Rheumatic joints have an altered cartilage turnover. Cartilage intermediate layer protein 1 (CILP-1) is secreted from articular chondrocytes and deposited into the cartilage extracellular matrix. We developed an immunoassay targeting a Matrix Metalloproteinase (MMP)-generated neo-epitope of CILP-1, named CILP-M. Human articular cartilage was cleaved with proteolytic enzymes and CILP-M levels were measured. We also quantified CILP-M in two studies from patients with rheumatoid arthritis (RA), ankylosing spondylitis (AS) and osteoarthritis (OA) and explored the monitoring and prognostic potential of CILP-M in TNF-α inhibitory treatment and modified Stoke AS Spine Score (mSASSS) progression. CILP-M was generated by MMP-1, -8 and -12. In the discovery study, CILP-M was significantly higher in patients with RA, AS and OA than healthy donors  with an area under the curve (AUC) between the diseased groups and healthy donors > 0.95 (p < 0.001). In the validation study, patients with RA and AS had significantly higher CILP-M levels than healthy controls (p < 0.001) and AUC > 0.90 (p < 0.001). Patients with AS treated with TNF- α inhibitory treatment in the validation study had significantly lower CILP-M levels after treatment (p = 0.004). CILP-M may provide useful insights into cartilage degradation processes in rheumatic diseases. To form the cartilage matrix, other non-collagenous extracellular matrix proteins, such as the cartilage intermediate layer protein (CILP) are also present 2. CILP-1 is a glycoprotein, first identified in articular cartilage, and believed to have a role in cartilage scaffolding2. It is secreted from articular chondrocytes2, which are responsible for maintaining cartilage homeostasis by low-turnover remodelling of matrix proteins3. A hallmark of rheumatic joint diseases is altered cartilage turnover, leading to dysregulated composition. After CILP-1 is secreted from articular chondrocytes, it is deposited into the cartilage extracellular matrix 2 and cleaved by proteases expressed during joint inflammation, generating neo-epitope fragments, which are released into the circulation 4. It has been shown that synthesis of CILP is increased in early osteoarthritis (OA) cartilage5 and is associated with other musculoskeletal disorders 7.Articular cartilage is a heterogenous tissue where cells are organized into a matrix. The matrix is formed by chondrocytes and its major constituent are fibril forming collagens, primarily, type II and IX collagen, and large aggregating proteoglycans8. In previous studies, CILP-1 was found to be highly expressed in intervertebral discs and its expression increased in lumbar disc disease11. Additionally, CILP functions as an antagonist of Insulin-like growth factor 1 (IGF1) by inhibiting ligand-induced IGF1 receptor. IGF1 is a critical factor involved in promoting chondrocyte anabolism and proliferation8. An immune response to CILP has also been suggested to contribute to the pathogenesis of inflammatory joint destruction present in rheumatoid arthritis (RA) and OA12. Moreover, CILP mRNA expression was observed in synovial tissue from patients with OA13, indicating its expression is not restricted to articular cartilage. These findings suggest that CILP proteins play a role in cartilage structure and reflect the extent of inflammation and tissue turnover in rheumatic diseases. Nevertheless, no data is currently available regarding the role of cleaved CILP-1 and its potential as a marker for disease activity, as exemplified in the monitoring of anti- inflammatory treatments such as anti-TNF-α, and for prognosis in terms of structural damage progression. Moreover, studies investigating the role of CILP-1 in other joint-related diseases such as ankylosing spondylitis (AS) are needed to further elucidate its function and implication in cartilage structure and pathology. Biomarkers reflecting disease activity and prognosis of rheumatic diseases such as RA, AS and OA are a high priority unmet clinical need to monitor patients on treatment for progression of these diseases.CILP inhibits tumour growth factor (TGF)β, preventing ECM cell proliferation and anabolism promoted by TGF in intervertebral disc diseaseIn this present work we developed an immunoassay targeting an MMP-1, MMP-8, and MMP-12-generated neo-epitope of CILP-1, named CILP-M. The clinical value of CILP-M was evaluated in joint-related diseases  in two exploratory patient studies. We also explored the disease activity monitoring and prognostic potential of CILP-M in AS patients receiving anti-TNF-α therapy using clinical measures of disease activity and the modified Stoke AS Spine Score (mSASSS) progression score.The monoclonal antibody (mAb) clone NB326#76 13B5-1C6-2C4 showed the best native reactivity, peptide affinity, and stability for the assay and was chosen for assay development. A summary of the technical evaluation of the CILP-M assay can be found in Table 14. A band at ~ 80 kDa was also detected when HAC was cleaved with MMP-1, MMP-2, MMP-3, MMP-8, MMP-9, and MMP-12. It could be that the fragment of interested was attached to glycans or other sugars due to post-translational modifications14.To investigate the responsible enzyme for CILP-M generation, human articular cartilage was cleaved with a panel of enzymes. Based on measurements of CILP-M ELISA assay, CILP-M was primarily generated by MMP-1, MMP-8 (with the highest concentration) and MMP-12 Fig. . The neoTables In the discovery cohort, patients with RA, AS and OA showed significantly higher levels of CILP-M compared to healthy donors . We observed that after 12 weeks of treatment, levels of CILP-M were significantly decreased  a technically robust and specific assay towards the CILP-M neo-epitope was developed; (2) CILP-M was primarily generated by MMP-1, MMP-8 and MMP-12, confirmed by the CILP-M ELISA assay and Western blot; (3) CILP-M was measurable in human serum, and the levels of CILP-M were increased in patients with RA, AS and OA as shown in the discovery and validation cohort; (4) CILP-M was also able to discriminate between patients with RA, AS or OA and healthy donors with AUC > 0.90–0.95) CILP-M levels decreased after anti-TNF-α therapy in patients with AS.4, which was primarily generated by ADAMTS-5. To confirm which protease generated the CILP-M cleavage site, we tested a series of proteases. In contrast with the previous finding, our in vitro cleavages showed that CILP-M epitope was generated primarily by MMP-1, MMP-8, and MMP-12 measured by the competitive chemiluminescence immunoassay (CLIA) assay and Western blot. However, the cleavage experiments differed from Zhen et al. in several aspects. In this study, we used end-stage osteoarthritic human articular cartilage, while Zhen et al. used samples derived from two separate subjects: one sample exhibiting OA pathologic features and another one with no signs of arthritis. In addition, we used a 3-day incubation period in the cleavage experiment, in contrast to the 21 days in the previous study; thus, the fragments of CILP-1 that Zhen et al. found might have been released on later days of the cleavage. In Western blot we observed the most prominent band at ~ 54 kDa when cleaved by MMP-8. Post-translational modifications (such as glycosylation) occurring close to the neo-epitope sequence could explain the larger sized immunoreactive bands observed in the Western blot14. Destruction and fusion of articular