Exploring the role of neuronal-enriched extracellular vesicle miR-93 and interoception in major depressive disorder.

Major depressive disorder (MDD) is associated with interoceptive processing dysfunctions, but the molecular mechanisms underlying this dysfunction are poorly understood. This study combined brain neuronal-enriched extracellular vesicle (NEEV) technology and serum markers of inflammation and metabolism with Functional Magnetic Resonance Imaging (fMRI) to identify the contribution of gene regulatory pathways, in particular micro-RNA (miR) 93, to interoceptive dysfunction in MDD. Individuals with MDD (n = 41) and healthy comparisons (HC; n = 35) provided blood samples and completed an interoceptive attention task during fMRI. EVs were separated from plasma using a precipitation method. NEEVs were enriched by magnetic streptavidin bead immunocapture utilizing a neural adhesion marker (L1CAM/CD171) biotinylated antibody. The origin of NEEVs was validated with two other neuronal markers - neuronal cell adhesion molecule (NCAM) and ATPase Na+/K+ transporting subunit alpha 3 (ATP1A3). NEEV specificities were confirmed by flow cytometry, western blot, particle size analyzer, and transmission electron microscopy. NEEV small RNAs were purified and sequenced. Results showed that: (1) MDD exhibited lower NEEV miR-93 expression than HC; (2) within MDD but not HC, those individuals with the lowest NEEV miR-93 expression had the highest serum concentrations of interleukin (IL)-1 receptor antagonist, IL-6, tumor necrosis factor, and leptin; and (3) within HC but not MDD, those participants with the highest miR-93 expression showed the strongest bilateral dorsal mid-insula activation during interoceptive versus exteroceptive attention. Since miR-93 is regulated by stress and affects epigenetic modulation by chromatin re-organization, these results suggest that healthy individuals but not MDD participants show an adaptive epigenetic regulation of insular function during interoceptive processing. Future investigations will need to delineate how specific internal and external environmental conditions contribute to miR-93 expression in MDD and what molecular mechanisms alter brain responsivity to body-relevant signals.

Exploring the role of neuronal-enriched extracellular vesicle miR-93 and interoception in major depressive disorder.

Major depressive disorder (MDD) is associated with interoceptive processing dysfunctions, but the molecular mechanisms underlying this dysfunction are poorly understood. This study combined brain Neuronal-Enriched Extracellular Vesicle (NEEV) technology and serum markers of inflammation and metabolism with Functional Magnetic Resonance Imaging (fMRI) to identify the contribution of gene regulatory pathways, in particular micro-RNA (miR) 93, to interoceptive dysfunction in MDD. Individuals with MDD (n = 44) and healthy comparisons (HC; n = 35) provided blood samples and completed an interoceptive attention task during fMRI. EVs were separated from plasma using a precipitation method. NEEVs were enriched by magnetic streptavidin bead immunocapture utilizing a neural adhesion marker (CD171) biotinylated antibody. NEEV specificities were confirmed by ow cytometry, western blot, particle size analyzer, and transmission electron microscopy. NEEV small RNAs were purified and sequenced. Results showed that: (1) MDD exhibited lower NEEV miR-93 expression than HC; (2) within MDD but not HC, those individuals with the lowest NEEV miR-93 expression had the highest serum concentrations of interleukin (IL)-1 receptor antagonist, IL-6, tumor necrosis factor, and leptin; and (3) within HC but not MDD, those participants with the highest miR-93 expression showed the strongest bilateral dorsal mid-insula activation. Since miR-93 is regulated by stress and affects epigenetic modulation by chromatin reorganization, these results suggest that healthy individuals but not MDD participants show an adaptive epigenetic regulation of insular function during interoceptive processing. Future investigations will need to delineate how specific internal and external environmental conditions contribute to miR-93 expression in MDD and what molecular mechanisms alter brain responsivity to body-relevant signals.

