Can common genetic variants be used to differentiate between treatment-resistant schizophrenia (TRS) and other forms of this disorder?Data from this genome-wide association study including 85 490 participants were used to estimate genome-wide single-nucleotide variation effect size differences between individuals with and without TRS, which were compatible with a polygenic model of treatment resistance. Results were used to generate a polygenic risk score, which was significantly associated with TRS status in independent incidence and prevalence samples.Findings of this study based on common genetic variants indicate that TRS is heritable with a modest but significant single-nucleotide variation–based heritability. This genome-wide association study examines the genetic architecture of treatment-resistant schizophrenia using genetic data from schizophrenia studies and generates a polygenic risk score for TRS status. treatment-resistant schizophrenia (TRS). The causes of treatment resistance and their relationships with causes underlying schizophrenia are largely unknown. Adequately powered genetic studies of TRS are scarce because of the difficulty in collecting data from well-characterized TRS cohorts.About 20% to 30% of people with schizophrenia have psychotic symptoms that do not respond adequately to first-line antipsychotic treatment. This clinical presentation, chronic and highly disabling, is known as To examine the genetic architecture of TRS through the reassessment of genetic data from schizophrenia studies and its validation in carefully ascertained clinical samples.Two case-control genome-wide association studies (GWASs) of schizophrenia were performed in which the case samples were defined as individuals with TRS (n = 10 501) and individuals with non-TRS (n = 20 325). The differences in effect sizes for allelic associations were then determined between both studies, the reasoning being such differences reflect treatment resistance instead of schizophrenia. Genotype data were retrieved from the CLOZUK and Psychiatric Genomics Consortium (PGC) schizophrenia studies. The output was validated using polygenic risk score (PRS) profiling of 2 independent schizophrenia cohorts with TRS and non-TRS: a prevalence sample with 817 individuals (Cardiff Cognition in Schizophrenia [CardiffCOGS]) and an incidence sample with 563 individuals (Genetics Workstream of the Schizophrenia Treatment Resistance and Therapeutic Advances [STRATA-G]).GWAS of treatment resistance in schizophrenia. The results of the GWAS were compared with complex polygenic traits through a genetic correlation approach and were used for PRS analysis on the independent validation cohorts using the same TRS definition.2 = 2.03%; P = .001), which was replicated in the STRATA-G incidence sample .The study included a total of 85 490 participants  in its GWAS stage and 1380 participants  in its PRS validation stage. Treatment resistance in schizophrenia emerged as a polygenic trait with detectable heritability (1% to 4%), and several traits related to intelligence and cognition were found to be genetically correlated with it . PRS analysis in the CardiffCOGS prevalence sample showed a positive association between TRS and a history of taking clozapine (rIn this GWAS, common genetic variants were differentially associated with TRS, and these associations may have been obscured through the amalgamation of large GWAS samples in previous studies of broadly defined schizophrenia. Findings of this study suggest the validity of meta-analytic approaches for studies on patient outcomes, including treatment resistance. Evidence from nonpsychiatric disorders points to distinct genetic bases for disorder susceptibility and prognosis,3 although few sufficiently powered psychiatric genetic studies have been conducted investigating disorder progression or outcome.4 Such study designs could offer valuable insights as to the feasibility of precision psychiatry approaches and their potential impact for patients who have poor outcomes because of the lack of effectiveness of current treatments.5Precision psychiatry provides a potential pathway to improve psychiatric classification and develop treatments that are better tailored to specific patients. Major advances in determining the role of genetic variation in the risk of developing psychiatric disorders are helping realize this potential,8 This clinical picture is known as treatment-resistant schizophrenia (TRS), which affects approximately 20% to 30% of people with this disorder. The only licensed treatment for TRS is clozapine, which is effective in approximately 60% of cases9 and improves most indicators of morbidity and mortality.11 While the mechanism of action of clozapine is still not fully understood,12 it has been proposed that its efficacy might be related to the biological underpinnings of treatment resistance, suggesting a distinct neurobiological etiology to that of non-TRS.13 Additionally, several studies have shown that delay in clozapine prescription is associated with resistance even to clozapine.15 This makes early identification of TRS critical and the ascertainment of correlates of TRS a priority for the field of schizophrenia research.Some of the most disadvantaged individuals in this respect are those with a diagnosis of schizophrenia whose symptoms do not respond adequately to conventional antipsychotic medication.16 but a recent systematic review did not find any individual genes robustly and specifically associated with this condition.17 Researchers have also investigated the long-standing hypothesis that TRS is a more severe form of schizophrenia resulting from a large burden of schizophrenia risk alleles.18 While 2 studies reported that aggregating risk variants into a polygenic risk score (PRS) revealed small differences between TRS and non-TRS samples,20 other analyses did not replicate this result.23 However, because each study used slightly different recruitment criteria and definitions of TRS, heterogeneity in the results is expected,24 making their collective interpretation difficult. This limitation likely reflects the previously discussed problem of individuals with treatment-resistant psychiatric symptoms being disproportionately underrepresented in research studies owing to poor health, limited capacity to consent, and other causes of attrition, including therapeutic pessimism,25 which have precluded the execution of well-powered genome-wide association studies (GWASs).26To date, there is considerable heterogeneity in the findings of genetic studies related to TRS. A family history of schizophrenia is likely associated with developing TRS,28 in which individuals with TRS can be formally defined. Our main hypotheses are that schizophrenia risk alleles will show different genetic associations in the analysis of individuals with and without TRS and that these differences reflect the underlying genetics of treatment resistance. When assessed at a genome-wide level, such differences could reveal commonalities with other complex traits or be validated in clinical cohorts using the PRS approach,29 helping us to better understand the biological and epidemiological characteristics of treatment resistance in schizophrenia.17In this study, we aim to characterize the contribution of common genetic variants to treatment resistance in schizophrenia by exploiting data generated by large consortium-based GWASs of schizophrenia,28 Some of the largest cohorts in these research initiatives were recruited based on clozapine prescription (a proxy of TRS status), and forming a case-case data set from them would require avoiding confounding factors, such as GWAS batch effects30 or population stratification,31 which are difficult to control in a multiple-cohort design.32 As a safeguard against these, we have used a meta-analytic procedure to assess the differences between GWAS in which individuals with TRS and non-TRS have been compared with matched sets of healthy controls, before comparing the allelic association effect sizes of these 2 GWASs on a genome-wide basis to create a GWAS specific to treatment resistance.For our main analysis, we used samples from large genomic studies of schizophrenia.33 All the individuals with TRS in these samples were prescribed clozapine in the UK after failure of at least 2 trials of antipsychotics, following National Institute for Health and Care Excellence guidelines for TRS.34 The use of a history of taking clozapine as equivalent to a research diagnosis of TRS has been validated in these samples,28 as well as in independent studies.36 Control individuals were collected from public databases or through collaboration with population sequencing projects in the UK.We used the CLOZUK1 and CLOZUK2 cohorts as our primary source of individuals with treatment-resistant symptoms, with a total sample size of 10 501 individuals with TRS and 24 542 controls. These cohorts have been described in previous studies.27 with a total sample size of 20 325 individuals with schizophrenia and 30 122 controls. In 14 of these studies, available clinical records allowed us to identify and remove all individuals with TRS ,37 Further details on the imputation, quality control, and association testing procedures are described in the eMethods in GWASs of individuals with TRS vs controls and individuals with non-TRS vs controls were carried out separately using the logistic regression model implemented in PLINK version 2.All studies used in the GWAS were reviewed and approved by their local ethical committee. Written informed consent  was obtained for all study participants except for those in the CLOZUK cohort, as this study used anonymized blood samples as approved by the UK Multicenter Research Ethics Committee.38 which is analogous to a fixed-effect test for moderators in the meta-analytic setting.39 This test allows us to calculate an estimate of the difference between 2 odds ratios (ORs), which in our case were those of individuals with TRS vs controls (OR1) and those with non-TRS vs controls (OR2). The difference, in the scale of the regression β coefficient, equates to difference = log(OR1) − log(OR2), with standard error SE(d) = √(SE[OR1]2 + SE[OR2]2). We transformed this effect size into a z score and calculated its associated P value at each overlapping single-nucleotide variation  between the TRS and non-TRS GWAS, excluding 146 SNVs with a minor allele frequency difference greater than 20% between both data sets to mirror preimputation quality control. For consistency, we also used the same definition of OR1 and OR2 in all our tests to preserve directions of effect, and thus, positive z scores in the interaction analysis reflect stronger SNV associations in individuals with TRS compared with non-TRS.To generate association statistics that reflect TRS vs non-TRS differences, we used the test for interaction proposed by Altman and Bland,40 after restricting to markers present in the HapMap3 study41 and a precomputed linkage disequilibrium reference panel based on the 1000 Genomes phase 3 samples. As point estimates of SNV-based heritability can differ based on model assumptions, we also estimated this quantity via the SumHer framework implemented in LDAK5,42 using a default parameter to define the relationship between minor allele frequency and effect size (α = −.25) and multiplicative inflation correction (genomic control). The LD reference for LDAK was estimated from the European samples of the public Haplotype Reference Consortium panel.43SNV-based heritability was estimated from the TRS interaction summary statistics using LD SCore version 1.01,44 We also used a new multiancestry incidence cohort of people with first-episode psychosis, the STRATA-G consortium, in which the participants have been followed up for at least 1 year after initial presentation to ascertain diagnosis and treatment response  and the other from a first-episode incidence cohort (Genetics