cartilage is a key feature in rheumatic diseases 17. Serine proteases and MMPs are highly expressed in the ECM when joint inflammation is occurring and are responsible for cartilage degradation17. ADAMTS4 and ADAMTS5 are considered the main aggrecan degrading enzymes in cartilage3, whereas MMP can degrade all components of the ECM18. MMP-1 and MMP-8 are two of the most common collagenases that are important for degradation of collagens in cartilage18. MMP-1 is produced primarily by synovial cells lining the joints and has a predominant role in OA since it controls the process of collagen degradation19. High levels of CILP-1 have been found in synovial tissue13, which could be further cleaved by MMP-1. MMP-8 is expressed at sites of inflammation and has been associated with a wide range of inflammatory disorders20, whereas MMP-12 is secreted by inflammatory macrophages21, and it has been found that increased MMP-12 expression in macrophages worsened the development of inflammatory arthritis in rabbits22. All these findings together suggest that higher expression of MMP-1, MMP-8, and MMP-12 in inflammation sites of the joints, specifically adjacent to articular cartilage, could increase the cleavage of CILP-1, generating the neo-epitope of CILP-M.The CILP-M assay was characterised as a technically robust and accurate assay by showing acceptable dilution recovery, interference, and stability tests. The inter and intra-variation was accepted with 10.3% and 7.2%, respectively. The epitope CILP-M target was previously found by mass spectrometry of human articular cartilage24. We found that levels of CILP-M were increased in RA, AS and OA, compared to healthy donors, but did not distinguish between the different rheumatological disorders. In previous studies, CILP-1 has been associated with cartilage degenerative diseases, especially OA7. It has also been shown that anti-CILP antibodies are present in OA and RA patients, suggesting that the autoimmune response against CILP is related to the pathogenesis of OA and RA12. From our current understanding, this is the first study investigating the role of CILP-1 in AS patients. Our findings are in line with previous studies indicating that patients with AS have a higher extracellular matrix remodelling in cartilage26. However, CILP-1 has also been related to pulmonary hypertension and cardiac fibrosis, suggesting it is not specific to articular cartilage 29.Previous studies have investigated cleavage products of CILP-1 in rheumatic diseases, specifically in OA30. However, the nature of this study was exploratory and further investigation is needed to elucidate the role of CILP-M as potential marker to monitor treatment response to anti-TNF-α therapy and disease activity.There is an unmet need for better tools to monitor disease activity and response to treatment in patients with AS. We therefore investigated if CILP-M was able to discriminate the treatment effect and response to anti-TNF-α therapy. We observed that levels of CILP-M were significantly lower after treatment, and patients responding to anti-TNF- α therapy had a trend of higher levels of CILP-M at baseline and after 12 weeks of treatment compared to non-responders. We also explored whether CILP-M was prognostic for radiographic progression, assessed by the mSASSS. Here we found patients who progressed during a 2-year period, tended to have higher levels of CILP-M but did not reach statistical significance. This is in agreement with previous findings, where cartilage degradation has been associated with radiographic progression in the spine of patients with ASLimitations of this study are the small population size, no information about biological variation , limited clinical data available in the discovery cohort, and serum sample availability in the validation cohort. Furthermore, the healthy patients did not come from the same source as the rheumatic patients in neither the discovery nor the validation cohort. The present work was an exploratory research and cohorts with larger samples and more accurate study design might be useful for validating CILP-M in future research.In conclusion, a novel neo-epitope biomarker CILP-M, measuring a MMP-1-, MMP-8- and MMP-12-mediated fragment of CILP-1 was developed and validated for use in human serum samples. CILP-M was upregulated in patients with RA and AS compared to healthy controls in two independent cohorts and declined levels were observed after TNF-α therapy in patients with AS. Based on these results, CILP-M may be useful to assess cartilage remodelling in joint degenerative diseases.All reagents used were high quality chemicals from Merck  and Sigma  unless stated otherwise. All synthetic peptides used for antibody production and assay validation were purchased from Genscript  Table .Table 4Shttps://blast.ncbi.nlm.nih.gov and https://npsa-prabi.ibcp.fr) with the 10-amino acid (aa) sequence SLNPDTGLWE. In the analysis, we focused on the first 6 amino acids (aa), checking for the presence of a single mismatch, as well as on the entire 10-aa sequence, allowing for a maximum of two mismatches. The results showed a complete 100% match with the CILP-1 protein, with no observed mismatches. Hence, mAbs were generated using the aa sequence ↓700SLNPDTGLWEas the antigen. Four-to six-week-old balb/C mice were immunized with a subcutaneous injection of 200 μl emulsified antigen and 100 μg immunogenic peptide (SLNPDTGLWE-GGC-KLH) using Stimmune (Thermo Fisher). Immunizations were repeated every two weeks until stable serum antibody concentrations were achieved. The mouse with the highest serum concentration was selected for fusion and rested for a month before being intravenously boosted with 50 μg immunogenic peptide in 100 μl 0.9% NaCl solution. Mouse spleen cells were fused with SP2/0 myeloma cells to produce hybridoma cells as described by Gefter et al.13. The clones were screened for reactivity using an indirect ELISA on streptavidin-coated plates using SLNPDTGLWE-K-Biotin as the screening peptide and SLNPDTGLWE as the standard peptide to test for specificity. Hybridoma cell supernatant was purified using HiTrap affinity columns  according to the manufacturer’s instructions, and the antibody isotype was determined using a Rapid ELISA Mouse mAb Isotyping Kit . The mAb was specifically selected to recognize the standard peptide , but not elongated peptide , or truncated peptide , ensuring the mAb is specific for the W↓S cleavage product.Before the mAb generation, a cross-reactivity assessment was conducted by performing a Blast analysis .The development of the CLIA included several preliminary optimizing experiments where reagents, concentrations, incubation-time and -temperature were analysed by several tests. The CILP-M CLIA procedure was as follows: a 96-well streptavidin-coated white microplate  was coated with 2.5 ng/mL biotinylated synthetic peptide (SLNPDTGLWE-K-Biotin) dissolved in assay buffer , 1% bovine serum albumin, 0.1% Tween-20, 0.36% Bronidox, 4 g/L NaCl, adjusted to pH 7.4 at 20 °C) and incubated for 30 min at 20 °C with constant shaking (300 rpm) in darkness. Next, 20 μL/well of standard peptide (100 ng/mL) and samples were added to the appropriate wells, followed by the addition of 100 μL/well of HRP-labelled antibody diluted in assay buffer to the concertation of 100 ng/mL and incubated for 1 h at 20 °C with constant shaking (300 rpm) in darkness. After each incubation