Yang et al. (2013), sourced from the Lung Tissue Research Consortium (LTRC) and National Jewish Health (NJH) cohorts, we identify an IPF gene expression signature. We performed optimal feature selection to reduce the number of transcripts required by our model to a parsimonious set of 15. This signature enables our model to accurately separate IPF patients from controls. Our model outperforms existing published models when tested with multiple independent clinical cohorts. Our study underscores the utility of elastic nets for gene signature/panel selection which can be used for the construction of a multianalyte biomarker of disease. We also filter the gene sets used for model input to construct a model reliant on secreted proteins. Using this approach, we identify the preclinical bleomycin rat model that is most congruent with human disease at day 21 post-bleomycin administration, contrasting with earlier timepoints suggested by other studies.Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive lung disease affecting ~5 million people globally. We have constructed an accurate model of IPF disease status using elastic net regularized regression on clinical gene expression data. Leveraging whole transcriptome microarray data from 230 IPF and 89 control samples from  Idiopathic Pulmonary Fibrosis (IPF) is a fatal disease of unknown etiology characterized by scarring of the lung parenchyma resulting in progressive loss of lung function and eventual death . AlthougComparative gene expression profiles of preclinical models of fibrosis with human tissue derived profiles have provided useful information on the utility of the models as well as insights into pathways or mechanisms that are altered during the induction, progression and resolution of fibrosis ,14. In mChaudhary et al. (2006) measured profibrotic gene expression including pro-collagen I, TGF-β1, fibronectin and collagen deposition, determining the fibrotic phase to begin between days 9 and 14 post bleomycin treatment. Bauer et al. (2015) found the most congruent rat bleomycin model (day post-treatment) by first extracting a differential-expression signature from the rat and subsequently using that gene set to construct a translational signature from IPF samples. Day 7 was identified as having the highest similarity to IPF based on gene expression measurements [In the most commonly studied preclinical model of IPF, the chemotherapeutic antibiotic bleomycin is intratracheally injected into rodents to induce an inflammatory response in the lung, damaging the epithelium, activating fibroblasts and ultimately leading to a fibrotic phase of increased collagen deposition and loss of alveolar structures . The indurements . The autHere, we have leveraged microarray profiling data from an extensive cohort of IPF and control samples within the Lung Tissue Research Consortium (LTRC) to develop an unbiased statistical model that defines a parsimonious 15-gene disease signature for IPF. The model has been trained and validated to accurately predict disease status across several IPF data sets. In addition, we identified a 29-gene secreted protein plasma signature for IPF and show that the bleomycin model of lung fibrosis at 21 days shows the largest congruence to the disease signature. Our work defines a robust genetic signature for IPF providing a potential multi-analyte biomarker panel for validation, as well as enables the identification of preclinical models that most closely resemble human IPF.Expression data for IPF and normal healthy patient lung samples were derived from 6 distinct cohorts . The bulMeltzer et al. (2011) when mapping GSE24206 Affymetrix training features to the GSE10667 Agilent features. Conveniently, due to the common source of LTRC lung tissue used to generate both GSE32537 and GSE47460, there exist 85 common patient samples with both Affymetrix and Agilent data, allowing us to directly map expression signal across platforms. We generated gene-level scaling factors, which were possible because the ratios of Affymetrix/Agilent for each gene had very low variance. Genes included in the model were present on both Affymetrix and Agilent platforms.In order to predict disease status of patients in the test cohorts we had to map Agilent expression measurements to the Affymetrix measurement space. This is similar to the scaling approach used by Bauer et al. (2013) intratracheally administered Sprague Dawley rats with a single instillation of saline or bleomycin and sacrified the animals along a time course of 3, 7, 14, 21, 28, 42, and 56 days post-treatment  . Bauer ereatment . Rat-HumGO:0005615) and not on the cell surface (GO:0009986). Our motivation was to exclude genes found on the cellular surface which were annotated as secreted.Secreted genes were annotated using Gene Ontology (GO) cellular component annotations ,23. GeneR version 3.4.1 and Bioconductor were used for expression data retrieval from GEO, normalization, filtering and scaling ,25. Whenhttps://github.com/ronammar/ipf_signature_elastic_net).All code and data required to execute the analysis described in this manuscript have been deposited in GitHub  was used as a categorial response with two possible outcomes in logistic regression, which models the probability of response using a binomial link function to define a model of disease. However, logistic regression can be unreliable when features ,30–32 . To identify the optimal combination with the highest performance, we conducted 10-fold balanced cross-validation for each α,λ pair in a grid search on the LTRC training data  on the LTRC training data. This generated the final model and set of selected features (genes). For completeness, we computed the inclusion frequencies for each feature  by the same authors . These tBefore training, we compared patients to one another in an unbiased manner with t-Distributed Stochastic Neighbor Embedding (t-SNE), a nonlinear dimensionality reduction method capable of reducing the entire transcriptome signal into just two (or three) dimensions for visualization . With trThe LTRC lung tissue expression data was used to train all models see . Four moeatures)  Tables. ures)  for rat samples and log2(IPF/control) for human samples. During initial comparisons between rat and IPF using the log2|≈0), introducing noise when computing similarity between rat and IPF expression. Therefore, when comparing rat to IPF, we used the The IPF signatures derived from each of our four models could be used to identify the preclinical rat bleomycin model with the highest congruence to IPF. Before comparing ortholog expression across species, we attempted to normalize species-specific expression by comparing ratios from rat to human, computed as ublished ). We fouublished , and we Bauer et al. (2015) described a 12-gene signature identified from about 100 IPF samples compared with control lungs and established the commonality of this signature with that derived from the rat model of bleomycin induced fibrosis at the 7-day time point. Our study complements and extends these findings by developing alternate signatures and establishing congruence with the rat model of bleomycin induced fibrosis. Tissue and peripheral gene/protein expression signatures provide complex information that could be poorly or incompletely understood in the absence of effective computational modeling. Our study identifies a novel 15-gene signature that accurately predicts IPF disease status  wherein the rat model of fibrosis day 7 was determined to be the most similar to human disease. This is likely due to our similarity being assessed only using the IPF-derived gene signatures and not larger sets of genes  showed that a 4 serum biomarker panel could be used to predict mortality and distinguish between stable and progressive IPF [In future work, we propose to predict disease progression or severity of IPF with the inclusion of FVC or DLCO lung function measures. This would be analogous to the PROFILE study where sive IPF . We alsoWe have discriminated effectively between IPF and control lung tissues which is relevant in translational models of disease, but the complexity in diagnosing IPF manifests largely in distinguising it from other idiopathic interstitial pneumonias (IIPs) . While tFrom the glmnet elastic nets R package ,31, we dyi = I(gi = 1).The response variable takes a value in We model With the log-odds transformation α combining both lasso  and ridge  penalties. The tuning parameter λ corresponds to the strength of the penalty.The objective function for the penalized logistic regression uses the negative binomial log-likelihood p≫n, regularized regression may be used to construct an accurate disease classifier based on transcript abundance, but each model represents one of many possible models [We note that due to the presence of multicollinearity in high-dimensional expression data, where e models . We estaS1 FigWe iterated over a grid of possible paired α and λ parameters for the elastic net module to determine optimal performance while reducing the number of features to create a minimal gene signature. Minimum classification error can be achieved at any value of α given an optimization for λ. The number of features included is annotated for each pair of parameters. The large red block represents a 0 gene feature model (only including an intercept β0).(TIF)Click here for additional data file.S2 Fign−1 principal components for an observation matrix of n×p where p≥n (n is the number of observations and p is the number of variables), where the variance is non-uniformly distributed across these eigenvectors. Instead the variance is typically spread across more than the first two or three eigenvectors yielding poorer separation between disease and control patients when only taking these eigenvectors into account.The proportions of variance accounted for by each of the first two principal components are indicated in parentheses. In this instance, t-SNE was more informative than Principal Components Analysis (PCA) because PCA yields (TIF)Click here for additional data file.S3 Figr = Pearson correlation coefficient where −1≤r≤1, with 1 meaning perfectly correlated and -1 perfectly anticorrelated.For the 30 gene expresion signature from (TIF)Click here for additional data file.S4 FigFDR<0.1), and identify the orthologs in the rat, we do not observe increased similarity at any time point post-bleomycin treatment to suggest maximal congruence with IPF. This motivates the use of a smaller gene expression signature to extract only IPF-relevant gene expression.If we examine only those genes that are differentially-expressed in IPF relative to controls ((TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S6 FigWe use the complete linkage method for hierarchical clustering with a Euclidean distance measure.(TIF)Click here for additional data file.S7 FigWe use the complete linkage method for hierarchical clustering with a Euclidean distance measure.(TIF)Click here for additional data file.S8 FigWe use the complete linkage method for hierarchical clustering with a Euclidean distance measure.(TIF)Click here for additional data file.S9 FigWe use the complete linkage method for hierarchical clustering with a Euclidean distance measure.(TIF)Click here for additional data file.S1 TableThe coefficients are extracted from (CSV)Click here for additional data file.S2 TableThe coefficients are extracted from (CSV)Click here for additional data file.S3 TableThe coefficients are extracted from (CSV)Click here for additional data file.S4 Table(CSV)Click here for additional data file.S5 Table(CSV)Click here for additional data file.S1 Supporting Information(XLSX)Click here for additional data file.


