Deep phenotyping of post-infectious myalgic encephalomyelitis/chronic fatigue syndrome.

Post-infectious myalgic encephalomyelitis/chronic fatigue syndrome (PI-ME/CFS) is a disabling disorder, yet the clinical phenotype is poorly defined, the pathophysiology is unknown, and no disease-modifying treatments are available. We used rigorous criteria to recruit PI-ME/CFS participants with matched controls to conduct deep phenotyping. Among the many physical and cognitive complaints, one defining feature of PI-ME/CFS was an alteration of effort preference, rather than physical or central fatigue, due to dysfunction of integrative brain regions potentially associated with central catechol pathway dysregulation, with consequences on autonomic functioning and physical conditioning. Immune profiling suggested chronic antigenic stimulation with increase in naïve and decrease in switched memory B-cells. Alterations in gene expression profiles of peripheral blood mononuclear cells and metabolic pathways were consistent with cellular phenotypic studies and demonstrated differences according to sex. Together these clinical abnormalities and biomarker differences provide unique insight into the underlying pathophysiology of PI-ME/CFS, which may guide future intervention.

Mock community taxonomic classification performance of publicly available shotgun metagenomics pipelines.

Shotgun metagenomic sequencing comprehensively samples the DNA of a microbial sample. Choosing the best bioinformatics processing package can be daunting due to the wide variety of tools available. Here, we assessed publicly available shotgun metagenomics processing packages/pipelines including bioBakery, Just a Microbiology System (JAMS), Whole metaGenome Sequence Assembly V2 (WGSA2), and Woltka using 19 publicly available mock community samples and a set of five constructed pathogenic gut microbiome samples. Also included is a workflow for labelling bacterial scientific names with NCBI taxonomy identifiers for better resolution in assessing results. The Aitchison distance, a sensitivity metric, and total False Positive Relative Abundance were used for accuracy assessments for all pipelines and mock samples. Overall, bioBakery4 performed the best with most of the accuracy metrics, while JAMS and WGSA2, had the highest sensitivities. Furthermore, bioBakery is commonly used and only requires a basic knowledge of command line usage. This work provides an unbiased assessment of shotgun metagenomics packages and presents results assessing the performance of the packages using mock community sequence data.

Geographic social vulnerability is associated with the alpha diversity of the human microbiome.

IMPORTANCE: As a risk factor for conditions related to the microbiome, understanding the role of SVI on microbiome diversity may assist in identifying public health implications for microbiome research. Here we found, using a sub-sample of the Human Microbiome Project phase 1 cohort, that SVI was linked to microbiome diversity across body sites and that SVI may influence race/ethnicity-based differences in diversity. Our findings, build on the current knowledge regarding the role of human geography in microbiome research, suggest that measures of geographic social vulnerability be considered as additional contextual factors when exploring microbiome alpha diversity.

Multi-amplicon microbiome data analysis pipelines for mixed orientation sequences using QIIME2: Assessing reference database, variable region and pre-processing bias in classification of mock bacterial community samples.

Microbiome research relies on next-generation sequencing and on downstream data analysis workflows. Several manufacturers have introduced multi-amplicon kits for microbiome characterization, improving speciation, but present unique challenges for analysis. The goal of this methodology study was to develop two analysis pipelines specific to mixed-orientation reads from multi-hypervariable (V) region amplicons. A secondary aim was to assess agreement with expected abundance, considering database and variable region. Mock community sequence data (n = 41) generated using the Ion16S™ Metagenomics Kit and Ion Torrent Sequencing Platform were analyzed using two workflows. Amplicons from V2, V3, V4, V6-7, V8 and V9 were deconvoluted using a specialized plugin based on CutPrimers. A separate workflow using Cutadapt is also presented. Three reference databases (Ribosomal Database Project, Greengenes and Silva) were used for taxonomic assignment. Bray-Curtis, Euclidean and Jensen-Shannon distance measures were used to evaluate overall annotation consistency, and specific taxon agreement was determined by calculating the ratio of observed to expected relative abundance. Reads that mapped to regions V2-V9 varied for both CutPrimers and Cutadapt-based methods. Within the CutPrimers-based pipeline, V3 amplicons had the best agreement with the expected distribution, tested using global distance measures, while V9 amplicons had the worst agreement. Accurate taxonomic annotation varied by genus-level taxon and V region analyzed. For the first time, we present a microbiome analysis pipeline that employs a specialized plugin to allow microbiome researchers to separate multi-amplicon data from the Ion16S Metagenomics Kit into V-specific reads. We also present an additional analysis workflow, modified for Ion Torrent mixed orientation reads. Overall, the global agreement of amplicons with the expected mock community abundances differed across V regions and reference databases. Benchmarking data should be referenced when planning a microbiome study to consider these biases related to sequencing and data analysis for multi-amplicon sequencing kits.