step, wells were washed five times with standard washing buffer . To prepare the chemiluminescence substrate , Basel, Switzerland), working solutions were mixed 15 min prior to use. Next, 100 μL/well of the solution was added to the plate and incubated for 3 min at 20 °C with constant shaking (300 rpm) in darkness. Relative light units were measured at all wavelengths within 5 min on a microplate luminometer reader . A standard curve was plotted using a 4-parameter logistic curve fit Y = (A − D)/(1 + (x/C)^B) + D, where R###0.9. Data were analysed using SoftMax Pro version 7.0.3 software.700SLNPDTGLWE, 10aa), elongated peptide , truncated peptide , and non-sense peptide . The intra- and inter-assay variation was determined by 10 independent runs of eight quality controls and two kit controls run in double determinations. Accuracy of the assay was measured in healthy human serum samples spiked with standard peptide and a serum sample with a known high CILP-M concentration and calculated as the percentage recovery of the measured value and the expected concentration of the peptide or the serum sample with high CILP-M plus the concentration of the analyte in serum. Analytical interference was performed by adding a low/high content of haemoglobin (2.50/5 mg/mL), lipemia/lipids (1.50/5 mg/mL) and biotin (3/9 ng/mL) to a serum sample of known concentration. Recovery percentage was calculated with the normal serum sample as reference. The standard reference values for haemoglobin, lipidaemia/lipids and biotin were 0–10 mg/dL (0–0.00161 mmol/L), < 150 mg/dL (< 1.6935 mmol/L) and 0.221–3.004 ng/mL, respectively. The interference was calculated as the percentage recovery of the analyte in non-spiked serum. The measurement range was defined as the range between LLOQ and ULOQ, which were determined from 10 independent runs with the standard peptide. Measurements below LLOQ or above ULOQ were assigned the value of LLOQ/ULOQ, respectively. IC50  was determined from the standard curve. The analyte stability was examined through temperature tests and repeated freeze–thaw cycles of serum samples. The temperature tests included different time point and temperatures where CILP-M levels were measured in three human serum samples after 0-, 2-, 4-, 24-, and 48-h incubation at either 4 °C or 20 °C. The recovery was estimated with 0 h sample as a reference. Furthermore, the effect of four repeated freeze/thaw cycles of three serum samples was assessed where freeze/thaw recovery was calculated with the zero cycle samples as a reference. Each sample was run in double determination.Two-fold dilutions of four human serum samples were used to assess linearity. Linearity was calculated as a percentage of recovery of the undiluted sample. Antibody specificity was calculated as percentage of signal inhibition by two-fold diluted standard peptide  were incubated with 4 μg of the individual enzyme with 250 μL digestion buffer as previously described4. The digestion was carried out for 72 h in replicates. The reaction was stopped by adding 5 mM EDTA. Cleaved products were measured in the CILP-M ELISA and run on Western blot. For the Western blot, the digested samples , and the ladder SeeBlue Plus2  were electrophoresed on a NuPAGE 4–12% Bis–Tris gel  under reducing conditions using NuPAGE® MES SDS running buffer . By using an iBlot® Dry blotting system , the proteins from the polyacrylamide gel were transferred onto an iBlot® nitrocellulose membrane . Subsequently, the membrane was blocked for 1h with 5% skim milk  in TBST  with 0.1%. Tween-20). The membrane was incubated overnight at 4 °C with CILP-M mAb. Next, the membrane was washed in TBST three times 10 min and incubated with the secondary peroxidase conjugated antibody  for 1 h. The membrane was washed in TBST and incubated for 5 min with SuperSignal west femto maximum sensitivity substrate . The bands were visualized through C-DiGit™ Blot Scanner . ImageLab software version 6.1 (Bio-Rad) was used for image acquisition.Articular cartilage biopsies from OA patients who underwent knee replacement surgery were obtained from Gentofte Hospital Denmark. A broad panel of enzymes, known to cleave human articular cartilage was testedThe clinical utility of CILP-M was evaluated in serum samples from a discovery and a validation cohort. The discovery cohort was acquired from the commercial vendor Proteogenex . The discovery cohort included a group of healthy donors (n = 13), patients diagnosed with RA (n = 18), patients diagnosed with ankylosing spondylitis (n = 14), and patients diagnosed with osteoarthritis (n = 8). The validation cohort was collected at University of Alberta, Canada, and included serum samples from patients diagnosed with RA (n = 23), patients diagnosed with AS (n = 146), and results were compared with age-matched healthy donors from the commercial vendor BioIVT  (n = 105). In the validation cohort, BASDAI and mSASSS were recorded for each AS patient. The AS samples were assayed prior to the start of treatment with a biologic. 97 patients with AS were treated with anti-TNF-α treatment. However, only 55 of those patients had samples available for CILP-M measurements before and after treatment. To assess the efficacy of anti-TNF-α treatment, patients were categorized as responders and non-responders based on a 50% reduction of the BASDAI score. To evaluate disease progression, patients were categorized as progressors and non-progressors based on whether they showed an increase in the mSASSS score (change of > 0) at the 2-year follow-up. Serum samples were collected and stored at -80 °C until ready for use.All animals were treated according to the guidelines for animal welfare. MAb production in mice was approved by the Danish National Authority  under approval number 2013-15-2934-00956. The collection and retrieval of the human cartilage complied with international ethical guidelines for handling human sample and patient information. All participants signed an informed consent, and the study was approved by the local ethical committee  under approval number Pro00000856. Samples from both cohorts were collected after informed consent and approval by the local Ethical Committee and in compliance with the Helsinki Declaration of 1975. The study is reported in accordance with ARRIVE guidelines.For all statistical analysis performed, a p-value below 0.05 was considered significant. Baseline characteristics are described as number (frequency) and percentage for categorical variables, and as mean (± SD) for continuous variables. Kruskal–Wallis rank test was used to examine baseline differences between groups of participants. One-way ANCOVA analyses were performed in both cohorts  to measure differences in CILP-M at baseline. Additionally, AUROC analyses were used to investigate the discrimination accuracy of CILP-M between patients and healthy controls at baseline in both studies. These analyses were adjusted for age and sex in both cohorts. Spearman’s correlations were performed with CILP-M and clinical scores  or BASDAI for RA or AS patients, respectively) at baseline in the validation study. A paired t-test was used to investigate differences in CILP-M levels before and after 3 months of TNF inhibitor  in patients with AS in the validation study .A linear mixed model was used to evaluate if CILP-M could discriminate response to TNF inhibitor based on 50% reduction of BASDAI, including response and visit as fixed effects and patient specific intercepts as random effect. A t-test was used to explore differences in CILP-M levels between mSASSS progressors and non-progressors.https://www.R-project.org).Statistical analysis and graphs were performed using GraphPad Prism version 9  and R studio version 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria. URL Supplementary Figure S1.