Workstream of the Schizophrenia Treatment Resistance and Therapeutic Advances [STRATA-G]). The CardiffCOGS prevalence cohort is a sample of individuals with schizophrenia that included both individuals with TRS and treatment-responsive schizophrenia, defined here again from a history of taking or not taking clozapine . This is a cross-sectional study with detailed clinical ratings based on research diagnostic interviews and contemporaneous records, as previously described.The CardiffCOGS study had the relevant UK National Health Service ethical approval, and studies included in STRATA-G were reviewed and approved by their local ethical committees. Written informed consent was obtained for all participants.45 and PRS-CS version June 4, 2021,46 as detailed in the eMethods in P values were corrected for multiple testing using the Benjamini and Hochberg false discovery rate (FDR) method47 as implemented in the R framework stats package.Our primary PRS analysis used the TRS interaction GWAS summary statistics as training set, and the prevalence and incidence cohorts as testing sets where their treatment-resistance phenotype was tested for association. We also investigated the association of PRS and treatment resistance status based on (1) the CLOZUK TRS vs healthy control GWAS and (2) the PGC non-TRS GWAS. Polygenic scores were estimated using PRSice-2 version 2.3548 again based on the LD-Score regression40 framework. We only performed the analysis for GWAS categories that had significant phenome-wide associations with psychiatric disorders, based on previous research.49 The categories chosen were education, cognitive, personality, psychiatric, and smoking, for a total of 28 summary statistics. Multiple testing correction of these results was also carried out using FDR, since most of the summary statistics tested have overlapping samples and thus yield partially dependent P values.Correlations between treatment resistance and other complex traits were computed using LD-Hub version 1.93,P < .05. All P values were 2-tailed.For all associations, statistical significance was set at P = 1.43 × 10−147) in which the 95% CI includes 1. No genome-wide SNVs were detected by the TRS interaction analysis .The study included a total of 85 490 participants  in its GWAS stage and 1380 participants  in its PRS validation stage. Discovery GWAS of individuals with TRS and non-TRS against independent controls showed highly consistent genome-wide significant signals , very similar to that obtained by testing the larger PGC non-TRS sample . However, the effect sizes of these PRSs were generally in opposite directions , explaining up to 2.03% of the variance on the liability scale of treatment resistance . In contrast, neither the CLOZUK nor PGC PRS were significantly associated with treatment resistance across any of the tested P value thresholds or using polygenic prediction via Bayesian regression and continuous shrinkage priors  showed that a greater interaction PRS burden was also associated with having treatment resistance in this setting, explaining up to 1.09% of the variance (e priors . Howevere priors .P < 1). As a reference point for this analysis, we retained a small subset of 242 screened unaffected control individuals that had been genotyped as part of the Genetics and Psychosis project,26 one of the STRATA-G cohorts. Splitting the combined sample by TRS status showed that the polygenic profile of treatment resistance is largely consistent between CardiffCOGS and STRATA-G, with the TRS interaction PRS burden showing no difference between incident and prevalent TRS individuals . None of the scores we tested were significantly different between the 2 groups of individuals with non-TRS, and all individuals with schizophrenia showed larger mean scores than the unaffected controls.To interpret the results from the prevalence and incidence cohorts alongside each other, we merged the genotyped SNVs from both samples  and estimated polygenic scores using all LD-independent SNVs  genetic correlations with TRS, of which 5 survived multiple testing correction  an association with TRS liability, even if our results cannot quantify the degree of contribution from nongenetic causes. In this regard, given that most of our analyses are based on European-based and UK-based samples, our conclusions might not be generalizable to non-European countries, where the diagnosis and treatment pathways of TRS might be influenced by racial and ethnic or cultural backgrounds.61Imperfect phenotyping and potential misclassification might also have attenuated our results, and given the small total heritability detected for our TRS GWAS, we note that the magnitude of genetic correlations with other traits should be interpreted cautiously. Also, similar to what has been argued in the study of etiologically heterogeneous phenotypes through GWAS,In this GWAS, TRS had a small but detectable heritability associated with common risk alleles. Validation work, via the PRS method, showed that the contribution of these alleles were similar across incidence and prevalence samples. This is despite differences in cohort characteristics, and the use of drug prescription data as a proxy for TRS instead of quantitative metrics. Altogether, these results highlight the usefulness of well-controlled clinical phenotype data in psychiatric genetics to explore beyond diagnostic classifications and into treatment outcome and response to aid precision psychiatry.


Background: Observational studies have identified impaired lung function accessed by forced expiratory volume in one second (FEV1), forced vital capacity (FVC) or the ratio of FEV1 over FVC (FEV1/FVC) as an independent risk factor for atrial fibrillation (AF). However, the result may be affected by confounders or reverse causality.Methods: We performed univariable MR (uvMR), multivariable MR (mvMR) and bidirectional two-sample MR to jointly estimate the causality of lung function with AF. Apart from the inverse variance weighted (IVW) approach as the main MR analysis, three complementary sensitive analyses approaches including MR-Egger regression, weighted median (WM) MR and Pleiotropy Residual Sum and Outlier (MR-PRESSO) in uvMR as well as mvMR-Egger and mvMR-PRESSO in mvMR were applied to control for pleiotropy. Linkage disequilibrium score (LDSC) regression was applied to estimate genetic correlation between lung function and AF.Results: All forward and reverse uvMR analyses consistently suggested absent causal relations between lung function and AF risk . The forward MR-Egger showed that each standard deviation (SD) increase in FEV1/FVC was related to a higher AF risk  without heterogeneity , but pleiotropy effect exist . However, this significant effect disappeared after adjustment of FEV1 and FVC  in mvMR. No evidence was found for independent causal effects of FEV1 and FVC on AF in mvMR analysis by using mvIVW method . Notably, the association between lung function and AF were replicated using the FinnGen cohort data.Conclusions: Our findings reported no coheritability between lung function and AF, and failed to find substantial causal relation between decreased lung function and risk of AF. However, lung function and AF were both associated with inflammation, which may be potential pathway, warranting further study. Atrial fibrillation (AF) is the most common persistent arrhythmia and its prevalence increases with age . With thLung function accessed by forced expiratory volume in one second (FEV1), forced vital capacity (FVC) and the ratio of FEV1 over FVC (FEV1/FVC) are used to predict morbidity and mortality associated with different airway disease –12 and aThe prevalence of AF and impaired lung function is increasing worldwide and seriously affect the quality of life , 16, so Mendelian randomization (MR) study may provide more credible evidence concerning the causality of lung function and AF. Genetic variants robustly related to lung function or AF would be used as instrumental variables (IVs), respectively. Due to the random allocation from parents to their offspring at conception, IVs are less susceptible to confounders or reverse causality , 21. MulSince this study only used publicly available summary statistic from relevant genome-wide-association studies (GWAS) and did not use the individual data, ethical approval was not required.The study of Biobank-based GWAS tested association between more than 30 million genetic variants and AF in 1,030,836 individuals  of European ancestry from the Nord-Trøndelag Health Study (HUNT), deCODE, the Michigan Genomics Initiative (MGI), DiscovEHR, UK Biobank, and the AFGen Consortium. Full details are provided elsewhere .2, gender, height, and smoking status. Given the datasets for AF included individuals from UK Biobank, we just selected SpiroMeta results from lung function GWAS to perform MR analyses. Because the independence and non-overlapping of exposure and outcome is a prominent prerequisite for the validity of the two sample MR method (via LD-Hub (http://ldsc.broadinstitute.org/ldhub/).Summary genetic association estimates for lung function were obtained from the largest currently available GWAS study in 400,102 individuals  of European ancestry and were adjusted for age, ageR method . Full deR method  and summhttps://www.finngen.fi/en).Considering the stability of our results, we replicated the two-sample MR analysis using the above lung function GWAS data including UK Biobank participants  and another latest publicly available data of AF  released from the FinnGen cohort  the IV is associated with the exposure robustly, (2) the IV is independent of confounders, and (3) the IV influences the outcome exclusively exposure .P < 5 × 10−8) associated with AF as IVs to investigate their effect on lung function , and vice versa. Among others, violations of the second and third assumption occur in case of population stratification, when different ethnic subgroups are present with different allele frequencies. We overcame this issue by focusing on individuals with European ancestry (r2 = 0.0001) based on European ancestry reference data from the 1,000 Genomes Project were retained.We performed MR using genetic variants (significance of SNPs: ancestry . Violatiancestry . In viewP < 5 × 10−8) from exposure GWAS; then prune SNPs for linkage disequilibrium (r2 = 0.0001); next, extract the retained SNPs from the outcome datasets; last, harmonize the exposure and outcome SNPs .In brief, the process for selecting IVs was as follows: we first extract significant SNPs (r2 < 0.0001) that were associated with FEV1, FVC, or FEV1/FVC at P < 5 × 10−8; then we merged all SNPs related to any lung-function traits by removing duplicate SNPs with higher p-values; next, we extracted the relevant information of the merged SNPs from the original exposure datasets; finally, we used the last extracted SNPs as IVs for mvMR analysis.The mvMR approach takes into account the inter-relationship between FEV1, FVC, and FEV1/FVC and the IVs used in the mvMR analyses are often associated with all exposures. This method is based on the above-mentioned standard IVs method which allow for multiple exposures. We selected independent SNPs (R2 [R2 = 2×EAF× (1-EAF) × Beta2] of each SNP and summed them up to calculate the overall R2 and F statistics  using the sample size of exposure GWAS  method as the main analysis. The IVW analysis does not consider the intercept (constrained to zero) in the regression and uses the reciprocal of the outcome variance as the weight for fitting . ResultsQ heterogeneity test and I2 statistics  in the Rstudio . Power calculation was performed using mRnd developed by Brion et al. (https://cnsgenomics.com/shiny/mRnd/).The MR results are expressed as odds ratios (ORs) with 95% confidence intervals (CIs) interpreted as AF risk per SD increase in log odds of the lung function traits . We use a two-sided α of 0.017 based on testing three lung function traits against