Despite the high global prevalence of chronic hepatitis B (CHB) infection, datasets covering the whole hepatitis B viral genome from large patient cohorts are lacking, greatly limiting our understanding of the viral genetic factors involved in this deadly disease. We performed deep sequencing of viral samples from patients chronically infected with HBV to investigate the association between viral genome variation and patients’ clinical characteristics. We discovered novel viral variants strongly associated with viral load and HBeAg status. Patients with viral variants C1817T and A1838G had viral loads nearly three orders of magnitude lower than patients without those variants. These patients consequently experienced earlier viral suppression while on treatment. Furthermore, we identified novel variants that either independently or in combination with precore mutation G1896A were associated with the transition from HBeAg positive to the negative phase of infection. These observations are consistent with the hypothesis that mutation of the HBeAg open reading frame is an important factor driving CHB patient’s HBeAg status. This analysis provides a detailed picture of HBV genetic variation in the largest patient cohort to date and highlights the diversity of plausible molecular mechanisms through which viral variation affects clinical phenotype. Although effective vaccines now exist to prevent HBV infection, most cases in highly endemic areas are the result of perinatal transmission leading to chronic infection, for which there is no cure.Chronic hepatitis B virus (HBV) infection is a significant public health burden with over 290 million chronically infected individuals worldwide3. These clinical factors are well known to be predictive of progression to cirrhosis, hepatocellular carcinoma, and other liver-related complications, including mortality8. Since the HBV genome is the blueprint for the viral interaction with the human host, understanding what genetic factors of the virus are correlated with these clinical parameters is essential for elucidating the molecular mechanisms of the disease progression. Previous studies investigating associations between viral genetic variants and patient clinical characteristics were largely restricted to the core promoter and precore regions of the virus or sequenced only a small sample of viral clones per patient9. We undertook a more comprehensive analysis of associations between HBV single nucleotide variants and patient clinical characteristics by looking at the whole viral genome across a large patient cohort.Current clinical practice guidelines of the European Association for the Study of the Liver (EASL) recognize five phases of HBV infection determined by a combination of clinical factors such as the viral load (serum levels of HBV DNA), the presence of viral hepatitis B e antigen (HBeAg), hepatitis B s antigen (HBsAg) levels, alanine aminotransferase (ALT) levels, and the stage of liver fibrosisWe performed an ultra-deep  whole HBV genome sequencing of 1467 patients  chronically infected with HBV at baseline. The patient population contained HBV genotypes A (N = 98), B (N = 285), C (N = 716), D (N = 356), E (N = 7), and F (N = 5) with 977 HBeAg-positive and 490 HBeAg-negative patients . It is well known that the transition from HBeAg positive to negative phase of HBV infection is generally marked by a reduction in viral replication and viral load15. In order to control for this confounding effect, patients’ HBeAg status was included as a covariate in our model during testing. Regardless of HBeAg status, however, C1817T and A1838G were both highly associated with viral load. The intra-patient frequency distribution of both variants across the patient cohort revealed a bimodal distribution, broadly dividing patients into low and high variant intra-patient frequency groups based on a naturally observed 10% variant frequency cutoff  than patients without or with low frequency of these variants . Both C1817T and A1838G associations were validated in an independent patient cohort  RNA17, suggesting an effect of C1817T on both the precore mRNA and pgRNA transcription. Indeed, C1817T is additionally associated with HBeAg status in our data as well (LRT p = 2.8 × 10−12).Although we identified C1817T association with viral load while controlling for HBeAg status, our findings do not exclude the possibility that this variant could have pleiotropic effects on multiple clinical factors. In fact, experimental evidence indicates that mutating the second precore codon to a stop codon results in the elimination of HBeAg expression−8, p = 6.7 × 10−8, and p = 2.3 × 10−7, respectively). Although their associations with viral load did not reach stringent p value cutoff (p < 1.6 × 10−6) in the validation cohort, their trending p values warrant future confirmation. Earlier studies suggest that these variants impact viral replication process via different molecular mechanisms. Specifically, C1653T alters the binding site of the CAAT enhancer-binding protein of the alpha-box within the HBV enhancer II region18, which is known to transcriptionally regulate the pgRNA. Variants G1896A and G1899A, on the other hand, impact the base pairing of the stem structure within the epsilon region of the pgRNA21, affecting the secondary RNA structure and consequently the encapsidation of the pgRNA, a crucial step prior to the generation of the rcDNA.Aside from C1817T and A1838G variants, it is worth mentioning that C1653T, G1896A, and G1899A displayed the next most significant association with viral load in the discovery patient cohort . Aside from its previously mentioned role in the epsilon region of the pgRNA, G1896A introduces a stop codon in the precore region abolishing HBeAg production15. The confirmation of this well-known mutation validates our approach. More importantly, we found that G1896A is not a necessary variant determining patient’s HBeAg status. Despite deep sequencing coverage at the G1896A locus (mediancoverage = 8949), a substantial portion of 24%  of HBeAg-negative patients had undetectable frequency of G1896A variant . We found that inclusion of additional variants yields a significantly better classification performance  DNA, an essential step of viral replication. The A1838G variant is located within this h3E region where nucleotide substitutions in the in vitro model resulted in a significant decrease in the production of rcDNA16. These experimental results are in keeping with our observation that patients carrying this variant in the h3E region display significantly reduced viral load when compared to wildtype.Due to the extremely compact and functionally overlapping architecture of the HBV genome, it is challenging to determine the precise mechanism of action for these variants. Some clues can be taken from an elegant study by Lewellyn and LoebThe C1817T variant lies in the second codon of precore inducing a stop codon. Coincidentally, this variant also resides directly upstream of the pgRNA which is a precursor to the HBV rcDNA. Given its proximity to the transcription start site, we hypothesize that C1817T affects the transcriptional regulation of pgRNA leading to the reduction of the viral load. Our association findings and this experimental evidence suggest these two variants likely utilize two different molecular mechanisms through which they negatively impact viral load - modification of h3E region involved in plus-strand DNA synthesis and a regulatory effect on pgRNA transcription.Although patients with high frequency of C1817T and A1838G variants experienced earlier viral suppression while on treatment, given the effectiveness of the latest reverse transcriptase inhibitors, the difference in viral load reduction diminishes after 48 weeks. We believe that monitoring of these specific variants in clinical trials investigating the efficacy of drugs with other mechanisms of action  may be more relevant to clinical practice.35 and other mutations become solely responsible for the disruption of HBeAg production. Previous studies described additional variants in the core promoter including C1653T, T1753C, A1762T, and G1764A and their correlation with the downregulation of precore mRNA37. The reported clinical impact of these variants entailed associations with HBeAg status; however, these connections have not always been consistent across all studies44. While our study did not identify any significant association with A1762T and G1764A, we found T1753C to be strongly associated with HBeAg status in our dataset , for example, the integrity of the HBeAg coding sequence seems to be the determining factor. Although G1896A had the most significant association with HBeAg status, we identified other variants that were important for understanding a patient’s HBeAg status. While G1896A disrupts the production of HBeAg via a premature stop codon in the precore region, other variants likely inhibit dimerization or protein folding of HBeAg. This observation further highlights the intriguing diversity of the molecular mechanisms through which viral variants affect a specific clinical phenotype. It is worth mentioning that G1896A variants in HBV genotype A and some genotype C strains are rare due to genotype-specific constraints in the base-pairing of the pgRNA secondary structure45, which would confound any possible association with viral variation.HBsAg is a hallmark serological marker of HBV infection and its reduction to undetectable levels along with patient’s development of anti-HBsAg antibodies (HBsAg seroconversion) is clinically considered a functional cure. Uncovering viral variants associated with HBsAg levels would be of tremendous interest to both scientists and clinicians, however, we did not observe any consistent associations. This finding is in line with clinical observations that it is very challenging to lower serum levels of HBsAg despite successful nucleoside inhibition of viral replication. One hypothesis would be that integrated HBV DNA significantly contributes to the serum levels of HBsAg, as has been shown in chimpanzees47. In summary, our data not only identifies novel variants associated with major clinical factors but will also serve as a resource to the research and medical community where novel treatment strategies (e.g. development of HBV surface/capsid inhibitors and DNA editing approaches) depend on the understanding of viral sequence variation across global patient cohorts.This work adds to the current knowledge of associations between viral genetic factors and patient clinical characteristics. Future employment of long-read technology capable of spanning the entire ~3.2Kb HBV genome could help us further resolve mutation linkage and give us insights into the joint effect of multiple variants. As this study examined associations between viral variation and clinical parameters, future investigations combining human genetic variations might provide additional perspective on viral-human genetic interaction and co-evolutionsBaseline serum samples analyzed in this study came from 1102 patients enrolled in two global phase 3 studies of tenofovir alafenamide (TAF) versus tenofovir disoproxil fumarate (TDF) for the treatment of HBeAg-negative and -positive chronic hepatitis B (GS-US-320-0108 and GS-US-320-0110) and 365 patients enrolled in GS-US-174-0149 clinical trial, evaluating TDF and peg-interferon alpha-2a combination therapy in treatment-naive patients chronically infected with HBV. Patient samples that were part of this analysis were collected from 21 countries . All patients signed an informed consent form prior to screening and in accordance with local regulatory and ethics committee requirements. Experimental protocol in these trials was approved by Gilead Sciences and all local regulatory