Individuals genetically susceptible to high schistosomiasis worm burden may contribute disproportionately to transmission and could be prioritized for control. Identifying genes involved may guide development of therapy.Schistosoma mansoni worm burden using the Up-Converting Particle Lateral Flow (UCP-LF) test detecting circulating anodic antigen (CAA), point-of-care Circulating Cathodic Antigen (POC-CCA) and Kato-Katz tests. Whole genome genotyping was conducted on 326 children comprising the top and bottom 25% of worm burden. Linear models were fitted to identify variants associated with worm burden in preselected candidate genes. Expression quantitative trait locus (eQTL) analysis was conducted for candidate genes with UCP-LF worm burden included as a covariate. Single Nucleotide Polymorphism loci associated with UCP-LF CAA included IL6 rs2066992  and rs7793163 ; IL21 SNP kgp513476  and IL17B SNP kgp708159 . A haplotype in the IL10 locus was associated with lower worm burden  and overlapped SNPs rs1800896, rs1800871 and rs1800872. Significant haplotypes  associated with worm burden were observed in IL6 and the Th17 pathway IL12B and IL17B genes. There were significant eQTL in the IL6, IL5, IL21, IL25 and IFNG regions.A cohort of 606 children aged 10–15 years were recruited in the Albert Nile region of Uganda and assessed for S. mansoni worm burden were in IL6, FCN2, RNASE3, IL10, IL12B and IL17B gene loci. However only eQTL associations remained significant after Bonferroni correction. In summary, immune balance, pathogen recognition and Th17 pathways may play a role in modulating Schistosoma worm burden. Individuals carrying risk variants may be targeted first in allocation of control efforts to reduce the burden of schistosomiasis in the community.Variants associated with  IL6, IL21 and IL17B genes, and a haplotype in the IL10 locus associated with lower worm burden. Expression quantitative trait locus (eQTL) analysis revealed significant gene and worm burden associations in the IL6 region, and expression associations in the IL5, IL21, IL25 and IFNG loci. These findings suggest that immune balance, pathogen recognition and Th17 pathways may be involved in modulating Schistosoma worm burden and that individuals carrying risk variants may be prioritized to improve control efforts.This study investigated genetic risk factors for high worm burden in a cohort of 606 children aged 10–15 years old in the Albert Nile region of Uganda. Genotyping was conducted on 326 children who comprised the top and bottom 25% of worm burden. Results showed single nucleotide polymorphisms (SNPs) associated with worm burden in the  Schistosomiasis affects more than 250 million people globally, but Africa bears the highest burden with approximately 80% of cases . The disDespite MDA, hotspots of transmission persist, and this could be due to a combination of lack of sanitation, re-infection, environmental factors and infection intensity . Host imSchistosoma mansoni worm burden in Ugandan children [Schistosoma mansoni worm burden.It is well established that there is a genetic component to susceptibility to high worm burden, with the earliest study identifying a cytokine cluster in chromosome 5 which explained approximately 66% of infection intensity in a Brazilian population . Candidachildren ,13. The children . It is tth largest contributor of the global total of 4.5 million disability adjusted life years(DALYs) in 2013 [S. mansoni worm burdens as measured by POC-CCA and UCP-LF CAA were recorded [S. mansoni infection intensity [IFNG, IL10, IL13, IL4, IL5, STAT6, CTLA4, FCN2, COLEC11, ABO, RNASE3), those in schistosomiasis infection QTL for which we could not find candidate gene studies in the literature  and some in the Th17 pathway, which is underrepresented in schistosomiasis genetic association studies , as described before [In sub-Saharan Africa which includes Uganda; schistosomiasis was the 7 in 2013 . Most of in 2013 ,16. In tntensity –19. We gThe full protocol for this study was published prior to undertaking the genotyping . Here weS. mansoni eggs in their stool, they were offered free treatment, which consisted of praziquantel at a dosage of 40mg/kg administered by trained Ministry of Health personnel, assisted by the district health worker.This study was approved by the Makerere University Ethics committee and the Uganda National Council of Science and Technology (UNCST No. HS 118). The study was conducted in the context of the National Control Program by the Vector Borne Disease Control Division of the Ministry of Health in Uganda that has implemented the Program in Pakwach (former Nebbi District) for over 19 years since 2003. The study targeted children 10 to 15 years old. Within the target villages in the Lake Albert region, parents were informed of the study and gave written informed consent on behalf of children under 12 years. In addition, those over 12 years gave their assent to participate in the study. As described before in this same population , if a chSample collection was done as previously described .The overS. mansoni eggs microscopically from stool as described previously [The Kato-Katz test was used to identify and quantify eviously . The POCeviously . The quaeviously ,23. UCP-eviously . It was eviously ; and theVenous blood samples were collected from the children into EDTA tubes for which 200μl was used for DNA extraction using the Qiagen DNA blood kit  following the manufacturer’s instructions. The resultant DNA was quantified  and approximately 1μg was sent for genotyping on the Illumina H3Africa chip at University College London Genomics Facility .Li and colleagues  describeIFNG, IL10, IL13, IL4, IL5, STAT6, CTLA4, FCN2, COLEC11, ABO, RNASE3, IL17A, IL17B, IL17F, IL6R, IL12B, IL1A, IL1B, TGFB1, IL6, IL21, IL23A, IL25, IL17RA. Literature supporting the candidate gene selection includes: IFNG[IL10 [IL13 [IL4 [CTLA4 [FCN2[COLEC11 [ABO [RNASE3 [IL12B, IL17A, IL17B, IL25, IL17F, IL17RA, IL1A, IL1B, TGFB1, IL6R, IL6, IL21, IL23A. We noted the possible role of these genes in our previous publication [We reviewed the literature on the genetics of schistosome worm burdens  and seleFNG[IL10 ,27, IL1310 [IL13 , IL4 [294 [CTLA4 , FCN2[31[COLEC11 , ABO [33 [RNASE3  and thosSamples were genotyped on the Illumina H3Africa custom chip  at the Up value < 0.001), sex check and close relatives pi-hat > 0.2 and extreme heterozygosity < 0.256. Principal component analyses were performed using Plink to identify and remove outliers which may have an undue influence on genetic results.Genotypes were called using the Illumina GenomeStudio package and then exported to PLINK format. The human reference genome version used was GRCh37. Quality control was done using standard methods for keeping individuals and SNPs as determined by visualization of results. The thresholds for exclusion were individual missingness > 0.025, genotype missingness > 0.05, minor allele frequency < 0.05, Hardy Weinberg equilibrium  QTL have been shown to be co-dominant [Tables and simulations of