Comprehensive characterization of pathogenic synovial fluid extracellular vesicles from knee osteoarthritis.

Synovial fluid (SF) extracellular vesicles (EVs) play a pathogenic role in osteoarthritis (OA). However, the surface markers, cell and tissue origins, and effectors of these EVs are largely unknown. We found that SF EVs contained 692 peptides that were positively associated with knee radiographic OA severity; 57.4% of these pathogenic peptides were from 46 proteins of the immune system, predominantly the innate immune system. CSPG4, BGN, NRP1, and CD109 are the major surface markers of pathogenic SF EVs. Genes encoding surface marker CSPG4 and CD109 were highly expressed by chondrocytes from damaged cartilage, while VISG4, MARCO, CD163 and NRP1 were enriched in the synovial immune cells. The frequency of CSPG4+ and VSIG4+ EV subpopulations in OA SF was high. We conclude that pathogenic SF EVs carry knee OA severity-associated proteins and specific surface markers, which could be developed as a new source of diagnostic biomarkers or therapeutic targets in OA.

Need for Emergent Intervention within 6 Hours: A Novel Prediction Model for Hospital Trauma Triage.

Objective: A tiered trauma team activation system allocates resources proportional to patients' needs based upon injury burden. Previous trauma hospital-triage models are limited to predicting Injury Severity Score which is based on > 10% all-cause in-hospital mortality, rather than need for emergent intervention within 6 hours (NEI-6). Our aim was to develop a novel prediction model for hospital-triage that utilizes criteria available to the EMS provider to predict NEI-6 and the need for a trauma team activation.Methods: A regional trauma quality collaborative was used to identify all trauma patients ≥ 16 years from the American College of Surgeons-Committee on Trauma verified Level 1 and 2 trauma centers. Logistic regression and random forest were used to construct two predictive models for NEI-6 based on clinically relevant variables. Restricted cubic splines were used to model nonlinear predictors. The accuracy of the prediction model was assessed in terms of discrimination.Results: Using data from 12,624 patients for the training dataset (62.6% male; median age 61 years; median ISS 9) and 9,445 patients for the validation dataset (62.6% male; median age 59 years; median ISS 9), the following significant predictors were selected for the prediction models: age, gender, field GCS, vital signs, intentionality, and mechanism of injury. The final boosted tree model showed an AUC of 0.85 in the validation cohort for predicting NEI-6.Conclusions: The NEI-6 trauma triage prediction model used prehospital metrics to predict need for highest level of trauma activation. Prehospital prediction of major trauma may reduce undertriage mortality and improve resource utilization.

Bariatric Surgery is Associated With Decreased Progression of Nonalcoholic Fatty Liver Disease to Cirrhosis: A Retrospective Cohort Analysis.

OBJECTIVE: This study sought to compare trends in the development of cirrhosis between patients with NAFLD who underwent bariatric surgery and a well-matched group of nonsurgical controls. SUMMARY OF BACKGROUND DATA: Patients with NAFLD who undergo bariatric surgery generally have improvements in liver histology. However, the long-term effect of bariatric surgery on clinically relevant liver outcomes has not been investigated. METHODS: From a large insurance database, patients with a new NAFLD diagnosis and at least 2 years of continuous enrollment before and after diagnosis were identified. Patients with traditional contraindications to bariatric surgery were excluded. Patients who underwent bariatric surgery were identified and matched 1:2 with patients who did not undergo bariatric surgery based on age, sex, and comorbid conditions. Kaplan-Meier analysis and Cox proportional hazards modeling were used to evaluate differences in progression from NAFLD to cirrhosis. RESULTS: A total of 2942 NAFLD patients who underwent bariatric surgery were identified and matched with 5884 NAFLD patients who did not undergo surgery. Cox proportional hazards modeling found that bariatric surgery was independently associated with a decreased risk of developing cirrhosis (hazard ratio 0.31, 95% confidence interval 0.19-0.52). Male gender was associated with an increased risk of cirrhosis (hazard ratio 2.07, 95% confidence interval 1.31-3.27). CONCLUSIONS: Patients with NAFLD who undergo bariatric surgery are at a decreased risk for progression to cirrhosis compared to well-matched controls. Bariatric surgery should be considered as a treatment strategy for otherwise eligible patients with NAFLD. Future bariatric surgery guidelines should include NAFLD as a comorbid indication when determining eligibility.