agencies .et al.48. Whole genome HBV amplicons were sequenced on Illumina MiSeq with 150 bp paired-end reads. Low quality bases (Q < 20) at 5′ and 3′ of each read were trimmed with Trimmomatic (v0.35) and reads shorter than 50 bp were removed. Subsequently, paired reads were merged based on overlapping regions and sequencing error correction was performed using PEAR (v0.9.6). Unmerged reads were discarded. Read mapping was performed with BWA (0.7.9a) and the reference genome for each sample was chosen from HBVdb.ibcp.fr49 given patient’s genotype, which was determined via laboratory genotyping assay. Accession numbers: genotype A – EU054331, genotype B – AB219428, genotype C - GQ924620, genotype D – FJ904433. It is important to note that these genotype specific reference sequences were only used to facilitate sequence alignment and were not used for variant calling. Therefore the selection of specific HBV reference sequences does not impact what constitutes a reference or variant allele .DNA isolation was performed using Qiagen MinElute kit for serum samples with viral load <100,000 IU/mL and Roche MagNA Pure robot 32 for samples with viral loads > 100,000 IU/ml. HBV whole genome was amplified following Gunther 50 (version 1.3). The parameters for base pair and mapping quality were -Q 20 -q 20 -d 50000 and variants were subsequently filtered with varscan51 (version 2.3.9) package given parameters for minimum number of reads, minimum frequency, and minimum p value . We further removed variants with a strand bias greater than 5. Assessing sequencing benchmarks from control plasmids and experimenting with minimum variant frequency cutoffs, we applied a minimum variant frequency cutoff of 2% to eliminate any background noise introduced via PCR and sequencing errors.Because collapsing viral genomes within each patient into a single viral consensus sequence will remove potentially valuable genetic information, we calculated wildtype frequency for each viral genome position and for each patient. Since several HBV genotypes can be genetically more than ~10% divergent, determining wildtype allele across all genotypes is not plausible and, furthermore, may also remove information for loci with more than two alleles. We therefore calculated wildtype frequency by calling variants from a genotype consensus generated from across our HBeAg positive patient population. This approach also partially controls for genotype specific effects during association testing. We chose HBeAg positive patients for genotype consensus reconstruction because this disease stage represents an earlier phase of infection. After read mapping, we generated primary consensus sequence  with custom scripts for each HBeAg positive patient. Primary consensus sequences were pooled given the HBV genotype determined from clinical assay and for each genotype we generated a secondary consensus. Because the depth of coverage of our sequencing experiments was very high (often exceeding 9000x coverage) across large patient cohort (977 HBeAg positive patients), the primary and secondary consensus call could always be made implementing the majority rule since no two variants within a patient or across patients had exactly the same frequency. Using an appropriate genotype specific secondary consensus, we called variants for each patient (both HBeAg positive and negative) using samtools mpileup0 (model without HBV variant frequency co-factor) with m1 (model with HBV variant frequency co-factor). Likelihood Ratio Test between m0 and m1 was performed to assess whether addition of HBV variant frequency significantly improved model fit. Models of clinical traits such as ALT, HBsAg levels, and HBV DNA levels were adjusted for HBV genotype, gender, age, race, and HBeAg status. Models of HBsAg and HBV DNA treatment response were adjusted for HBV genotype, gender, race, age, HBeAg status, treatment arm, and treatment experience. Model of HBeAg status was adjusted for HBV genotype, gender, race, and age. Models of HBeAg and HBsAg loss were adjusted for HBV genotype, gender, race, age, baseline levels of HBV DNA and HBsAg, treatment arm, and treatment experience. Multiple testing correction via Bonferroni method led to a significance threshold of approximately 1.6 × 10−6. All modeling was performed independently on discovery and validation sets of patients. Associations significant in both cohorts are reported here and became candidates for further exploration.Associations between viral variants  and patient’s clinical variables were analyzed using Generalized Linear Models (GLM) while adjusting for appropriate co-factors. Specifically, for each response variable tested, we compared m52. Specifically, we combined top 10 variants individually found to be associated with HBeAg status with variants selected by Elastic Net, which explained at least ~50% of HBeAg status variation. The reason for this strategy was two-fold. First, we wanted to select top variants by the fraction of HBeAg status variation they collectively explain, rather than by choosing specific association p value cutoff. Second, we decided to supplement the selection of top variants supplied by Elastic Net due to a known behavior, where one of two highly correlated variants gets dropped by Elastic Net. This approach yielded 37 variants. Using these 37 variants we then built a random forest model (number of trees to grow = 2000) for the classification of patient’s HBeAg status. Validation of the random forest model was performed on an independent set of patient data (N = 365).To investigate how well HBV variants can predict patient’s HBeAg status, we built a random forest classification model. Because the number of features (N = 2281) greatly exceeded the number of patients (N = 1102) in the discovery cohort, we performed a feature selection assisted with Elastic Net method53, this experiment may reflect a conservative estimate of mutation effect on HBV DNA production.Site directed mutants (SDM) were created for the C1817T and A1838G mutations, both individually and in combination. Primers were designed for the SDM mutagenesis process in order to introduce the substitutions into a plasmid vector containing the HBV genotype A 1.1x genome length laboratory strain pHY92 under the control of a CMV promoter. The entire HBV genome of the construct was sequenced to confirm that no additional mutations had been introduced during the SDM process. Intracellular and extracellular HBV DNA were assessed 7 days after transient transfection of the SDM and pHY92 constructs into HepG2 cells  using FuGENE 6 . HBV DNA in the supernatant was quantified using the branched DNA technology QuantiGene® 2.0  per manufacturer’s instructions. HBV DNA in the cells was quantified using quantitative PCR specific to the HBV X gene. For SDM constructs, fold changes in HBV DNA production with respect to the corresponding reference sample pHY92 were reported. The transfection experiments were performed only in the pHY92 genotype A background context and may not fully extrapolate to other HBV genotypes. However, given the comparatively lower replication rate of genotype A strainswww.rcsb.org). The structure was prepared using the ProteinPrep utility in Maestro (www.schrodinger.com) and visualizations and analysis were generated using Pymol (www.schrodinger.com). The residue numbering in the discussion is offset by +19 from the residue numbering in the X-ray.The structure of HBeAg dimer was downloaded from the Protein Data Bank (SUPPLEMENTARY INFORMATION


Diabrotica virgifera virgifera LeConte, is an important pest of maize (Zea mays L.). Published WCR diets contain corn root powder, which is not available for purchase, thereby limiting the practical use of diets containing this ingredient. We applied response surface modeling combined with mixture designs to formulate a WCR diet that does not require corn root powder. We developed the new formulation by systematically exploring eight protein ingredients from animal, plant, and yeast sources based on simultaneous evaluation of three life history parameters . This formulation (WCRMO-2) without corn root powder supported approximately 97% of larval survival and successful molting. Larval weight gain after 10 days of feeding on WCRMO-2 was 4-fold greater than that of larvae feeding on the current best published WCR diet. Additionally, there was no significant difference in these larval performance traits when larvae were reared on WCRMO-2 and the best proprietary WCR diet. A commercial version of WCRMO-2 was tested and found to perform comparably for these traits. These improvements met our goal of a diet comprised of available ingredients that supports performance of WCR larvae equal to or better than publicly available formulations and proprietary formulations.The western corn rootworm (WCR),  Diabrotica virgifera virgifera LeConte, is an economically important pest of maize (Zea mays L.) in North America and parts of Europe. The impact of corn rootworm has been estimated at approximately $2 billion annually1. Yield reduction by this pest primarily results from larval feeding on maize roots3 that causes detrimental effects on nutrient and water uptake4, facilitation of pathogen infestation6, and unharvested grain due to lodging7. A variety of management tactics  have faltered due to adaptation of WCR populations15. Insect resistance management (IRM) plans for western corn rootworm have been directed toward monitoring the development of resistance to each of the Bt products16. Diet bioassays can be a key component of IRM plans. An ideal diet17 used in diet bioassays should be publicly available, compatible with insecticide toxins, easy to use, low in contamination, and support larval development similar to larvae feeding on corn roots.The western corn rootworm (WCR), 18, an optimization of the ingredients in a previously published diet for WCR. Pleau et al.19 developed the first WCR diet, which was modified from a diet for southern corn rootworm (SCR), D. undecimpunctata howardi Barber22, by adding corn root powder, removing formalin, optimizing the pH and changing the concentrations of wheat germ and linseed oils. These modifications resulted in a diet that doubled larval weight gain compared to larvae reared on the SCR formulation19. We improved the Pleau et al. diet by optimizing the concentration of several ingredients  and by adding wheat germ oil18. This resulted in a formulation that supported approximately 99% of larvae for survival and molting and further doubled the weight gain after 11 days compared with larvae reared on the Pleau et al. diet. Additionally, this formulation is publicly available, compatible with each of the four marketed Bt toxins23 and, under our laboratory conditions, resulted in fewer than 1% of infested diet wells becoming contaminated24.We previously developed an improved diet for WCR larvae, hereafter referred to as “WCRMO-1”18, northern corn rootworm, D. barberi Smith & Lawrence25, and another coleopteran species, Diaprepes abbreviatus (Linnaeus)27. Since insect diets are a mixture of several ingredients, a change in the number of ingredients and in the proportion of any ingredient results in a change in the relative composition of all ingredients. Mixture experiments are commonly analyzed based on Scheffé polynomials28 wherein the proportions of mixture components are varied and total amount of the mixture remains constant. Later, Piepel and Cornell29 developed a mixture-amount experiment expressing the parameters of the Scheffé model while varying the total amount of the mixture. The application of mixture experiments in combination with response surface modeling allows concurrently varying multiple ingredients to identify and characterize key components and predict an optimum formulation that