Traumatic Brain Injury Alters the Trajectory of Age-Related Mitochondrial Change.

Background: Some epidemiologic studies associate traumatic brain injury (TBI) with Alzheimer's disease (AD). Objective: To test whether a TBI-induced acceleration of age-related mitochondrial change could potentially mediate the reported TBI-AD association. Methods: We administered unilateral controlled cortical impact (CCI) or sham injuries to 5-month-old C57BL/6J and tau transgenic rTg4510 mice. In the non-transgenics, we assessed behavior (1-5 days, 1 month, and 15 months), lesion size (1 and 15 months), respiratory chain enzymes (1 and 15 months), and mitochondrial DNA copy number (mtDNAcn) (1 and 15 months) after CCI/sham. In the transgenics we quantified post-injury mtDNAcn and tangle burden. Results: In the non-transgenics CCI caused acute behavioral deficits that improved or resolved by 1-month post-injury. Protein-normalized complex I and cytochrome oxidase activities were not significantly altered at 1 or 15 months, although complex I activity in the CCI ipsilesional cortex declined during that period. Hippocampal mtDNAcn was not altered by injury at 1 month, increased with age, and rose to the greatest extent in the CCI contralesional hippocampus. In the injured then aged transgenics, the ipsilesional hippocampus contained less mtDNA and fewer tangles than the contralesional hippocampus; mtDNAcn and tangle counts did not correlate. Conclusions: As mice age their brains increase mtDNAcn as part of a compensatory response that preserves mitochondrial function, and TBI enhances this response. TBI may, therefore, increase the amount of compensation required to preserve late-life mitochondrial function. If TBI does modify AD risk, altering the trajectory or biology of aging-related mitochondrial changes could mediate the effect.

Impact of Weight Management on Obesity-Driven Biomarkers of Prostate Cancer Progression.

PURPOSE: Excess body and visceral fat increase the risk of death from prostate cancer (PCa). This phase II study aimed to test whether weight reduction by > 5% total body weight counteracts obesity-driven PCa biomarkers. MATERIALS AND METHODS: Forty men scheduled for prostatectomy were randomized into intervention (n = 20) or control (n = 20) arms. Intervention participants followed a weight management program for 4 to 16 weeks before and 6 months after surgery. Control participants received standardized educational materials. All participants attended visits at baseline, 1 week before surgery, and 6 months after surgery. Circulating immune cells, cytokines, and chemokines were evaluated. Weight loss, body composition/distribution, quality of life, and nutrition literacy were assessed. Prostate tissue samples obtained from biopsy and surgery were analyzed. RESULTS: From baseline to surgery (mean = 5 weeks), the intervention group achieved 5.5% of weight loss (95% CI, 4%-7%). Compared to the control, the intervention also reduced insulin, total cholesterol, LDL cholesterol, leptin, leptin:adiponectin ratio, and visceral adipose tissue. The intervention group had reduced c-peptide, plasminogen-activator-inhibitor-1, and T cell count from baseline to surgery. Myeloid-derived suppressor cells were not statistically different by group. Intervention group anthropometrics improved, including visceral and overall fat loss. No prostate tissue markers changed significantly. Quality of life measures of general and emotional health improved in the intervention group. The intervention group maintained or kept losing to a net loss of 11% initial body weight (95% CI, 8%-14%) at the study end. CONCLUSIONS: Our study demonstrated improvements in body composition, PCa biomarkers, and quality of life with a weight management intervention.

Randomized singular value decomposition for integrative subtype analysis of 'omics data' using non-negative matrix factorization.

Integration of multiple 'omics datasets for differentiating cancer subtypes is a powerful technic that leverages the consistent and complementary information across multi-omics data. Matrix factorization is a common technique used in integrative clustering for identifying latent subtype structure across multi-omics data. High dimensionality of the omics data and long computation time have been common challenges of clustering methods. In order to address the challenges, we propose randomized singular value decomposition (RSVD) for integrative clustering using Non-negative Matrix Factorization: intNMF-rsvd. The method utilizes RSVD to reduce the dimensionality by projecting the data into eigen vector space with user specified lower rank. Then, clustering analysis is carried out by estimating common basis matrix across the projected multi-omics datasets. The performance of the proposed method was assessed using the simulated datasets and compared with six state-of-the-art integrative clustering methods using real-life datasets from The Cancer Genome Atlas Study. intNMF-rsvd was found working efficiently and competitively as compared to standard intNMF and other multi-omics clustering methods. Most importantly, intNMF-rsvd can handle large number of features and significantly reduce the computation time. The identified subtypes can be utilized for further clinical association studies to understand the etiology of the disease.