Machine learning methods to predict child posttraumatic stress: a proof of concept study.

BACKGROUND: The care of traumatized children would benefit significantly from accurate predictive models for Posttraumatic Stress Disorder (PTSD), using information available around the time of trauma. Machine Learning (ML) computational methods have yielded strong results in recent applications across many diseases and data types, yet they have not been previously applied to childhood PTSD. Since these methods have not been applied to this complex and debilitating disorder, there is a great deal that remains to be learned about their application. The first step is to prove the concept: Can ML methods - as applied in other fields - produce predictive classification models for childhood PTSD? Additionally, we seek to determine if specific variables can be identified - from the aforementioned predictive classification models - with putative causal relations to PTSD. METHODS: ML predictive classification methods - with causal discovery feature selection - were applied to a data set of 163 children hospitalized with an injury and PTSD was determined three months after hospital discharge. At the time of hospitalization, 105 risk factor variables were collected spanning a range of biopsychosocial domains. RESULTS: Seven percent of subjects had a high level of PTSD symptoms. A predictive classification model was discovered with significant predictive accuracy. A predictive model constructed based on subsets of potentially causally relevant features achieves similar predictivity compared to the best predictive model constructed with all variables. Causal Discovery feature selection methods identified 58 variables of which 10 were identified as most stable. CONCLUSIONS: In this first proof-of-concept application of ML methods to predict childhood Posttraumatic Stress we were able to determine both predictive classification models for childhood PTSD and identify several causal variables. This set of techniques has great potential for enhancing the methodological toolkit in the field and future studies should seek to replicate, refine, and extend the results produced in this study.

Immune system-related plasma extracellular vesicles in healthy aging.

Objectives: To identify age-related plasma extracellular vehicle (EVs) phenotypes in healthy adults. Methods: EV proteomics by high-resolution mass spectrometry to evaluate EV protein stability and discover age-associated EV proteins (n=4 with 4 serial freeze-thaws each); validation by high-resolution flow cytometry and EV cytokine quantification by multiplex ELISA (n=28 healthy donors, aged 18-83 years); quantification of WI-38 fibroblast cell proliferation response to co-culture with PKH67-labeled young and old plasma EVs. The EV samples from these plasma specimens were previously characterized for bilayer structure, intra-vesicle mitochondria and cytokines, and hematopoietic cell-related surface markers. Results: Compared with matched exo-EVs (EV-depleted supernatants), endo-EVs (EV-associated) had higher mean TNF-α and IL-27, lower mean IL-6, IL-11, IFN-γ, and IL-17A/F, and similar mean IL-1ß, IL-21, and IL-22 concentrations. Some endo-EV and exo-EV cytokine concentrations were correlated, including TNF-α, IL-27, IL-6, IL-1ß, and IFN-γ, but not IL-11, IL-17A/F, IL-21 or IL-22. Endo-EV IFN-γ and exo-EV IL-17A/F and IL-21 declined with age. By proteomics and confirmed by flow cytometry, we identified age-associated decline of fibrinogen (FGA, FGB and FGG) in EVs. Age-related EV proteins indicated predominant origins in the liver and innate immune system. WI-38 cells (>95%) internalized similar amounts of young and old plasma EVs, but cells that internalized PKH67-EVs, particularly young EVs, underwent significantly greater cell proliferation. Conclusion: Endo-EV and exo-EV cytokines function as different biomarkers. The observed healthy aging EV phenotype reflected a downregulation of EV fibrinogen subpopulations consistent with the absence of a pro-coagulant and pro-inflammatory condition common with age-related disease.

New methods for separating causes from effects in genomics data.