A lower sleep regularity index (SRI) is associated with relapse in individuals with alcohol use disorder following inpatient treatment.

The sleep regularity index (SRI) is used to measure an individual's sleep/wake consistency over time. The SRI has been associated with certain health risks; to date, research investigating the relationship between the SRI and relapse in individuals with alcohol use disorder (AUD) is lacking. The aim of this work was to evaluate the SRI and relapse in individuals with AUD following inpatient treatment. Individuals with AUD (n = 77, mean age = 49.5 ± 10.86) were assessed for 28-days following discharge from an inpatient treatment program. Logistic regression was applied to examine the impact of SRI on relapse as the outcome variable of interest. Sleep quality was lower in individuals who relapsed compared to those who did not. Moreover, SRI scores were significantly worse in those who relapsed compared to those who did not. Over the entire patient cohort, lower weekly SRI scores were significantly correlated with longer weekly nap duration. Logistic regression model results indicated that the overall SRI was a significant predictor of relapse. The SRI represents a relevant aspect of sleep health and should be considered when assessing an individual's sleeping patterns. Behavior based interventions related to the importance of individualized consistency in sleep and wake patterns may be particularly important for treatment seeking individuals with AUD not only during inpatient treatment, but also once these individuals have transitioned into their outpatient phase of recovery. These findings support the notion of SRI as a separate facet of sleep health worth investigating in at-risk, disease specific groups.

The role of the oral microbiome in smoking-related cardiovascular risk: a review of the literature exploring mechanisms and pathways.

Cardiovascular disease is a leading cause of morbidity and mortality. Oral health is associated with smoking and cardiovascular outcomes, but there are gaps in knowledge of many mechanisms connecting smoking to cardiovascular risk. Therefore, the aim of this review is to synthesize literature on smoking and the oral microbiome, and smoking and cardiovascular risk/disease, respectively. A secondary aim is to identify common associations between the oral microbiome and cardiovascular risk/disease to smoking, respectively, to identify potential shared oral microbiome-associated mechanisms. We identified several oral bacteria across varying studies that were associated with smoking. Atopobium, Gemella, Megasphaera, Mycoplasma, Porphyromonas, Prevotella, Rothia, Treponema, and Veillonella were increased, while Bergeyella, Haemophilus, Lautropia, and Neisseria were decreased in the oral microbiome of smokers versus non-smokers. Several bacteria that were increased in the oral microbiome of smokers were also positively associated with cardiovascular outcomes including Porphyromonas, Prevotella, Treponema, and Veillonella. We review possible mechanisms that may link the oral microbiome to smoking and cardiovascular risk including inflammation, modulation of amino acids and lipids, and nitric oxide modulation. Our hope is this review will inform future research targeting the microbiome and smoking-related cardiovascular disease so possible microbial targets for cardiovascular risk reduction can be identified.

An exploratory study of pro-inflammatory cytokines in individuals with alcohol use disorder: MCP-1 and IL-8 associated with alcohol consumption, sleep quality, anxiety, depression, and liver biomarkers.