maximizes all desired developmental traits31.Mixture designs combined with response surface modeling have been highly efficient for use in diet improvement for WCR32. Corn root powder is the only ingredient that is not available for commercial purchase, thus limiting its use. In order to develop an ideal diet for WCR, this study focused on the development of a WCR diet with improved larval development and without corn root powder, making the WCR diet more widely available for researchers. By applying mixture designs combined with response surface modeling, we explored eight different protein ingredients, which are referred to diet ingredients that contain proteins as major components and may contain other nutritional components, derived from animal, plant, and yeast sources to identify key proteins. We then optimized proteins blends based on WCR life history parameters  while limiting contamination.Previous diet work for WCR highlighted an important role of corn root powder as a key component that had positive effects on WCR development. Exclusion of this ingredient resulted in a slower development of WCRp < 0.0001, F9,20 = 42.26) with insignificant lack of fit (LOF) (p = 0.5382), molting  with significant LOF (p = 0.0397), and survival  with significant LOF (p = 0.0009) by varying 8 different protein sources: corn gluten meal, cottonseed meal, casein, plant protein, whey protein, perfect amino, yeast extract, and egg powder  were three animal protein sources: casein, egg powder, and whey protein Fig. . For plap < 0.0001, F4,26 = 12.48) with significant LOF (p < 0.0001), molting  with significant LOF (p = 0.0011), and survival  with insignificant LOF (p = 0.0584)  yielded significant response surface models for all three measures, including larval weight  were held constant Fig. . The resIncreases in egg powder up to a proportion of 4% (w/w) and 2% (w/w) resulted in positive larval weight gain Fig.  and molt19, WCRMO-2 had no corn root powder, additions of egg powder and glucose, and removal of oil components, casein and sucrose. At 10 days post infestation, larval dry weight increased 4 fold with WCRMO-2 as compared to WCRMO-1 and survival and molting rate of WCR larvae reared on WCRMO-2 were approximately 97%. Additionally, there was no significant difference in all measured developmental traits when WCR larvae were reared on WCRMO-2 or the current best proprietary diet . By using geometric and mathematical approaches, we evaluated the influence of eight different protein sources to identify key protein sources and characterized and maximized benefits of the key protein, resulting in an improved formulation that will be more accessible to users.Current published WCR diets require corn root powder as a key component because of its positive influence on larval performanceet al.19 reported a similar pattern in which WCR larvae performed better with proteins (casein and sodium caseinate) derived from animals compared with proteins (lima bean and pinto beans) derived from plants. Research with lepidopteran insects has also documented similar results. The African cotton leafworm, Spodoptera littoralis , grew better on an artificial diet that contained casein as its protein supplement compared to those reared on a diet formulation that casein was substituted with zein, a maize-derived protein34. Damodaran and Parkin35 documented that nutritional quality in animal proteins are better than that of plant proteins. Animal proteins  usually provide a complete and balanced level of essential amino acids, while the essential amino acids in proteins of plants  are often not fully presented and are usually not presented at the well-balanced proportions. Bioavailability of proteins of animal origin are better than those of plant origin as well35.The eight-protein mixture experiment indicated that animal protein sources supported all larval developmental traits  better than other protein sources such as plant and yeast proteins Fig. . All thr19. These findings suggest that egg powder was the single best protein source. Egg powder used in the current study is derived from whole eggs and therefore is itself a complex mixture that mainly contains proteins but also additional nutritional components . Further efforts to identify which components in egg powder are driving the effect would require fractionating egg powder into its components followed by a series of multivariate experiments.The three-protein mixture-amount experiment to characterize the key protein components revealed that egg powder was the best protein source and egg powder alone at higher amount of diet blends could yield the best formulation based on all developmental traits  compared to possible combinations of three animal proteins, i.e. casein, egg powder, whey protein Figs  and 3. T27. By using this approach, we characterized eight different protein ingredients from animal, plant and yeast sources and maximized the key protein component based on all larval response measures. This resulted in a superior formulation (WCRMO-2) with the addition of egg powder, removal of corn root powder, removal of lipid components (linseed oils and wheat germ oils) and a substitution of sucrose with glucose compared with WCRMO-118. At 10 days post infestation, the level of diet improvement included a 4-fold increase in larval weight gain with WCRMO-2 as compared to WCRMO-1 whereas survival and molting rate of WCR larvae on WCRMO-2 at approximately 97%. There was no significant difference on overall larval performance when WCR larvae were reared on WCRMO-2 compared with the current superior proprietary diet  ratio than diet containing a high protein: carbohydrate ratio (90:10)41. If nutrition similarly affects coleopteran response to toxins, then differences in the artificial diets between individual companies will complicate direct comparisons between assays; and consequently may provide an inaccurate phenotypic picture of the test populations as it relates to toxin susceptibility. A single optimized artificial diet is necessary to facilitate standardized corn rootworm resistance monitoring assays. All researchers, seed companies, and those performing EPA required monitoring should have access to the same diet formulation and preparation method. However, that level of diet standardization will only happen if the diet is commercially produced. We have collaborated with Frontier Scientific Services to establish a commercial version of WCRMO-2 (Frontier WCRMO-2) that is designed to service and be available for all researchers whether they are members of industry or university laboratories. We believe this formulation would facilitate resistance monitoring efforts and new research discoveries.Artificial diets for WCR larvae are used in diet assays to detect the susceptibility of WCR populations to Bt toxins and other sources. Currently, each of the major maize seed companies have developed their own proprietary diet formulations and evaluated their own proteins on their own diets. There is considerable variation among proprietary diets and published diets currently in use for rearing rootworm larvaeet al.19 (see Supplementary Methods).Eggs of WCR (primary diapausing and non-diapausing strains) were provided by the USDA-ARS laboratories in Brooking, SD and in Columbia, MO. The eggs were surface-sterilized using a procedure described in Pleau 24 provided by industry was used within a week of receipt. Other diets were poured using a procedure described in Huynh et al.18 (see Supplementary Methods), except for a commercial version of WCRMO-2 . A Frontier WCRMO-2 batch includes agar, a dry mix pack, and KOH solution. To make 1 L of Frontier WCRMO-2, agar (15.8 g) was added to 926 ml of cold water the solution was brought to a full boil for 1 minute while stirring regularly or until agar was completely melted. The agar solution was then transferred to a blender placed in a biological safety cabinet. When the agar solution had cooled to 65 °C, 147.4 g of the dry mix pack was added, and the mixture was blended for 1 minute or until mixed thoroughly. Subsequently, 26.3 ml of KOH solution were added and blended for 1 minute or until mix thoroughly. The diet solution was dispensed immediately into a 96-well plate using a repeater pipette (200 µl per well) or proper containers. The diet plate was opened to evaporate excess moisture in the biological cabinet for 10 min, stored in a refrigerator at 4 °C and used for assays within a week.The best proprietary WCR dietet al.18. All materials used in the diet assays were surface-treated via exposure to UV light for 10 min in biological cabinet. Each formulation was randomly assigned to a 12-well row of the 96-well plate and replicated 5 times for a total of 60 larvae per each formulation. Each well was infested with one larva, which hatched within 24 h, using a fine paintbrush. The plate was covered with a sealing film . For ventilation, a hole was made with a number zero insect pin in the sealing film over each well. The plates were kept in an incubator  at 25 °C in darkness for 10 days. Larval weight, molting, survival, and evidence of diet contamination were recorded at 10 days. For larval dry weight, all live larvae were pooled per replicate (12 possible) into 95% ethanol, dried in an oven  at 50 °C for 2 days, and weighed using a micro balance .The diet bioassays were conducted as described by Huynh et al.19 and Huynh et al.18 illustrated that protein components were key ingredients that had positive effects on overall WCR larval performance. To eliminate corn root powder, we explored contributions of eight different protein sources  to three life history parameters  by constructing an eight-component I-optimal mixture design sufficient to satisfy a Scheffé quadratic polynomial response surface model30. I-optimal mixture designs refer to a mathematical algorithm that identifies diet blends that predict precise responses by minimizing the average variance of prediction across the design space43. Additional points to estimate lack of fit, which measures how well the model is fitted by the data, and replicated points to obtain sufficient degree of freedom for estimating pure error and to attain a uniform leverage for design spaces44 were also generated. This design was generated with Design-Expert®  that had 30 design points with 7 model, 17 lack of fit and 5 pure error degrees of freedom  to characterize the effects of varying three animal proteins  from 0 to 1. The amounts of these proteins were varied in blends from 1 gram to 6 grams. This design consisted of 32 design points with 17 model, 6 lack of fit and 8 pure error degrees of freedom  were used to construct a D-optimal mixture-amount design sufficient for modeling a Scheffé quadratic-quadratic polynomial response surface model18. The proportions of water were changed to incorporate changes in egg powder proportions and additions of lipid components and corn root powder. Other ingredients were kept constant . Additionally, formulations containing 3% egg powder with additions of lipid components (linseed oils and wheat germ oils) and lipid components plus corn root powder were tested. The amount of lipid components and corn root powder added were the same as in WCRMO-1 dietThe percentage of larvae surviving and molting was determined by dividing the number of larvae surviving and molting by the number of larvae initially infested and multiplying by 100. Weight per larva was determined by dividing the dry weight by the number of larvae that survived.® . Model selection was based on several criteria including low model P-value, lack of fit P-value, low standard deviation, high R-values 26, close agreement between adjusted R-squared and predicted R-squared, and a low PRESS value48. Once more than one satisfactory model was generated, adequacy tests were performed to further evaluate the selected model as described by Anderson and Whitcomb50.In the eight-protein mixture and three-protein mixture-amount experiments, the best fit model for each measured response  was selected from all possible models from linear to quartic polynomials generated with Design Expert51.In the egg powder experiment, all response measures  were analyzed as a randomized complete block design using PROC MIXED in SASSupplementary information