BACKGROUND: The discovery of molecular pathways is a challenging problem and its solution relies on the identification of causal molecular interactions in genomics data. Causal molecular interactions can be discovered using randomized experiments; however such experiments are often costly, infeasible, or unethical. Fortunately, algorithms that infer causal interactions from observational data have been in development for decades, predominantly in the quantitative sciences, and many of them have recently been applied to genomics data. While these algorithms can infer unoriented causal interactions between involved molecular variables (i.e., without specifying which one is the cause and which one is the effect), causally orienting all inferred molecular interactions was assumed to be an unsolvable problem until recently. In this work, we use transcription factor-target gene regulatory interactions in three different organisms to evaluate a new family of methods that, given observational data for just two causally related variables, can determine which one is the cause and which one is the effect. RESULTS: We have found that a particular family of causal orientation methods (IGCI Gaussian) is often able to accurately infer directionality of causal interactions, and that these methods usually outperform other causal orientation techniques. We also introduced a novel ensemble technique for causal orientation that combines decisions of individual causal orientation methods. The ensemble method was found to be more accurate than any best individual causal orientation method in the tested data. CONCLUSIONS: This work represents a first step towards establishing context for practical use of causal orientation methods in the genomics domain. We have found that some causal orientation methodologies yield accurate predictions of causal orientation in genomics data, and we have improved on this capability with a novel ensemble method. Our results suggest that these methods have the potential to facilitate reconstruction of molecular pathways by minimizing the number of required randomized experiments to find causal directionality and by avoiding experiments that are infeasible and/or unethical.

A comprehensive assessment of methods for de-novo reverse-engineering of genome-scale regulatory networks.

De-novo reverse-engineering of genome-scale regulatory networks is an increasingly important objective for biological and translational research. While many methods have been recently developed for this task, their absolute and relative performance remains poorly understood. The present study conducts a rigorous performance assessment of 32 computational methods/variants for de-novo reverse-engineering of genome-scale regulatory networks by benchmarking these methods in 15 high-quality datasets and gold-standards of experimentally verified mechanistic knowledge. The results of this study show that some methods need to be substantially improved upon, while others should be used routinely. Our results also demonstrate that several univariate methods provide a "gatekeeper" performance threshold that should be applied when method developers assess the performance of their novel multivariate algorithms. Finally, the results of this study can be used to show practical utility and to establish guidelines for everyday use of reverse-engineering algorithms, aiming towards creation of automated data-analysis protocols and software systems.

Prediction of clinical trial enrollment rates.

Clinical trials represent a critical milestone of translational and clinical sciences. However, poor recruitment to clinical trials has been a long standing problem affecting institutions all over the world. One way to reduce the cost incurred by insufficient enrollment is to minimize initiating trials that are most likely to fall short of their enrollment goal. Hence, the ability to predict which proposed trials will meet enrollment goals prior to the start of the trial is highly beneficial. In the current study, we leveraged a data set extracted from ClinicalTrials.gov that consists of 46,724 U.S. based clinical trials from 1990 to 2020. We constructed 4,636 candidate predictors based on data collected by ClinicalTrials.gov and external sources for enrollment rate prediction using various state-of-the-art machine learning methods. Taking advantage of a nested time series cross-validation design, our models resulted in good predictive performance that is generalizable to future data and stable over time. Moreover, information content analysis revealed the study design related features to be the most informative feature type regarding enrollment. Compared to the performance of models built with all features, the performance of models built with study design related features is only marginally worse (AUC = 0.78 ± 0.03 vs. AUC = 0.76 ± 0.02). The results presented can form the basis for data-driven decision support systems to assess whether proposed clinical trials would likely meet their enrollment goal.

Mental health progress requires causal diagnostic nosology and scalable causal discovery.

Nine hundred and seventy million individuals across the globe are estimated to carry the burden of a mental disorder. Limited progress has been achieved in alleviating this burden over decades of effort, compared to progress achieved for many other medical disorders. Progress on outcome improvement for all medical disorders, including mental disorders, requires research capable of discovering causality at sufficient scale and speed, and a diagnostic nosology capable of encoding the causal knowledge that is discovered. Accordingly, the field's guiding paradigm limits progress by maintaining: (a) a diagnostic nosology (DSM-5) with a profound lack of causality; (b) a misalignment between mental health etiologic research and nosology; (c) an over-reliance on clinical trials beyond their capabilities; and (d) a limited adoption of newer methods capable of discovering the complex etiology of mental disorders. We detail feasible directions forward, to achieve greater levels of progress on improving outcomes for mental disorders, by: (a) the discovery of knowledge on the complex etiology of mental disorders with application of Causal Data Science methods; and (b) the encoding of the etiological knowledge that is discovered within a causal diagnostic system for mental disorders.