Differential expression of MDGA1 in major depressive disorder.

The identification of gene expression-based biomarkers for major depressive disorder (MDD) continues to be an important challenge. In order to identify candidate biomarkers and mechanisms, we apply statistical and machine learning feature selection to an RNA-Seq gene expression dataset of 78 unmedicated individuals with MDD and 79 healthy controls. We identify 49 genes by LASSO penalized logistic regression and 45 genes at the false discovery rate threshold 0.188. The MDGA1 gene has the lowest P-value (4.9e-5) and is expressed in the developing brain, involved in axon guidance, and associated with related mood disorders in previous studies of bipolar disorder (BD) and schizophrenia (SCZ). The expression of MDGA1 is associated with age and sex, but its association with MDD remains significant when adjusted for covariates. MDGA1 is in a co-expression cluster with another top gene, ATXN7L2 (ataxin 7 like 2), which was associated with MDD in a recent GWAS. The LASSO classification model of MDD includes MDGA1, and the model has a cross-validation accuracy of 79%. Another noteworthy top gene, IRF2BPL, is in a close co-expression cluster with MDGA1 and may be related to microglial inflammatory states in MDD. Future exploration of MDGA1 and its gene interactions may provide insights into mechanisms and heterogeneity of MDD.

Design and methods of a tailored approach for diabetes prevention in women with previous gestational diabetes.

Aim: To describe the design and methods of an intervention that engaged women with previous gestational diabetes mellitus in a tailored approach for diabetes prevention. Methods: Women participated in biometric tests for BMI and hemoglobin A1c, psychosocial questionnaires and an informed decision-making process to select a lifestyle change program for Type 2 diabetes prevention based on their needs and priorities. Measure time points were at baseline, 6 months and 12 months. Results: The authors recruited 116 women. The outcomes of this study will evaluate the effect of this strategy on participant engagement in lifestyle change programs for Type 2 diabetes prevention. Conclusion: This paper describes a variety of lifestyle change programs and an informed decision-making process for tailoring diabetes prevention programs for a high-risk population.

"We are a family with diabetes": Parent perspectives on siblings of youth with type 1 diabetes.

Having a child with type 1 diabetes (T1D) impacts the entire family system. Parental distress and burden have been well studied, but other family members, including siblings, have received little attention. Based on research about family life and sibling experiences in other chronic condition populations (e.g., autism, cancer), we expected parents of youth with T1D would report that siblings participated in T1D management and that T1D had a psychological impact on siblings. As part of a larger qualitative study, parents of youth with T1D age 5-17 (M = 10.8 ± 3.6 years) participated in semistructured interviews about T1D-specific health-related quality of life. For this study, we conducted secondary analyses on transcripts from 20 parents (95% mothers) from households with at least 1 sibling of the child with T1D. Three themes emerged: (a) siblings share the workload and help with T1D management, (b) T1D takes an emotional toll on siblings, and (c) parents feel guilty about prioritizing T1D over siblings' needs and desires. Parents recognized siblings have impactful roles in T1D management and family functioning. Future research into these themes can guide clinical and research efforts to develop sibling-inclusive resources and interventions for families with T1D. Enhancing family-focused interventions to recognize and support the needs of siblings may ultimately improve family T1D-related quality of life. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Common Data Elements, Scalable Data Management Infrastructure, and Analytics Workflows for Large-Scale Neuroimaging Studies.

Neuroscience studies require considerable bioinformatic support and expertise. Numerous high-dimensional and multimodal datasets must be preprocessed and integrated to create robust and reproducible analysis pipelines. We describe a common data elements and scalable data management infrastructure that allows multiple analytics workflows to facilitate preprocessing, analysis and sharing of large-scale multi-level data. The process uses the Brain Imaging Data Structure (BIDS) format and supports MRI, fMRI, EEG, clinical, and laboratory data. The infrastructure provides support for other datasets such as Fitbit and flexibility for developers to customize the integration of new types of data. Exemplar results from 200+ participants and 11 different pipelines demonstrate the utility of the infrastructure.

