity and control in different genotypes unless there has a significant interaction of obesity and genotype on the NPS level. Response; We are grateful for your direction, that helped to analyses the data more effectively. Secondly we agree with you and have removed the comparison of NPS level between obese and controls in revised submission. Comment # 6. The authors reported rs=0.884, p=0.0004 in the result (what is the meaning of rs?), but in the figure, they show r2=0.1797, p<0.0001? And it also seemed there did not have 116 plots on the figure, please confirmed and provided a more cleared one. Response;.Yes, thank you again actually it is r2 values that was wrong presentation now has been fixed. Comment # 7. Molecular simulation can only provide a hypothesis that the SNP may influence the interaction of NPS and Asn107lle. But no real data supported the interaction was affected, and furthermore, this effect has an influence on obesity. Indeed, the authors should perform a moderation effect analysis using the genotype as a moderating variable or perform a mediation effect analysis using the NPS as a mediating variable, these analyses can check if the interaction of genotypes and NPS has an effect on obesity. Response; Yes, we agree with your suggestion, Moderation effect analysis by A.Fhayes really is useful tool for such analyses. We have added section in results and Supplementary tableS2. Comment # 8. Too many figures. Figure 1 does not need to be provided in the main text; Figure 2 and 3 need to be re-figured according to a new two-way ANOVA analysis; Figure5-7 can be put together into one figure. Response. Yes right we reorganized the figures accordingly. Comment # 9. The simulation is just a prediction; I would not write three long paragraphs to report these results, even longer than the results from real data. Response. Yes we have summarized the simulation data in revised submission. Comment # 10. The RESULTS may be better presented with separate paragraphs accompanied by sub-titles. Response. Yes we have organized our results accordingly. Thank you Attachment Submitted filename: Response to Reviewers.docx Click here for additional data file. 10.1371/journal.pone.0243205.r005 Decision Letter 2 Li Zezhi Academic Editor © 2020 Zezhi Li2020Zezhi LiThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Submission Version2 1 Sep 2020 PONE-D-19-35919R2 Neuropeptide S receptor gene Asn107 polymorphism in obese male individuals in Pakistan PLOS ONE Dear Dr. Bokhari, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Oct 16 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols We look forward to receiving your revised manuscript. Kind regards, Zezhi Li, Ph.D., M.D. Academic Editor PLOS ONE [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #2: (No Response) ********** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #2: Partly ********** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #2: No ********** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #2: Yes ********** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #2: Yes ********** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #2: Comment1#: The author does not understand the meaning of Hardy Weinberg's equation, and the formula replied by the author can only prove that our basic laws of mathematics have not yet failed. Deviation from HWE means that there is a significant difference between the genetic distribution of the sample and the theoretical distribution which calculated based on the least allele frequency; such difference is generally analyzed by using chi-square test, and the P value of this test needs to be reported. For more details about HWE, authors can refer to genetic textbooks or Wikipedia. With regard to the calculation of HWE, authors can use software such as SPSS or some online calculators on the Internet. Comment2#: According to your previous data, this is not an age-matched case-control study. Age-match means that the age is the same, not similar, between case and control. Therefore, I suggest to remove the statement of age-matched. Furthermore, the data of age should not be deleted in table1. The author also did not use age as a covariable as I suggested previously. Is it because the correlation no longer significance after age controlled? Even so, please report the results with covariates and discuss the results. Because it directly affects the conclusion. Comment3# I suggested using obesity and genotype as independent variables, and BMI, NPS and blood indicators as dependent variables to perform