Circulating Tumor Cell Subpopulations Predict Treatment Outcome in Pancreatic Ductal Adenocarcinoma (PDAC) Patients.

There is a high clinical unmet need to improve outcomes for pancreatic ductal adenocarcinoma (PDAC) patients, either with the discovery of new therapies or biomarkers that can track response to treatment more efficiently than imaging. We report an innovative approach that will generate renewed interest in using circulating tumor cells (CTCs) to monitor treatment efficacy, which, in this case, used PDAC patients receiving an exploratory new therapy, poly ADP-ribose polymerase inhibitor (PARPi)-niraparib-as a case study. CTCs were enumerated from whole blood using a microfluidic approach that affinity captures epithelial and mesenchymal CTCs using anti-EpCAM and anti-FAPα monoclonal antibodies, respectively. These antibodies were poised on the surface of two separate microfluidic devices to discretely capture each subpopulation for interrogation. The isolated CTCs were enumerated using immunophenotyping to produce a numerical ratio consisting of the number of mesenchymal to epithelial CTCs (denoted "Φ"), which was used as an indicator of response to therapy, as determined using computed tomography (CT). A decreasing value of Φ during treatment was indicative of tumor response to the PARPi and was observed in 88% of the enrolled patients (n = 31). Changes in Φ during longitudinal testing were a better predictor of treatment response than the current standard CA19-9. We were able to differentiate between responders and non-responders using ΔΦ (p = 0.0093) with higher confidence than CA19-9 (p = 0.033). For CA19-9 non-producers, ΔΦ correctly predicted the outcome in 72% of the PDAC patients. Sequencing of the gDNA extracted from affinity-selected CTC subpopulations provided information that could be used for patient enrollment into the clinical trial based on their tumor mutational status in DNA repair genes.

Adipose-Tumor Crosstalk contributes to CXCL5 Mediated Immune Evasion in PDAC.

Background: CXCR1/2 inhibitors are being implemented with immunotherapies in PDAC clinical trials. Cytokines responsible for stimulating these receptors include CXCL ligands, typically secreted by activated immune cells, fibroblasts, and even adipocytes. Obesity has been linked to poor patient outcome and altered anti-tumor immunity. Adipose-derived cytokines and chemokines have been implicated as potential drivers of tumor cell immune evasion, suggesting a possibility of susceptibility to targeting specifically in the context of obesity. Methods: RNA-sequencing of human PDAC cell lines was used to assess differential influences on the cancer cell transcriptome after treatment with conditioned media from peri-pancreatic adipose tissue of lean and obese PDAC patients. The adipose-induced secretome of PDAC cells was then assessed by cytokine arrays and ELISAs. Lentiviral transduction and CRISPR-Cas9 was used to knock out CXCL5 from a murine PDAC cell line for orthotopic tumor studies in diet-induced obese, syngeneic mice. Flow cytometry was used to define the immune profiles of tumors. Anti-PD-1 immune checkpoint blockade therapy was administered to alleviate T cell exhaustion and invoke an immune response, while the mice were monitored at endpoint for differences in tumor size. Results: The chemokine CXCL5 was secreted in response to stimulation of PDAC cells with human adipose conditioned media (hAT-CM). PDAC CXCL5 secretion was induced by either IL-1ß or TNF, but neutralization of both was required to limit secretion. Ablation of CXCL5 from tumors promoted an immune phenotype susceptible to PD-1 inhibitor therapy. While application of anti-PD-1 treatment to control tumors failed to alter tumor growth, knockout CXCL5 tumors were diminished. Conclusions: In summary, our findings show that known adipokines TNF and IL-1ß can stimulate CXCL5 release from PDAC cells in vitro. In vivo , CXCL5 depletion alone is sufficient to promote T cell infiltration into tumors in an obese setting, but requires checkpoint blockade inhibition to alleviate tumor burden. DATA AVAILABILITY STATEMENT: Raw and processed RNAseq data will be further described in the GEO accession database ( awaiting approval from GEO for PRJ number ). Additional raw data is included in the supplemental material and available upon reasonable request. WHAT IS ALREADY KNOWN ON THIS TOPIC: Obesity is linked to a worsened patient outcome and immunogenic tumor profile in PDAC. CXCR1/2 inhibitors have begun to be implemented in combination with immune checkpoint blockade therapies to promote T cell infiltration under the premise of targeting the myeloid rich TME. WHAT THIS STUDY ADDS: Using in vitro/ex vivo cell and tissue culture-based assays with in vivo mouse models we have identified that adipose derived IL-1ß and TNF can promote tumor secretion of CXCL5 which acts as a critical deterrent to CD8 T cell tumor infiltration, but loss of CXCL5 also leads to a more immune suppressive myeloid profile. HOW THIS STUDY MIGHT AFFECT RESEARCH PRACTICE OR POLICY: This study highlights a mechanism and emphasizes the efficacy of single CXCR1/2 ligand targeting that could be beneficial to overcoming tumor immune-evasion even in the obese PDAC patient population.