Theoretical properties of distance distributions and novel metrics for nearest-neighbor feature selection.

The performance of nearest-neighbor feature selection and prediction methods depends on the metric for computing neighborhoods and the distribution properties of the underlying data. Recent work to improve nearest-neighbor feature selection algorithms has focused on new neighborhood estimation methods and distance metrics. However, little attention has been given to the distributional properties of pairwise distances as a function of the metric or data type. Thus, we derive general analytical expressions for the mean and variance of pairwise distances for Lq metrics for normal and uniform random data with p attributes and m instances. The distribution moment formulas and detailed derivations provide a resource for understanding the distance properties for metrics and data types commonly used with nearest-neighbor methods, and the derivations provide the starting point for the following novel results. We use extreme value theory to derive the mean and variance for metrics that are normalized by the range of each attribute (difference of max and min). We derive analytical formulas for a new metric for genetic variants, which are categorical variables that occur in genome-wide association studies (GWAS). The genetic distance distributions account for minor allele frequency and the transition/transversion ratio. We introduce a new metric for resting-state functional MRI data (rs-fMRI) and derive its distance distribution properties. This metric is applicable to correlation-based predictors derived from time-series data. The analytical means and variances are in strong agreement with simulation results. We also use simulations to explore the sensitivity of the expected means and variances in the presence of correlation and interactions in the data. These analytical results and new metrics can be used to inform the optimization of nearest neighbor methods for a broad range of studies, including gene expression, GWAS, and fMRI data.

Machine Learning Analysis of Electronic Nose in a Transdiagnostic Community Sample With a Streamlined Data Collection Approach: No Links Between Volatile Organic Compounds and Psychiatric Symptoms.

Non-intrusive, easy-to-use and pragmatic collection of biological processes is warranted to evaluate potential biomarkers of psychiatric symptoms. Prior work with relatively modest sample sizes suggests that under highly-controlled sampling conditions, volatile organic compounds extracted from the human breath (exhalome), often measured by an electronic nose ("e-nose"), may be related to physical and mental health. The present study utilized a streamlined data collection approach and attempted to replicate and extend prior e-nose links to mental health in a standard research setting within large transdiagnostic community dataset (N = 1207; 746 females; 18-61 years) who completed a screening visit at the Laureate Institute for Brain Research between 07/2016 and 05/2018. Factor analysis was used to obtain latent exhalome variables, and machine learning approaches were employed using these latent variables to predict three types of symptoms independent of each other (depression, anxiety, and substance use disorder) within separate training and a test sets. After adjusting for age, gender, body mass index, and smoking status, the best fitting algorithm produced by the training set accounted for nearly 0% of the test set's variance. In each case the standard error included the zero line, indicating that models were not predictive of clinical symptoms. Although some sample variance was predicted, findings did not generalize to out-of-sample data. Based on these findings, we conclude that the exhalome, as measured by the e-nose within a less-controlled environment than previously reported, is not able to provide clinically useful assessments of current depression, anxiety or substance use severity.

Correction: Identification and replication of RNA-Seq gene network modules associated with depression severity.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Intervention Delivery Matters: What Mothers at High Risk for Type 2 Diabetes Want in a Diabetes Prevention Program-Results from a Comparative Effectiveness Trial.

Participants in the ENCOURAGE Healthy Families Study, a family-focused, modified Diabetes Prevention Program, reported challenges to and preferences for engaging in a diabetes prevention program. Challenges with flexible intervention delivery, accessibility, the traditional group-based format, and Coronavirus Disease 2019 (COVID-19) exposure risk can be mitigated by participant preferences for one-on-one, virtual/online intervention delivery.Trial Registration: ClinicalTrials.gov identifier, NCT01823367.