Characterization of influenza A virus induced transposons reveals a subgroup of transposons likely possessing the regulatory role as eRNAs.

Although many studies have observed genome-wide host transposon expression alteration during viral infection, the mechanisms of induction and the impact on the host remain unclear. Utilizing recently published influenza A virus (IAV) time series data and ENCODE functional genomics data, we characterized virus induced host differentially expressed transposons (virus-induced-TE) by investigating genome-wide spatial and functional relevance between the virus-induced-TEs and epigenomic markers (e.g. histone modification and chromatin remodelers). We found that a significant fraction of virus-induced-TEs are derived from host enhancer regions, where CHD4 binding and/or H3K27ac occupancy is high or H3K9me3 occupancy is low. By overlapping virus-induced-TEs to human enhancer RNAs (eRNAs), we discovered that a proportion of virus-induced-TEs are either eRNAs or part of enhancer RNAs. Upon further analysis of the eRNA targeted genes, we found that the virus-induced-TE related eRNA targets are overrepresented in differentially expressed host genes of IAV infected samples. Our results suggest that changing chromatin accessibility from repressive to permissive in the transposon docked enhancer regions to regulate host downstream gene expression is potentially one of the virus and host cell interaction mechanisms, where transposons are likely important regulatory genomic elements. Our study provides a new insight into the mechanisms of virus-host interaction and may lead to novel strategies for prevention and therapeutics of IAV and other virus infectious diseases.

Causal analysis identifies small HDL particles and physical activity as key determinants of longevity of older adults.

BACKGROUND: The hard endpoint of death is one of the most significant outcomes in both clinical practice and research settings. Our goal was to discover direct causes of longevity from medically accessible data. METHODS: Using a framework that combines local causal discovery algorithms with discovery of maximally predictive and compact feature sets (the "Markov boundaries" of the response) and equivalence classes, we examined 186 variables and their relationships with survival over 27 years in 1507 participants, aged ≥71 years, of the longitudinal, community-based D-EPESE study. FINDINGS: As few as 8-15 variables predicted longevity at 2-, 5- and 10-years with predictive performance (area under receiver operator characteristic curve) of 0·76 (95% CIs 0·69, 0·83), 0·76 (0·72, 0·81) and 0·66 (0·61, 0·71), respectively. Numbers of small high-density lipoprotein particles, younger age, and fewer pack years of cigarette smoking were the strongest determinants of longevity at 2-, 5- and 10-years, respectively. Physical function was a prominent predictor of longevity at all time horizons. Age and cognitive function contributed to predictions at 5 and 10 years. Age was not among the local 2-year prediction variables (although significant in univariable analysis), thus establishing that age is not a direct cause of 2-year longevity in the context of measured factors in our data that determine longevity. INTERPRETATION: The discoveries in this study proceed from causal data science analyses of deep clinical and molecular phenotyping data in a community-based cohort of older adults with known lifespan. FUNDING: NIH/NIA R01AG054840, R01AG12765, and P30-AG028716, NIH/NIA Contract N01-AG-12102 and NCRR 1UL1TR002494-01.

Analysis and computational dissection of molecular signature multiplicity.

Molecular signatures are computational or mathematical models created to diagnose disease and other phenotypes and to predict clinical outcomes and response to treatment. It is widely recognized that molecular signatures constitute one of the most important translational and basic science developments enabled by recent high-throughput molecular assays. A perplexing phenomenon that characterizes high-throughput data analysis is the ubiquitous multiplicity of molecular signatures. Multiplicity is a special form of data analysis instability in which different analysis methods used on the same data, or different samples from the same population lead to different but apparently maximally predictive signatures. This phenomenon has far-reaching implications for biological discovery and development of next generation patient diagnostics and personalized treatments. Currently the causes and interpretation of signature multiplicity are unknown, and several, often contradictory, conjectures have been made to explain it. We present a formal characterization of signature multiplicity and a new efficient algorithm that offers theoretical guarantees for extracting the set of maximally predictive and non-redundant signatures independent of distribution. The new algorithm identifies exactly the set of optimal signatures in controlled experiments and yields signatures with significantly better predictivity and reproducibility than previous algorithms in human microarray gene expression datasets. Our results shed light on the causes of signature multiplicity, provide computational tools for studying it empirically and introduce a framework for in silico bioequivalence of this important new class of diagnostic and personalized medicine modalities.