Combined PI3K and MAPK inhibition synergizes to suppress PDAC.

Oncogenic KRAS mutations are nearly ubiquitous in pancreatic ductal adenocarcinoma (PDAC), yet therapeutic attempts to target KRAS as well as its target MAPK pathway effectors have shown limited success due to the difficulty to pharmacologically target KRAS, inherent drug resistance in PDAC cells, and acquired resistance through activation of alternative mitogenic pathways such JAK-STAT and PI3K-AKT. While KRAS canonically drives the MAPK signaling pathway via RAF-MEK-ERK, it is also known to play a role in PI3K-AKT signaling. Our therapeutic study targeted the PI3K-AKT pathway with the drug Omipalisib (p110α/ß/δ/γ and mTORC1/2 inhibitor) in combination with MAPK pathway targeting drug Trametinib (MEK1/2 inhibitor) or SHP099-HCL (SHP099), which is an inhibitor of the KRAS effector SHP2. Western blot analysis demonstrated that application of Trametinib or SHP099 alone selectively blocked ERK phosphorylation (pERK) but failed to suppress phosphorylated AKT (pAKT) and in some instances increased pAKT levels. Conversely, Omipalisib alone successfully inhibited pAKT but failed to suppress pERK. Therefore, we hypothesized that a combination therapeutic comprised of Omipalisib with either Trametinib or SHP099 would inhibit two prominent mitogenic pathways, MEK and PI3K-AKT, to more effectively suppress pancreatic cancer. In vitro studies demonstrated that both Omipalisib/Trametinib and Omipalisib/SHP099 combination therapeutic strategies were generally more effective than treatment with each drug individually at reducing proliferation, colony formation, and cell migration compared to vehicle controls. Additionally, we found that while combination Omipalisib/SHP099 treatment reduced implanted tumor growth in vivo , the Omipalisib/Trametinib treatment was significantly more effective. Therefore, we additionally tested the Omipalisib/Trametinib combination therapeutic in the highly aggressive PKT (Ptf1a cre , LSL-Kras G12D , TGFbR2 fl/fl ) spontaneous mouse model of PDAC. We subsequently found that PKT mice treated with the Omipalisib/Trametinib combination therapeutic survived significantly longer than mice treated with either drug alone, and more than doubled the mean survival time of vehicle control mice. Altogether, our data support the importance of a dual treatment strategy targeting both MAPK and PI3K-AKT pathways.

Improving the accuracy and internal consistency of regression-based clustering of high-dimensional datasets.