Background: High levels of sleep disturbances reported among individuals with alcohol use disorder (AUD) can stimulate inflammatory gene expression, and in turn, may alter pro-inflammatory cytokines levels. We aimed to investigate associations between pro-inflammatory cytokine markers with subjective measures of sleep quality, psychological variables and alcohol consumption among individuals with AUD. Methods: This exploratory study is comprised of individuals with AUD (n = 50) and healthy volunteers (n = 14). Spearman correlation was used to investigate correlations between plasma cytokine levels and clinical variables of interest (liver and inflammatory markers, sleep quality, patient reported anxiety/depression scores, and presence of mood and/or anxiety disorders (DSM IV/5); and history of alcohol use variables. Results: The AUD group was significantly older, with poorer sleep quality, higher anxiety/depression scores, and higher average drinks per day as compared to controls. Within the AUD group, IL-8 and MCP-1 had positive significant correlations with sleep, anxiety, depression and drinking variables. Specifically, higher levels of MCP-1 were associated with poorer sleep (p = 0.004), higher scores of anxiety (p = 0.006) and depression (p < 0.001), and higher number of drinking days (p = 0.002), average drinks per day (p < 0.001), heavy drinking days (p < 0.001) and total number of drinks (p < 0.001). The multiple linear regression model for MCP-1 showed that after controlling for sleep status and heavy drinking days, older participants (p = 0.003) with more drinks per day (p = 0.016), and higher alkaline phosphatase level (p = 0.001) had higher MCP-1 level. Conclusion: This exploratory analysis revealed associations with cytokines MCP-1 and IL-8 and drinking consumption, sleep quality, and anxiety and depression in the AUD group. Furthermore, inflammatory and liver markers were highly correlated with certain pro-inflammatory cytokines in the AUD group suggesting a possible relationship between chronic alcohol use and inflammation. These associations may contribute to prolonged inflammatory responses and potentially higher risk of co-morbid chronic diseases.

Dietary Intakes of Patients with Alcohol Use Disorder During a 4-Week Protocol on an Inpatient Treatment Unit Found to Meet Dietary Reference Intakes for Macronutrients, but Have Variability in Energy Balance and Adequacy of Micronutrient Intake.

BACKGROUND: Despite literature supporting the importance of diet during rehabilitation, minimal research quantifies dietary intake during treatment for alcohol use disorder (AUD). OBJECTIVE: The aim was to quantify dietary intake and energy balance of patients with AUD during inpatient treatment. DESIGN: This was a secondary analysis of data from a 4-week observational protocol. Participants self-selected food from a room service menu. Dietary intake was recorded by patients and reviewed by nutrition staff. To quantify nutrient and food group intake, data were coded into Nutrition Data Systems for Research software, versions 2016 and 2017. Daily average intake was calculated for all dietary variables. PARTICIPANTS/SETTING: Participants (n = 22) were adults seeking treatment for AUD at the National Institutes of Health Clinical Center (Bethesda, MD) between September 2016 and September 2017 and who were enrolled in a study examining the microbiome during AUD rehabilitation. Four participants discontinued protocol participation before study week 4 and were not included in analyses examining change over time. MAIN OUTCOME MEASURES: Weight change, daily energy, and macronutrient and select micronutrient intakes were the main outcome measures included. STATISTICAL ANALYSES PERFORMED: Mean differences in intake and weight were assessed using nonparametric tests. RESULTS: Sixty-four percent of participants were male; mean ± SD age was 46.3 ± 13.0 years, mean ± SD body mass index (calculated as kg/m2) was 23.9 ± 2.5, and mean intake was 2,665 kcal/d (consisting of 45.9% carbohydrate, 34.9% fat, and 19.1% protein). Eighty percent or more of this sample met the Estimated Average Requirement for 10 of 16 micronutrients assessed. Male participants consumed more energy than estimated needs (P = .003) and gained a mean ± SD of 2.67 ± 1.84 kg (P = .006) when an outlier with weight loss and acute pancreatitis was removed from analysis. Female participants did not gain weight or consume more than estimated energy needs. CONCLUSIONS: Overall macronutrient intake was within recommended ranges, but intake of other dietary components and weight gain were variable, supporting the need for individualized nutrition care during AUD treatment.

Comparing Published Gut Microbiome Taxonomic Data Across Multinational Studies.

