Return to the Pre-Injury Level of Sport after Anterior Cruciate Ligament Reconstruction: A Practical Review with Medical Recommendations.

Returning to sport after anterior cruciate ligament reconstruction (ACLR) can be a challenging and complex process for the athlete, with the rate of return to the pre-injury level of sport observed to be less than athlete expectations. Of the athletes that do return to sport (RTS), knee re-injury rates remain high, and multiple studies have observed impaired athletic performance upon RTS after ACLR as well as reduced playing time, productivity, and career lengths. To mitigate re-injury and improve RTS outcomes, multiple RTS after ACLR consensus statements/clinical practice guidelines have recommended objective RTS testing criteria to be met prior to medical clearance for unrestricted sports participation. While the achievement of RTS testing criteria can improve RTS rates after ACLR, current criteria do not appear valid for predicting safe RTS. Therefore, there is a need to review the various factors related to the successful return to the pre-injury level of sport after ACLR, clarify the utility of objective performance testing and RTS criteria, further discuss safe RTS decision-making as well as present strategies to reduce the risk of ACL injury/re-injury upon RTS. This article provides a practical review of the current RTS after ACLR literature, as well as makes medical recommendations for rehabilitation and RTS decision-making after ACLR.

Kinetic and functional analysis of abundant microRNAs in extracellular vesicles from normal and stressed cultures of Chinese hamster ovary cells.

Chinese hamster ovary (CHO) cells release and exchange large quantities of extracellular vesicles (EVs). EVs are highly enriched in microRNAs (miRs, or miRNAs), which are responsible for most of their biological effects. We have recently shown that the miR content of CHO EVs varies significantly under culture stress conditions. Here, we provide a novel stoichiometric ("per-EV") quantification of miR and protein levels in large CHO EVs produced under ammonia, lactate, osmotic, and age-related stress. Each stress resulted in distinct EV miR levels, with selective miR loading by parent cells. Our data provide a proof of concept for the use of CHO EV cargo as a diagnostic tool for identifying culture stress. We also tested the impact of three select miRs (let-7a, miR-21, and miR-92a) on CHO cell growth and viability. Let-7a-abundant in CHO EVs from stressed cultures-reduced CHO cell viability, while miR-92a-abundant in CHO EVs from unstressed cultures-promoted cell survival. Overexpression of miR-21 had a slight detrimental impact on CHO cell growth and viability during late exponential-phase culture, an unexpected result based on the reported antiapoptotic role of miR-21 in other mammalian cell lines. These findings provide novel relationships between CHO EV cargo and cell phenotype, suggesting that CHO EVs may exert both pro- and antiapoptotic effects on target cells, depending on the conditions under which they were produced.

Causal association between subtypes of osteoarthritis and common comorbidities: A Mendelian randomisation study.

Objective: To investigate the causal association between Osteoarthritis (OA) and five comorbidities: depression, tiredness, multisite chronic pain, irritable bowel syndrome (IBS) and gout. Design: This study used two-sample Mendelian Randomisation (MR). To select the OA genetic instruments, we used data from the largest recent genome-wide association study (GWAS) of OA (GO Consortium), with a focus on OA of the knee (62,497 cases, 333,557 controls), hip (35,445 cases, 316,943 controls) and hand (20,901 cases, 282,881 controls). Genetic associations for comorbidities were selected from GWAS for depression (246,363 cases, 561,190 controls), tiredness (449,019 participants), multisite chronic pain (387,649 participants), IBS (53,400 cases, 433,201 controls) and gout (6543 cases, 456,390 controls). We performed a bidirectional MR analysis using the inverse variance weighted method, for both joint specific and overall OA. Results: Hip OA had a causal effect on multisite chronic pain (per unit change 0.02, 95% CI 0.01 to 0.04). Multisite chronic pain had a causal effect on knee (odd ratio (OR) 2.74, 95% CI 2.20 to 3.41), hip (OR 2.12, 95% CI 1.54 to 2.92), hand (OR 2.24, 95% CI 1.59 to 3.16) and overall OA (OR 2.44, 95% CI, 2.06 to 2.86). In addition, depression and tiredness had causal effects on knee and hand, but not hip, OA. Conclusions: Apart from Hip OA to multisite chronic pain, other joint OA did not have causal effects on these comorbidities. In contrast, multisite chronic pain had a causal effect on any painful OA.