Component-wise Sparse Mixture Regression (CSMR) is a recently proposed regression-based clustering method that shows promise in detecting heterogeneous relationships between molecular markers and a continuous phenotype of interest. However, CSMR can yield inconsistent results when applied to high-dimensional molecular data, which we hypothesize is in part due to inherent limitations associated with the feature selection method used in the CSMR algorithm. To assess this hypothesis, we explored whether substituting different regularized regression methods (i.e. Lasso, Elastic Net, Smoothly Clipped Absolute Deviation (SCAD), Minmax Convex Penalty (MCP), and Adaptive-Lasso) within the CSMR framework can improve the clustering accuracy and internal consistency (IC) of CSMR in high-dimensional settings. We calculated the true positive rate (TPR), true negative rate (TNR), IC and clustering accuracy of our proposed modifications, benchmarked against the existing CSMR algorithm, using an extensive set of simulation studies and real biological datasets. Our results demonstrated that substituting Adaptive-Lasso within the existing feature selection method used in CSMR led to significantly improved IC and clustering accuracy, with strong performance even in high-dimensional scenarios. In conclusion, our modifications of the CSMR method resulted in improved clustering performance and may thus serve as viable alternatives for the regression-based clustering of high-dimensional datasets.

Statistical Methods for Integrative Clustering of Multi-omics Data.

Cancers are heterogeneous diseases caused by accumulated mutations or abnormal alterations at multi-levels of biological processes including genomics, epigenomics, transcriptomics, and proteomics. There is a great clinical interest in identifying cancer molecular subtypes for disease prognosis and personalized medicine. Integrative clustering is a powerful unsupervised learning method that has been increasingly used to identify cancer molecular subtypes using multi-omics data including somatic mutations, DNA copy numbers, DNA methylation, and gene expression. Integrative clustering methods are generally classified into model-based or nonparametric approaches. In this chapter, we will give an overview of the frequently used model-based methods, including iCluster, iClusterPlus, and iClusterBayes, and the nonparametric method, integrative nonnegative matrix factorization (intNMF). We will use the integrative analyses of uveal melanoma and lower-grade glioma to illustrate these representative methods. Finally, we will discuss the strengths and limitations of these representative methods and give suggestions for performing integrative analyses of cancer multi-omics data in practice.

Persistent circulation of soluble and extracellular vesicle-linked Spike protein in individuals with postacute sequelae of COVID-19.

SARS-CoV-2, the causative agent of COVID-19 disease, has resulted in the death of millions worldwide since the beginning of the pandemic in December 2019. While much progress has been made to understand acute manifestations of SARS-CoV-2 infection, less is known about post-acute sequelae of COVID-19 (PASC). We investigated the levels of both Spike protein (Spike) and viral RNA circulating in patients hospitalized with acute COVID-19 and in patients with and without PASC. We found that Spike and viral RNA were more likely to be present in patients with PASC. Among these patients, 30% were positive for both Spike and viral RNA; whereas, none of the individuals without PASC were positive for both. The levels of Spike and/or viral RNA in the PASC+ve patients were found to be increased or remained the same as in the acute phase; whereas, in the PASC-ve group, these viral components decreased or were totally absent. Additionally, this is the first report to show that part of the circulating Spike is linked to extracellular vesicles without any presence of viral RNA in these vesicles. In conclusion, our findings suggest that Spike and/or viral RNA fragments persist in the recovered COVID-19 patients with PASC up to 1 year or longer after acute SARS-CoV-2 infection.

Assessing equivalent and inverse change in genes between diverse experiments.

Background: It is important to identify when two exposures impact a molecular marker (e.g., a gene's expression) in similar ways, for example, to learn that a new drug has a similar effect to an existing drug. Currently, statistically robust approaches for making comparisons of equivalence of effect sizes obtained from two independently run treatment vs. control comparisons have not been developed. Results: Here, we propose two approaches for evaluating the question of equivalence between effect sizes of two independent studies: a bootstrap test of the Equivalent Change Index (ECI), which we previously developed, and performing Two One-Sided t-Tests (TOST) on the difference in log-fold changes directly. The ECI of a gene is computed by taking the ratio of the effect size estimates obtained from the two different studies, weighted by the maximum of the two p-values and giving it a sign indicating if the effects are in the same or opposite directions, whereas TOST is a test of whether the difference in log-fold changes lies outside a region of equivalence. We used a series of simulation studies to compare the two tests on the basis of sensitivity, specificity, balanced accuracy, and F1-score. We found that TOST is not efficient for identifying equivalently changed gene expression values (F1-score = 0) because it is too conservative, while the ECI bootstrap test shows good performance (F1-score = 0.95). Furthermore, applying the ECI bootstrap test and TOST to publicly available microarray expression data from pancreatic cancer showed that, while TOST was not able to identify any equivalently or inversely changed genes, the ECI bootstrap test identified genes associated with pancreatic cancer. Additionally, when investigating publicly available RNAseq data of smoking vs. vaping, no equivalently changed genes were identified by TOST, but ECI bootstrap test identified genes associated with smoking. Conclusion: A bootstrap test of the ECI is a promising new statistical approach for determining if two diverse studies show similarity in the differential expression of genes and can help to identify genes which are similarly influenced by a specific treatment or exposure. The R package for the ECI bootstrap test is available at https://github.com/Hecate08/ECIbootstrap.

