Preliminary Evidence for Anhedonia as a Marker of Sexual Trauma in Female Adolescents

Ayse Irem Sonmez,1,2 Charles P Lewis,1 Arjun P Athreya,3 Julia Shekunov,2 Paul E Croarkin2 1Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, MN, USA; 2Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA; 3Department of Molecular Pharmacology &Experimental Therapeutics, Mayo Clinic, Rochester, MN, USACorrespondence: Ayse Irem SonmezDepartment of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, 2312 S 6th St, Minneapolis, MN, 55454, USATel +1 612-273-8383Email [email protected]: Major depressive disorder (MDD) is a common condition with heterogeneous presentations that often include predominant anhedonia. Previous studies have revealed that childhood trauma is a potent risk factor for the development of MDD; however, the clinical implications of this finding are not fully understood.Methods: Participants were adolescents (age 13– 21 years) with a diagnosis of moderate-to-severe major depressive disorder and healthy controls. We used generalized linear models to assess the relationship between anhedonia severity and trauma severity in a cross-sectional dataset.Results: This cross-sectional analysis of an adolescent sample that underwent clinical evaluations and a trauma assessment, suggested that anhedonia was associated with historical trauma severity. The association between anhedonia and sexual abuse was greater in female participants compared to male participants.Discussion: Our results were partially in line with the reported literature in adult samples. Future studies aiming to characterize the trauma–anhedonia relationship in adolescents should utilize scales designed specifically to measure these constructs in young populations, and scales that assess specific subtypes of anhedonia.Keywords: adolescent, anhedonia, emotional abus

Model-based unsupervised learning informs metformin-induced cell-migration inhibition through an AMPK-independent mechanism in breast cancer

We demonstrate that model-based unsupervised learning can uniquely discriminate single-cell subpopulations by their gene expression distributions, which in turn allow us to identify specific genes for focused functional studies. This method was applied to MDA-MB-231 breast cancer cells treated with the antidiabetic drug metformin, which is being repurposed for treatment of triple-negative breast cancer. Unsupervised learning identified a cluster of metformin-treated cells characterized by a significant suppression of 230 genes (p-value < 2E-16). This analysis corroborates known studies of metformin action: a) pathway analysis indicated known mechanisms related to metformin action, including the citric acid (TCA) cycle, oxidative phosphorylation, and mitochondrial dysfunction (p-value < 1E-9); b) 70% of these 230 genes were functionally implicated in metformin response; c) among remaining lesser functionally-studied genes for metformin-response was CDC42, down-regulated in breast cancer treated with metformin. However, CDC42's mechanisms in metformin response remained unclear. Our functional studies showed that CDC42 was involved in metformin-induced inhibition of cell proliferation and cell migration mediated through an AMPK-independent mechanism. Our results points to 230 genes that might serve as metformin response signatures, which needs to be tested in patients treated with metformin and, further investigation of CDC42 and AMPK-independence's role in metformin's anticancer mechanisms

Model-based unsupervised learning informs metformin-induced cell-migration inhibition through an AMPK-independent mechanism in breast cancer

We demonstrate that model-based unsupervised learning can uniquely discriminate single-cell subpopulations by their gene expression distributions, which in turn allow us to identify specific genes for focused functional studies. This method was applied to MDA-MB-231 breast cancer cells treated with the antidiabetic drug metformin, which is being repurposed for treatment of triple-negative breast cancer. Unsupervised learning identified a cluster of metformin-treated cells characterized by a significant suppression of 230 genes (p-value < 2E-16). This analysis corroborates known studies of metformin action: a) pathway analysis indicated known mechanisms related to metformin action, including the citric acid (TCA) cycle, oxidative phosphorylation, and mitochondrial dysfunction (p-value < 1E-9); b) 70% of these 230 genes were functionally implicated in metformin response; c) among remaining lesser functionally-studied genes for metformin-response was CDC42, down-regulated in breast cancer treated with metformin. However, CDC42's mechanisms in metformin response remained unclear. Our functional studies showed that CDC42 was involved in metformin-induced inhibition of cell proliferation and cell migration mediated through an AMPK-independent mechanism. Our results points to 230 genes that might serve as metformin response signatures, which needs to be tested in patients treated with metformin and, further investigation of CDC42 and AMPK-independence's role in metformin's anticancer mechanisms

Towards Individualized Prediction of Response to Methotrexate in Early Rheumatoid Arthritis: a Pharmacogenomics-driven Machine Learning Approach

Objective To test the ability of machine learning (ML) approaches with clinical and genomic biomarkers to predict methotrexate treatment response in patients with early rheumatoid arthritis (RA). Methods Demographic, clinical and genomic data from 643 patients of European ancestry with early RA (mean age 54 years; 70% female) subdivided into a training (n=336) and validation cohort (n=307) were used. The genomic data comprised 160 single nucleotide polymorphisms (SNPs) previously associated with RA or methotrexate metabolism. Response to methotrexate monotherapy was defined as good or moderate by the European League Against Rheumatism (EULAR) response criteria at 3-month follow-up. Supervised ML methods were trained with 5-repeats and 10-fold cross-validation using the training cohort. Prediction performance was validated in the independent validation cohort. Results Supervised ML methods combining age, sex, smoking, rheumatoid factor, baseline Disease Activity Score with 28-joint count (DAS28) and 160 SNPs predicted EULAR response at 3 months with the area under the receiver operating curve of 0.84 (p=0.05) in the training cohort and achieved a prediction accuracy of 76% (p=0.05) in the validation cohort (sensitivity 72%, specificity 77%). Intergenic SNPs rs12446816, rs13385025, rs113798271, and ATIC (rs2372536) had variable importance above 60.0 and along with baseline DAS28 were among the top predictors of methotrexate response. Conclusion Pharmacogenomic biomarkers combined with baseline DAS28 can be useful in predicting response to methotrexate in patients with early RA. Applying ML to predict treatment response holds promise for guiding effective RA treatment choices, including timely escalation of RA therapies

Systemic sclerosis in childhood - Clinical and immunologic features of 153 patients in an international database

Objective. To determine the clinical and immunologic features of systemic sclerosis (SSc) in a large group of children and describe the clinical evolution of the disease and compare it with the adult form. Methods. Data on 153 patients with juvenile SSc collected from 55 pediatric rheumatology centers in Europe, Asia, and South and North America were analyzed. Demographic, clinical, and immunologic characteristics of children with juvenile SSc at the onset, at diagnosis, and during the disease course were evaluated. Results. Raynaud's phenomenon was the most frequent symptom, followed by skin induration in similar to 75% of patients. Musculoskeletal symptoms were present in one-third of patients, and the most frequently involved internal organs were respiratory and gastrointestinal, while involvement of renal, cerebral, and cardiovascular systems was extremely rare. Antinuclear antibodies were present in the sera of 81% of patients. Antitopoisomerase I (Scl-70) and anticentromere antibodies were found to be positive in 34% and 7.1% of patients, respectively. Involvement of the respiratory, gastrointestinal, and cardiovascular systems was more frequent and occurred earlier in patients who died than in those who survived. Compared with the adult form, juvenile SSc appears to be less s evere, with the involvement of fewer internal organs, particularly at the time of diagnosis, and has a less characterized immunologic profile. Conclusion. This study provides information on the largest collection of patients with juvenile SSc ever reported. Juvenile SSc appears to be less severe than in adults because children have less internal organ involvement, a less specific autoantibody profile, and a better long-term outcome