BACKGROUND: Nurse researchers are well poised to study the connection of the microbiome to health and disease. Evaluating published microbiome results can assist with study design and hypothesis generation. OBJECTIVES: This article aims to present and define important analysis considerations in microbiome study planning and to identify genera shared across studies despite methodological differences. This methods article will highlight a workflow that the nurse scientist can use to combine and evaluate taxonomy tables for microbiome study or research proposal planning. METHODS: We compiled taxonomy tables from 13 published gut microbiome studies that had used Ion Torrent sequencing technology. We searched for studies that had amplified multiple hypervariable (V) regions of the 16S rRNA gene when sequencing the bacteria from healthy gut samples. RESULTS: We obtained 15 taxonomy tables from the 13 studies, comprised of samples from four continents and eight V regions. Methodology among studies was highly variable, including differences in V regions amplified, geographic location, and population demographics. Nevertheless, of the 354 total genera identified from the 15 data sets, 25 were shared in all V regions and the four continents. When relative abundance differences across the V regions were compared, Dorea and Roseburia were statistically different. Taxonomy tables from Asian subjects had increased average abundances of Prevotella and lowered abundances of Bacteroides compared with the European, North American, and South American study subjects. DISCUSSION: Evaluating taxonomy tables from previously published literature is essential for study planning. The genera found from different V regions and continents highlight geography and V region as important variables to consider in microbiome study design. The 25 shared genera across the various studies may represent genera commonly found in healthy gut microbiomes. Understanding the factors that may affect the results from a variety of microbiome studies will allow nurse scientists to plan research proposals in an informed manner. This work presents a valuable framework for future cross-study comparisons conducted across the globe.

Extracellular vesicle associated and soluble immune marker profiles of psychoneurological symptom clusters in men with prostate cancer: an exploratory study.

Psychoneurological symptom clusters are co-occurring and interrelated physiological symptoms that may include cancer-related fatigue, pain, depressive symptoms, cognitive disturbances, and sleep disturbances. These symptoms are hypothesized to share a common systemic proinflammatory etiology. Thus, an investigation of systemic immune biomarkers is an important approach to test this hypothesis. Here, we investigated the associations between extracellular vesicle (EV)-associated and soluble cytokines with immune markers and symptom clusters in men with non-metastatic prostate cancer. This observational study included 40 men with non-metastatic prostate cancer at the start (T1) of external beam radiation therapy (EBRT) and 3 months post treatment (T2), as well as 20 men with non-metastatic prostate cancer on active surveillance (AS) seen at one time point. Collected questionnaires assessed patient-reported fatigue, sleep disturbances, depressive symptoms, and cognitive fatigue. In total, 45 soluble and EV-associated biomarkers in plasma were determined by multiplex assays. Principal component analysis (PCA) was used to identify psychoneurological symptom clusters for each study group and their time points. Bivariate correlation analysis was run for each identified PCA cluster with the concentrations of EV-associated and soluble cytokines and immune markers. Both EV-associated and soluble forms of RANTES significantly correlated with the symptom cluster for EBRT at T1, whereas, at T2, soluble IFNα2, IL-9, and IL-17 correlated with the corresponding symptom cluster. For the AS group, soluble survivin correlated with psychoneurological symptoms. Linking specific inflammatory cytokines with psychoneurological symptom clusters in men receiving prostate cancer treatment can enhance understanding of the underlying mechanisms of this phenomenon and aid in developing targeted interventions.

Steroid Hormone Biosynthesis Metabolism Is Associated With Fatigue Related to Androgen Deprivation Therapy for Prostate Cancer.

BACKGROUND: Androgen deprivation therapy (ADT) is a cornerstone treatment for prostate cancer. Despite the clinical benefits, ADT is associated with multiple adverse effects including fatigue. The goal of the study was to examine metabolomic changes to better understand cancer-related fatigue specific to ADT treatment. METHODS: A total of 160 plasma samples collected from participants with (+ADT, n = 58) or without neoadjuvant ADT (-ADT, n = 102) prior to radiation therapy for treatment of non-metastatic localized prostate cancer were included in the study. Fatigue and sleep-related impairment were measured using the Patient Reported Outcomes Measurement Information System. Plasma metabolites were identified and measured using untargeted ultrahigh-performance liquid chromatography/mass spectrometry metabolomics analyses. Partial least square discriminant analysis was used to identify discriminant metabolite features, and the diagnostic performance of selected classifiers was quantified using AUROC curve analysis. Pathway enrichment analysis was performed using metabolite sets enrichment analyses. FINDINGS: Steroid hormone biosynthesis pathways, including androstenedione metabolism as well as androgen and estrogen metabolism, were overrepresented by metabolites that significantly discriminated samples in the +ADT from the -ADT group. Additional overrepresented metabolic pathways included amino acid metabolism, glutathione metabolism, and carnitine synthesis. Of the metabolites that were significantly different between the groups, steroid hormone biosynthesis metabolites were most significantly correlated with fatigue severity. Sleep-related impairment was strongly correlated with fatigue severity and inversely correlated with ADT-induced reduction in androsterone sulfate. CONCLUSIONS: Patients with non-metastatic prostate cancer receiving neoadjuvant ADT prior to radiation therapy reported relatively more severe fatigue. Increased fatigue in this population may be attributable to sleep-related impairment associated with alterations in steroid hormone biosynthesis. Findings in this study provide a basis for further research of changes in sleep patterns and their role in this specific subcategory of cancer-related fatigue caused by the treatment.