Integration of differential expression and network structure for 'omics data analysis.

Differential expression (DE) analysis has been routinely used to identify molecular features that are statistically significantly different between distinct biological groups. In recent years, differential network (DN) analysis has emerged as a powerful approach to uncover molecular network structure changes from one biological condition to the other where the molecular features with larger topological changes are selected as biomarkers. Although a large number of DE and a few DN-based methods are available, they have been usually implemented independently. DE analysis ignores the relationship among molecular features while DN analysis does not account for the expression changes at individual level. Therefore, an integrative analysis approach that accounts for both DE and DN is required to identify disease associated key features. Although, a handful of methods have been proposed, there is no method that optimizes the combination of DE and DN. We propose a novel integrative analysis method, DNrank, to identify disease-associated molecular features that leverages the strengths of both DE and DN by calculating a weight using resampling based cross validation scheme within the algorithm. First, differential expression analysis of individual molecular features is carried out. Second, a differential network structure is constructed using the differential partial correlation analysis. Third, the molecular features are ranked in the order of their significances by integrating their DE measures and DN structure using the modified Google's PageRank algorithm. In the algorithm, the optimum combination of DE and DN analyses is achieved by evaluating the prediction performance of top-ranked features utilizing support vector machine classifier with Monte Carlo cross validation. The proposed method is illustrated using both simulated data and three real data sets. The results show that the proposed method has a better performance in identifying important molecular features with respect to predictive discrimination. Also, as compared to existing feature selection methods, the top-ranked features selected by our method had a higher stability in selection. DNrank allows the researchers to identify the disease-associated features by utilizing both expression and network topology changes between two groups.

A Mitochondrial DNA Haplogroup Defines Patterns of Five-Year Cognitive Change.

BACKGROUND: Mitochondrial DNA (mtDNA) may play a role in Alzheimer's disease (AD) and cognitive decline. A particular haplogroup of mtDNA, haplogroup J, has been observed more commonly in patients with AD than in cognitively normal controls. OBJECTIVE: We used two mtDNA haplogroups, H and J, to predict change in cognitive performance over five years. We hypothesized that haplogroup J carriers would show less cognitive resilience. METHODS: We analyzed data from 140 cognitively normal older adults who participated in the University of Kansas Alzheimer's Disease Research Center clinical cohort between 2011 and 2020. We used factor analysis to create three composite scores (verbal memory, attention, and executive function) from 11 individual cognitive tests. We performed latent growth curve modeling to describe trajectories of cognitive performance and change adjusting for age, sex, years of education, and APOE ɛ4 allele carrier status. We compared haplogroup H, the most common group, to haplogroup J, the potential risk group. RESULTS: Haplogroup J carriers had significantly lower baseline performance and slower rates of improvement on tests of verbal memory compared to haplogroup H carriers. We did not observe differences in executive function or attention. CONCLUSION: Our results reinforce the role of mtDNA in changes to cognitive function in a domain associated with risk for dementia, verbal memory, but not with other cognitive domains. Future research should investigate the distinct mechanisms by which mtDNA might affect performance on verbal memory as compared to other cognitive domains across haplogroups.

Challenging the Vestibular System Affects Gait Speed and Cognitive Workload in Chronic Mild Traumatic Brain Injury and Healthy Adults.