Acyrthosiphon pisum) show differential fitness on host legume species, while generalist aphid parasitoids can show variable fitness on different host aphid species, suggesting that maternal effects could operate in a plant-aphid-parasitoid system. We tested whether the maternal rearing environment affected the behaviour and fitness of aphids by rearing aphids on two plant hosts that were either the same as or different from those experienced by the mothers. A similar approach was used to test the behaviour and fitness of parasitoid wasps in response to maternal rearing environment. Here, the host environment was manipulated at the plant or plant and aphid trophic levels for parasitoid wasps. We also quantified the quality of host plants for aphids and host aphids for parasitoid wasps. In choice tests, aphids and parasitoid wasps had no preference for the plant nor plant and aphid host environment on which they were reared. Aphid offspring experienced 50.8% higher intrinsic rates of population growth, 43.4% heavier offspring and lived 14.9% longer when feeding on bean plants compared to aphids feeding on pea plants, with little effect of the maternal rearing environment. Plant tissue nitrogen concentration varied by 21.3% in response to aphid mothers’ rearing environment, and these differences correlated with offspring fitness. Maternal effects in parasitoid wasps were only observed when both the plant and aphid host environment was changed: wasp offspring were heaviest by 10.9–73.5% when both they and their mothers developed in bean-reared pea aphids. Also, parasitoid wasp fecundity was highest by 38.4% when offspring were oviposited in the maternal rearing environment. These findings indicate that maternal effects have a relatively small contribution towards the outcome of plant-aphid-parasitoid interactions.The maternal rearing environment can affect offspring fitness or phenotype indirectly via ‘maternal effects’ and can also influence a mother’s behaviour and fecundity directly. However, it remains uncertain how the effects of the maternal rearing environment cascade through multiple trophic levels, such as in plant-insect herbivore-natural enemy interactions. Pea aphids ( The maternal rearing environment can have cascading effects on organisms and their offspring. The environment that offspring are reared in can result in preference for a particular habitat when they become adults, a process termed natal habitat preference induction . For exaGryllus pennsylvanicus) whose mothers had been exposed to a wolf spider, Hogna helluo, exhibited greater immobility, an anti-predatory behaviour, and higher survival rates in environments with predators than crickets produced by naïve mothers [Current environmental conditions can affect directly the behaviour and fecundity of organisms, while offspring phenotype and fitness can be affected by their maternal environment via ‘maternal effects’ . For exa mothers . Materna mothers . The conAphis nerii) [Rhopalosiphum padi) [Myzus persicae mother aphids did not differ when feeding on chemically defended and non-defended plant hosts, but their daughters were able to anticipate a stressful environment by modifying gene expression depending on the plant host that the mother fed on [Aphids mostly have a parthenogenetic, telescopic reproduction strategy , meanings nerii)  and birdum padi) . Conversr fed on .Aphis fabae) influenced oviposition by a parasitoid wasp (Aphidius matricariae). They showed that mother parasitoid wasps were more attracted to the plant host environment that produced offspring with the lowest fitness, but oviposition frequency was highest on the plant species that resulted in the fittest offspring [Diaeretiella rapae attacking two aphid species (Myzus persicae and Brevicoryne brassicae) despite significant effects on aphid fitness and aphid nutritional quality[Transgenerational effects in aphids and parasitoid wasps can result from maternal perception of their abiotic  and biotic  environment. A specific component of the biotic environment is the quality of the plant and aphid species forming the parasitoid wasp maternal environment, which can alter the offspring’s oviposition choice. For example, Chesnais et al.  tested hffspring . Althougffspring ,17. Paraffspring ,19, indil quality. HoweverAcyrthosiphon pisum) and the generalist parasitoid wasp Aphidius ervi. Although information is limited in the literature, both generalist and specialist insects are likely to experience fitness costs when transferred between different maternal hosts, with higher costs detected for specialist aphids [Vicia faba) and pea (Pisum sativum) on pea aphid offspring fitness. Second, we assessed whether changing the maternal environment of parasitoid wasps at a single trophic level, using pea aphids reared on either bean or pea, affected fitness of parasitoid wasps and their offspring. Third, we tested parasitoid wasp fitness in response to manipulation of the parasitoid wasp host environment at two trophic levels using pea aphids reared on bean or potato aphid (Macrosiphum euphorbiae) reared on tomato (Solanum lycopersicum). We hypothesised that the maternal environment would have cascading effects on the fitness of aphid and parasitoid wasp offspring. We predicted that, first, adult pea aphids and adult parasitoid wasps  would prefer the plant or plant+aphid host environment that formed their own host environment (Prediction 1). Second, we predicted that pea aphid and parasitoid wasp offspring  would have higher fitness on the plant or plant+aphid host environment that formed the G0 host environment (Prediction 2). Finally, we predicted that wasp mothers  would have higher fecundity on the plant or plant+aphid host environment that formed her own developmental host environment (Prediction 3).In this study, we investigate whether the maternal environment affects the behaviour and fitness of pea aphid (t aphids –23. We t0 (Generation 0) pea aphids and parasitoid wasps prefer the host environment they had been reared in. Adult G0 insects were given a choice comprising either the host environment they had been reared in or an alternative host environment (vs. pea); and parasitoid wasp preferences for ii) pea aphids on different plant hosts  or iii) different plant+aphid host combinations .To test the predictions, choice tests and performance assays were performed on pea aphids and parasitoid wasps. We tested the first prediction that adult Gironment . Three c0 host environment affects offspring G1 performance (Prediction 2) and her fecundity (Prediction 3). This assay compared G1 performance in the G0 host environment with G1 performance in an alternative host environment (vs. pea) and parasitoid wasp offspring fitness was examined in response to maternal experience of ii) pea aphids on two different plant hosts  or iii) two different plant+aphid host combinations . G0 pea aphids and parasitoid wasps were raised to adulthood on their designated host environment and transferred to either the same host environment or an alternative host environment and allowed to deposit G1 pea aphid nymphs or oviposit G1 parasitoid wasps. Transfer of reproductive G0 adults ensured that G1 offspring had no prior exposure to their maternal environment. For aphids, G0 adults were removed within 24–48 h of G1 nymph deposition to minimise the G0 mother’s effect on G1 offspring. We also measured the dry weight of G2 nymphs produced from each G1 adult. G0 mother parasitoid wasps were removed from their oviposition environment when they had oviposited in 30 aphids (see details below).Performance assays were conducted for pea aphids and parasitoid wasps to test the predictions that the mother Gironment . The expPlant and aphid host quality was assessed by quantifying tissue dry weight and nitrogen concentrations although these data could not be collected for parasitoid wasp performance assays as this would have compromised collection of mummification data.Vicia faba cv. Sutton Dwarf) and pea (Pisum sativum cv. Douce Provence) seeds were planted individually into 3L pots filled with compost . Tomato (Solanum lycopersicum cv. Money Maker) seeds were germinated at 22°C then transferred individually into 1L pots filled with compost . Plants were grown in a greenhouse with 16:8 h (L:D) and 20°C:14°C and watered daily. Aphid clonal lines were confirmed to be free of facultative endosymbionts, such as Hamiltonella defensa [0, were reared on excised bean or pea cuttings  or excised tomato cuttings , which were replaced weekly, in ventilated plastic cups for at least four generations prior to use. This approach produced both winged and non-winged aphids in the same cup suitable for use in experiments. Winged aphid density was generally low, due to low numbers of aphids and use of high quality plant material, and these culture conditions produced a small number of winged aphids, which were sufficient in number for the olfactometer studies. Between 10–20 cups were used to culture aphids and aphids from different culture cups were mixed and randomly selected for experiments. Parasitoid wasps (Aphidius ervi) were obtained from Syngenta  and all wasps used in experiments, including G0, were reared on pea aphids on bean or pea plants, or potato aphids on tomato plants, using aphids that were less than four days old, for at least one generation prior to experiments. When mummies had formed on plants, the leaves that the mummies had formed on were removed and placed into ventilated plastic boxes until used in experiments. Adult parasitoid wasps were fed a diluted honey solution (50% v/v) presented in cotton wool. Female parasitoid wasps, aged two to five days old and presumed mated, were used in experiments. Insect cultures were maintained at 16:8 h (L:D), 20°C:14°C and 70% humidity.Bean  reared on the appropriate plant host were placed in mesh-covered clip cages  that were fixed onto leaves of experimental plants. Each arm of the olfactometer was attached to a small hole created in the corner of the PET bag enclosing experimental plants and sealed using polytetrafluoroethylene (PTFE) tape. Inert fabric, attached by PTFE tape, covered the ends of the olfactometer to prevent insect escape but allowing air flow (see Two-way choice tests were conducted with adult Gid wasps . Experimflow see . After o0 adult pea aphids were transferred from plant cuttings to a 3-week old plant of the appropriate host environment (1 nymphs (within 24–48 h of transferring the G0 adult), the G0 aphids and all but three G1 nymphs were removed. The development of three G1 nymphs was monitored to adulthood, after which aphids were removed from the plant so that a single aphid remained in each cage. Performance of the one remaining G1 aphid was monitored for a maximum of twelve days of adulthood in order to calculate intrinsic rate of population growth (Rm) [Two Gironment  and secuwth (Rm)  by:Rm=01 aphid took to deposit its first nymph (development time) and the number of nymphs (G2) the adult G1 aphid produced daily (fecundity). If G1 pea aphids died during the experiment, the number of days the G1 aphid had lived was recorded as ‘survival’. Additionally, the dry weight of all G2 nymphs produced by each G1 adult was measured. Plants used in performance assays were harvested whole and dried at 60°C for