AF outcome to define significant or not. MR analyses were performed using the packages “MendelianRandomization,” “TwoSampleMR,” and “MR-PRESSO” packages , namely no coheritability between two traits. There was evidence for higher genetic correlations of FEV1 with FVC and FEV1/FVC   .P < 5 × 10−8) for FEV1/FVC, FEV1, FVC, and AF, respectively, then we extracted those SNPs from corresponding outcome dataset. Finally, 16/9/8/82 SNPs remained after harmonizing the datasets of exposure and outcome .In the univariable bidirectional MR analyses, we obtained 18/11/10/93 LD-independent SNPs which achieved genome wide significance (r2 < 0.0001) SNPs associated with FEV1, FVC, and FEV1/FVC at P < 5 × 10−8 were included. Five of these SNPs  represent the same signal, so we aggregate a list of SNP (34 = 39 – 5) by selecting the SNPs with the lowest P-value. This resulted in 34 SNPs, of which seven were related to FEV1 but not FVC and FEV1/FVC at P < 5 × 10−8, 14 were associated with FEV1/FVC but not FEV1 and FVC at P < 5 × 10−8, eight were associated with FVC but not FEV1 and FEV1/FVC at P < 5 × 10−8, and five were associated with any two traits at P < 5 × 10−8. Then, we extracted relevant information of 34 SNPs from the original exposure datasets, respectively. Finally, 28  SNPs for any lung function trait were obtained after harmonizing with SNP-outcome  above-mentioned LD-independent (R2): 0.6%], FVC , FEV1/FVC  were small and the statistical power was insufficient. However, the F statistics  for each SNP and the power of IVs in the mvMR : 2%; power: 100%) were sufficient, indicating that those SNPs satisfy the strong relevance assumption and the weak IV bias would not likely affect the causal inference. The number of IVs included for exposures were listed in The number of SNPs for FEV1 [nine, variance explained  without evidence of heterogeneity  nor pleiotropy effect . The WM method estimates were more precise than the MR-Egger method . The leave-one-out sensitivity analysis  with no evidence of pleiotropy  but moderate heterogeneity . The leave-one-out sensitivity analysis (I2 = 35%) after rerun the MR by removing the outlier SNP, the MR estimates did not alter significantly   . No sign= 0.339) .P = 0.182; WM: OR = 1.099, 95% CI = 0.969–1.246, P = 0.139) without evidence of heterogeneity . Although MR Egger estimate was significant , overall horizontal pleiotropic effects existed , highlighting the need for mvMR analysis. The leave-one-out sensitivity analysis , FVC  and FEV1/FVC . Pleiotropy bias  was not detected and heterogeneity  was not found. These main estimates from IVW were broadly consistent with estimates from the WM and MR-Egger sensitivity analyses. All reverse leave-one-out sensitivity analyses  controlling for another two lung function arguments was similar with the univariable setting. Additionally, no effect of FEV1/FVC  on AF could be observed, which was inconsistent with the univariable MR Egger result. Consistent with standard IVW regression results, mvMR-Egger regression (orientated to FEV1/FVC) and MR-PRESSO (detect no significant outliers) results also showed no significant association between lung function and AF, suggesting that the mvMR method successfully overcomes the bias caused by horizontal pleiotropy in the univariable MR analysis  can be found in the article/QZ conceived this study, conducted data analysis, and wrote this manuscript. XZ, JieZ, BW, XM, QT, JinZ, MJ, YZ, DZ, LW, and WW contributed to methodological guidance and manuscript revision. YW and BW oversaw the implementation of the statistical and analytical methods and revised and finalized this manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the National Nature Science Foundation of China (NFSC 81673247), a China-Australian collaborative grant (NSFC 81561128020-NHMRC APP1112767), and National Key R&D Program of China (2018YFC2000704).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Staphylococcus aureus, aimed to test the genetic mechanisms underlying the phenotypic plasticity of this species to vancomycin exposure and Escherichia coli coexistence. We reconstruct the two most comprehensive genetic networks for abiotic and biotic phenotypic plasticity. Pathway analysis shows that SNP-SNP epistasis for phenotypic plasticity can be annotated to protein-protein interactions through coding genes. Our model can unveil the regulatory mechanisms of significant loci and excavate missing heritability from some insignificant loci. Our multilayer genetic networks provide a systems tool for dissecting environment-induced evolution.Phenotypic plasticity represents a capacity by which the organism changes its phenotypes in response to environmental stimuli. Despite its pivotal role in adaptive evolution, how phenotypic plasticity is genetically controlled remains elusive. Here, we develop a unified framework for coalescing all single nucleotide polymorphisms (SNPs) from a genome-wide association study (GWAS) into a quantitative graph. This framework integrates functional genetic mapping, evolutionary game theory, and predator-prey theory to decompose the net genetic effect of each SNP into its independent and dependent components. The independent effect arises from the intrinsic capacity of a SNP, only expressed when it is in isolation, whereas the dependent effect results from the extrinsic influence of other SNPs. The dependent effect is conceptually beyond the traditional definition of epistasis by not only characterizing the strength of epistasis but also capturing the bi-causality of epistasis and the sign of the causality. We implement functional clustering and variable selection to infer multilayer, sparse, and multiplex interactome networks from any dimension of genetic data. We design and conduct two GWAS experiments using  Genetic plasticity drives phenotypic differences. Here, the authors develop a framework to quantify the individual and combinatorial contributions of SNPs on a phenotype of interest and use it to identify SNP-SNP interactions associated with variations in bacteria’s response to external changes. This so-called phenotypic plasticity is coded by a plastic developmental program that allows the organisms to sense environmental cues in early stages of life and develop phenotypes better adapted to environments encountered later in life7. Phenotypic plasticity is thought to be under genetic control12; i.e., specific genes may occur to govern its pattern, sign, and magnitude. In modern ecological, evolutionary, and medical genetics, the genetic mechanisms underlying phenotypic plasticity and its evolutionary novelty have emerged as an important topic of research in both theory and application18. Genetic mapping and genome-wide association studies (GWASs) have been used to map the genome-wide distribution of quantitative trait loci (QTLs) responsible for phenotypic plasticity in a variety of species25. With the advent of high-throughput genotyping techniques, there is a pressing demand to chart a more detailed and precise genetic atlas of this phenomenon.Most organisms are equipped with a capacity to produce multiple phenotypes in response to environmental change31. A recently emerging theory, known as omnigenic theory, states that complex traits are controlled by all genes carried by an organism32. Taken together, to better interpret phenotypic plasticity, we need to coalesce all genes into genetic interaction networks and trace the roadmap of each gene toward this phenomenon. Networks are mathematical graphs, connecting nodes  via edges (genetic interactions), which are particularly powerful for disentangling complex systems34. However, there is a formidable challenge in reconstructing genetic networks. First, for ordinary GWAS, hundreds of thousands of SNPs genotyped for each sample are not uncommon, making it computationally infeasible to reconstruct an omnigenic network. Second, the detection of genetic interactions requires a sample size that is hardly met in practice. Computer simulation shows that as many as 10,000 samples are needed to detect genetic interactions between a pair of genes in a GWAS35, let alone millions of millions of gene pairs.Existing approaches are founded on reductionist thinking, which can only identify individual key QTLs at a time. However, it is becoming increasingly clear that a deeper genetic understanding of phenotypic plasticity as a complex trait requires not only a detailed characterization of its underlying individual genes, but also of their interactions as a cohesive whole38. Statistical approaches currently used in the literature cannot reveal the casualty of epistasis and the sign of casualty. Understanding the direction of regulation from one gene to next is of utmost significance to disentangle the genetic architecture of complex traits and design an efficient and effective gene editing program for trait improvement and disease control. Fourth, genetic variation in phenotypic plasticity is displayed as gene–environment interactions of which most studies are based on linear additive models39. However, the organism often responds to environmental change in a nonlinear manner owing to substantial uncertainties and random effects across timescales42. Nonlinearities between genes and environment can help trait phenotypes maintain their robustness to random perturbations in environmental exposures43.Third, in classic quantitative genetics, genetic interactions are defined as epistasis, which describes how much the effect of one gene reciprocally depends on the other gene44 allows us to interpret how the genetic effect of a SNP is determined by its own intrinsic capacity and the epistatic influences of other loci on it. The integration of evolutionary game theory with predator-prey theory49 makes it possible to derive a generalized nonlinear Lotka–Volterra (nLV) equations that decompose the net genetic effect of a SNP into its two components, the independent effect due to its intrinsic capacity and the dependent effect resulting from regulation by other SNPs50. According to network theory, we encapsulate independent effects of individual SNPs as nodes and dependent effects of SNP pairs as edges into a graph, in which edges represent bidirectional, signed, and weighted (bDSW) genetic interactions . Viewing all SNPs together as a quantitative system, the distribution of bDSW interactions portrays a picture of the biological role of epistasis. By incorporating developmental modularity theory51 and high-dimensional statistical models, we can reconstruct multilayer, multiplex, large-scale, and sparse genetic networks from any number of SNPs.Here, we develop a computational model to infer omnigenic interactome networks underlying phenotypic plasticity. The model absorbs and integrates the elements of multiple previously disjointed disciplines into a unified framework. Association analysis based on individual SNPs estimates the marginal (net) genetic effect of each SNP on a complex trait from a pool of genome-wide markers. Evolutionary game theoryS. aureus. As one of the most important members of the Firmicutes, S. aureus often cause skin infections, but can also lead to pneumonia, heart valve infections, and bone infections52. The abiotic GWAS experiment is to study how the growth of S. aureus responds to vancomycin, a glycopeptide antibiotic designed to control and treat S. aureus-caused diseases by inhibiting cell wall biosynthesis54. In clinical practice, the use of this drug has incurred vancomycin-intermediate S. aureus (VISA) isolates, with the minimal inhibitory concentration (MIC) of 4–8 µg/mL55. Vancomycin non-susceptibility in S. aureus involves a genetic component. Several nucleotide substitutions that distinguish the vancomycin-susceptible S. aureus from VISA isolates have been identified by a whole-genome sequencing approach58. Some studies further used GWAS to characterize common genetic variants associated with antibiotic resistance in