People with persistent symptoms after mild traumatic brain injury (mTBI) report imbalance during walking with head movements. The purpose of this study was (1) to compare usual walk gait speed to walking with head turns (HT) between people with mTBI and controls, (2) to compare the cognitive workload from usual walk to HT walk between groups, and (3) to examine if gaze stability deficits and mTBI symptoms influence gait speed. Twenty-three individuals (mean age 55.7 ± 9.3 years) with persistent symptoms after mTBI (between 3 months to 2 years post-injury) were compared with 23 age and sex-matched controls. Participants walked a 12-inch wide, 60-foot walkway when looking ahead and when walking with HT to identify letters and their colors. Gait speed during usual walk and HT walk were calculated. Pupillary responses during both walks were converted to the Index of Cognitive Activity (ICA) as a measure of cognitive workload. Gaze stability was examined by the dynamic visual acuity (DVA) test in the yaw plane. The post-concussion symptom scale (PCSS) was used to collect symptom severity. Within group analysis showed that gait speed was lower during HT walk compared to usual walk in the people with mTBI (p < 0.001) as well as in controls (p < 0.001). ICA was higher with HT compared to usual walk in the mTBI group in the right eye (p = 0.01) and left eye (p = 0.001), and in controls in the right eye (p = 0.01) and left eye (p = 0.01). Participants in the mTBI group had slower usual (p < 0.001), and HT gait speed (p < 0.001) compared to controls. No differences were noted in ICA in the right or left eye during usual walk and HT walk between groups (p > 0.05). DVA loss in the yaw plane to the right and left was not different between groups (p > 0.05) and were not correlated with gait speed. PCSS scores were correlated with usual walk (r = -0.50, p < 0.001) and HT gait speed (r = -0.44, p = 0.002). Slower gait speed, poorer stability, and higher cognitive workload during walking with head turns may reduce community participation in people with mTBI and persistent symptoms.

Influences of Gastrointestinal Microbiota Dysbiosis on Serum Proinflammatory Markers in Epithelial Ovarian Cancer Development and Progression.

GI microbiota has been implicated in producing the inflammatory tumor microenvironment of several cancers. Women with ovarian cancer often report GI-related symptoms at diagnosis although minimal is known about the possible GI bacteria that may trigger pro-tumorigenic immune responses in early EOC. The purpose of this study was to investigate the influences of GI microbiota dysbiosis on serum inflammatory markers during EOC utilizing a rodent model. This experimental design consisted of C57BL/6 mice randomly assigned to either the microbiota dysbiosis group (n = 6) or control group (n = 5). The CD7BL/6 mice assigned to the microbiota dysbiosis group were administered a mixture of broad-spectrum antibiotics (bacitracin and neomycin) for 2 weeks. Both groups were injected intraperitoneally with mouse ovarian epithelial cells that induce ovarian tumorigenesis. Levels of C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) were assessed in the serum, and the composition of the GI microbiota in fecal samples was measured using 16S rRNA gene sequencing. Overall CRP serum levels were significantly lower and TNFα levels were significantly higher in the microbiota dysbiosis group compared to the control group. The abundances of microbiota that correlated with CRP serum levels in the combined groups were genus Parabacteroides, Roseburia, and Emergencia and species Ruminococcus faecis, Parabacteroides distasonis, Roseburia Faecis, and Emergencia timonensis. This study provides evidence to support for further investigation of the GI microbial profiles in patients at risk of EOC.

Assessing Omega-3 Intake in Sport: the Brief Food Frequency Questionnaire and the Omega-3 Index in Collegiate Women Soccer Players.

CONTEXT: Omega-3 fatty acids modulate inflammatory processes and are considered beneficial for sport populations, highlighting a need to assess omega-3 intake in a practical manner. Food frequency questionnaires (FFQs) are inexpensive, noninvasive tools aimed at evaluating nutrient intakes such as omega-3 fatty acids. In healthy adults, a tailored, brief FFQ for estimating omega-3 intake was associated with the erythrocyte omega-3 fatty acid level, a biomarker for omega-3 tissue status and indicative of intake. However, the association between a brief omega-3 FFQ and erythrocyte levels, particularly the Omega-3 Index (eicosapentaenoic acid [EPA], docosahexaenoic acid [DHA], and EPA + DHA), has yet to be explored in a sport population. OBJECTIVE: To examine the association between omega-3 intake using a brief FFQ and the Omega-3 Index in collegiate women soccer players. DESIGN: Cross-sectional study. SETTING: University sport team. PATIENTS OR OTHER PARTICIPANTS: Thirty-one National Collegiate Athletic Association Division I collegiate women soccer players. MAIN OUTCOME MEASURE(S): The brief omega-3 FFQ assessed dietary omega-3 intake: DHA and EPA. The OmegaQuant blood test measured erythrocyte omega-3 fatty acid (EPA, DHA) and Omega-3 Index (EPA + DHA) levels. RESULTS: Brief FFQ intakes of EPA, DHA, and EPA + DHA were positively correlated with the erythrocyte EPA (r = 0.48, P = .007), DHA (r = 0.73, P < .001), and Omega-3 Index (r = 0.73, P < .001). CONCLUSIONS: In a sample of collegiate women soccer players, the brief omega-3 FFQ was correlated with erythrocyte omega-3 fatty acid levels and may offer health practitioners a practical tool for assessing omega-3 intake in this collegiate sport population.