Similar but distinct: The impact of biomechanical forces and culture age on the production, cargo loading, and biological efficacy of human megakaryocytic extracellular vesicles for applications in cell and gene therapies.

Megakaryocytic extracellular vesicles (MkEVs) promote the growth and megakaryopoiesis of hematopoietic stem and progenitor cells (HSPCs) largely through endogenous miR-486-5p and miR-22-3p cargo. Here, we examine the impact of biomechanical force and culture age/differentiation on the formation, properties, and biological efficacy of MkEVs. We applied biomechanical force to Mks using two methods: shake flask cultures and a syringe pump system. Force increased MkEV production in a magnitude-dependent manner, with similar trends emerging regardless of whether flow cytometry or nanoparticle tracking analysis was used for MkEV counting. Both methods produced MkEVs that were relatively depleted of miR-486-5p and miR-22-3p cargo. However, while the shake flask-derived MkEVs were correspondingly less effective in promoting megakaryocytic differentiation of HSPCs, the syringe pump-derived MkEVs were more effective in doing so, suggesting the presence of unique, unidentified miRNA cargo components. Higher numbers of MkEVs were also produced by "older" Mk cultures, though miRNA cargo levels and MkEV bioactivity were unaffected by culture age. A reduction in MkEV production by Mks derived from late-differentiating HSPCs was also noted. Taken together, our results demonstrate that biomechanical force has an underappreciated and deeply influential role in MkEV biology, though that role may vary significantly depending on the nature of the force. Given the ubiquity of biomechanical force in vivo and in biomanufacturing, this phenomenon must be grappled with before MkEVs can attain clinical relevance.

The role of biomechanical stress in extracellular vesicle formation, composition and activity.

Extracellular vesicles (EVs) are cornerstones of intercellular communication with exciting fundamental, clinical, and more broadly biotechnological applications. However, variability in EV composition, which results from the culture conditions used to generate the EVs, poses significant fundamental and applied challenges and a hurdle for scalable bioprocessing. Thus, an understanding of the relationship between EV production (and for clinical applications, manufacturing) and EV composition is increasingly recognized as important and necessary. While chemical stimulation and culture conditions such as cell density are known to influence EV biology, the impact of biomechanical forces on the generation, properties, and biological activity of EVs remains poorly understood. Given the omnipresence of these forces in EV preparation and in biomanufacturing, expanding the understanding of their impact on EV composition-and thus, activity-is vital. Although several publications have examined EV preparation and bioprocessing and briefly discussed biomechanical stresses as variables of interest, this review represents the first comprehensive evaluation of the impact of such stresses on EV production, composition and biological activity. We review how EV biogenesis, cargo, efficacy, and uptake are uniquely affected by various types, magnitudes, and durations of biomechanical forces, identifying trends that emerge both generically and for individual cell types. We also describe implications for scalable bioprocessing, evaluating processes inherent in common EV production and isolation methods, and propose a path forward for rigorous EV quality control.

miR-486-5p and miR-22-3p Enable Megakaryocytic Differentiation of Hematopoietic Stem and Progenitor Cells without Thrombopoietin.

Megakaryocytes release submicron size microparticles (MkMPs) in circulation. We have shown that MkMPs target CD34+ hematopoietic stem/progenitor cells (HSPCs) to induce megakaryocytic differentiation, and that small RNAs in MkMPs play an important role in the development of this phenotype. Here, using single-molecule real-time (SMRT) RNA sequencing (RNAseq), we identify the synergetic effect of two microRNAs (miRs), miR-486-5p and miR-22-3p (highly enriched in MkMPs), in driving the Mk differentiation of HSPCs in the absence of thrombopoietin (TPO). Separately, our data suggest that the MkMP-induced Mk differentiation of HSPCs is enabled through JNK and PI3K/Akt/mTOR signaling. The interaction between the two signaling pathways is likely mediated by a direct target of miR-486-5p and a negative regulator of PI3K/Akt signaling, the phosphatase and tensin homologue (PTEN) protein. Our data provide a possible mechanistic explanation of the biological effect of MkMPs in inducing megakaryocytic differentiation of HSPCs, a phenotype of potential physiological significance in stress megakaryopoiesis.

TMEM67, TMEM237, and Embigin in Complex With Monocarboxylate Transporter MCT1 Are Unique Components of the Photoreceptor Outer Segment Plasma Membrane.