S. aureus59. However, despite these advances, a comprehensive portrait of the genetic control mechanisms underlying vancomycin resistance is still unclear. The biotic GWAS experiment aims to investigate how S. aureus changes its growth when encountered with another species, Escherichia coli. Studying the plastic response of one bacterial species to its co-existing species has become increasingly interesting to researchers from many fields including the gut microbiomes61, but the genetic architecture of this phenomenon has been little explored. Jiang et al60. characterized a set of specific QTLs in S. aureus that mediates the change of microbial growth from a socially isolated environment to a socialized environment. It is likely that phenotypic plasticity to biotic factors includes a complex, still unknown genetic machinery.We design and conduct two independent GWAS experiments of phenotypic plasticity to abiotic and biotic factors, respectively, using bacterial species S. aureus responds to an abiotic factor (drug) and biotic factor (species coexistence). Phenotypic plasticity can be quantitatively defined as the difference of phenotypic values for the same genotype expressed in two different environments11. We define the difference of microbial growth for the same S. aureus strain expressed in vancomycin-free and vancomycin-exposed media or in isolated and E. coli-socialized media as the abiotic or biotic phenotypic plasticity of this strain, respectively. First, we globally view the genetic landscape of each type of phenotypic plasticity, filled by a complete set of bDSW interactions, from hundreds of thousands of SNPs and delimit this landscape into functionally distinct network communities. Second, we characterize the detailed roadmap through which individual key genes determine phenotypic plasticity directly and/or through multiple indirect pathways. Our model opens up a new avenue to unveil the genetic complexities of how genes interact with the environment to help the organism better adapt to environmental and biological cues.We apply our new model to systemically dissect the genetic architecture of how We derive a computational model for reconstructing omnigenic interactome networks underlying phenotypic plasticity. We outline the procedure of model derivation in Fig. S. aureus strains were cultured in vancomycin-free medium (control) and vancomycin-exposed medium (stress). The mean abundance growth of these strains obeys an S-shaped logistic equation, as observed in many previous studies62, but the amount and pattern of growth vary from control to stress , more rapidly reach maximum relative growth rate (tI = 12.1 h vs. 21.4 h), and displays greater asymptotic growth (a = 1.54 vs. 1.10) in control than stress conditions. Increasing variability in growth trajectory is produced when strains are exposed to vancomycin. In contrast to all individual strains displaying S-shaped growth curves in control, a portion of strains in stress grow a little, having high susceptibility to vancomycin. Those that grow in an S shape in stress imply their high vancomycin resistance, marginally associated with an elevated parental MIC (P = 0.12)  Table  and part2) Table . By plot2) Table , we illuSAOUHSC_00544 (SdrC) that codes fibrinogen-binding protein. In a genomic analysis of highly vancomycin-resistant S. aureus strains developed by in vitro mutagenesis, gene SdrC was identified to play an important role in vancomycin resistance64. QTL Q25261 is located at gene SAOUHSC_00020  encoding cell envelope biogenesis65. walRK phosphorylation activates the expression of YycH and YycI to control cell wall metabolism and biofilm formation in S. aureus67.Unlike a general complex trait, developmental phenotypic plasticity is a composite trait derived from phenotypic values at multiple time points expressed in different environments. To better dissect it, we implement composite functional mapping (coFunMap), an extended model of functional mapping. By analyzing microbial growth trajectories that have been adjusted for population structure Fig. S, coFunMaWe further estimate the genetic effect curves of each QTL and tested in which temporal pattern a QTL affects the phenotypic plasticity of microbial growth Fig. . All QTLThe analytical procedure described above represents a standard flowchart of QTL detection from genetic mapping or association studies. Our model can address several unanswered questions: (1) how and why significant QTLs are significant, (2) why insignificant SNPs are not significant, and (3) whether insignificant SNPs can become significant. In the end, we will depict the functional roadmap of each SNP towards phenotypic plasticity.71. Developmental modularity theory states that a complex system is often broken down into multiple distinct network communities within which components are connected more tightly with each other than those from other communities73. We introduce this theory to divide a large-scale network into its delimited communities in which genes are linked more tightly with each other than with those from other communities74. It is possible that genes with the same community follow a similar temporal pattern of genetic effects on phenotypic plasticity, as previously detected in a longitudinal GWAS76. We use coFunMap to estimate genetic effect curves for each SNP and then implement functional clustering (FunClu)78 to classify all SNPs into distinct modules  based on the similarity of their effect curves.To systematically characterize the role of each SNP in mediating phenotypic plasticity, an appropriate approach is to reconstruct a genetic network that covers all pairwise links of SNPs considered. However, for a GWAS with thousands of thousands of SNPs, this is computationally prohibited. More importantly, full interconnections of genes do not exist in a cell since this violates the basic biological rule, i.e., buffering against random perturbations through sparsityS. aureus to vancomycin. The 16 QTLs detected by coFunMap are distributed in communities M7, M10, M12, and M14, rather than randomly distributed in different communities, suggesting that QTLs detected tend to function in a similar manner. This argument is strengthened by the fact that the QTL-containing communities, especially M7, M10, and M12, display a similar pattern of time-varying genetic effects. However, gene enrichment analysis annotates these QTL modules with different biological functions  of different sizes among 25,173 SNPs based on Bayesian information criterion (BIC), with each module displaying a different pattern of genetic effects Fig. . The oveons Fig. . For exa44 to model how one module mean is affected by its own strategy and the strategies of other module means. According to this argument, we reconstruct a 14-node inter-module network, which represents a top genetic network at the first layer  to 0 (M1 and M11), but incoming links are more uniformly distributed among modules  is a hub with more links than average, and its coding protein AID38557.1 also serves as a hub in the protein network. In the same network, S95811 interacts with S94818 (residing in SAOUHSC_00089) and, this interaction code the interaction between two proteins AID38557.1 and AID38556.1, each coded by one of these two SNPs. In the SM25 SNP network, two linked SNPs S319914 and S324256 code the interaction between their coding proteins AID38813.1and AID38818.1  serves as a hub in the SM17 SNP network and, correspondingly, protein AID41345.1 it codes is a hub in the protein network. Also, the interaction of S2728755 with S2748895 (located at SAOUHSC_02982) coding protein AID41358.1 is reflected by the link of their coding proteins  establishes an “antagonistic” relationship with SNP S2748895 (SAOUHSC_02982); i.e., while this QTL inhibits the latter, it is also inhibited by the latter , S2773290 (SAOUHSC_03000), and S2085718 (SAOUHSC_02250). Together, although Q2728755 has a small independent effect, i.e., a limited intrinsic capacity to express itself in isolation, its net genetic effect detected by coFunMap is still remarkable, thanks to large favorable contributions from multiple positive regulators  is stronger than the promotion it receives from a promoter (S2748895).Beyond most existing undirected networks, our networks can characterize causal epistasis. In the third-layer network of SM17, QTL Q2728755  has a large independent effect that is amplified through the promotion of Q229384 (in a non-coding region) at the early stage of growth, leading to a larger net genetic effect , making Q2072412 a significant net genetic effect.The third-layer networks of submodules SM29 and SM25 each contains two QTLs Fig. . In eachect Fig. . Q229384ect Fig.  because ect Fig. . These tSAOUHSC_00544) in the submodule SM25 of M14 . gidA is found to play a role in regulating the expression of salA associated with lantibiotic production in bacteria 83.Like the case of module M12, as described above, in the second-layer submodule networks of modules M14 Fig. S, M7 Fig, and M1014 Fig. S has a suSAOUHSC_00020), Q193712 (at SAOUHSC_00176), Q92210 (at SAOUHSC_00085), and Q2644158 (at SAOUHSC_02871), have significant net effects mainly due to favorable accumulated promotion from regulators, although the strength of promotion and number of regulators differ from QTL to QTL , its independent effect is even larger than its net effect because it receives a negative regulation from S119306 (in a non-coding region). As has been seen from the above gene enrichment analysis  encoding peptide ABC transporter permeases  and S964453 (located at SAOUHSC_00992) code two interacting proteins AID39489.1 and AID39490.1 , although found to be insignificant by functional mapping, display large independent genetic effects on phenotypic plasticity. The former is inhibited by two SNPs, whereas the latter is promoted by one SNP but inhibited, to a much larger extent, by a second SNP. The negative regulation they receive from other SNPs cancels out their independent effect, making their net genetic effects insignificant. The other three SNPs have consistently small independent effects, but their small net effects undergo different mechanisms. S855041 (at SAOUHSC_00891) receives a strong positive regulation and, in the meanwhile, a strong negative regulation, which cancels each other so that its net effect makes no change. S395434 (at SAOUHSC_00392) and S678553 (at SAOUHSC_00693) each receive a small regulation from one SNP, which does not influence their independent effects. Taken together, although many SNPs are detected to be insignificant by traditional approaches, they may not be necessarily unimportant rather they are intrinsically impactful in nature (like S366529 and S964411). Even for those intrinsically insignificant SNPs (such as S855041), their net effect can be changed through altering the expression of its regulators. These results suggest a possibility to retrieve missing heritability hidden in genetic architecture by editing the relevant regulators that act through genetic networks.As a popular regulator, submodule SM437 (containing 24 SNPs) in the M4 network influences many other submodules Fig. . We reco0.1 Fig. , suggest0.1 Fig.  for theiS. aureus strains in monoculture and co-culture with E. coli. In general, S. aureus strains display a reduced growth from monoculture to co-culture60, showing remarkable plastic response to species coexistence  is a 9-node inter-module network  and S1464685 (at SAOUHSC_01516) code proteins AID38810.1 and AID38809.1, respectively, which are linked. A similar finding is true for another QTL-containing module M4; i.e., AID39944.1-AID39945.1 link interpreted by S1456366 (at SAOUHSC_01502)-S1456591 (at SAOUHSC_01503) interaction in the SM3 network, norB-AID39899.1 link by S1385625 (at SAOUHSC_01448)-S1456591 (at SAOUHSC_01450) interaction in the SM7 network, and AID39952.1-AID39952.1 link by S1507067 (at SAOUHSC_01580)-S1507354 (at