two days to quantify shoot and root dry weight and leaf tissue nitrogen concentration as described above.We measured the number of days the G0 wasps reared in each plant or plant+aphid host environment were transferred into a parasitism ‘arena’ comprising an excised leaf, abaxial surface uppermost, immobilised in 1% agarose (w/v) in a plastic petri dish . The leaf was infested with 30 aphid nymphs (2nd or 3rd instar); the plant or plant+aphid host environment presented in the arena reflected either the same G0 parasitoid wasp developmental host environment or an alternative host environment  was recorded as a measure of the GBinomial general linear mixed models (GLMMs) were used to test for effects of host environment on pea aphid and parasitoid wasp behaviour (Prediction 1) in choice tests, using date of assay (tests were conducted over 3–7 d) and replicate as random factors. The proportion of insects  spending time in each sector of the olfactometer chamber was calculated for each of the thirty time points for ten replicates per treatment, or eight for parasitoid wasps reared on potato aphids and tomato plants, and used in the analysis. Paired t-tests were used to test for differences in tissue dry weight and nitrogen concentration between bean and pea plant pairs used in pea aphid choice tests and for aphids used in parasitoid wasp choice tests.1 intrinsic rate of population growth and G2 nymph weight (Prediction 2). Fixed effects in the models included the G0 and G1 host environment, and the interaction, and the position of the pea aphid assay in the greenhouse was included as a random effect. Twelve replicates were used. Two-way ANOVAs were also used to test the bean and pea host dry weight and tissue nitrogen concentration with the same fixed and random effects, as described above. The correlation was tested between G1 intrinsic rate of population growth and host plant tissue nitrogen concentration. Pea aphid survival (measured as the number of days the G1 aphid lived) was analysed by fitting the survival data to a Cox proportional hazards regression model. The effect of the G0 and G1 host environment, and the interaction, was tested using analysis of deviance. During model simplification, analysis of deviance  and Akaike’s Information Criterion (AIC) were used to check for model suitability.For pea aphids, two-way analysis of variance (ANOVA) was used to test for maternal effects of host environment on G1 parasitoid wasps were tested using separate binomial GLMMs and logit links (due to using binary datasets) (Prediction 2). Maternal effects on G1 parasitoid wasp dry weight were tested using a linear mixed model (LMM). All models included the G0 and G1 host environment, and the interaction, as fixed effects and the date on which the parasitoid wasp assay was performed and the assay replicate number as a random effect. Ten replicates were used. In addition, the LMM used to analyse G1 parasitoid wasp dry weight included G1 wasp sex as a fixed factor. The ‘Anova’ function was used to obtain chi-squared, degrees of freedom and p values to establish significant differences between levels of each fixed effect. The effects of host environment on G0 fecundity (i.e. time taken for G0 parasitoid wasps to attack 30 aphid nymphs and number of wasp mummies) were analysed using two-way ANOVAs, with G0 and G1 host environment, and the interaction, as fixed effects and the date on which the wasp assay was performed as a random effect (Prediction 3). Model simplification was carried out as described above. Note that data collected for G1 parasitoid wasps which developed on bean-reared pea aphids, whose G0 parasitoid mothers also developed on bean-reared pea aphids, were used for statistical analysis in both the plant and plant-aphid comparisons.Maternal effects on wasp survival (emerged vs. un-emerged wasps) and the sex  of the emerged G0 and G1 host environment was significant at the 5% level.The paired t-tests and ANOVAs were conducted using GenStat . Data sa0 pea aphids showed no preference for bean or pea plants irrespective of the plant host environment they experienced , G2 nymphs were heavier  and G1 aphids survived longer . This was due to lower N concentrations in pea plants when infested with G1 pea aphids whose G0 mothers had been reared on pea plants .There was little evidence for maternal effects on Ga plants . Indeed,1 parasitoid wasp survival did not vary with the mother G0 nor offspring G1 host environment , and G1 host environment had a significant effect on the sex of successfully emerged G1 parasitoid wasps , but the interaction between the G0 and G1 host environment was significant , but sex did interact with the G1 host environment: Females were heavier with a bean-reared pea aphid G1 host environment whilst males were heavier with a tomato-reared potato aphid G1 host environment. Also, the G0 and G1 host environment interaction was significant  were highest when the maternal and offspring rearing environments were bean-reared pea aphid  was observed when the G0 and G1 plant+aphid developmental host environments were the same  were unaffected in this particular host environment.We had expected pea aphids to prefer the host plant they were reared on, as previous studies found that pea aphid genotypes specialised to different legume plants preferred the legume they were adapted to rather than other leguminous species . The olfAphidius ervi has been shown to respond to similar volatiles, including methyl salicylate, released from both pea aphid-infested bean plants [Like aphids, parasitoid wasps use a variety of methods to assess host quality in order to make the ultimate decision to oviposit using a variety of visual and physical cues. These include aphid size (instar), shape, colour and movement ,36, probn plants  and potan plants , and we Glycine max), nitrogen-starved plants had a similar volatile composition to that of nitrogen-fertilised plants, but three compounds were released in different quantities; however, these differences were undetectable when plants were attacked by fall armyworms (Spodoptera frugiperda) and parasitoid wasps (Cotesia marginiventris) showed no preference between infested plants that had been nitrogen starved or fertilised [The nitrogen concentration of pea aphids reared on either bean or pea plants were similar, but potato aphids had higher nitrogen concentration than pea aphids in choice tests. However, pea aphids were bigger than potato aphids, and hence did not offer a larger nitrogen resource overall. Plant nitrogen status has been shown to influence volatile emissions: in soybean plants , lending a little support for our second prediction. Pea aphid offspring had a higher intrinsic rate of population increase, heavier nymphs and survived longer on a bean host environment. Maternal environment might be anticipated to be a key determinant of offspring fitness in clonally-reproducing aphids. Previous research has shown, however, that pea aphids perform as well or better on bean plants regardless of their maternal hosts  and McLeAphid infestation  or infesA. ervi has a haploid-diploid reproduction strategy [0 wasps oviposited more rapidly in the largest aphid hosts (bean-reared pea aphids) in both the plant and plant-aphid comparisons, but this preference was not observed in choice tests. However, smaller wasps were produced in a different plant+aphid host environment compared to only a different plant host environment: Offering wasps an aphid host that represented a resource of differing quality seemed to have a stronger impact on wasp fitness than changing the plant aspect of the host environment, although these factors are linked, making interpretation complex.Our second prediction, that parasitoid wasp offspring fitness proxies would be highest when offspring experienced their mother’s environment, was not supported by offspring wasp emergence and wasp sex ratio data when only maternal plant environment was manipulated (i.e. plant comparison). However, in the plant-aphid comparison, parasitoid offspring had a lower male bias with a tomato-reared potato aphid maternal host environment and bean-reared pea aphid offspring host environment compared to other host environment combinations. strategy , allowinstrategy . In densstrategy , 48; masstrategy , but in strategy . The benThese findings can help inform biological control methods. For example, by choice of a favourable host environment combination, mass rearing of parasitoid wasps could be optimised to maximise parasitoid wasp mothers’ fecundity and improve the potential of their offspring to regulate aphid pests. However, the fitness of the offspring is likely to be maximised using larger aphid hosts, which could result in mother-offspring conflict if mothers preferentially oviposit in smaller aphids, resulting in short-term negative maternal effects on offspring fitness that compromise long-term aphid biocontrol. Further work is needed to understand the impact of environmental heterogeneity and trophic complexity on maternal effects. For example, positive maternal effects on offspring fitness are likely to be compromised by fluctuations in the insect environment that lead to unpredictability in host quality and suitability. An interesting avenue for further work is to determine whether the positive maternal effects observed in this study could affect other trophic levels (e.g. hyperparasitoids) and lead to cascading maternal effects through the food web. The hypotheses tested in this study could be applied to additional trophic groups, although challenging to test empirically due to the experiment size doubling each time a treatment level is added.Our study shows that the plant and aphid host environments of wasps can interact to affect wasp fitness, but the exact outcomes are complex and inconsistent. Specifically, wasps reared on bean plants and pea aphids appeared to have the highest fitness, possibly as pea aphids themselves had the highest fitness on bean plants. In order to better gather evidence for maternal effects in wasps, more plant and aphid host environment combinations need to be tested. This information could help facilitate the most appropriate rearing environments used in the biocontrol industry.S1 FigAphid dry weight (mg) for (A) the Plant Comparison and (C) the Plant-Aphid Comparison. Aphid nitrogen concentration for (B) the Plant Comparison and (D) the Plant-Aphid Comparison. Values are means (± SEM) of n = 20 for pea aphids reared on bean plants and n = 19 pea aphids reared on pea plants in the Plant Comparison and n = 18 for pea aphids reared on bean plants and n = 18 for potato aphids reared on tomato plants for the Plant-Aphid Comparison. ** p<0.01, *** p<0.001.(TIF)Click here for additional data file.S1 TableNegative estimates and z values indicate preference for the ‘alternative’ compared to the ‘same’ host environment  presente(DOCX)Click here for additional data file.S2 TableWeight (g) and leaf nitrogen concentration (% dry mass) of 3-week old bean. Values are means (± SEM) of n = 12 plants. Significant differences are highlighted in bold.(DOCX)Click here for additional data file.S3 Table1 intrinsic rate of population increase (Rm), G2 nymph dry weight and G1 survival for pea aphids used in performance assays (Ge assays . Signifi(DOCX)Click here for additional data file.S4 Table1 pea aphids for three weeks. Significant differences are highlighted in bold.Weight (g) and leaf nitrogen concentration (% dry mass) of six-week old bean and pea plants, after being infested with G(DOCX)Click here for additional data file.S5 Table1 wasp survival and sex, and LMMs for G1 wasp weight were used to test for maternal effects for the Plant and Plant-Aphid Comparison (GLMMS for Gmparison . Statist(DOCX)Click here for additional data file.