a two-way ANOVA, but the authors did not do that. The influence of genotype on these indexes (BMI, NPS, and blood indicators) is need to be reported, which can be shown by two-way ANOVA, and it can also show whether there is interaction between obesity and genotypes on different indicators. Comment4# The data reported about moderation effect analysis is confusing. There are three results should be reported, the effects of BMI, the genotype and their interactions on NPS respectively. The interaction seemed not be reported. The significant moderation of genotype means that the correlations between BMI and NPS are significant difference between the two genotypes, so please report the correlation coefficient between BMI and NPS in the two genotypes respectively, and make a figure for it. Comment5# The peak map of sequencing does not need to be provided in the main body of the manuscript, because it does not show the data, but use to prove the reliability of the genotyping. ********** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #2: No [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. 10.1371/journal.pone.0243205.r006 Author response to Decision Letter 2 Submission Version3 28 Oct 2020 Response Letter addressing “Revision” Dear Editor PLOS ONE, With Reference ; PONE-D-19-35919R2 Neuropeptide S receptor gene Asn107 polymorphism in obese male individuals in Pakistan The Manuscript Revision comments & Response here for your consideration. The authors appreciate the time the reviewers have invested in reviewing our manuscript carefully and for their valuable input which has significantly enhanced the impact of this manuscript. Following are the responses to reviewer comments and subsequent changes in the revised manuscript. The specific comments and their responses are mentioned below. Reviewer #2: Comment1#: The author does not understand the meaning of Hardy Weinberg's equation, and the formula replied by the author can only prove that our basic laws of mathematics have not yet failed. Deviation from HWE means that there is a significant difference between the genetic distribution of the sample and the theoretical distribution which calculated based on the least allele frequency; such difference is generally analyzed by using chi-square test, and the P value of this test needs to be reported. For more details about HWE, authors can refer to genetic textbooks or Wikipedia. With regard to the calculation of HWE, authors can use software such as SPSS or some online calculators on the Internet. Response: Methods: We thank the reviewer for this extremely helpful suggestion. We have implemented this in the new manuscript as follows: The allele frequency was obtained by counting the number of alleles in the total population and dividing by twice the number of study subjects. For example, AA has two A alleles, AT has one A allele and one T allele and TT has two TT alleles. The expected genotype probability was calculated using the following table based upon the Hardy-Weinberg equation with p being the probability of the A allele and q being the probability of the T allele. A (p) T (q) A (p) AA(p2) AT(pq) T (q) AT(qp) TT (q2) The genotype frequencies were then multiplied by the number of study subjects to get the expected genotype frequencies. The chi-squared statistic was derived using standard methods using Excel. Results: Using the allele frequency calculate from the collected data, the expected distribution of genotypes was calculated using the Hardy-Weinberg equation. These genotype frequencies were used as the expected values in a chi-squared test to see if the observed genotype frequency is significantly different from the expected frequency. The allele frequency in the observed population is 0.69 for A and 0.31 for T (160/232=0.69 for A and 72/232=0.31 for T). The frequency for the genotypes were calculated and are shown below. Expected-genotype Frequency A T A 0.47562426 0.21403092 T 0.21403092 0.09631391 The resulting chi-squared tables for observed and expected for the null hypothesis that the genotype frequencies are the same are observed expected obese control obese control AA 28 40 68 AA 27.11058 28.0618312 55.1724138 TT 14 10 24 TT 5.489893 5.68252081 11.1724138 AT 15 9 24 AT 24.39952 25.255648 49.6551724 57 59 116 57 59 116 The chi-squared statistic with 2 degrees of freedom is 35.66 which is greater than 10.597 (p=0.005). Therefore, we reject the null hypothesis that the observed and expected genotype frequencies ae the same. Comment2#: According to your previous data, this is not an