SAOUHSC_01582) interaction in the SM5 network. Even for modules without QTLs, SNP-SNP interactions detected can correspond to protein-protein interactions. For example, in the SM437 network of M4, how S964411 (at SAOUHSC_00991) interacts with S64453 (at SAOUHSC_00992) determines the link of their coding proteins AID39489.1 and AID39490.1 , Q407377 (at SAOUHSC_00405), Q1349428 (in a non-coding region), and Q1464883 (at SAOUHSC_01515), observed genetic effects are larger than their intrinsic independent effects due to up-regulation they receive from regulators. This is especially true for Q1349428 with an almost neglected independent effect, whose significance is derived from a tremendous dependent effect it receives from S407368 (at SAOUHSC_00405). This finding suggests that the practical transformation of Q1349428 can only be made possible through a joint selection or editing of S407368. Q1831526 (at SAOUHSC_01926) is significant by coFunMap, but its independent effect is even larger than what can be observed. Down-regulation this QTL receives from S563832 (in a non-coding region) cancels its intrinsic capacity to affect phenotypic plasticity. Thus, by silencing the expression of S563832, the effect of Q1831526 can be better released.Figure SAOUHSC_00535) has a subtle independent effect. Because it only receives a subtle dependent effect, its net effect is very small. Thus, this SNP makes no contribution to phenotypic plasticity. However, a dramatically different pattern is observed for S784955 (at SAOUHSC_00802 encoding carboxylesterase). This SNP has a formidable independent effect, but because of striking down-regulation by SNP595377 (in a non-coding region) its observed effect almost can be neglected. In practice, the real effect of S784955 on biotic defense can be released if its negative regulator is knocked out. We argue that those SNPs, like S784955, can be used to retrieve missing heritability.In the preceding section, we argue that our network can potentially detect missing heritability. This argument is confirmed by our biotic GWAS study. We randomly chose two insignificant SNPs to draw their effect curves Fig. . Althoug85. The advantage of LMM lies in its capacity to correct population structure and kinships so that genetic architecture can be more precisely characterized. Our network model is qualitatively different from LMM in two key aspects. First, LMM can only analyze static phenotypes measured at a single time point, whereas our model makes use of dynamic phenotypes that are viewed as curves. Second, most LMM attempt to detect single significant loci at a time from a pool of genome-wide SNPs, but our model simultaneously analyzes all SNPs from which all possible pairwise interactions are characterized. Third, LMM assumes a linear genotype-phenotype relationship, whereas our model can capture any nonlinear relationship. Despite these differences, we can still make a rough comparison between the two models. We first reduce the growth dimension from dynamic to static by calculating growth rates for each strain and implement an LMM to estimate the genetic control of growth rate under each condition. In the abiotic phenotypic plasticity experiment of S. aureus, the total heritability of growth rate explained by all QTLs is 0.516 in vancomycin-free condition and 0.021 in vancomycin-exposed condition. By combining data from two conditions, we estimate the overall heritability of growth rates  as 0.763, larger than the total QTL heritability, suggesting the existence of tremendous missing heritability. Similarly, we identify tremendous missing heritability in the biotic phenotypic plasticity experiment, where the overall heritability of growth rate is 0.602, whereas the total heritability explained by all QTLs is 0.170 in monoculture and 0.335 in co-culture. Taken together, although LLM can precisely identify individual QTLs, they may be encountered with a formidable issue of missing heritability. Because of a joint analysis of all SNPs and their pairwise interactions, our model show its power to detect missing heritability. Furthermore, our model can provide the roadmap of gene regulation, helping geneticists to retrieve and excavate some hidden missing heritability by blocking negative regulation.Linear mixed models (LMM) are one of the most commonly used computational approaches for GWAS12, but its genetic analysis is based on reductionist thinking of individual gene identification. As a composite trait derived multiple phenotypes expressed in different environments, phenotypic plasticity is likely to comply with omnigenic theory30; i.e., it may be controlled by all genes an organism may carry. By integrating mapping theory and elements of multiple disciplines, we develop a computational model to encapsulates all SNPs from a GWAS into a phenotypic plasticity-driven genetic network. Different from gene regulatory networks from expression data87, our genetic networks unravel a direct link from DNA genotype to phenotype, providing a broader picture of genetic architecture.Phenotypic plasticity involves a complex genetic componentS. aureus strains in two contrast environments. These two experiments are complementary, one focusing on the genetic mapping of phenotypic plasticity to an abiotic environment (vancomycin-free vs. vancomycin-exposed) and the second on the genetic mapping of phenotypic plasticity to a biotic environment . We use our model to reconstruct a large-scale, multilayer, and maximally informative genetic network underlying abiotic phenotypic plasticity and biotic phenotypic plasticity, respectively. The two networks unravel many previously undetected genetic mechanisms by traditional mapping approaches and, also, produce findings that are reciprocally supported from the two GWAS experiments.One major advantage of our model lies in its capacity to chart a comprehensive roadmap of how each SNP flows its genetic signal towards phenotypic plasticity, whether a SNP exerts its effect either in its own capacity (independent effect) or through regulation by promotors or inhibitors (dependent effect), or both, and how the intrinsic effect of a SNP is masked by other loci. To validate the biological relevance of our model, we design and conduct two independent GWAS experiments by culturing each panel of S. aureus’ response to vancomycin and to E. coli coexistence, respectively, in the abiotic and biotic GWAS. We postulate three mechanisms underlying the significance of these QTLs.From the genetic networks reconstructed from two GWAS experiments, we identify common causes of the significance of QTLs detected by genetic mapping based on marginal effects of single SNPs. coFunMap finds 16 and eight key QTLs of significant marginal effects that mediate SAOUHSC_00544 (SdrC) in mediating vancomycin resistance64 at which this QTL is located  does not affect vancomycin susceptibility in its own intrinsic capacity, but it does exert a remarkable effect, especially in the early stage of vancomycin susceptibility, through positive regulation from S2085718 (at the SAOUHSC_02250 location), S2773077 and S2773290 (both at the SAOUHSC_03000 location)  gene is involved in the biosynthesis of outer capsules of cell walls88, which provides material support for Q2728755 to mediate S. aureus colonization, pathogenesis, and bacterial evasion of the host immune defenses. The net effect of Q2728755 decreases considerably with time because it receives negative regulation by S2748895 (at SAOUHSC_02982). Thus, by inhibiting the expression of S2748895 and/or promoting the expression of S2085718, S2773077, and S2773290, it is possible to amplify and maintain the genetic effect of Q2728755 in response to vancomycin.Most of the QTLs detected from both GWAS experiments affect phenotypic plasticity through this mechanism, i.e., making use of favorable regulation from other regulatory genes rather than their own intrinsic capacity. For example, Q2728755 located at n) Figs. , S8. SAOSAOUHSC_00544) that is proximal to the fibrinogen-binding protein SdrD gene encoding serine-aspartate repeat-containing protein D. This protein can promote the adhesion of bacteria to host cells, help resist the killing of innate immune components, such as neutrophils in the blood, and, thus, weaken bacterial clearance89. Q2783126 (detected from the abiotic GWAS experiment), located at gene SAOUHSC_03008 coding multiple phosphatase-related processes, is not expressed in its own capacity when its growth is at a linear stage  and S183439 (at SAOUHSC_00169), making its net effect below its increasing independent effect. As an inhibitor, SAOUHSC_00169 encodes ABC transporter permease that reduces intracellular antibiotic concentration through MDR proteins91, thereby leading to change in drug resistance.The expression of Q193712 detected in the abiotic GWAS experiment is promoted jointly by three SNPs and also inhibited, but to a lesser extent, by one SNP Figs. SC, S6, lege Fig. S; Fig. S8SAOUHSC_00169 (oppA) that plays a critical role in antibiotic resistance through encoding peptide ABC transporter permeases83  is involved in the formation of biofilms92 and can promote blood coagulation by activating prothrombin to convert fibrinogen into fibrin93. By changing the expression of regulators, the role of Q183488 in mediating vancomycin resistance can be maximized.Some QTLs are significant because of both intrinsic capacity and extrinsic regulation. For example, Q183488 detected from the abiotic GWAS experiment exerts a large independent effect on vancomycin susceptibility Fig. S probably83 Table . This QTSAOUHSC_00397 encoding type I restriction-modification system subunit M ). Taken together, our model can detect any epistasis and identify its mechanism from any number of SNPs from a GWAS, but much less reliant upon sample size compared to traditional approaches.Despite its importance for trait control, epistasis is difficult to estimate without a considerable large sample size that is often hardly met in practice. For example, Zuk et al39. By borrowing ingredients of various disciplines, these two theories are coalesced into a unified framework by which the genetic architecture of phenotypic variation and plasticity can be systemically characterized. However, our model has several limitations. First, it needs dynamic data of traits, which may not be widely available in ordinary general GWAS. Yet, Wu and Jiang’s50 strategy for extracting dynamic information from static snapshots may be incorporated into our model to expand its use to static phenotypic data. Second, our model can only be used for a single phenotype, but the organism can be better described by its multifaced correlated features. However, how to reconstruct genetic networks for the phenotypic plasticity of multiple phenotypes is not a simple extension of the current model because these phenotypes may be interdependent of each other in a complex, likely nonlinear, manner. Third, our model only considers two environments. To extract the complete genetic mechanisms underlying how complex traits function, vary, and evolve along a spatiotemporal gradient, a series of environments should be sampled, but joint modeling of multiple environments cannot be made possible without the implementation of sophisticated spatial statistical methods. Despite these areas remaining to be explored, the precise mapping of SNP-SNP interactions identified by our model to the interactions between coding proteins, confirmed with two independent S. aureus GWAS experiments, implies its biological relevance and potential promise to be used as a general quantitative genetic approach at a new but higher standard. If this is a case, our model can likely make a paradigm shift in trait mapping from reductionist thinking of individual genes to a holistic approach that portrays a complete picture of genetic mechanisms.Our model integrates two emerging theories in quantitative genetics, i.e., omnigenic control and