The outer segment (OS) organelle of vertebrate photoreceptors is a highly specialized cilium evolved to capture light and initiate light response. The plasma membrane which envelopes the OS plays vital and diverse roles in supporting photoreceptor function and health. However, little is known about the identity of its protein constituents, as this membrane cannot be purified to homogeneity. In this study, we used the technique of protein correlation profiling to identify unique OS plasma membrane proteins. To achieve this, we used label-free quantitative MS to compare relative protein abundances in an enriched preparation of the OS plasma membrane with a preparation of total OS membranes. We have found that only five proteins were enriched at the same level as previously validated OS plasma membrane markers. Two of these proteins, TMEM67 and TMEM237, had not been previously assigned to this membrane, and one, embigin, had not been identified in photoreceptors. We further showed that embigin associates with monocarboxylate transporter MCT1 in the OS plasma membrane, facilitating lactate transport through this cellular compartment.

Using Stepwise Pharmacogenomics and Proteomics to Predict Hepatitis C Treatment Response in Difficult to Treat Patient Populations.

PURPOSE: In the interferon era of hepatitis C virus (HCV) therapies, genotype/subtype, cirrhosis, prior treatment failure, sex, and race predicted relapse. Our objective is to validate a targeted proteomics platform of 17 peptides to predict sustained virologic response (SVR). EXPERIMENTAL DESIGN: Stored plasma from three, open-label, trials of HIV/HCV-coinfected subjects receiving interferon-containing regimens is identified. LC-MS/MS is used to quantitate the peptides directly from plasma, and IL28B genotyping is completed using stored peripheral blood mononuclear cells (PBMC). A logistic regression model is built to analyze the probability of SVR using responders and nonresponders to interferon-based regimens. RESULTS: The cohort (N = 35) is predominantly black (51.4%), male (86%), and with median age 48 years. Most patients achieve SVR (54%). Using multivariable models, it is verified that three human corticosteroid binding globulin (CBG) peptides are predictive of SVR in patients with the unfavorable IL28B genotypes (CT/TT). The model performs better than IL28B alone, with an area under the curve of 0.870. CONCLUSIONS AND CLINICAL RELEVANCE: In HIV/HCV-coinfected patients, three human CBG peptides that accurately predict treatment response with interferon-based therapy are identified. This study suggests that a stepwise approach combining a genetic predictor followed by targeted proteomics can improve the accuracy of clinical decision-making.

A study paradigm integrating prospective epidemiologic cohorts and electronic health records to identify disease biomarkers.

Defining the full spectrum of human disease associated with a biomarker is necessary to advance the biomarker into clinical practice. We hypothesize that associating biomarker measurements with electronic health record (EHR) populations based on shared genetic architectures would establish the clinical epidemiology of the biomarker. We use Bayesian sparse linear mixed modeling to calculate SNP weightings for 53 biomarkers from the Atherosclerosis Risk in Communities study. We use the SNP weightings to computed predicted biomarker values in an EHR population and test associations with 1139 diagnoses. Here we report 116 associations meeting a Bonferroni level of significance. A false discovery rate (FDR)-based significance threshold reveals more known and undescribed associations across a broad range of biomarkers, including biometric measures, plasma proteins and metabolites, functional assays, and behaviors. We confirm an inverse association between LDL-cholesterol level and septicemia risk in an independent epidemiological cohort. This approach efficiently discovers biomarker-disease associations.

Investigating the Genetic Architecture of the PR Interval Using Clinical Phenotypes.

BACKGROUND: One potential use for the PR interval is as a biomarker of disease risk. We hypothesized that quantifying the shared genetic architectures of the PR interval and a set of clinical phenotypes would identify genetic mechanisms contributing to PR variability and identify diseases associated with a genetic predictor of PR variability. METHODS AND RESULTS: We used ECG measurements from the ARIC study (Atherosclerosis Risk in Communities; n=6731 subjects) and 63 genetically modulated diseases from the eMERGE network (Electronic Medical Records and Genomics; n=12 978). We measured pairwise genetic correlations (rG) between PR phenotypes (PR interval, PR segment, P-wave duration) and each of the 63 phenotypes. The PR segment was genetically correlated with atrial fibrillation (rG=-0.88; P=0.0009). An analysis of metabolic phenotypes in ARIC also showed that the P wave was genetically correlated with waist circumference (rG=0.47; P=0.02). A genetically predicted PR interval phenotype based on 645 714 single-nucleotide polymorphisms was associated with atrial fibrillation (odds ratio=0.89 per SD change; 95% confidence interval, 0.83-0.95; P=0.0006). The differing pattern of associations among the PR phenotypes is consistent with analyses that show that the genetic correlation between the P wave and PR segment was not significantly different from 0 (rG=-0.03 [0.16]). CONCLUSIONS: The genetic architecture of the PR interval comprises modulators of atrial fibrillation risk and obesity.