power in extreme phenotype sampling are described in . In a mudominant ,37 and tSNPs were phased using Shapeit2 , haplotyhttps://zzz.bwh.harvard.edu/plink/) [Demographic factors were tested for their associations with UCP-LF CAA antigen using Wilcoxon rank sum test for continuous variables and Pearson’s chi-square test for categorical variables. Linear regression analysis in Plink V1.90 (/plink/)  was used/plink/) ,42,43 an/plink/) ,45. Prin/plink/) . These p/plink/) .https://www.r-project.org/) version 4 for plotting graphics. Bonferroni correction was used to adjust results for multiple testing. False Discovery Rate(<0.05) was applied on the top p-value per haplotype block.The whole genome genotyping data after quality control as described in the genotype calling and quality control section, was used in Plink to calculate the first 20 principal components of the data which were also used as covariates in the regression analysis. Plink cannot process multi-allelic haplotypes so associations with haplotypes were detected using linear regression in R 3.5, with logarithm of UCP-LF CAA as the dependent variable and haplotype alleles, age, sex, BMI and collection site as explanatory variables. The dependent variable UCP-LF CAA was chosen over POC-CCA and Kato-Katz as it is the most sensitive to worm burden levels . IndividFrom a subset of participants (44 cases and 20 controls as defined by UCP-LF CAA), we collected peripheral blood in PAXgene Blood RNA  tubes and extracted RNA using Trizol . Participants were selected during the POC-CCA screening in the field. The POC-CCA scoring gave information on the intensity of infection for which Paxgene blood was taken from only those individuals who were to participate in the transcriptome analysis study, that is High (>3+), Low (<1+) and Negative (0). Samples with RNA concentrations of more than 1μg as measured by Qubit  were sent for sequencing at the Center for Genomics Research at the University of Liverpool. The total RNA was subjected to rRNA removal using the QIAseq FastSelect rRNA HMR kits (Qiagen)  and the libraries constructed using the NEBNext Ultra II Directional RNA Library Prep Kit. On the Illumina NovaSeq S4 (Illumina), the libraries were sequenced in a 2x150 read format with a target depth of 30m read pairs per sample. Using HiSat2 , FASTQ rEpidemiological characteristics of participants are presented in S1 Table lists the SNPs and individuals included after each quality control filter. After quality control steps for SNPs, we had 297 individuals who were included for SNP and haplotype analysis.We selected individuals within the top and bottom 25% of worm burden UCP-LF CAA signals for genotyping. We conducted quality control of SNPs and individuals by filtering using thresholds as defined in methods section. Nine outliers in the principal component analysis (PCA) were dropped from further analysis. p<0.05 are shown in S2 Table. However, no SNP remained significant after Bonferroni correction for multiple testing. The Bonferroni threshold was 0.00015  as those with <5% are considered rare [IL10, RNASE3, FCN2, TGFB1. However, these did not remain significant after Bonferroni correction. Haplotype blocks contained several alleles which were not significant after FDR correction. We considered the top p-value allele signal per haplotype block since all alleles in a block are linked. Using this approach, 69 out of 332 haplotype loci showed an FDR<0.05. The FDR for the top signal and corresponding alleles are shown in S4 Table).Haplotypes in candidate genes fitted to the linear model predicting CAA yielded results with p<0.05 as shown in red rare . These iThe IL6 locus showed both haplotypes and SNPs .Some haplotypes within our candidate genes contain SNPs with published associations with schistosomiasis Tables  and S5. Table 5, for the 100kb analysis (part A) and 1Mb analysis (part B).We conducted eQTL analysis for the candidate genes. RNA sequence data and covariate data were available for 58 of the samples, comprising 38 infected and 20 uninfected children. Analysis was conducted for 100kb and 1Mb flanking regions respectively. The results are as shown in IL5 and IL6. The IL5 and IL6 loci include haplotypes that were significantly associated with worm burden in For the 100kb analysis, we identified significant eQTL in IL25, IFNG and IL21. In the 1Mb analysis, the IL5 locus, which was significant in the 100Kb analysis, was no longer significant because the larger number of SNP tested led to a stronger correction for multiple testing, but shows a trend towards significance (p = 0.08). Although there were multiple SNP associated with expression of IL5 these were not associated with worm burden as measured by UCP-LF CAA  were replicated in multiple studies [We have tested 24 candidate genes for association with  studies ,29,55,56IL6 locus. These SNPs were in regions of the genome that had elevated conservation and DNAse sensitivity scores (IL6 expression (Bonferroni unadjusted p<0.05), even though SNP rs114644824 was not itself associated with log CAA. The above evidence suggests that IL6 may play an important role in modulating worm burden. In vitro evidence has shown that IL6 is expressed in cells of acute inflammatory granulomas induced by S. mansoni eggs in mice and that it enhances the cytotoxic activity of human platelets against S. mansoni larvae. This suggests that IL6 may play a role in the immune response to schistosome eggs and the development of egg-induced immunopathology such as liver granulomas during schistosomiasis [S. mansoni infected individuals compared with non-infected individuals, and that levels correlated with egg load [Several top-ranking SNPs and haplotypes with p<0.05 are in the y scores  suggestisomiasis ,58. A stegg load . Differeegg load .IL10 contains SNPs and haplotypes with p<0.05. This association appeared to be mediated only by particular haplotypes of IL10 polymorphisms rather than the SNP in isolation, since in our results, the IL10 promoter SNPs rs1800871, rs1800872 and rs1800896 were not individually associated with worm burden despite the haplotype GC of these SNP being associated with CAA levels  on chromosome 6q22-q23 is part of the second locus to be associated with S. mansoni outcomes, termed SM2 locus [IL25 and IL21 polymorphisms in the Th17 pathway have not been previously associated with worm burden but we have recently hypothesized that they might play a role in worm infection response as they are Th17 pathway genes within SM1 and SM2 loci [S. japonicum [S. japonicum-infected mice [S.mansoni soluble egg antigen-induced granuloma formation is through regulating granulocyte recruitment and functions [S. japonicum infection in mice [IL17B and IL12B, which were significant in SNP and haplotype analysis. Further published evidence of Th17 pathway involvement in schistosomiasis include elevation of IL17 in a murine immunopathology model [S. haematobium compared to those that are uninfected [Further, through eQTL analysis, we have showed multiple significant SNPs modulating m burden . These eM2 locus  which waSM2 loci ,20. Th17SM2 loci ,63–65. Tunctions . The evi in mice .Other gegy model  and elevinfected .RNASE3, also known as ECP, which is a secretory protein of eosinophil granulocytes that efficiently kills the larval stage of S. mansoni. RNASE3 has been associated with schistosomiasis infection, infection intensity and fibrosis in a study