Histologically, infiltration of T-cells, extension of brown fat and overall collagen deposition were characteristics of GO in male mice. In contrast, female mice developed predominately macrophage infiltration in muscle and connective tissue, and muscle hypertrophy. Apart from sex-dependent variabilities in pathogenesis, disease classification revealed minor sex-differences in incidence and total outcome. In conclusion, sex does not predispose for autoimmune hyperthyroidism and associated GO.Graves’ orbitopathy (GO) is the most common extra thyroidal complication of Graves’ disease (GD) and occurs predominantly in women but more severe in men. The reason for this effect of gender on GO is unknown. Herein we studied the manifestation of GO in both sexes of an induced mouse model in absence of additional risk factors present in patients like advanced age, genetic variabilities or smoking. Male and female mice were immunized with human TSHR A-subunit encoding plasmid. Both sexes comparably developed autoimmune hyperthyroidism characterized by TSHR stimulating autoantibodies, elevated T4 values, hyperplastic thyroids and hearts. Autoimmune mice developed inflammatory eye symptoms and proptosis, although males earlier than females. Serial  GO symptoms are variable and depend on extend of orbital tissue inflammation and edema, expansion of adipose tissue and muscle fibrosis, resulting in disfiguring forward protrusion of the eye (proptosis) and/or muscle dysfunction (squint)3. The eye disease significantly impairs quality of life, may be sight-threatening and limited therapeutic options are available5. There is an urgend need for disease prevention or even early treatment, but confounding factors underlay the difficulty as the condition has a multifactorial etiology where no single factor predict the clinical outcome8. Manifestation of GO in GD patients is correlated with elevated serum concentrations of TSHR antibodies and severe hyperthyroidism10. Risk factors for development or worsening of GO are gender, age and multiple genetic variabilities in combination with exogenous factors like smoking, stress, infections or iodine intake7. Of note, among risk factors, female gender appears to conferr a greater risk than any other single factor for development of GO where depending on the different populations examined GO is found with female-to-male ratio of 4 to 614. However, female-bias depend on severity of GO: The female to male ratio was 9.3 in patients with mild orbitopathy, 3.2 in those with moderate orbitopathy and 1.4 with severe orbitopathy15. Altogether the eye disease tends to be more common in younger woman but more severe in men and older patients18. The reason underlying gender as a predisposing factor is unclear although, hormones and sex linked genetic susceptibility may be major factors. Moreover, the gender-specific impact of additional risk factors like advanced age and smoking on GO remains unclear and could not yet be investigated experimentally because of a lack of a male mouse model for GD and associated GO.Graves’ disease (GD) is an autoimmune thytoid disorder that is caused by antibodies directed against the TSH-receptor (TSHR) leading to hyperthyroidism. In 30–50% of GD patients overt orbitopathy occurs, termed Graves’ orbitopathy (GO) or thyroid eye diseasesin vivo expression of the extracellular region of the human TSHR (termed hTSHR A-subunit) by genetic immunizytion leads to the induction of experimental GD in the female model21. Recently we reported on the development of a robust and reproducible model of experimental autoimmune hyperthyroidism with associated GO in female BALB/c mice immunized by electroporation with a plasmid encoding hTSHR A-subunit and more recently, further shown that by this method we can disturb the normal mechanism of immune tolerance by inducing disease with self-anitgen, mouse TSHR in the model24. The female GO mouse model induced with hTSHR A-subunit closely resembles GO of patients sharings many pathological features with the condition. Importantly, different animals undergoing experimental GO show disease heterogeneity with varying degrees of orbital muscle inflammation and extension of adipose tissue, similar to that observed in patients with GO25.Experimental mouse models of autoimmune hyperthyroidism have been reported where In the present study, we aimed to elucidate the impact of gender in the experimental GO mouse model in absence of additional risk factors. We show that immunization with the hTSHR A-subunit plasmid induced auto-antibodies and leads to comparable manifestation of autoimmune hyperthyroidism and orbitopathy in male mice as well as in females. Our study demonstrates sex-dependent variabilities in onset and progression of orbitopathy rather than female-biased prevalence and incidence of disease. Our study suggests a minor contribution of sex on its own for manifestation of GO. However, additional risk factors linked to gender in patients most likely genetic variabilities, advanced age and/or smoking may be major determinants for development of substantial female-bias in GO.To investigate whether immunization with hTSHR A-subunit encoding plasmid has led to an immune reaction we evaluated total TSHR antibodies (TRAbs) in the mice sera by measuring TSH binding inhibitory immunoglobulin values in competition with labeled bTSH to human TSHR. 100% of GO mice of both sexes equally developed TRAbs with an inhibition activity range of 80% inhibition of TSH binding Fig. . To eval26. Likerwise we observed a statistically significant increase in the thyroid area as well as heart length in hTSHR mice compared to the corresponding ß-Gal mice indicating thyroid and cardiac hypertrophy in both sexes  was carried out. 19F MRI revealed elevated infiltration of macrophages/monocytes in the peri- and retro- orbital tissue at muscles and nerve tissue of male and female mice  such as hyaluronic acid in orbital tissues27. Quantification of the CEST signal in the retro orbital area including the harderian gland revealed a statistically significant increase in GAG deposition in female hTSHR mice while the males tended to an increase in GAG deposition in comparison to the corresponding ß-Gal mice  that expressed TSHR to high levels suggesting BAT to be a potential target tissue in miceBAT Figs  and S4. BAT Figs . Just liBAT Figs .We next analyzed Sirius Red staining of orbital slices to investigate collagen deposition in the retro orbital area compromising adipose, perineural and extraocular muscle tissue Figs  and S5. 19F Integral, CEST Contrast) or by histology  were normalized by using the Z-Score method. The Z-Score of all data sets revealed statistically significant autoimmune hyperthyroidism and orbitopathy in the males, as well as the female GO mouse model to a comparable extent  and orbitopathy investigated by MRI  reported a substantial female-bias in outbred NMRI mice immunized with hTSHR holoreceptor cDNA32. Although incidence of disease remained low the study might indicate that the use of an outbred mice strain is in favor of female-bias in induced GD mouse models. However, in mouse models of other autoimmune diseases, in induced systemic lupus erythematosus and experimental autoimmune encephalomyelitis (EAE) female-bias existed in inbred SJL mice33. In contrast, male B10.PL and PL/J mice had more susceptibility to EAE than females while no sex-bias induced in NOD and C57BL/6 mice33. The presence of female-bias in one inbred mouse strain, but not another, indicates that genetic background of the mice can influence sex differences in experimental autoimmunity. In addition also different approaches to develop TSHR autoimmunity in animals may effect the sex-specific outcome. In a transgenic hTSHR/NOD.H2h4 mouse model female mice were shown to be more prone than male mice in spontaneously developing TSHR antibodies and elevated T4 levels34. Although some of the females exhibited elevated T4 levels none of the thyroid glands showed signs of hypertrophy as typically observed in induced models of GD (24). Compared to earlier studies addressing the influence of sex in GD models our study demonstrate the incidence of TSAbs , hyperthyroidism  and orbitopathy  significantly improved by hTSHR A-subunit immunization of BALB/c mice in both sexes.Several experimental mouse models of GD have been developed in female mice and among several mice strains BALB/c have been found most susceptible to different immunization methodsSex differences exist in the animal model regarding disease onset during the experimental course. Male mice develop acute inflammatory eye symptoms earlier than female mice indicating a faster response toward the immunization. Although in this study we gained little information on the TSHR antibody level at this early time point of disease induction it is likely that the immune male mice underwent a faster and stronger immune-response initially. However, at the end stage, the TSHR antibody levels were comparable in both sexes suggesting similar immune-reactivity while ongoing disease.35. However, sex-based immune mechanisms leading to female-bias in thyroid autoimmunity are poorly understood39. It appears that potential sex dependent mechanisms which would have caused female-bias in autoimmunity did not take effects in the GO mouse model. These finding indicates that either endogenous and/or exogenous risk factors additionally linked to gender are most likely critical determinants for female-bias in thyroid autoimmunity and GO rather than sex on its own. Potential endogenous risk factors absent in the mouse model which could have predisposed female-bias in GO are most likely advanced age in conjunction with susceptibility genes and (epi-) genetic variations. Genetic predisposition for GO is believed to overlap with Graves’ hyperthyroidism40. Immune response modulating genes HLH DR3, CTLA4, FOXP3, CD40L, and thyroid specific genes TSHR has been suggested to predispose for GD and GO43. Also