nonlinear gene–environment interactionsS. aureus. These two experiments aim to address the same biological question about phenotypic plasticity from different perspectives. The first experiment is designed to study how S. aureus responds to an abiotic environment, whereas the second experiment attempts to answer the question of how this species alters its phenotype in response to a biotic environment.The motivation of this study is to develop a computational model for mapping the complete genetic architecture of phenotypic plasticity in a GWAS. To better describe the rationale and derivation procedure of the model, we start with two real GWAS experiments for an important bacterial species S. aureus strains , of which 41 are vancomycin-resistant to different extents and 58 are vancomycin-sensitive, were obtained from parental strains via a 60-day in vitro vancomycin treatment. All original parental vancomycin-sensitive strains  are VSSA (MIC = 1–1.5) (Table We sampled and cultured 99 5) Table . The str600 to represent growth abundance at 1 h after culture, once every 2 h after 2 h till 12 h, once every 4 h after 12 h till 24 h, and once every 6 h after 24 h till 48 h. In total, there are 14 time points for measurement. Genomic DNA of strains was extracted by TIANamp Bacteria DNA Kit  according to the manufacturer’s protocol. Whole-genome sequencing was performed using Illumina HiSeq 4000 instrument  at Allwegene . To obtain initial alignment results, sequencing data were compared with the reference genome (S. aureus subsp. aureus NCTC 8325) using BWA mapper v0.7.899. SAMtools v0.1.18100 was used to sort the alignment results, producing 110,678 SNPs, of which 25,173 pass quality control for association mapping. All sequence data could access in the Sequence Read Archive (SRA) data NCBI (https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA722566).To obtain growth data, we inoculated each strain in the same-volume liquid medium under control and stress and measured their ODS. aureus strains and 100 E. coli strains from the National Infrastructure of Microbial Resources, China. We pre-cultivate these microbial strains using a procedure as described previously101. After pre-cultivation, we inoculated 100 strains of S. aureus (monoculture) and 100 independent S. aureus–E. coli interspecific strain pairs (co-culture) in different flasks filled with the same media, i.e., 25 mL two-time diluted brain heart infusion medium . For co-culture, we mix two species in a 1:1 ratio. All the flasks were incubated at 30 °C and shaken at 130 rpm. To minimize the position effect, we placed the flasks in a randomized layout. The experiment was repeated three times.We collected 100 S. aureus were performed according to the previously reported procedure60. The qPCR counts of each strain, averaged over three replicates, are used as abundance phenotypes for genetic mapping. Whole-genome sequencing of 100 E. coli strains was performed on the Illumina HiSeq platform at Allwegene . In total, we obtain 12,849 SNPs for S. aureus. The resequencing statistics for the genomes of S. aureus are summarized in Tables Each strain in a flask was sampled at time once every 0.5 h before 2 h of culture, followed by once every 2 h after 2 h and once every 4 h after 12 h, with a total culture period of 36 h. Quantitative PCR (qPCR) measurements of 103. In general, these equations produce biologically similar results104, from which we choose the logistic equation to fit our data, expressed asa is the asymptotic growth of the strain, b is the parameter that describes the initial growth of the strain, and r is the average specific growth rate of the curve. To characterize the growth properties of an organism, Sun et al105. proposed to use several key heterochronic parameters that can specify the timing, period, rate, and event of development. Two of the heterochronic parameters, specific growth rate (r) and the timing of inflection point (tI) at which the strain reaches a maximum relative growth rate, calculated as tI = (In b)/r, were used to compare growth curves.Changes of abundance with time are important phenotypes that describe the biological properties of microbes. Time-varying abundance of a strain in both biotic and abiotic GWAS experiments can be fitted by growth equations. A number of growth equations have been developed, among which Gompertz, logistic, and Richards equations are most commonly used to fit microbial growthn members, i.e., strain, as described above, genotyped by p genome-wide SNPs. To study the phenotypic plasticity of microbial abundance, we culture each strain in two contrast environments and measure its abundance repeatedly at a series of T time points after culture. Let xi = (xi(t1), …, xi(tT)) and yi = (yi(t1), …, yi(tT)) denote a vector of abundance values measured for the ith strain at time schedule  in environment X and Y, respectively. The time-dependent differences of microbial abundance for the ith strain between two environments is calculated asi responds to environmental change from X to Y over the time course. Thus, we define differences shown in equation (2) as the phenotypic plasticity trajectory of abundance for the ith strain. If there is variability in plasticity trajectory among n strains, this implies the possible existence of QTLs that mediate phenotypic plasticity.Consider a GWAS population of 106 to test the population structure of each GWAS population and use SNPhylo software107 to build up the phylogenetic trees of S. aureus strains from each experiment . We formulate the likelihood of time-dependent differences of microbial abundance at this SNP asfs(·) is a T-dimensional normal distribution with mean vector Σ at SNP s. Based on the definition, the mean vector can be expressed asjs of SNP s at time point t  and js of SNP s at time point t, expressed in environments X and Y, respectively. We consider that the time-dependent change of microbial abundance follows a S-shaped curve, as shown in equation (1). Thus, we model the genotypic means of microbial abundance at each genotype, specifically for each environment, asa, b, r) are genotype- and environment-specific, i.e., two sets of growth parameters for each genotype are used for curve fitting for two different environments.FunMap is a statistical model, proven to be powerful for mapping growth traits by integrating biologically meaningful growth equations110. The residual covariance matrix of phenotypic plasticity at SNP s has the following structure:T) of phenotypic plasticity are the sum of the time-variances 98, is used to fit the structure of the covariances, increasing the model’s parsimony. The advantages of SAD(1) include the allowance of variances and covariances to change with time without need of additional parameters and the existence of the closed forms of matrix inverse and determinant favorable for efficient computation.The statistical power of coFunMap can also attribute to the covariance modelings for the phenotypic plasticity at any time point t, expressed asWe implemented the simplex algorithm to obtain the maximum likelihood estimates (MLEs) of the model parameters constituting the likelihood (3). Using the MLEs of the parameters that model the mean vector (4 A), we can calculate the genetic standard deviation of a SNP under each of which we calculate the likelihood values and use them to calculate the LR value as a test statistic. Permutation tests can be used to determine the genome-wide critical threshold. The procedures described above are used to identify key QTLs in traditional genetic mapping, but lacking a capacity to chart a complete picture of quantitative genetic architecture.111. This decision process continues until the Nash equilibrium  is reached112. Combined with evolutionary theory, game theory is leveraged to evolutionary game theory, where evolutionarily stable strategies refine the Nash equilibrium without assuming players’ rationality in interactions44. We integrate evolutionary game theory and predator-prey theory, an argument that describes the effects of predation on prey populations49, to derive a generalized nLV equations for modeling “strategic” interactions among nonrational genes.A better way of comprehending genetic control is to encapsulate all loci into an omnigenic network that shapes phenotypic variation. To do so, we need to introduce elements of multiple disciplines. We assume that all loci act and interact together to form a complex system, whose behavior can be interpreted through the lens of game theory. As a mathematical formulation of strategic interactions, game theory states that, to maximize its payoff, a rational player would decide an optimal strategy in response to the strategies of other playerss on phenotypic plasticity estimated by coFunMap. Based on evolutionary game theory, the net genetic effect of a SNP can be decomposed into its independent effect and dependent effect component31. This decomposition is mathematically specified by a system of nLV-based ordinary differential equations (ODEs), expressed ass on phenotypic plasticity at time t, s that occurs when it is assumed to be in isolation, s that arises from the influence of another SNP 114, to fit the independent and dependent functions.Let p – 1 SNPs. However, such a full connection of epistasis in a cell is likely to be absent because this does not help the organism buffer against random perturbations and maintain its stability, as widely recognized in other network systems71. As such, the number of SNPs whose influences a given SNP can receive is limited, leading to the sparsity of genetic networks. A similar argument, called Dunbar’s law, has been observed in animal social networks, where an individual can only stably handle a certain number of relationships with others due to a limited size of its neocortex—the part of the brain associated with cognition and language115. In statistics, to select a small set of SNPs that are the most significant for a given SNP, we incorporate regularization-based variable selection, such as LASSO and its variants118. This can be done by regressing the net genetic effect of a SNP on those of all other SNPs across time points. LASSO-related approaches can particularly handle variable selection when the number of SNPs is larger than the number of time points. In the case, where the relative number of SNPs over time points is so exceedingly large that LASSO may errantly perform, we may interpolate additional values on the fitting curve of genetic effects over time as a multiple regression model.Equation  represends (ds ≪ p) denote the number of the most significant genes for gene s. By replacing p by ds in the second term of equation (9), the full model is reduced to a sparse model, by which a sparsely-connected network can be reconstructed. We formulate a likelihood or a least-squares approach to solve the sparse ODEs by implementing the fourth-order Kunge-Kutta algorithm50. We code the estimates of independent genetic effects of different SNPs as nodes and the estimates of dependent genetic effects of gene pairs as edges into a graph. This graph represents a maximally informative networks filled of bDSW interactions 50.Through variable selection, we obtain a small set of the most significant genes for each gene. Let 74, a phenomenon essential for the system to maintain its robustness and stability71. A module is defined as one whose components are more tightly with each other than those from other modules120. We implement a bottom-up approach for detecting network communities50. This approach is based on the assumption that SNPs within the same network communities have a more similar temporal pattern of genetic effects than those from other communities. In a GWAS of a trait measured at 500 time points, Verweig et al84. found that SNPs are clustered based on the similarity of their genetic effects at all time points. Thus, we implement FunClu78 to divide all p SNPs into