that was conducted among the Alur speaking community of Lake Albert [Another suggestive worm burden association was with e Albert . NotablyComparing several studies for replication of SNP effects is challenging since different SNP may regulate a particular response in different populations . This phStrengths of the current study include the design to capture differences due to worm burden at the three levels of phenotyping, genotyping and analysis. For the phenotyping, we used the most sensitive diagnostic detecting worm-derived circulating antigen, UCP-LF CAA which is more sensitive than KK, the current WHO gold standard ,22. In gL5,IL6, IFNG,IL21 and IL25) were significant. Bonferroni correction may also have resulted in overcorrection, missing actual genetic markers associated with worm burden. The markers presented here represent hypotheses that could be tested in additional studies to confirm their associations with worm burden. It is also likely that more SNPs control worm burden than those analyzed. The results presented are also from one timepoint in a cross-sectional study. Longitudinal studies are more powerful to capture time course and re-infection events which may generate causal evidence of the effect of immune markers [However, there are some limitations, such as insufficient sample size for whole genome analysis. We have mitigated this by focusing only on candidate genes and conducting extreme phenotype sampling to maximize the power to detect effects. None of the markers for SNP and haplotype analysis were significant post-Bonferroni, but eQTL markers in 5 genesClick here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S1 Appendix(XLSX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.


Scholars have posited that adolescent emotion regulation (ER) may serve as an underlying mechanism in the link between parenting and adolescent adjustment. Supportive parent-adolescent relationships  may be a key emotion socialization mechanism influencing adolescent ER.The purpose of this investigation was to examine the influence of supportive parent-adolescent relationships on adolescent adjustment  both directly and indirectly (via adolescent ER) effects of supportive parent-adolescent relationships on adolescent adjustment. We also explored whether these indirect and direct effects varied by adolescent sex and age.The sample included 206 adolescents  and one primary caregiver . Structural equation modeling (SEM) was implemented to investigate the direct and indirect (via adolescent ER) links between supportive parent-adolescent relationships and adolescent prosocial behavior, aggressive behavior, and depressive symptoms. Moreover, evidence indicated that many of these pathways were significant for boys but not girls. No evidence of youth age as a moderator was found.Results suggested direct and indirect (These findings highlight the important role supportive parent-adolescent relationships play in adolescent emotional and behavioral adjustment. Parenting programs could focus on facilitating a mutually responsive parent-adolescent relationship with a specific focus on the dynamic nature of emotion socialization during adolescence. The devvia adolescent ER) effects of dyadic, supportive parent-adolescent relationships on positive and negative developmental outcomes among youth in a predominantly low-income, single-parent, and ethnic minority sample.Studies of parental influences on child adjustment can be categorized into three conceptual models: (1) parent-driven effects focusing on parent behavior; (2) child-driven effects recognizing the influence of child characteristics on parent behavior; and (3) relationship models based on the notion that parent–child relationships are comprised of more than both the parent’s and child’s behaviors . RelatioER provides a critical link to understanding developmental psychopathology during adolescence - a period marked by dramatic increases in mental health issues such as depression and anxiety . RutherfSupportive relationships between parents and adolescents are characterized by openness, acceptance, and emotional responsiveness . In the The capacity for emotional responsiveness, a component of supportive parent-adolescent relationships, is defined as the awareness and responsivity to another’s emotions during social exchanges . ResearcAssociations between supportive parent-adolescent relationships, ER, and adolescent outcomes may differ by sex and age. Some studies have found the link between parental emotional support and adolescent externalizing symptoms is stronger among girls ; whereasvia adolescent anger and sadness regulation) links between supportive parent-adolescent relationships and adolescent adjustment  in a predominantly low-income, single parent, ethnic minority sample. It was hypothesized that supportive parent-adolescent relationships would be directly and indirectly (via adolescent anger and sadness regulation) related to adolescent adjustment. In addition, we explored whether these pathways were moderated by adolescent age and sex. No specific hypotheses regarding sex and age differences were made, as previous research findings related to these factors were inconclusive.Previous studies have explored ER as a mediator in the link between parenting characteristics and adolescent adjustment e.g., . While t2.2.1.M age = 13.38 years, SD = 2.32, Age Range = 10–18 years; 51% female; 32% African American, 29.6% European American, 19.4% Latino American, 19% multiple racial/ethnic groups) and their primary caregivers . The sample was predominantly comprised of low-income  families with an average of 4.35 people living in each home and 38.7% headed by single parents.The sample consisted of 206 families with adolescents who participated in the Family and Youth Development Project . The purpose of the FYDP was to examine predictors and outcomes of adolescent ER. Data were collected from urban areas of a southern Midwest region of the United States from both adolescents  to 3 (a lot like her/him) and includes items such as “My mother/father is a person who makes me feel better talking over my worries with her/him,” and “makes me feel like the most important person in her/his life.” Mean scores were calculated with higher scores indicating greater parental acceptance. Cronbach’s α was 0.92.Adolescents completed the parental acceptance scale which assesses the degree to which the parent displays supportive, accepting, and emotionally responsive behavior when interacting with the adolescent . The par2.2.2.Definitely not) to 5 (Definitely). This instrument was adapted from the Student-Teacher Relationship Scale .Interactions during the task were coded using a revised coding scheme developed originally by Rand Conger and his colleagues . Researc2.2.4.Not true) to 2 (Very true). Means scores were calculated for both subscales, with higher scores indicating greater emotion coping strategies.Parents and adolescents each reported on adolescents’ abilities to regulate their emotions using the Children’s Emotion Management Scale: Sadness and Anger scales CSMS; . The sad2.2.5.Not true), 1 (Sometimes), to 2 (True). This measure  of adolescent and parent ratings.Parents and adolescents each reported on adolescents’ prosocial behavior during the past year on a 3-point scale from 0 (2.2.6.Never), 2 (1–2 times), 3 (3–4 times), 4 (5–6 times), to 5 (7 or more times). Mean scores were