Letter to the Editor: on the stability and internal consistency of component-wise sparse mixture regression-based clustering.

Understanding the relationship between molecular markers and a phenotype of interest is often obfuscated by patient-level heterogeneity. To address this challenge, Chang et al. recently published a novel method called Component-wise Sparse Mixture Regression (CSMR), a regression-based clustering method that promises to detect heterogeneous relationships between molecular markers and a phenotype of interest under high-dimensional settings. In this Letter to the Editor, we raise awareness to several issues concerning the assessment of CSMR in Chang et al., particularly its assessment in settings where the number of features, P, exceeds the study sample size, N, and advocate for additional metrics/approaches when assessing the performance of regression-based clustering methodologies.

Oculomotor Deficits and Symptom Severity Are Associated With Poorer Dynamic Mobility in Chronic Mild Traumatic Brain Injury.

Oculomotor deficits, vestibular impairments, and persistent symptoms are common after a mild traumatic brain injury (mTBI); however, the relationship between visual-vestibular deficits, symptom severity, and dynamic mobility tasks is unclear. Twenty-three individuals (mean age 55.7 ± 9.3 years) with persistent symptoms after mTBI, who were between 3 months to 2 years post-injury were compared with 23 age and sex-matched controls. Oculomotor deficits [depth perception, near-point convergence, baseline visual acuity (BLVA), perception time], vestibular deficits (dynamic visual acuity in the pitch and yaw planes), dynamic mobility measured by the Functional Gait Assessment (FGA), and symptoms measured by the Post-Concussion Symptom Scale (PCSS) and Dizziness Handicap Inventory (DHI) were compared between groups. Participants with mTBI had poorer performance on the FGA (p < 0.001), higher symptom severity on the PCSS (p < 0.001), and higher DHI scores (p < 0.001) compared to controls. Significant differences were seen on specific items of the FGA between individuals with mTBI and controls during walking with horizontal head turns (p = 0.002), walking with vertical head tilts (p < 0.001), walking with eyes closed (p = 0.003), and stair climbing (p = 0.001). FGA performance was correlated with weeks since concussion (r = -0.67, p < 0.001), depth perception (r = -0.5348, p < 0.001), near point convergence (r = -0.4717, p = 0.001), baseline visual acuity (r = -0.4435, p = 0.002); as well as with symptoms on the PCSS (r = -0.668, p < 0.001), and DHI (r = -0.811, p < 0.001). Dynamic balance deficits persist in chronic mTBI and may be addressed using multifaceted rehabilitation strategies to address oculomotor dysfunction, post-concussion symptoms, and perception of handicap due to dizziness.

Extracellular vesicle-mediated endothelial apoptosis and EV-associated proteins correlate with COVID-19 disease severity.

Coronavirus disease-2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has lead to a global pandemic with a rising toll in infections and deaths. Better understanding of its pathogenesis will greatly improve the outcomes and treatment of affected patients. Here we compared the inflammatory and cardiovascular disease-related protein cargo of circulating large and small extracellular vesicles (EVs) from 84 hospitalized patients infected with SARS-CoV-2 with different stages of disease severity. Our findings reveal significant enrichment of proinflammatory, procoagulation, immunoregulatory and tissue-remodelling protein signatures in EVs, which remarkably distinguished symptomatic COVID-19 patients from uninfected controls with matched comorbidities and delineated those with moderate disease from those who were critically ill. Specifically, EN-RAGE, followed by TF and IL-18R1, showed the strongest correlation with disease severity and length of hospitalization. Importantly, EVs from COVID-19 patients induced apoptosis of pulmonary microvascular endothelial cells in the order of disease severity. In conclusion, our findings support a role for EVs in the pathogenesis of COVID-19 disease and underpin the development of EV-based approaches to predicting disease severity, determining need for patient hospitalization and identifying new therapeutic targets.