Nearest-Neighbor Projected Distance Regression for Epistasis Detection in GWAS With Population Structure Correction.

Nearest-neighbor Projected-Distance Regression (NPDR) is a feature selection technique that uses nearest-neighbors in high dimensional data to detect complex multivariate effects including epistasis. NPDR uses a regression formalism that allows statistical significance testing and efficient control for multiple testing. In addition, the regression formalism provides a mechanism for NPDR to adjust for population structure, which we apply to a GWAS of systemic lupus erythematosus (SLE). We also test NPDR on benchmark simulated genetic variant data with epistatic effects, main effects, imbalanced data for case-control design and continuous outcomes. NPDR identifies potential interactions in an epistasis network that influences the SLE disorder.

Evaluating the resource allocation index as a potential fMRI-based biomarker for substance use disorder.

BACKGROUND: There is a lack of neuroscience-based biomarkers for the diagnosis, treatment and monitoring of individuals with substance use disorders (SUD). The resource allocation index (RAI), a measure of the interrelationship between salience, executive control and default-mode brain networks (SN, ECN, and DMN), has been proposed as one such biomarker. However, the RAI has yet to be extensively tested in SUD samples. METHODS: The present analysis compared RAI scores between individuals with stimulant and/or opioid use disorders (SUD; n = 139, abstinent 4-365 days) and healthy controls (HC; n = 56) who had completed resting-state functional magnetic resonance imaging (fMRI) scans within the context of the Tulsa 1000 cohort. First, we used independent component analysis (ICA) to identify the SN, ECN, and DMN and extract their time series data. Second, we used multiple permutations of automatically identified networks to compute RAI as reported in the fMRI literature. RESULTS: First, the RAI as a metric depended substantially on the approach that was used to define the network components. Second, regardless of the selection of networks, after controlling for multiple testing there was no difference in RAI scores between SUD and HC. Third, the RAI was not associated with any substance use-related self-report measures. CONCLUSION: Taken together, these findings do not provide evidence that RAI can be used as an fMRI-derived biomarker for the severity or diagnosis of individuals with SUD.

Short-Term Change in Measures of Glycemia in Obese Youth Meeting Criteria for Prediabetes: A Retrospective Chart Review.

BACKGROUND: The prevalence of youth diagnosed with prediabetes is increasing, yet there is a lack of guidelines on how to manage this condition clinically. OBJECTIVES: The aim was to determine the short-term outcomes of patients referred with prediabetes and to determine predictors of worsening dysglycemia in youth. STUDY DESIGN: This is a retrospective chart review of patients referred to our Youth Diabetes Prevention Clinic (YDPC) with laboratory tests indicating an increased risk for type 2 diabetes (T2D). We defined glycemic categories by HbA1c with normoglycemia as HbA1c <5.7%, prediabetes I (P1) as HbA1c 5.7 to <6.0%, and prediabetes II (P2) as HbA1c 6.0 to <6.5%. We compared HbA1c at the time of referral (screening HbA1c) and at the YDPC visit (YDPC HbA1c) to assess for improvement or worsening. Multinomial logistic regression was used to assess predictors of prediabetes. RESULTS: Among 562 patients seen, 336 had both screening and YDPC HbA1c values. Race (p < 0.001) and screening glycemic category (p < 0.001) were significantly associated with dysglycemia at the YDPC visit, while sex (p = 0.50), BMI z-score change (p = 0.27), and days from referral (p = 0.83) were not. As compared to those who reverted to normoglycemia, patients with prediabetes at YDPC were 7 times more likely to have a higher screening HbA1c (both P1 and P2). The majority of patients referred with prediabetes had lower HbA1c at the YDPC (75.4-82.6%). CONCLUSION: Patients with screening HbA1c <6% might benefit from a 4-month follow-up at primary care while recommending lifestyle changes. Patients of minority race and screening HbA1c ≥6% are more likely to have a persistent elevation of HbA1c.