calculated, with higher scores reflecting greater aggressive behavior. Cronbach’s α was 0.88 for adolescent report and 0.90 for parent report. The adolescent and parent ratings were averaged  to create the adolescent aggressive behavior composite score.Parents and adolescents each reported on adolescents’ aggressive behavior using 14 items from the Problem Behavior Frequency Scale , which a2.2.7.Not true), 1 (Sometimes), to 2 (True). This measure includes 33 items such as “I felt miserable or unhappy,” and “I thought there was nothing for me in the future.” Cronbach’s α was 0.93 and scores were averaged with higher scores indicating greater depressive symptoms.Adolescents reported on their own depressive symptoms during the last 2 weeks using the Child Mood & Feelings Questionnaire MFQ-C,  using a 3.3.1.t-tests. To answer our research question, structural equation modeling (SEM) was implemented to test the theoretical models using Mplus version 6.12  based on early and middle age ranges of adolescence put forth by the American Academy of Pediatrics (χ2) test.First, mean sex and age group differences on study variables were tested using ion 6.12 . Parentaion 6.12 , measureion 6.12 . Indirecdiatrics . Factor 3.2.t (204) = 3.02, p < 0.01 and were rated to have higher parent-adolescent relationship quality than adolescent males, t (197) = 2.27, p = 0.02. Parents reported adolescent females to have higher scores on anger regulation than males, t (202) = 2.18, p = 0.03. Both adolescent-reports and parent-reports indicated females had higher levels of prosocial behavior than males, t (196.84) = 4.29, p < 0.001, and t (191.80) = 2.76, p < 0.01, respectively. Adolescent males reported slightly more aggressive behavior than females, t (204) = −1.99, p < 0.05. Depressive symptoms did not differ by sex, t (203) = −0.69, p = 0.49.Adolescent females reported greater openness with their parents compared to adolescent males, t (199.59) = 3.53, p = 0.001, and t (204) = 2.28, p = 0.02, respectively. Younger adolescents also were rated to have higher relationship quality with parents, t (197) = 2.22, p = 0.03, and rated by parents to have higher levels of openness and prosocial behavior, t (202) = 2.38, p = 0.02, and t (202) = 2.24, p = 0.03, respectively. Younger adolescents reported marginally fewer depressive symptoms, t (203) = −1.86, p = 0.06. No age differences in ER or aggressive behavior were found. Descriptive statistics for all variables for the full sample and by age and sex can be found in Younger adolescents reported greater parental acceptance, and openness than older adolescents, 3.3.3.3.1.χ2 (7) = 7.05, p = 0.42; CFI = 1.00; RMSEA = 0.01. Analysis of the structural model fit the data very well, χ2 (19) = 32.15, p = 0.03; CFI = 0.97; RMSEA = 0.06. Supportive parent-adolescent relationships were positively associated with anger regulation, which in turn was positively associated with adolescent prosocial behavior and negatively associated with adolescent aggressive behavior and depressive symptoms  = 3.41, p = 0.84; CFI = 1.00; RMSEA = 0.00. Analysis of the structural model fit the data very well, χ2 (19) = 25.01, p = 0.16; CFI = 0.98; RMSEA = 0.04. Supportive parent-adolescent relationships were positively associated with sadness regulation, which in turn was positively associated with adolescent prosocial behavior and negatively associated with adolescent aggressive behavior but not significantly associated with depressive symptoms  = 26.01, p = 0.63; CFI = 1.00; RMSEA = 0.00 for anger regulation model, and χ2 (29) = 20.92, p = 0.86; CFI = 1.00; RMSEA = 0.00 for sadness regulation model.The multi-group measurement model was fit to the data first. The modification indices suggested that only the intercepts for parental acceptance and adolescent report of openness varied by sex, i.e., the intercept of parental acceptance was slightly higher among males, and the intercept of openness was higher among females. The final measurement model fit the data well, 3.4.1.χ2 (1) = 7.35, p < 0.01, with the model fit, χ2 (59) = 80.69, p = 0.03; CFI = 0.96; RMSEA = 0.06. Results suggested that the association between supportive parent-adolescent relationships and adolescent aggressive behavior was marginally significant for males and was not significant for females  = 8.45, p < 0.01. After releasing this link, the modification indices for the new model suggested that the link between supportive parent-adolescent relationships and sadness regulation should also be freely estimated, and the final model fit the data better, χ2 (58) = 60.13, p = 0.40; CFI = 1.00; RMSEA = 0.02, than the previous model, Δχ2 (1) = 6.50, p < 0.05. The results suggested the association between supportive parent-adolescent relationships and adolescent sadness regulation was significant for males but not for females, and the association between supportive parent-adolescent relationships and aggressive behavior was also significant for males but not for females. Bootstrapping results showed that the indirect effects of supportive parent-adolescent relationships on prosocial and aggressive behavior were only significant for males  = 28.79, p = 0.58; CFI = 1.00; RMSEA = 0.00 for anger regulation model, and χ2 (31) = 20.48, p = 0.93; CFI = 1.00; RMSEA = 0.00 for sadness regulation model. All the path coefficients in the structural model were constrained to be equal at first. The fully constrained models fit the data well, χ2 (62) = 67.28, p = 0.30; CFI = 0.99; RMSEA = 0.03 for anger model, and χ2 (62) = 50.71, p = 0.85; CFI = 1.00; RMSEA = 0.00 for sadness model. The modification indices did not show meaningful constraint releasing suggestions. Thus, there were no significant age differences among the links.The multi-group measurement models fit the data well, 4.via adolescent sadness and anger regulation) links between supportive parent-adolescent relationships and adolescent adjustment  using a predominantly low-income, single-parent, and ethnic minority sample. Results suggested that ER may serve as one mechanism underlying the association between supportive parent-adolescent relationships and adolescent adjustment outcomes. Specifically, as hypothesized, this study found the effects of supportive parent-adolescent relationships on adolescent depression, aggression, and prosocial behavior were indirect through adolescent ER. Further, we found direct links between supportive parent-adolescent relationships and adolescent prosocial behavior and aggression but not depression. The analyses also showed that adolescent sex (but not age) served as a moderator in some of the pathways.The current study extended the literature by examining direct and indirect  and agency (independence or self-determination) in children and adolescents  while al4.1.In support of the first hypothesis, results of the anger model revealed significant indirect effects, such that greater supportive parent-adolescent relationships were associated with greater adolescent anger regulation, which in turn was related to greater prosocial behaviors and less aggression and depressive symptoms. These findings are consistent with Studies examining adolescent anger regulation as a predictor of adolescent outcomes have demonstrated that high levels of anger regulation were significantly related to low levels of adolescent externalizing and internalizing behaviors . In the Building upon past research, our findings suggest that