Defining a Contemporary Ischemic Heart Disease Genetic Risk Profile Using Historical Data.

BACKGROUND: Continued reductions in morbidity and mortality attributable to ischemic heart disease (IHD) require an understanding of the changing epidemiology of this disease. We hypothesized that we could use genetic correlations, which quantify the shared genetic architectures of phenotype pairs and extant risk factors from a historical prospective study to define the risk profile of a contemporary IHD phenotype. METHODS AND RESULTS: We used 37 phenotypes measured in the ARIC study (Atherosclerosis Risk in Communities; n=7716, European ancestry subjects) and clinical diagnoses from an electronic health record (EHR) data set (n=19 093). All subjects had genome-wide single-nucleotide polymorphism genotyping. We measured pairwise genetic correlations (rG) between the ARIC and EHR phenotypes using linear mixed models. The genetic correlation estimates between the ARIC risk factors and the EHR IHD were modestly linearly correlated with hazards ratio estimates for incident IHD in ARIC (Pearson correlation [r]=0.62), indicating that the 2 IHD phenotypes had differing risk profiles. For comparison, this correlation was 0.80 when comparing EHR and ARIC type 2 diabetes mellitus phenotypes. The EHR IHD phenotype was most strongly correlated with ARIC metabolic phenotypes, including total:high-density lipoprotein cholesterol ratio (rG=-0.44, P=0.005), high-density lipoprotein (rG=-0.48, P=0.005), systolic blood pressure (rG=0.44, P=0.02), and triglycerides (rG=0.38, P=0.02). EHR phenotypes related to type 2 diabetes mellitus, atherosclerotic, and hypertensive diseases were also genetically correlated with these ARIC risk factors. CONCLUSIONS: The EHR IHD risk profile differed from ARIC and indicates that treatment and prevention efforts in this population should target hypertensive and metabolic disease.

PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability.

OBJECTIVE: Health care generated data have become an important source for clinical and genomic research. Often, investigators create and iteratively refine phenotype algorithms to achieve high positive predictive values (PPVs) or sensitivity, thereby identifying valid cases and controls. These algorithms achieve the greatest utility when validated and shared by multiple health care systems.Materials and Methods We report the current status and impact of the Phenotype KnowledgeBase (PheKB, http://phekb.org), an online environment supporting the workflow of building, sharing, and validating electronic phenotype algorithms. We analyze the most frequent components used in algorithms and their performance at authoring institutions and secondary implementation sites. RESULTS: As of June 2015, PheKB contained 30 finalized phenotype algorithms and 62 algorithms in development spanning a range of traits and diseases. Phenotypes have had over 3500 unique views in a 6-month period and have been reused by other institutions. International Classification of Disease codes were the most frequently used component, followed by medications and natural language processing. Among algorithms with published performance data, the median PPV was nearly identical when evaluated at the authoring institutions (n = 44; case 96.0%, control 100%) compared to implementation sites (n = 40; case 97.5%, control 100%). DISCUSSION: These results demonstrate that a broad range of algorithms to mine electronic health record data from different health systems can be developed with high PPV, and algorithms developed at one site are generally transportable to others. CONCLUSION: By providing a central repository, PheKB enables improved development, transportability, and validity of algorithms for research-grade phenotypes using health care generated data.

Multifunctional roles for the protein translocation machinery in RNA anchoring to the endoplasmic reticulum.