Plasma metabolomic profile associated with fatigue in cancer patients.

BACKGROUND: Metabolomics is the newest -omics methodology and allows for a functional snapshot of the biochemical activity and cellular state. The goal of this study is to characterize metabolomic profiles associated with cancer-related fatigue, a debilitating symptom commonly reported by oncology patients. METHODS: Untargeted ultrahigh performance liquid chromatography/mass spectrometry metabolomics approach was used to identify metabolites in plasma samples collected from a total of 197 participants with or without cancer. Partial least squares-discriminant analysis (PLS-DA) was used to identify discriminant metabolite features, and diagnostic performance of selected classifiers was quantified using area under the receiver operating characteristics (AUROC) curve analysis. Pathway enrichment analysis was performed using Fisher's exact test and the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway database. FINDINGS: The global metabolomics approach yielded a total of 1120 compounds of known identity. Significant metabolic pathways unique to fatigued cancer versus control groups included sphingolipid metabolism, histidine metabolism, and cysteine and methionine metabolism. Significant pathways unique to non-fatigued cancer versus control groups included inositol phosphate metabolism, primary bile acid biosynthesis, ascorbate and aldarate metabolism, starch and sucrose metabolism, and pentose and glucuronate interconversions. Pathways shared between the two comparisons included caffeine metabolism, tyrosine metabolism, steroid hormone biosynthesis, sulfur metabolism, and phenylalanine metabolism. CONCLUSIONS: We found significant metabolomic profile differences associated with cancer-related fatigue. By comparing metabolic signatures unique to fatigued cancer patients with metabolites associated with, but not unique to, fatigued cancer individuals (overlap pathways) and metabolites associated with cancer but not fatigue, we provided a broad view of the metabolic phenotype of cancer-related fatigue.

The Oral and Gut Bacterial Microbiomes: Similarities, Differences, and Connections.

Background: The oral cavity is associated with local and systemic diseases, although oral samples are not as commonly studied as fecal samples in microbiome research. There is a gap in understanding between the similarities and differences in oral and gut microbiomes and how they may influence each other. Methods: A scoping literature review was conducted comparing oral and gut microbiome communities in healthy humans. Results: Ten manuscripts met inclusion criteria and were examined. The oral microbiome sites demonstrated great variance in differential bacterial abundance and the oral microbiome had higher alpha diversity as compared to the gut microbiome. Studies using 16S rRNA sequencing analysis resulted in overall community differences between the oral and gut microbiomes when beta diversity was analyzed. Shotgun metagenomics sequencing increased taxonomic resolution to strain level (intraspecies) and demonstrated a greater percentage of shared taxonomy and oral bacterial translocation to the gut microbiome community. Discussion: The oral and gut microbiome bacterial communities may be more similar than earlier research has suggested, when species strain is analyzed through shotgun metagenomics sequencing. The association between oral health and systemic diseases has been widely reported but many mechanisms underlying this relationship are unknown. Although future research is needed, the oral microbiome may be a novel interventional target through its downstream effects on the gut microbiome. As nurse scientists are experts in symptom characterization and phenotyping of patients, they are also well posed to lead research on the connection of the oral microbiome to the gut microbiome in health and disease.

Grade C molar-incisor pattern periodontitis subgingival microbial profile before and after treatment.