adolescent anger regulation is an important mechanism through which the latent construct of supportive parent-adolescent relationships is linked to adolescent prosocial behavior, aggression, and depressive symptoms. In relationships characterized by high levels of acceptance and emotional support, parents may be more likely to validate adolescent’s negative emotional expressions  and consider these opportunities for supportive emotion socialization practices, such as problem-solving and social support . In turnIn addition to these indirect effects, a significant direct effect was found between supportive parent-adolescent relationships and adolescent prosocial behaviors. This is consistent with past research that showed positive, balanced parent-adolescent interactions were positively related to adolescent prosocial behavior . Specifi4.2.Next, we examined whether the links between supportive parent-adolescent relationships and adolescent adjustment were mediated by sadness regulation. Results of the second model revealed significant indirect effects, such that supportive parent-adolescent relationships were positively and significantly associated with adolescent sadness regulation, which in turn was significantly related to prosocial behavior and aggression in expected directions. However, no indirect effect was found in the pathway to adolescent depressive symptoms. Interestingly, our findings do not support the aforementioned emotion specificity hypothesis, as we found anger regulation was associated with depressive symptoms and aggression, whereas sadness regulation was associated with aggression but not depressive symptoms. However, our findings are consistent with prior research suggesting anger regulation is related to both internalizing and externalizing symptoms . As mentSimilar to the first model, a significant direct effect was found between supportive parent-adolescent relationships and greater adolescent prosocial behavior. A significant direct effect was found between greater supportive parent-adolescent relationships and less adolescent aggression; however, this association only remained significant for males following moderation analyses. Overall, anger regulation appeared to play a larger role in the links between supportive parent-adolescent relationships and adjustment outcomes compared to sadness regulation. Perhaps in the context of a low-income sample, parents may focus their energy on facilitating anger regulation given the implications of ineffective anger regulation at home, in one’s neighborhood, or at school .4.3.To explore our second research question, we examined whether sex and age moderated the links between supportive parent-adolescent relationships and adolescent adjustment outcomes. In regard to the first model (anger regulation), we found the effect of sex resulted in no changes in the findings aside from a marginally significant direct effect between supportive parent-adolescent relationships and aggression for males compared to a nonsignificant effect for females. Past research suggests parent-adolescent support and openness is associated with less aggressive behaviors for both males and females , howeverIn regard to the second model (sadness regulation), after examining the influence of adolescent sex, the link between supportive parent-adolescent relationships and adolescent sadness regulation remained significant for adolescent males only. Further, the association between supportive parent-adolescent relationships and adolescent aggression remained significant for males only as well. Considering socialization pressures that discourage displays of sadness in boys compared to girls , it may 4.4.There were a number of strengths reflected in this study. The current study contributed to our understanding of the role of supportive parent-adolescent relationships and ER in adolescent adjustment. With a focus on the parent-adolescent relationship and the emotional exchanges within that relationship, we used an observational measure of relationship quality as an indicator of emotional responsiveness in the parent-adolescent relationship. The other indicator of support, openness in communication, as well as measures of adolescent prosocial behavior and externalizing symptoms were also based on both parent and adolescent reports. Because relationship models encompass the inherently dyadic nature of the parent-adolescent relationship, they are more effective than either just examining parent-driven or child-driven models . As suchDespite the mentioned strengths, one limitation of the investigation was the cross-sectional design which limited the ability to determine causality or examine change in adolescent adjustment. Future research would benefit from the use of longitudinal designs. It is possible and likely that a bidirectional relationship exists between variables. Adolescents with less depressive symptoms and aggressive behavior, or greater prosocial behaviors may be better at regulating their emotions, which in turn improves interactions with parents. Further, a reciprocal relationship may exist between adolescent adjustment variables and supportive parent-adolescent relationships indicators given our knowledge of the influence of adolescent psychopathology on parenting behaviors . While w4.5.This study highlights the importance of parent-adolescent relationships characterized by high levels of acceptance, openness, and emotional support as a foundation for supportive emotion socialization strategies in promoting positive adolescent outcomes in a sample of adolescents at-risk. Moreover, this study provides greater insight into the dyadic nature of supportive parent-adolescent relationships, which may have important implications for targeted prevention and intervention programs. Our findings extend the literature by increasing our understanding of the role of parent-adolescent relationships in which both members of the dyad engage in emotionally responsive, open communication and how these interactions may relate to both ER skills and adolescent outcomes in a low-income, ethnic minority sample. Relationship models represent more than the sum of parent-driven effects and child-driven effects and acknowledge the dyadic aspect of the parent–child relationship in which these skills develop. Taking these findings into consideration, parenting programs could focus on facilitating a mutually responsive parent-adolescent relationship with a specific focus on the dynamic nature of emotion socialization during adolescence.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Oklahoma State University Institutional Review Board. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.ER, AM, LC, JJ, JS, and MC contributed to the interpretation and application of results. AM, LC, and MC contributed conception and design of the study and data collection and management. AM, LC, JS, and MC contributed to data analysis. LC and ER wrote the first draft of the manuscript. All authors contributed to the article and approved the submitted version.This research was supported by a United States Department of Agriculture, Oklahoma Agriculture Experiment Station Project grant (AB-1-13921), the Oklahoma Center for the Advancement of Science and Technology (OCAST) grant , and a National Institute of Child Health and Human Development (AA-5-43382 and 1R15HD072463–01) R15 grant.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.