Signal sequence-encoding mRNAs undergo translation-dependent localization to the endoplasmic reticulum (ER) and at the ER are anchored via translation on Sec61-bound ribosomes. Recent investigations into the composition and membrane association characteristics of ER-associated mRNAs have, however, revealed both ribosome-dependent (indirect) and ribosome-independent (direct) modes of mRNA association with the ER. These findings raise important questions regarding our understanding of how mRNAs are selected, localized, and anchored to the ER. Using semi-intact tissue culture cells, we performed a polysome solubilization screen and identified conditions that distinguish polysomes engaged in the translation of distinct cohorts of mRNAs. To gain insight into the molecular basis of direct mRNA anchoring to the ER, we performed RNA-protein UV photocross-linking studies in rough microsomes and demonstrate that numerous ER integral membrane proteins display RNA binding activity. Quantitative proteomic analyses of HeLa cytosolic and ER-bound polysome fractions identified translocon components as selective polysome-interacting proteins. Notably, the Sec61 complex was highly enriched in polysomes engaged in the translation of endomembrane organelle proteins, whereas translocon accessory proteins, such as ribophorin I, were present in all subpopulations of ER-associated polysomes. Analyses of the protein composition of oligo(dT)-selected UV photocross-linked ER protein-RNA adducts identified Sec61α,ß and ribophorin I as ER-poly(A) mRNA-binding proteins, suggesting unexpected roles for the protein translocation and modification machinery in mRNA anchoring to the ER. In summary, we propose that multiple mechanisms of mRNA and ribosome association with ER operate to enable an mRNA transcriptome-wide function for the ER in protein synthesis.

Design patterns for the development of electronic health record-driven phenotype extraction algorithms.

BACKGROUND: Design patterns, in the context of software development and ontologies, provide generalized approaches and guidance to solving commonly occurring problems, or addressing common situations typically informed by intuition, heuristics and experience. While the biomedical literature contains broad coverage of specific phenotype algorithm implementations, no work to date has attempted to generalize common approaches into design patterns, which may then be distributed to the informatics community to efficiently develop more accurate phenotype algorithms. METHODS: Using phenotyping algorithms stored in the Phenotype KnowledgeBase (PheKB), we conducted an independent iterative review to identify recurrent elements within the algorithm definitions. We extracted and generalized recurrent elements in these algorithms into candidate patterns. The authors then assessed the candidate patterns for validity by group consensus, and annotated them with attributes. RESULTS: A total of 24 electronic Medical Records and Genomics (eMERGE) phenotypes available in PheKB as of 1/25/2013 were downloaded and reviewed. From these, a total of 21 phenotyping patterns were identified, which are available as an online data supplement. CONCLUSIONS: Repeatable patterns within phenotyping algorithms exist, and when codified and cataloged may help to educate both experienced and novice algorithm developers. The dissemination and application of these patterns has the potential to decrease the time to develop algorithms, while improving portability and accuracy.

Portability of an algorithm to identify rheumatoid arthritis in electronic health records.

OBJECTIVES: Electronic health records (EHR) can allow for the generation of large cohorts of individuals with given diseases for clinical and genomic research. A rate-limiting step is the development of electronic phenotype selection algorithms to find such cohorts. This study evaluated the portability of a published phenotype algorithm to identify rheumatoid arthritis (RA) patients from EHR records at three institutions with different EHR systems. MATERIALS AND METHODS: Physicians reviewed charts from three institutions to identify patients with RA. Each institution compiled attributes from various sources in the EHR, including codified data and clinical narratives, which were searched using one of two natural language processing (NLP) systems. The performance of the published model was compared with locally retrained models. RESULTS: Applying the previously published model from Partners Healthcare to datasets from Northwestern and Vanderbilt Universities, the area under the receiver operating characteristic curve was found to be 92% for Northwestern and 95% for Vanderbilt, compared with 97% at Partners. Retraining the model improved the average sensitivity at a specificity of 97% to 72% from the original 65%. Both the original logistic regression models and locally retrained models were superior to simple billing code count thresholds. DISCUSSION: These results show that a previously published algorithm for RA is portable to two external hospitals using different EHR systems, different NLP systems, and different target NLP vocabularies. Retraining the algorithm primarily increased the sensitivity at each site. CONCLUSION: Electronic phenotype algorithms allow rapid identification of case populations in multiple sites with little retraining.

Automatically detecting problem list omissions of type 2 diabetes cases using electronic medical records.

As part of a large-scale project to use DNA biorepositories linked with electronic medical record (EMR) data for research, we developed and validated an algorithm to identify type 2 diabetes cases in the EMR. Though the algorithm was originally created to support clinical research, we have subsequently re-applied it to determine if it could also be used to identify problem list gaps. We examined the problem lists of the cases that the algorithm identified in order to determine if a structured code for diabetes was present. We found that only just over half of patients identified by the algorithm had a corresponding structured code entered in their problem list. We analyze characteristics of this patient population and identify possible reasons for the problem list omissions. We conclude that application of such algorithms to the EMR can improve the quality of the problem list, thereby supporting satisfaction of Meaningful Use guidelines.