Aim: This study evaluated the influence of periodontal therapy on the microbiological profile of individuals with Grade C Molar-Incisor Pattern Periodontitis (C/MIP). Methods: Fifty-three African-American participants between the ages of 5-25, diagnosed with C/MIP were included. Patients underwent full mouth mechanical debridement with systemic antibiotics (metronidazole 250 mg + amoxicillin 500 mg, tid, 7 days). Subgingival samples were collected from a diseased and a healthy site from each individual prior to treatment and at 3, 6, 12, 18 and 24 months after therapy from the same sites. Samples were subjected to a 16S rRNA gene based-microarray. Results: Treatment was effective in reducing the main clinical parameters of disease. Aggregatibacter actinomycetemcomitans (A.a.) was the strongest species associated with diseased sites. Other species associated with diseased sites were Treponema lecithinolyticum and Tannerella forsythia. Species associated with healthy sites were Rothia dentocariosa/mucilaginosa, Eubacterium yurii, Parvimonas micra, Veillonella spp., Selenomonas spp., and Streptococcus spp. Overall, treatment was effective in strongly reducing A.a. and other key pathogens, as well as increasing health-associated species. These changes were maintained for at least 6 months. Conclusions:Treatment reduced putative disease-associated species, particularly A.a., and shifted the microbial profile to more closely resemble a healthy-site profile. (Clinicaltrials.gov registration #NCT01330719).

Longitudinal gut microbiome changes in alcohol use disorder are influenced by abstinence and drinking quantity.

Many patients with alcohol use disorder (AUD) consume alcohol chronically and in large amounts that alter intestinal microbiota, damage the gastrointestinal tract, and thereby injure other organs via malabsorption and intestinal inflammation. We hypothesized that alcohol consumption and subsequent abstinence would change the gut microbiome in adults admitted to a treatment program. Stool and oral specimens, diet data, gastrointestinal assessment scores, anxiety, depression measures and drinking amounts were collected longitudinally for up to 4 weeks in 22 newly abstinent inpatients with AUD who were dichotomized as less heavy drinkers (LHD, <10 drinks/d) and very heavy drinkers (VHD, 10 or more drinks/d). Next-generation 16 S rRNA gene sequencing was performed to measure the gut and oral microbiome at up to ten time points/subject and LHD and VHD were compared for change in principal components, Shannon diversity index and specific genera. The first three principal components explained 46.7% of the variance in gut microbiome diversity across time and all study subjects, indicating the change in gut microbiome following abstinence. The first time point was an outlier in three-dimensional principal component space versus all other time points. The gut microbiota in LHD and VHD were significantly dissimilar in change from day 1 to day 5 (p = .03) and from day 1 to week 3 (p = .02). The VHD drinking group displayed greater change from baseline. The Shannon diversity index of the gut microbiome changed significantly during abstinence in five participants. In both groups, the Shannon diversity was lower in the oral microbiome than gut. Ten total genera were shared between oral and stool in the AUD participants. These data were compared with healthy controls from the Human Microbiome Project to investigate the concept of a core microbiome. Rapid changes in gut microbiome following abstinence from alcohol suggest resilience of the gut microbiome in AUD and reflects the benefits of refraining from the highest levels of alcohol and potential benefits of abstinence.

TREML4 Promotes Inflammatory Programs in Human and Murine Macrophages and Alters Atherosclerosis Lesion Composition in the Apolipoprotein E Deficient Mouse.

The Triggering Receptor Expressed on Myeloid cells-like 4 (TREML4) is a member of the TREM receptor family, known modulators of inflammatory responses. We have previously found that TREML4 expression positively correlates with human coronary arterial calcification (CAC). However, the role of TREML4 in the pathogenesis of cardiovascular disease remains incompletely defined. Since macrophages play a key role in inflammatory conditions, we investigated if activated macrophages selectively expressed TREML4 and found that carriage of either one of the eQTL SNP's previously associated with increased TREML4 expression conferred higher expression in human inflammatory macrophages (M1) compared to alternatively activated macrophages (M2). Furthermore, we found that TREML4 expression in human M1 dysregulated several inflammatory pathways related to leukocyte activation, apoptosis and extracellular matrix degradation. Similarly, murine M1 expressed substantial levels of Treml4, as did oxLDL treated macrophages. Transcriptome analysis confirmed that murine Treml4 controls the expression of genes related to inflammation and lipid regulation pathways, suggesting a possible role in atherosclerosis. Analysis of Apoe-/-/Treml4-/- mice showed reduced plaque burden and lesion complexity as indicated by decreased stage scores, macrophage content and collagen deposition. Finally, transcriptome analysis of oxLDL-loaded murine macrophages showed that Treml4 represses a specific set of genes related to carbohydrate, ion and amino acid membrane transport. Metabolomic analysis confirmed that Treml4 deficiency may promote a beneficial relationship between iron homeostasis and glucose metabolism. Together, our results suggest that Treml4 plays a role in the development of cardiovascular disease, as indicated by Treml4-dependent dysregulation of macrophage inflammatory pathways, macrophage metabolism and promotion of vulnerability features in advanced lesions.