Forever-Fit Summer Camp: The Impact of a 6-Week Summer Healthy Lifestyle Day Camp on Anthropometric, Cardiovascular, and Physical Fitness Measures in Youth With Obesity.

Pediatric obesity is a public health concern with lifestyle intervention as the first-line treatment. Forever-Fit Summer Camp (FFSC) is a 6-week summer day program offering physical activity, nutrition education, and well-balanced meals to youth at low cost. The aim of the study was to assess the efficacy of this program that does not emphasize weight loss rather emphasizes healthy behaviors on body mass index, cardiovascular and physical fitness. Methods: The inclusion criteria were adolescents between 8 and 12 years and body mass index (BMI) ≥85th percentile. The data were collected at baseline and week 6 (wk-6) and was analyzed for 2013-2018 using paired-sample t tests. Results: The participants' (N = 179) average age was 10.6 ± 1.6 years with a majority of females (71%) and black race/ethnicity (70%). At wk-6, BMI and waist circumference decreased by 0.8 ± 0.7 kg/m2 and 1.0 ± 1.3 in, respectively. Resting heart rate, diastolic and systolic blood pressure decreased by 8.5 ± 11.0 bpm, 6.3 ± 8.8 mmHg, and 6.4 ± 10.1 mmHg, respectively. The number of pushups, curl-ups, and chair squats were higher by 5.8 ± 7.5, 6.7 ± 9.1, and 7.7 ± 8.5, respectively. Conclusion: The FFSC is efficacious for improving BMI, cardiovascular, and physical fitness in the short term. The effect of similar episodic efforts that implement healthy lifestyle modifications throughout the school year should be investigated.

Correction: Tissue-Specific Expressed Antibody Variable Gene Repertoires.

[This corrects the article DOI: 10.1371/journal.pone.0100839.].

Nearest-neighbor Projected-Distance Regression (NPDR) for detecting network interactions with adjustments for multiple tests and confounding.

SUMMARY: Machine learning feature selection methods are needed to detect complex interaction-network effects in complicated modeling scenarios in high-dimensional data, such as GWAS, gene expression, eQTL and structural/functional neuroimage studies for case-control or continuous outcomes. In addition, many machine learning methods have limited ability to address the issues of controlling false discoveries and adjusting for covariates. To address these challenges, we develop a new feature selection technique called Nearest-neighbor Projected-Distance Regression (NPDR) that calculates the importance of each predictor using generalized linear model regression of distances between nearest-neighbor pairs projected onto the predictor dimension. NPDR captures the underlying interaction structure of data using nearest-neighbors in high dimensions, handles both dichotomous and continuous outcomes and predictor data types, statistically corrects for covariates, and permits statistical inference and penalized regression. We use realistic simulations with interactions and other effects to show that NPDR has better precision-recall than standard Relief-based feature selection and random forest importance, with the additional benefit of covariate adjustment and multiple testing correction. Using RNA-Seq data from a study of major depressive disorder (MDD), we show that NPDR with covariate adjustment removes spurious associations due to confounding. We apply NPDR to eQTL data to identify potentially interacting variants that regulate transcripts associated with MDD and demonstrate NPDR's utility for GWAS and continuous outcomes. AVAILABILITY AND IMPLEMENTATION: Available at: https://insilico.github.io/npdr/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Consensus features nested cross-validation.