Extracellular Vesicle TGF-ß1 Is Linked to Cardiopulmonary Dysfunction in Human Immunodeficiency Virus.

Extracellular vesicles (EVs) have emerged as important mediators in cell-cell communication; however, their relevance in pulmonary hypertension (PH) secondary to human immunodeficiency virus (HIV) infection is yet to be explored. Considering that circulating monocytes are the source of the increased number of perivascular macrophages surrounding the remodeled vessels in PH, this study aimed to identify the role of circulating small EVs and EVs released by HIV-infected human monocyte-derived macrophages in the development of PH. We report significantly higher numbers of plasma-derived EVs carrying higher levels of TGF-ß1 (transforming growth factor-ß1) in HIV-positive individuals with PH compared with individuals without PH. Importantly, levels of these TGF-ß1-loaded, plasma-derived EVs correlated with pulmonary arterial systolic pressures and CD4 counts but did not correlate with the Dl CO or viral load. Correspondingly, enhanced TGF-ß1-dependent pulmonary endothelial injury and smooth muscle hyperplasia were observed. HIV-1 infection of monocyte-derived macrophages in the presence of cocaine resulted in an increased number of TGF-ß1-high EVs, and intravenous injection of these EVs in rats led to increased right ventricle systolic pressure accompanied by myocardial injury and increased levels of serum ET-1 (endothelin-1), TNF-α, and cardiac troponin-I. Conversely, pretreatment of rats with TGF-ß receptor 1 inhibitor prevented these EV-mediated changes. Findings define the ability of macrophage-derived small EVs to cause pulmonary vascular modeling and PH via modulation of TGF-ß signaling and suggest clinical implications of circulating TGF-ß-high EVs as a potential biomarker of HIV-associated PH.

Progesterone promotes immunomodulation and tumor development in the murine mammary gland.

BACKGROUND: Clinical studies have linked usage of progestins (synthetic progesterone [P4]) to breast cancer risk. However, little is understood regarding the role of native P4, signaling through the progesterone receptor (PR), in breast tumor formation. Recently, we reported a link between PR and immune signaling pathways, showing that P4/PR can repress type I interferon signaling pathways. Given these findings, we sought to investigate whether P4/PR drive immunomodulation in the mammary gland and promote tumor formation. METHODS: To determine the effect of P4 on immune cell populations in the murine mammary gland, mice were treated with P4 or placebo pellets for 21 days. Immune cell populations in the mammary gland, spleen, and inguinal lymph nodes were subsequently analyzed by flow cytometry. To assess the effect of PR overexpression on mammary gland tumor development as well as immune cell populations in the mammary gland, a transgenic mouse model was used in which PR was overexpressed throughout the entire mouse. Immune cell populations were assessed in the mammary glands, spleens, and inguinal lymph nodes of 6-month-old transgenic and control mice by flow cytometry. Transgenic mice were also monitored for mammary gland tumor development over a 2-year time span. Following development of mammary gland tumors, immune cell populations in the tumors and spleens of transgenic and control mice were analyzed by flow cytometry. RESULTS: We found that mice treated with P4 exhibited changes in the mammary gland indicative of an inhibited immune response compared with placebo-treated mice. Furthermore, transgenic mice with PR overexpression demonstrated decreased numbers of immune cell populations in their mammary glands, lymph nodes, and spleens. On long-term monitoring, we determined that multiparous PR-overexpressing mice developed significantly more mammary gland tumors than control mice. Additionally, tumors from PR-overexpressing mice contained fewer infiltrating immune cells. Finally, RNA sequencing analysis of tumor samples revealed that immune-related gene signatures were lower in tumors from PR-overexpressing mice as compared with control mice. CONCLUSION: Together, these findings offer a novel mechanism of P4-driven mammary gland tumor development and provide rationale in investigating the usage of antiprogestin therapies to promote immune-mediated elimination of mammary gland tumors.