Sleep Regularity Index in Patients with Alcohol Dependence: Daytime Napping and Mood Disorders as Correlates of Interest.

Alcohol use disorder (AUD) is often accompanied by comorbid conditions, including sleep disturbances related to sleep regularity and timing. The Sleep Regularity Index (SRI) is a novel measure that assesses the probability that an individual is awake (vs. asleep) at any two time points 24 h apart. We calculated actigraphy-based SRI on 124 participants with alcohol dependence to capture the effects of changes in sleep timing and duration among patients enrolled in an inpatient alcohol treatment program. During the course of the study, the mean SRI increased between weeks 1 and 3 (75.4 to 77.8), thus indicating slightly improved sleep quality and regularity during alcohol treatment. Individuals within the bottom quartile of SRI scores at week 1 improved significantly over time. Average total SRI for individuals with no mood disorders was slightly higher than that for individuals with one or more mood disorders. Increased SRI scores were associated with lower total nap duration from week 1 to week 3. Increased SRI scores were associated with decreased mental/physical exhaustion scores from week 1 to week 3. The SRI could be a target for assessment/intervention in certain sub-groups of individuals undergoing inpatient treatment for AUD.

An exploratory analysis of extracellular vesicle-associated and soluble cytokines in cancer-related fatigue in men with prostate cancer.

BACKGROUND: Cancer Related Fatigue (CRF) is one of the most prevalent and distressing symptoms associated with cancer treatments. The exact etiology of CRF and its mechanisms are poorly understood. Cytokine dysregulation was hypothesized to be one of these mechanisms. Here, we explored the associations of soluble and extracellular vesicle (EV)-associated markers that include cytokines, heat shock proteins (hsp27, hsp70, hsp90), and neurotrophic factors (BDNF) with CRF. METHODS: Plasma was collected from men (n â€‹= â€‹40) with non-metastatic prostate cancer receiving external beam radiation therapy (EBRT) at the start of the treatment, and three months after EBRT. CRF was assessed using the Functional Assessment of Cancer Therapy - Fatigue (FACT-F) from all participants. EVs were characterized via Nanoparticle Tracking Analysis, electron microscopy, and Western blot. Concentrations of EV-associated and soluble markers were measured with a multiplexed immunoassay system. Bivariate correlation analyses and independent T tests analyzed the relationships of CRF with the markers. FINDINGS: As CRF worsened, concentrations of EV-associated markers were upregulated. EV-associated fold changes of Eotaxin, hsp27, IP-10, MIP-3α, were significantly higher in fatigued participants compared to non-fatigued EBRT participants three months after treatment. This was not observed in soluble markers. Concentrations of EV-associated CRP and MCP-1, soluble survivin, IFNα2, IL-8, IL-12p70, and MCP-1 significantly correlated with lower (worsening) CRF scores at the start of and three months after treatment. INTERPRETATION: Concentrations of EV-associated markers increased in fatigued men with prostate cancer three months after EBRT. Both EV-associated and soluble markers correlated with worsening CRF. EV-associated markers, which have not been previously studied in depth, may provide additional insights and serve as potential biomarkers for CRF.

Using Machine Learning to Classify Individuals With Alcohol Use Disorder Based on Treatment Seeking Status.