associations between polymorphysms in immunological factors like INFγ, TNFα or ICAM1 and GO were suggested. However, the reported associations vary between populations and lacked associations with gender45. Furthermore exogenous risk factors may linked to gender in GO like smoking or infection which could have influenced immunity have been absent in the mouse model48. On the other hand female-bias in GO patients might be overdiagnosed due to gender differences in behavior of the health system, which is of course, absent in the model49.In general female-bias in autoimmunity is explained by stronger humoral and cellular immune responses of females due to sex hormone and x-chromosomal dependent mechanisms24. Instead most of the immunized male mice established overall collagen deposition indicating chronical fibrotic changes have already set in the orbital tissue. The results suggest that in comparison to the females the male GO mice started earlier acute inflammation and underwent faster progression into a chronic state of GO. However, apparent proptosis was observed only in 33% of the male but in 57% of the female hTSHR mice. Interestingly orbital fat expansion and browning where comparable in both sexes suggesting that muscle hypertrophy and GAG deposition contributed to proptosis in females. However, simple visual assessment of pathological eye changes may be liable to subjective judgement and histological evaluation of middle area slices of the orbit may not reflect the total orbital disease. To overcome these limitations we exploited small animal MRI to evaluate pathological features of GO in the whole orbita of living mice. Moreover, we scored distinct orbital abnormalities in individual mice to enable comparative analysis of total orbital disease between sexes.Sex differences exist regarding the progression of pathological tissue abnormalities. Male mice developed acute inflammatory eye signs earlier which finally resulted in extensive infiltration of inflammatory cells in the orbit. Inflammation was restricted to the muscle and perineural tissue in both sexes, but males did not show any muscle hypertrophy like the females. Muscle hypertrophy appeared to be a characteristic of female mice since female mice immunized with homologous mouse TSHR A-subunit developed muscle hypertrophy as wellIn the mouse model incidence and severity of autoimmune hyperthyroidism, as well as orbitopathy, was comparable in both sexes although some sex differences existed in onset and progression of pathological features. In conclusion, sex on its own is not a predisposing factor for development or worsening of GO. Additional risk factors linked to sex are likely most important determinants for deveopement of substantial sex differences. The GO mouse model provides a preclinical murine model for autoimmune hyperthyroid associated orbitopathy in both sexes with the opportunity to dissect the impact of critical endogenous or exogenous risk factors as well as treatments of orbital pathology in future studies.23. Briefly, mice were anesthetized with isoflurane and immunized by injection and electroporation of 50 µg (1 mg/ml) plasmid into each biceps femoris muscle four times three weeks apart. A total of 15 male mice and 14 female were immunized with hTSHR A-subunit plasmid (referred as hTSHR mice), ten male and 17 female mice were immunized with control ß-Gal plasmid . All animals were monitored daily for general conditions, spontaneous behavior and clinical outcome of orbitopthy. Mice were inspected daily for eye symptoms indicating orbital disease by two different observers during the entire experimental course. Inflammatory eye symptoms like red eyes and lids, swelling of the lid and upcoming pus indicated early acute disease. Persistent eye symptoms like swelling, pus and proptosis for at least 5 days indicated chronic disease. Squint like eye signs were not observed in any mouse. Mouse weights were measured weekly. Mice were sacrificed when all hTSHR mice have shown eye symptoms six weeks after the last immunization. After scarification serum was obtained from inferior vena cava puncture. Microsurgical excision of thyroid glands and orbits for histology was performed. Hearts were eviscerated and length was measured. One male hTSHR mouse died before sacrification for unknown reasons.Male and female inbred BALB/c OlaHsd (H2d) mice were purchased from commercial supplier Envigo GmbH Netherlands and housed in temperature (23 ± 1 °C) and light –controlled (inverse 12:12 light-dark cycle) conditions. Each four male or female mice were maintained in single ventilated cages in specific pathogen free conditions to avoid environmental exposure. Drinking water and food pellets were provided ad libitum. Mice were immunized at the age of 6–8 weeks and sacrificed at the age of 21–23 weeks. All animal procedures were reviewed and approved by North Rhine Westphalian State Agency for Nature, Environment and Consumer Protection (LANUV), Germany. We confirm that all experiments were performed in accordance with relevant guidelines and regulations. The eukaryotic expression plasmid, pTriEx1.1Neo-human (h)TSHR A-subunit  and the control pTri1Ex1.1Neo-ß-Gal plasmid were used and referred to as hTSHR A-subunit plasmid and ß-Gal plasmid respectivelyin situ labeling of macrophages/monocytes and subsequent tracking by 19F MRI. Combined 1H/19F MRI was carried out at a Bruker AVANCEIII 9.4 T wide bore NMR spectrometer driven by ParaVision 5.1 . Images were acquired using the Bruker microimaging unit Micro 2.5 with actively shielded gradient sets (1.5 T/m) and a 25 mm birdcage resonator tunable to both 1H and 19F as described previously50. Mice were anaesthetized with 1.5% isoflurane and kept at 37 °C. T2 mapping was carried out using a multi-slice multi-echo (MSME) sequence covering the entire orbit for different tissue types essentially as reported51. Chemical exchange saturation transfer (CEST) was performed to analyze glycosaminoglycans (GaGs)52. After the acquisition of all 1H datasets, the resonator was tuned to 19F, and morphologically matching 19F images were recorded. For superimposing the images of both nuclei, the “hot iron” color look-up table provided by ParaVision was applied to 19F images. To fade out the background noise from 19F images a constant threshold was applied to 19F data. Using the ROI tool of ParaVision, inflamed regions were determined from 19F images by planimetric analysis of PFC signals of both eyes. The full experimental protocol took around 60–90 min, vital functions were supervised during the whole process, which was well tolerated by all mice. For a detailed description of MRI procedures refer to27.Magnetic resonance imaging (MRI) was performed six weeks after the last immunization immediately before sacrificing the mice. Four to six mice per group were randomly selected for MRI. At least 48 h before MRI analysis the animals were injected i.v. with 300 µl perfluorocarbon nanoemulsion (PFC) for 23. Briefly, TRAbs were measured in commercial TRAK kits using 25 µl serum plus 75 µl human control serum as TBII activity in competition with labeled bTSH to the human TSHR . TSAbs were measured in 3 µl serum plus 147 µl buffer in a bioassay using stably transfected mouse TSHR-CHO cells kindly provided by Sandra McLachlan as described before54. The concentration of TSAbs is directly correlated to the cAMP production of the cells. cAMP concentration in 100 µl of the mouse TSHR-CHO cells supernatants was measured by ELISA . Total T4 concentrations were measured in 25 µl serum by ELISA .Anti-TSHR antibodies (TRAbs) and their subtypes stimulating (TSAbs) and total T4 were evaluated in mouse serum as described beforeThe orbits were formalin fixed and paraffin embedded. Orbital slices (1 µm) at the anterior, middle and posterior area of the mouse orbits were H&E stained and examined. Consecutive slices of the middle area were selected and subjected to specific staining procedures. Muscle fibers of extraocular inferior rectus and medial rectus muscle were analyzed in H&E stained slices by CellProfiler . The total area of the muscle, muscle fiber area, number of muscle fibers and stroma was quantitatively analyzed. Slices of the middle area were stained Picrosirius red to evaluate collagen deposition in the orbital tissue. After dewaxing, the sections were stained with Weigert’s hematoxylin solution , followed by Picrosirius Red solution . Picrosirius red staining of the slices was analyzed quantitatively with CellProfiler. Furthermore, consecutive slices of the middle area were stained for UCP-1 , CD31 , F4/80 , CD3  and with HRP-conjugated-Polymer-System in accordance with the manufacturers manual (Zytomed Systems). Total fat area and UCP-1 positive stained adipose tissue were determined with ImageJ. CD31 positive vessels, F4/80 and CD3 positive cells were counted in muscle, peri-neural connective tissue and fat tissue.The thyroids were formalin fixed and paraffin embedded and sections (1 µm) stained H&E. The total thyroid gland area was analyzed objectively in central representative sections of the whole lobe by using CellProfiler . Thyroidal morphology was blindly evaluated by two different observers and indexed as hyperactive, hypoactive or normal in comparison to the thyroid morphology of the respective Ctrl mice. Images were generated using an Olympus BX51 microscope.All results are presented as mean ± s.e.m. Graph Pad software  was used for statistical analyses and Z-score analysis. Statistical analysis of all data except MRI data were performed with one-way ANOVA with Bonferroni’s multiple comparison test. Statistical analysis of MRI data was performed with unpaired two-tailed multiple Student’s t test and Mann-Whitney-test. Assumption of normal distribution of values was proven by Kolmogorov-Smirnov test. Statistical analysis of paired groups was performed with two-way ANOVA. P values less than 0.05  were considered significant and are indicated.Supporting information S1-S5 Figure