different modules according to the similarity of the temporal pattern of their genetic effects. This clustering allows us to reconstruct multiple interconnected smaller networks from a big network to overcome the curse of network dimensionality.For a high-dimensional genetic network, sparsity is not only due to the limited number of links for a given SNP, but also to its division into delimited network communities. The existence of network communities is consistent with developmental modularity theory by which a complex system can be divided into different moduless on phenotypic plasticity calculated by Eq.  to which a specific SNP s belongs can be determined on the basis of the estimate of the posterior probability, expressed asWe implement a hybrid of the EM algorithm and simplex algorithm to solve the likelihood (10). An optimal number of mixture components  is determined according to AIC. The module  network, is equivalent to the optimal number of modules by FunClu determined by information criteria, such as BIC. We then reconstruct genetic networks at the SNP level, called bottom networks, for each module, with the number of bottom networks being equal to the optimal number of modules. In current GWAS, it is not uncommon to have tens of thousands—thousands of thousands of SNPs, in which case the number of SNPs within a module is still too big to reconstruct a meaningful SNP network. We further use FunClu, implemented with the LOP of a higher order, to classify such a module into its distinct submodules and reconstruct the second-layer network among these submodules. If needed, we classify a submodule into its sub-submodules and reconstruct the third-layer networks among these sub-submodules. This process continues until a small-size unit, allowing meaningful SNP networks to be reconstructed, is obtained. Taken together, we will reconstruct a multilayer, multiplex, and multiscale sparse network from any number of SNPs in a GWAS. While the top network is constituted by coarse-grained genetic interactions, the bottom network provides a detailed roadmap of how each SNP works together with every other SNP to determine phenotypic plasticity through fine-grained genetic interactions.Further information on research design is available in the Supplementary FiguresPeer Review FileEditorial Assessment ReportList of Supplementary Data filesDataset 1Dataset 2Dataset 3Dataset 4Reporting Summary


Despite the widespread use of oxytocin for induction of labor, mechanistic insights into fetal/neonatal wellbeing are lacking because of the absence of an animal model that recapitulates modern obstetric practice. Here, we create and validate a hi-fidelity pregnant rat model that mirrors labor induction with oxytocin in laboring women. The model consists of an implantable preprogrammed microprocessor-controlled infusion pump that delivers a gradually escalating dose of intravenous oxytocin to induce birth at term gestation. We validated the model with molecular biological experiments on the uterine myometrium and telemetry-supported assessment of changes in intrauterine pressure. Finally, we applied this model to test the hypothesis that labor induction with oxytocin would be associated with oxidative stress in the newborn brain. Analysis of biomarkers of oxidative stress and changes in the expression of associated genes were no different between oxytocin-exposed and saline-treated pups, suggesting that oxytocin-induced labor was not associated with oxidative stress in the developing brain. Collectively, we provide a viable and realistic animal model for labor induction and augmentation with oxytocin that would enable new lines of investigation related to the impact of perinatal oxytocin exposure on the mother-infant dyad. Taken together with a concomitant increase in the use of Oxt for augmentation of labor, approximately 50–70% of parturients are exposed to Oxt during labor5. Despite this widespread use for over 60 years, most research has focused on the contractile effects of Oxt on the uterus and associated obstetric outcomes9. Whether Oxt affects the fetus remains sparsely studied, despite evidence that it can reduce placental perfusion and enter the fetal circulation17.Oxytocin (Oxt) is one of the most widely used medications to induce labor and facilitate delivery. Between 1998 and 2007, the incidence of induction of labor more than doubled from 9.8 to 23%, with a large majority of patients receiving Oxt21, making them contextually less germane. Consequentially, the impact of pharmacological interventions during childbirth on the mother-infant dyad remains poorly investigated. In this report, we surmount these challenges and present a hi-fidelity pregnant rat model for elective labor induction and augmentation with Oxt using an implantable, programmable, microprocessor-controlled precision drug delivery pump to closely mimic obstetric management of human labor. We chose a rat model because of (i) anatomical similarities between the rat and the human placenta 23, (ii) better characterization of Oxt metabolism in the rat uterus24, (iii) a relatively larger size which makes multiple invasive procedures  less challenging, and (iv) the possibility of leveraging the rich social behavioral repertoire of rats for future neurobehavioral studies in the offspring26.An important scientific roadblock is the absence of an animal model that mirrors induction of labor in pregnant women with Oxt, one of the most common interventions in modern obstetric practice. This is in stark contrast to the availability of numerous animal models that recapitulate various facets of healthy and maladapted pregnancy. A major reason for the lack of an animal model for labor induction is the technical difficulty associated with delivering a gradually escalating dose of intravenous Oxt in a free-moving animal. Current preclinical studies examining the effect of Oxt on the fetus do so without inducing birth33. An outstanding concern with these studies is the inability to distinguish the need for labor induction from its possible effects, and to delineate the effect of an individual pharmacological agent from a combination of drugs used for labor induction. With the rising popularity of elective induction of labor9, where labor is not induced for maternal or fetal indications, there is a compelling need to simultaneously pursue preclinical investigations to better understand the impact of these practices on long-term neurodevelopmental health of children. Here, we endeavor to address this knowledge gap by performing foundational experiments regarding the potential mechanisms by which labor induction with oxytocin can impact the developing brain. We rule out oxidative stress as a likely possibility, suggesting that the effects of Oxt on the fetus, if any, are likely to be directly mediated.Recent epidemiological studies suggest a modest but controversial association between labor induction and neurodevelopmental disorders such as autism spectrum and attention deficit hyperactivity disorders in the offspringOverall, 44 timed-pregnant Sprague Dawley dams were used for the study . Unbiased RNA-seq analyses of the cerebral cortex of vaginally delivered newborn pups revealed no significant changes in the cortical transcriptome after exposure to surgery and anesthesia as shown by the lack of significantly differentially expressed genes in the volcano plot  a decrease in OxtR protein expression in the uterine myometrium, and (ii) confirmation of increased intrauterine pressure with Oxt. Furthermore, we provide evidence for the translational utility of the model by showing that labor induction with Oxt was not associated with oxidative stress in the fetal brain.44, epigenetic regulation of OxtR in the fetal brain16, relationship between intrapartum Oxt use and breastfeeding success47, and the complex association between Oxt and postpartum depression50. Furthermore, by scaling down with appropriate equipment (iPRECIO SMP 310-R with a dedicated wireless communication device), transgenic mouse models could be used to investigate complex gene-environment interaction studies in the perinatal period. Motivated by recently published work in on the fetal effects of Oxt in prairie voles and mice21, ongoing studies in our laboratory are focused on the transfer of maternally administered Oxt across the placental and fetal blood–brain barriers and its impact on Oxt-ergic signaling in the fetal brain.Ethical and logistic challenges significantly limit the scope of mechanistic research on pregnant women and their newborn. Our contextually relevant animal model, by providing unrivaled access to maternal and fetal tissue, has wide-ranging implications for translational research related to perinatal Oxt exposure. In the absence of a clinically relevant animal model for labor induction with Oxt, our work provides the opportunity to investigate lingering concerns about the impact of Oxt on neurobehavioral development of the offspring51. Because 1 IU = 1.68 μg of Oxt peptide, this would translate to approximately 3.4–6.8 μg of Oxt, similar to what we used in our model. Nevertheless, considering that we took a ‘top-down’ approach and settled for the first Oxt infusion regimen that was associated with birth of healthy pups without labor dystocia, it is likely that the cumulative Oxt dose is relatively higher. Despite this, our model simulates clinical practice to a large extent, and it is likely that research knowledge generated using this model is more likely to provide reliable and actionable mechanistic data than other currently available models.The biggest strength of our model is how it mirrors labor induction with Oxt in clinical practice. Though we prefer not to anthropomorphize our study , we provide information on the cumulative Oxt dose for reassurance that it was not abnormally high by orders-of-magnitude. Approximately 3–7 μg Oxt was required to induce birth, which is comparable to the dose ranges typically used during human labor. For example, parturients receive on average, a cumulative Oxt dose of 2000–4000 mIU or 2–4 IU  during the course of labor6. However, to our surprise, expression of the OxtR gene was significantly increased after labor induction with Oxt. We believe that these apparently contradictory findings could be due to the choice of myometrial samples by Phaneuf et al.6; samples were collected from patients who underwent cesarean delivery after dystocic labor with Oxt suggesting the possibility of abnormal transcription during intrapartum arrest of labor. In contrast, we performed cesarean delivery during uncomplicated labor to facilitate sample collection. This line of thought is supported by the 4-fivefold increased myometrial expression of OxtR gene during uncomplicated labor in rodents53. The only other relevant preclinical study that examines this question is that of Hirayama et al. which used an osmotic pump to deliver a continuous subcutaneous infusion of Oxt in pregnant mice54. However, the experimental paradigm did not allow for escalation of Oxt dose, nor did the study assess the impact of Oxt administration on the uterine myometrium. Furthermore, the pup survival rate was low, suggesting the possibility that the high dose of Oxt could have caused tetanic, rather than cyclical, uterine contraction, labor dystocia, and fetal demise. That this is a strong possibility is reinforced by similar observations during the iterative phase of model creation in our study; higher doses of Oxt were associated with significant dystocia and perinatal demise of the pups. Our demonstration of cyclical changes in intrauterine pressure after initiation of Oxt, predictable delivery of alive and thriving pups, and a nurturing mother, provides reassurance that our model most closely resembles pharmacological management of human labor.As a G protein-coupled receptor that is sensitive to downregulation, our findings of reduced membrane bound OxtR protein