age-matched case-control study. Age-match means that the age is the same, not similar, between case and control. Therefore, I suggest to remove the statement of age-matched. Furthermore, the data of age should not be deleted in table1. The author also did not use age as a covariable as I suggested previously. Is it because the correlation no longer significance after age controlled? Even so, please report the results with covariates and discuss the results. Because it directly affects the conclusion. Response: We have corrected the manuscript as suggested. The age-matched statement has been replaced and age data has been restored in table 1. Furthermore Age has been considered as variable in two way ANOVA analysis. Comment3# I suggested using obesity and genotype as independent variables, and BMI, NPS and blood indicators as dependent variables to perform a two-way ANOVA, but the authors did not do that. The influence of genotype on these indexes (BMI, NPS, and blood indicators) is need to be reported, which can be shown by two-way ANOVA, and it can also show whether there is interaction between obesity and genotypes on different indicators. Response: Methods: We appreciate this thoughtful suggestion. We have implemented the two-way ANOVA as follows: A two-way Analysis Of Variance (ANOVA) was performed to test if the means of the measured dependent variables NPS, triglycerides, blood sugar, BMI, HDL, LDL, age, and cholesterol were different for different values of the independent variables, obesity and genotype. The Tukey’s Honestly-Significant-Difference (TukeyHSD) test was performed as a post-hoc procedure to see which groups are different from one another. Statistics were done using R 3.6.31, RStudio 1.3.1093 2, the car3, the rstatix4, and the emmeans5 packages. Figures were produced using the ggplot26 and the ggpubr7 packages. The full reproducible code is available in Supplementary Materials. Results: We applied two way ANOVA modelto compare the means of independent variables obesity and genotype against the dependent variables Age, NPS and lipid profile parameters both in obese and control groups. In this study we found the means for NPS and triglycerides were significantly (p=0.0001) different for different genotypes in obese and control group. Furthermore, means for Cholesterol, BMI, HDL, blood sugar and LDL are significantly different in obese and control group (p<0.01). Interestingly only in the case of triglycerides there is an interaction between the independent variables obesity and genotype. Table 3 also shows the Tukey HSD p-values for the two-way analysis. These values indicate that we reject the hypothesis that for NPS AT and AA have equal means and that TT and AA have equal mean. We found a statistically significant difference in average NPS by both SNP (f(2)= 26.0613, p <5.435e-10 ) and by obesity (f(1)= 186.6127, p<2.2e-16), though the interaction between these terms was not significant. A Tukey post-hoc test revealed significant pairwise differences between genotype “AA” and genotype “AT” (diff= 47.930945), between “AA” and genotype TT” (diff= 47.930945). No significant difference was found between “AT” and genotype TT” (diff = -2.531281). Moreover, We found a statistically significant difference in average Triglycerides by both SNP (f(2)= 4.7670, p = 0.01034) and by obesity (f(1)= 399.2172, p<2.2e-16); however the interaction between these terms was not significant. NPS and Triglycerides are the only dependent variables that show a significant difference in both SNP and obesity. Comment4# The data reported about moderation effect analysis is confusing. There are three results should be reported, the effects of BMI, the genotype and their interactions on NPS respectively. The interaction seemed not be reported. The significant moderation of genotype means that the correlations between BMI and NPS are significant difference between the two genotypes, so please report the correlation coefficient between BMI and NPS in the two genotypes respectively, and make a figure for it. Response: Method The moderation effect analysis was performed to analyze the effect of obesity as independent variable directly on NPS concentration considering genotype as moderator using PROCESS Procedure for SPSS Version 3.5 by Andrew F. Hayes. ***************** PROCESS Procedure for SPSS Version 3.5 ***************** Written by Andrew F. Hayes, Ph.D. www.afhayes.com Documentation available in Hayes (2018). www.guilford.com/p/hayes3 ************************************************************************** Model : 1 Y : NPS X : Obesity W : Genotype Sample Size: 116 ************************************************************************** OUTCOME VARIABLE: NPS Model Summary R R-sq MSE F(HC4) df1 df2 p .691 .478 1962.044 31.460 3.000 112.000 .000 Model coeff se(HC4) t p LLCI ULCI constant 121.352 15.050 8.063 .000 91.532 151.171 Obesity -10.962 2.565 -4.273 .000 -16.045 -5.879 Genotype 17.447 8.617 2.025 .045 .373 34.521 Int_1 2.873 1.432 2.006 .047 .035 5.711 Product terms key: Int_1 : Obesity x Genotype Test(s) of highest order unconditional interaction(s): R2-chng F(HC4) df1 df2 p X*W .020 4.022 1.000 112.000 .047 ---------- Focal predict: Obesity (X) Mod var: Genotype (W) Conditional effects of the focal predictor at values of the moderator(s): Genotype Effect se(HC4) t p LLCI ULCI AA -8.089 1.229 -6.583 .000 -10.524 -5.654 AT/TT -5.216 .736 -7.085 .000 -6.675 -3.757 Data for visualizing the conditional effect of the focal predictor: Paste text below into a SPSS syntax window and execute to produce plot. DATA LIST FREE/ Obesity Genotype NPS . BEGIN DATA. -6.275 1.000 189.558 .000 1.000 138.798 6.275 1.000 88.039 -6.275 2.000 188.977 .000 2.000 156.245 6.275 2.000 123.513 END DATA. GRAPH/SCATTERPLOT= Obesity WITH NPS BY Genotype . Result NPS concentrations were significantly (p=0.000) moderated by NPSR1 gene Asn107Ile polymorphism with increasing obesity (Fig 4). Furthermore moderation effect analyses revealed a more negative effect of obesity on NPS concentration with AT+TT vs AA genotype as shown in (Table S2). *********************** ANALYSIS NOTES AND ERRORS ************************ Level of confidence for all confidence intervals in output: 95 NOTE: A heteroscedasticity consistent standard error and covariance matrix estimator was used. NOTE: The following variables were mean centered prior to analysis: Obesity ------ END MATRIX ----- Comment5# The peak map of sequencing does not need to be provided in the main body of the manuscript, because it does not show the data, but use to prove the reliability of the genotyping. Response. Yes Agreed, the Figure have been removed from the main body of text. Thanks References 1. R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. 2. RStudio Team (2020). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA URL http://www.rstudio.com/. 3. John Fox and Sanford Weisberg (2019). An {R} Companion to Applied Regression, Third Edition. Thousand Oaks CA: Sage. URL: https://socialsciences.mcmaster.ca/jfox/ Books/Companion/. 4. Alboukadel Kassambara (2020). rstatix: Pipe-Friendly Framework for Basic Statistical Tests. R package version 0.6.0. https://CRAN.R-project.org/package=rstatix. 5. Russell Lenth (2020). emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.5.1. https://CRAN.R-project.org/package=emmeans. 6. H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016. 7. Alboukadel Kassambara (2020). ggpubr: “ggplot2” Based Publication Ready Plots. R package version 0.4.0. https://CRAN.R-project.org/package=ggpubr. 10.1371/journal.pone.0243205.r007 Decision Letter 3 Li Zezhi Academic Editor © 2020 Zezhi Li2020Zezhi LiThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Submission Version3 18 Nov 2020 Neuropeptide S receptor gene Asn107 polymorphism in obese male individuals in Pakistan PONE-D-19-35919R3 Dear Dr. Bokhari, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. 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PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #2: (No Response) ********** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #2: (No Response) ********** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #2: No 10.1371/journal.pone.0243205.r008 Acceptance letter Li Zezhi Academic Editor © 2020 Zezhi Li2020Zezhi LiThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 26 Nov 2020 PONE-D-19-35919R3 Neuropeptide S receptor gene Asn107 polymorphism in obese male individuals in Pakistan Dear Dr. Bokhari: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Zezhi Li Academic Editor PLOS ONE ==== Refs References 1 World Health Organization (WHO). Fact Sheet:http://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight. 2 Boughton CK , Murphy KG . Can neuropeptides treat obesity? A review of neuropeptides and their potential role in the treatment of obesity . British journal of pharmacology . 2013 12 1 ;170 (7 ):1333 –48 . 10.1111/bph.12037 23121386 3 Ghazal P . The physio-pharmacological role of the NPS/NPSR system in psychiatric disorders: a translational overview . 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