A comparison of evaluation metrics for biomedical journals, articles, and websites in terms of sensitivity to topic.

Evaluating the biomedical literature and health-related websites for quality are challenging information retrieval tasks. Current commonly used methods include impact factor for journals, PubMed's clinical query filters and machine learning-based filter models for articles, and PageRank for websites. Previous work has focused on the average performance of these methods without considering the topic, and it is unknown how performance varies for specific topics or focused searches. Clinicians, researchers, and users should be aware when expected performance is not achieved for specific topics. The present work analyzes the behavior of these methods for a variety of topics. Impact factor, clinical query filters, and PageRank vary widely across different topics while a topic-specific impact factor and machine learning-based filter models are more stable. The results demonstrate that a method may perform excellently on average but struggle when used on a number of narrower topics. Topic-adjusted metrics and other topic robust methods have an advantage in such situations. Users of traditional topic-sensitive metrics should be aware of their limitations.

Pharmacogenomics education, research and clinical implementation in the state of Minnesota.

Several healthcare organizations across Minnesota have developed formal pharmacogenomic (PGx) clinical programs to increase drug safety and effectiveness. Healthcare professional and student education is strong and there are multiple opportunities in the state for learners to gain workforce skills and develop advanced competency in PGx. Implementation planning is occurring at several organizations and others have incorporated structured utilization of PGx into routine workflows. Laboratory-based and translational PGx research in Minnesota has driven important discoveries in several therapeutic areas. This article reviews the state of PGx activities in Minnesota including educational programs, research, national consortia involvement, technology, clinical implementation and utilization and reimbursement, and outlines the challenges and opportunities in equitable implementation of these advances.

Computational causal discovery for post-traumatic stress in police officers.

This article reports on a study aimed to elucidate the complex etiology of post-traumatic stress (PTS) in a longitudinal cohort of police officers, by applying rigorous computational causal discovery (CCD) methods with observational data. An existing observational data set was used, which comprised a sample of 207 police officers who were recruited upon entry to police academy training. Participants were evaluated on a comprehensive set of clinical, self-report, genetic, neuroendocrine and physiological measures at baseline during academy training and then were re-evaluated at 12 months after training was completed. A data-processing pipeline-the Protocol for Computational Causal Discovery in Psychiatry (PCCDP)-was applied to this data set to determine a causal model for PTS severity. A causal model of 146 variables and 345 bivariate relations was discovered. This model revealed 5 direct causes and 83 causal pathways (of four steps or less) to PTS at 12 months of police service. Direct causes included single-nucleotide polymorphisms (SNPs) for the Histidine Decarboxylase (HDC) and Mineralocorticoid Receptor (MR) genes, acoustic startle in the context of low perceived threat during training, peritraumatic distress to incident exposure during first year of service, and general symptom severity during training at 1 year of service. The application of CCD methods can determine variables and pathways related to the complex etiology of PTS in a cohort of police officers. This knowledge may inform new approaches to treatment and prevention of critical incident related PTS.

Evidence That Established Lung Cancer Mortality Disparities in American Indians Are Not Due to Lung Cancer Genetic Testing and Targeted Therapy Disparities.