SUMMARY: Feature selection can improve the accuracy of machine-learning models, but appropriate steps must be taken to avoid overfitting. Nested cross-validation (nCV) is a common approach that chooses the classification model and features to represent a given outer fold based on features that give the maximum inner-fold accuracy. Differential privacy is a related technique to avoid overfitting that uses a privacy-preserving noise mechanism to identify features that are stable between training and holdout sets.We develop consensus nested cross-validation (cnCV) that combines the idea of feature stability from differential privacy with nCV. Feature selection is applied in each inner fold and the consensus of top features across folds is used as a measure of feature stability or reliability instead of classification accuracy, which is used in standard nCV. We use simulated data with main effects, correlation and interactions to compare the classification accuracy and feature selection performance of the new cnCV with standard nCV, Elastic Net optimized by cross-validation, differential privacy and private evaporative cooling (pEC). We also compare these methods using real RNA-seq data from a study of major depressive disorder.The cnCV method has similar training and validation accuracy to nCV, but cnCV has much shorter run times because it does not construct classifiers in the inner folds. The cnCV method chooses a more parsimonious set of features with fewer false positives than nCV. The cnCV method has similar accuracy to pEC and cnCV selects stable features between folds without the need to specify a privacy threshold. We show that cnCV is an effective and efficient approach for combining feature selection with classification. AVAILABILITY AND IMPLEMENTATION: Code available at https://github.com/insilico/cncv. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Diabetes disclosure strategies in adolescents and young adult with type 1 diabetes.

OBJECTIVE: Adolescence and young adulthood have social and developmental challenges that can impact type 1 diabetes (T1D) management. New relationships (e.g. friends, schoolmates, dating partners, teachers, employers) introduce opportunities for disclosure of T1D status. Characterizing how adolescents and young adults (AYAs) disclose having T1D to others may help inform clinical strategies to help AYAs ensure their safety by obtaining social support. METHODS: As part of a study about diabetes health-related quality of life across the lifespan, transcriptions of semi-structured qualitative interviews with AYAs with T1D (n = 16, age 12-25 years, mean age 18.7 ±â€¯4.9, 38% female) were coded to derive themes related to T1D disclosure. RESULTS: Participants described three disclosure strategies: (1) Open Disclosure: shares T1D status in straightforward, direct manner and readily requests diabetes-related support; (2) Disclosure Hesitancy: reluctant to tell others about or actively hides having T1D; (3) Passive Disclosure: discloses T1D via other people (e.g., parents) or through others' observation of T1D management tasks. CONCLUSION: AYAs may benefit from guidance in approaches to informing others about having T1D in different contexts. Identifying individuals' use of these strategies can inform education and intervention strategies aimed at engaging AYAs in healthy T1D-related disclosure to seek and receive support.

A Humanized Yeast Phenomic Model of Deoxycytidine Kinase to Predict Genetic Buffering of Nucleoside Analog Cytotoxicity.

Knowledge about synthetic lethality can be applied to enhance the efficacy of anticancer therapies in individual patients harboring genetic alterations in their cancer that specifically render it vulnerable. We investigated the potential for high-resolution phenomic analysis in yeast to predict such genetic vulnerabilities by systematic, comprehensive, and quantitative assessment of drug-gene interaction for gemcitabine and cytarabine, substrates of deoxycytidine kinase that have similar molecular structures yet distinct antitumor efficacy. Human deoxycytidine kinase (dCK) was conditionally expressed in the Saccharomycescerevisiae genomic library of knockout and knockdown (YKO/KD) strains, to globally and quantitatively characterize differential drug-gene interaction for gemcitabine and cytarabine. Pathway enrichment analysis revealed that autophagy, histone modification, chromatin remodeling, and apoptosis-related processes influence gemcitabine specifically, while drug-gene interaction specific to cytarabine was less enriched in gene ontology. Processes having influence over both drugs were DNA repair and integrity checkpoints and vesicle transport and fusion. Non-gene ontology (GO)-enriched genes were also informative. Yeast phenomic and cancer cell line pharmacogenomics data were integrated to identify yeast-human homologs with correlated differential gene expression and drug efficacy, thus providing a unique resource to predict whether differential gene expression observed in cancer genetic profiles are causal in tumor-specific responses to cytotoxic agents.

Correction: Dynamical Hurst analysis identifies EEG channel differences between PTSD and healthy controls.

[This corrects the article DOI: 10.1371/journal.pone.0199144.].