OBJECTIVE: The authors used a decision tree classifier to reduce neuropsychological, behavioral and laboratory measures to a subset of measures that best predicted whether an individual with alcohol use disorder (AUD) seeks treatment. METHOD: Clinical measures (N = 178) from 778 individuals with AUD were used to construct an alternating decision tree (ADT) with 10 measures that best classified individuals as treatment or not treatment-seeking for AUD. ADT's were validated by two methods: using cross-validation and an independent dataset (N = 236). For comparison, two other machine learning techniques were used as well as two linear models. RESULTS: The 10 measures in the ADT classifier were drinking behavior, depression and drinking-related psychological problems, as well as substance dependence. With cross-validation, the ADT classified 86% of individuals correctly. The ADT classified 78% of the independent dataset correctly. Only the simple logistic model was similar in accuracy; however, this model needed more than twice as many measures as ADT to classify at comparable accuracy. INTERPRETATION: While there has been emphasis on understanding differences between those with AUD and controls, it is also important to understand, within those with AUD, the features associated with clinically important outcomes. Since the majority of individuals with AUD do not receive treatment, it is important to understand the clinical features associated with treatment utilization; the ADT reported here correctly classified the majority of individuals with AUD with 10 clinically relevant measures, misclassifying < 7% of treatment seekers, while misclassifying 38% of non-treatment seekers. These individual clinically relevant measures can serve, potentially, as separate targets for treatment. FUNDING: Funding for this work was provided by the Intramural Research Programs of the National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institute on Drug Abuse (NIDA) and the Center for Information Technology (CIT). RESEARCH IN CONTEXT: Evidence Before This Study: Less than 10% of persons who meet lifetime criteria for Alcohol Use Disorder (AUD) receive treatment. As the etiology of AUD represents a complex interaction between neurobiological, social, environmental and psychological factors, low treatment utilization likely stems from barriers on multiple levels. Given this issue, it is important from both a research and clinical standpoint to determine what characteristics are associated with treatment utilization in addition to merely asking individuals if they wish to enter treatment. At the level of clinical research, if there are phenotypic differences between treatment and nontreatment-seekers that directly influence outcomes of early-phase studies, these phenotypic differences are a potential confound in assessing the utility of an experimental treatment for AUD. At the level of clinical practice, distinguishing between treatment- and nontreatment-seekers may help facilitate a targeted treatment approach. Previous efforts to understand the differences between these populations of individuals with AUD leveraged the multidimensional data collected in clinical research settings for AUD that are not well suited to traditional regression methods.Added Value of This Study: Alternating decision trees are well suited to deep-phenotyping data collected in clinical research settings as this approach handles nonparametric, skewed, and missing data whose relationships are nonlinear. This approach has proved to be superior in some cases to conventional clinical methods to solve diagnostic problems in medicine. We used a decision tree classifier to understand treatment- and non-treatment seeking group differences. The decision tree classifier approach chose a subset of factors arranged in an alternating decision tree that best predicts a given outcome. Assuming that the input measures are clinically relevant, the alternating decision tree that is generated has clinical value. Unlike other machine learning approaches, in addition to its predictive value, the nodes in the tree and their arrangement in a hierarchy have clinical utility. With the "if-then" logic of the tree, the clinician can learn what features become important and which recede in importance as the logic of the tree is followed. The decision tree classifier approach reduced 178 characterization measures (both categorical and continuous) in multiple domains to a decision tree comprised of 10 measures that together best classified subjects by treatment seeking status (yes/no).Implications After All the Available Evidence: We leveraged a large data set comprised of 178 clinical measures and using the decision tree approach, we have reduced these to a subset of 10 measures that accurately classified individuals with alcohol dependence by treatment utilization. From this analysis, drinking behavior variables and depression measures are strong treatment seeking predictors. Having identified a cluster of factors that predicts treatment seeking, we can assess the influence of these factors directly on the clinical study outcome measures themselves. In clinical practice these factors can be separate targets for treatment. In clinical research, the group differences my directly influence research outcomes for treatment of AUD.

NRL-Regulated Transcriptome Dynamics of Developing Rod Photoreceptors.

Gene regulatory networks (GRNs) guiding differentiation of cell types and cell assemblies in the nervous system are poorly understood because of inherent complexities and interdependence of signaling pathways. Here, we report transcriptome dynamics of differentiating rod photoreceptors in the mammalian retina. Given that the transcription factor NRL determines rod cell fate, we performed expression profiling of developing NRL-positive (rods) and NRL-negative (S-cone-like) mouse photoreceptors. We identified a large-scale, sharp transition in the transcriptome landscape between postnatal days 6 and 10 concordant with rod morphogenesis. Rod-specific temporal DNA methylation corroborated gene expression patterns. De novo assembly and alternative splicing analyses revealed previously unannotated rod-enriched transcripts and the role of NRL in transcript maturation. Furthermore, we defined the relationship of NRL with other transcriptional regulators and downstream cognate effectors. Our studies provide the framework for comprehensive system-level analysis of the GRN underlying the development of a single sensory neuron, the rod photoreceptor.