BACKGROUND: American Indians and Alaska Natives (AI/AN) continue to experience extreme lung cancer health disparities. The state of Minnesota is home to over 70,000 AI/AN, and this population has a 2-fold increase in lung cancer mortality compared to other races within Minnesota. Genetic mutation testing in lung cancer is now a standard of high-quality lung cancer care, and EGFR mutation testing has been recommended for all adenocarcinoma lung cases, regardless of smoking status. However, genetic testing is a controversial topic for some AI/AN. PATIENTS AND METHODS: We performed a multisite retrospective chart review funded by the Minnesota Precision Medicine Grand Challenge as a demonstration project to examine lung cancer health disparities in AI/AN. We sought to measure epidemiology of lung cancer among AI receiving diagnosis or treatment in Minnesota cancer referral centers as well as rate of EGFR testing. The primary outcome was the rate of EGFR mutational analysis testing among cases and controls with nonsquamous, non-small-cell lung cancer. We secured collaborations with 5 health care systems covering a diverse geographic and demographic population. RESULTS: We identified 200 cases and 164 matched controls from these sites. Controls were matched on histology, smoking status, sex, and age. In both groups, about one third of subjects with adenocarcinoma received genetic mutation testing. CONCLUSION: There was no significant difference in mutation testing in AI compared to non-AI controls at large health care systems in Minnesota. These data indicate that other factors are likely contributing to the higher mortality in this group.

Dilution of Molecular-Pathologic Gene Signatures by Medically Associated Factors Might Prevent Prediction of Resection Status After Debulking Surgery in Patients With Advanced Ovarian Cancer.

PURPOSE: Predicting surgical outcome could improve individualizing treatment strategies for patients with advanced ovarian cancer. It has been suggested earlier that gene expression signatures (GES) might harbor the potential to predict surgical outcome. EXPERIMENTAL DESIGN: Data derived from high-grade serous tumor tissue of FIGO stage IIIC/IV patients of AGO-OVAR11 trial were used to generate a transcriptome profiling. Previously identified molecular signatures were tested. A theoretical model was implemented to evaluate the impact of medically associated factors for residual disease (RD) on the performance of GES that predicts RD status. RESULTS: A total of 266 patients met inclusion criteria, of those, 39.1% underwent complete resection. Previously reported GES did not predict RD in this cohort. Similarly, The Cancer Genome Atlas molecular subtypes, an independent de novo signature and the total gene expression dataset using all 21,000 genes were not able to predict RD status. Medical reasons for RD were identified as potential limiting factors that impact the ability to use GES to predict RD. In a center with high complete resection rates, a GES which would perfectly predict tumor biological RD would have a performance of only AUC 0.83, due to reasons other than tumor biology. CONCLUSIONS: Previously identified GES cannot be generalized. Medically associated factors for RD may be the main obstacle to predict surgical outcome in an all-comer population of patients with advanced ovarian cancer. If biomarkers derived from tumor tissue are used to predict outcome of patients with cancer, selection bias should be focused on to prevent overestimation of the power of such a biomarker.See related commentary by Handley and Sood, p. 9.

De novo prediction of cell-type complexity in single-cell RNA-seq and tumor microenvironments.

Recent single-cell transcriptomic studies revealed new insights into cell-type heterogeneities in cellular microenvironments unavailable from bulk studies. A significant drawback of currently available algorithms is the need to use empirical parameters or rely on indirect quality measures to estimate the degree of complexity, i.e., the number of subgroups present in the sample. We fill this gap with a single-cell data analysis procedure allowing for unambiguous assessments of the depth of heterogeneity in subclonal compositions supported by data. Our approach combines nonnegative matrix factorization, which takes advantage of the sparse and nonnegative nature of single-cell RNA count data, with Bayesian model comparison enabling de novo prediction of the depth of heterogeneity. We show that the method predicts the correct number of subgroups using simulated data, primary blood mononuclear cell, and pancreatic cell data. We applied our approach to a collection of single-cell tumor samples and found two qualitatively distinct classes of cell-type heterogeneity in cancer microenvironments.

A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification.

BACKGROUND: Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain. RESULTS: In the present paper we identify methodological biases of prior work comparing random forests and support vector machines and conduct a new rigorous evaluation of the two algorithms that corrects these limitations. Our experiments use 22 diagnostic and prognostic datasets and show that support vector machines outperform random forests, often by a large margin. Our data also underlines the importance of sound research design in benchmarking and comparison of bioinformatics algorithms. CONCLUSION: We found that both on average and in the majority of microarray datasets, random forests are outperformed by support vector machines both in the settings when no gene selection is performed and when several popular gene selection methods are used.