expression after Oxt exposure is broadly consistent with published data in human studies33. Though the mechanisms remain unexplored, both oxidative stress and a direct effect of Oxt on the Oxt receptor in the fetal brain have been suggested as possibilities44. We focused on oxidative stress because Oxt causes a higher rate of rise in intrauterine pressure than spontaneous labor36, and is reported to cause lipid peroxidative injury55, decrease the anti-oxidant glutathione in cord blood56, and increase amniotic fluid lactate58. Because the developing brain is vulnerable to oxidative stress59, we applied our model to determine if induction and augmentation of labor with Oxt was associated with oxidative stress. Our findings were reassuring in that even after 8–12 h of exposure to Oxt-induced uterine contractions, there was no evidence for oxidative stress in the newborn brain. Lack of oxidative stress after prolonged exposure to repetitive Oxt-induced uterine contractions in a polytocous species where labor typically lasts between 90 and 120 min60, gives us more confidence that this is unlikely to be a concern for the human fetus. Taken together, our preclinical data suggest that the impact of Oxt on the fetus, if any, is likely to be a direct effect via changes in Oxt-ergic signaling rather than an indirect effect via oxidative stress. This notion is partly supported by evidence for changes in DNA methylation of the OxtR and downregulation of the OxtR gene in fetal brains exposed to Oxt in utero21.Epidemiological evidence, albeit conflicting, suggests a link between the use of Oxt and neurodevelopmental disorders such as autism spectrum disorder (ASD)62. Second, is the poor neurodevelopmental correlation between rats  and humans  which would limit some of the translational aspects of our work. This could be partially offset by performing Oxt exposure studies in postnatal pups at day 10 or beyond, but it would, however, be limited by the lack of context and the absence of Oxt-provoked strong uterine contractions that precede delivery. More importantly, viewing all events solely from a neurodevelopmental perspective ignores the vastly more important evolutionary role of oxytocin during the birthing process as a facilitator of intrauterine to extrauterine transition of life 64. Nevertheless, we expect our work to inspire other researchers to either develop a more neurodevelopmentally appropriate non-human primate model or pursue potentially new avenues for clinical research directly in pregnant human subjects. Third, our sample size for RNA-seq, though considered acceptable practice65, only captures big-fold changes in significant differential gene expression. Therefore, we cannot the exclude the possibility of a small effect of anesthesia and surgery on the cortical transcriptome, but we are reassured nonetheless because such effects would be similarly distributed across both treatment conditions. Fourth, even though our low-dose Oxt infusion for the first 4 h would have resulted in cervical ripening as demonstrated in laboring women66, we are unable to provide objective evidence to support that assumption. Nevertheless, because birth of the pups occurred predictably, it is unlikely to be a concern. Fifth, we did not compare the extent to which Oxt increases intrauterine pressure compared to saline. Because we had biological validation of pup birth, our objective was to capture the temporal relationship between the initiation of Oxt and the rise in intrauterine pressure rather than assess differences in intrauterine pressure between Oxt-induced and spontaneous labor that have been characterized previously67. Sixth, the absence of oxidative stress at birth does not exclude the possibility of an effect at a later time point. Finally, a thorough pharmacokinetic profiling of Oxt would have been ideal but was beyond the scope of our study. This limitation, however, is partly offset by the poor correlation between maternal plasma Oxt level and uterine contractility in laboring women68.Our research has a few limitations. First, our model can be perceived as contrived. Considering the technical challenges of delivering an escalating dose of intravenous Oxt in a free-moving animal to simulate obstetric practice, we considered all possibilities before pursuing this model. Importantly, our model is in no way more traumatic or less realistic than the unilateral carotid artery ligation/ anoxia model to investigate perinatal asphyxia in rodentsIn conclusion, we provide a viable and realistic animal model for labor induction and augmentation with Oxt and demonstrate its utility in addressing clinically relevant questions in obstetric practice. Adoption of our model by other researchers would enable new lines of investigation related to the impact of perinatal Oxt exposure on the mother-infant dyad.All animal experiments and methods reported here were approved by the Institutional Animal Care and Use Committee at Washington University in St. Louis (#20170010) and conducted in accordance with the institutional and ARRIVE guidelines.The system consists of a subcutaneously placed iPRECIO infrared-controlled microinfusion pump  connected to the right internal jugular vein in an embryonic day (E)18 Sprague Dawley dam (Charles River Laboratories) Fig. .i.OxtR gene expression: Briefly, approximately 0.5 cm × 1 cm rectangular piece of myometrial tissue was harvested from the anti-mesometrial aspect of the uterus after 8–12 h of exposure to either Oxt (100 mcg/mL concentration) or saline. Sample processing and OxtR qPCR was performed with a custom TaqMan® OxtR probe as described by us previously69.ii.Western blot for OxtR expression: Membrane-associated proteins were isolated from approximately 100 mg of uterine myometrial tissue using Mem-PER Plus Membrane Protein Extraction Kit  following manufacturer's instructions and subjected to immunoblotting with appropriate positive and negative controls . Approximately 10 μg of protein per lane was electrophoresed and transferred to membrane using Bolt western blot reagents . The membrane was subsequently blocked with TBST buffer  containing 5% milk for 1 h at room temperature on a shaker. Following a brief wash with TBST buffer, the membrane was immunoblotted overnight at 4 °C on a shaker with primary rabbit anti-OxtR antibody , at a dilution of 1:250 in 5% milk-TBST buffer. HRP-conjugated secondary antibody  was used at a dilution of 1:1000 for 1 h at room temperature on a shaker. Immunoblots were incubated with ProSignal® Dura ECL Reagent  for 2 min at room temperature and detection of bound antibody was achieved with LI-COR Fc imaging system (LI-COR Biosciences Inc.) and the band concentrations were analyzed with Image Studio™ Ver. 5.2. For loading control, the membrane was stripped using Western ReProbe PLUS  according to manufacturer’s instructions and immunoblotted with HRP-conjugated β-Actin , at a dilution of 1:1000  for 1 h.iii.Immunohistochemistry: To qualitatively confirm our western blot results, we performed immunohistochemistry for OxtR expression in the uterine myometrium. Briefly, 5-μm frozen sections of uterine myometrium embedded in OCT compound were obtained using Leica CM1510 S cryostat and immunostained for phosphorylated myosin light chain kinase  and imaged with the Zeiss Axioskop 40 microscope. OxtR protein expression was assessed by immunostaining with goat anti-rat OXTR antibody  and revealed with Alexa Fluor® 594 labeled rabbit anti-goat antibody . All primary antibodies were incubated overnight at 4ºC followed by a 1 h incubation with secondary antibodies at room temperature. Imaging was performed with Olympus BX60 fluorescence microscope with designated filter sets.iv.Uterine telemetry: To assess whether initiation of Oxt was temporally associated with increase in intrauterine pressure, we performed pressure recordings with telemetry as described previously by us for mice71. Briefly, under isoflurane anesthesia and sterile precautions, we inserted a pressure catheter in the right horn between the uterine wall and the fetus under sterile precautions during pump implantation in E18 dams. To minimize the possibility that telemetry recordings could represent spontaneous labor, we advanced the time of replacement of saline with Oxt to 48 h instead of 72 h . The pressure catheter was connected to a PhysioTel PA-C10 transmitter  placed in the lower portion of the abdominal cavity. Telemetry recordings were performed at 500 Hz with Dataquest ART data acquisition system version 4.10 (DSI) sampling every 5 min for 15 s intervals for 6 h at baseline, followed by recordings 48 h later when Oxt was initiated and continued until the birth of the pups.Though the witnessed birth of pups offered functional validation, we examined the effect of Oxt on the uterine myometrium with molecular biological assays, immunohistochemistry, and telemetric assessment of changes in uterine pressure.42, we examined the cortical transcriptome of newborn pups delivered spontaneously by unhandled vs. surgically implanted dams. Briefly, 2 brains from spontaneously delivered newborn pups of either sex were collected within 2 h of birth from 6 dams . Total RNA was extracted from the right cerebral cortex using RNAeasy kit (Qiagen) and subjected to RNA-seq (Genome Technology Access Center core facility). Only RNA with RIN > 9.5 were used for RNA-seq. Processing of samples, sequencing, and analysis were done as described by us previously69 and detailed in the Supplementary Materials and Methods.To rule out the possibility of adverse effects on the fetal brain from intrauterine exposure to anesthesia and surgery during pump implantation72.Briefly, brains were isolated from vaginally delivered newborn pups within 30 min to 2 h after birth, snap frozen, and stored at -80 °C for oxidative stress assays. Cortical lysates were prepared according to the assay type and protein concentration was determined using BCA Protein Assay Kit (ThermoFisher Scientific) prior to the assays. All assays were performed in duplicate, and fluorescence/absorbance was read with Tecan Infinite® M200 PRO multimode plate reader using appropriate filter sets as recommended by the manufacturer. We assayed for total free radicals , 4-hydroxynonenal , protein carbonyl , total glutathione , and total antioxidant capacity . All assays were purchased from Cell BioLabs, Inc  and performed as described by us in detail previously69. Expression levels of 16 genes relevant to oxidative stress  and antioxidant  pathways were assayed in duplicate along with four endogenous housekeeping control genes  and reported as described previously69.From the same set of experiments as above, brains were isolated from additional pups born after exposure to either Oxt or saline (n = 6–8 per group), snap frozen, and immediately stored at -80 °C. Processing of total RNA for gene expression experiments was performed as described by us previously69. Quantitative data were analyzed with Welch’s t-test with p ≤ 0.05 considered significant, while oxidative stress gene expression data were analyzed with unpaired student’s t-test followed by Bonferroni correction with an adjusted p-value ≤ 0.003 considered significant. All analyses, with the exception of RNA-seq data, were performed on Prism 8 for Mac OS X  and expressed as mean ± S.E.M.Data outliers were detected and eliminated using ROUT  with Q set to 10%. Because our pilot experiments with a higher dose of Oxt (100 mcg/mL concentration) showed no sex differences in the expression of oxidative stress markers in the newborn brain, all subsequent analyses were performed regardless of sex of the offspring. RNA-seq data were analyzed as described by us previouslyThis abstract was presented for Oral Presentation at the 50th Annual Meeting of the Society for Obstetric Anesthesia and Perinatology, Miami, FL, May 9–13, 2018.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Video 1.Supplementary Video 2.