Traumatic brain injury alters the effects of class II invariant peptide (CLIP) antagonism on chronic meningeal CLIP + B cells, neuropathology, and neurobehavioral impairment in 5xFAD mice.

BACKGROUND: Traumatic brain injury (TBI) is a significant risk factor for Alzheimer's disease (AD), and accumulating evidence supports a role for adaptive immune B and T cells in both TBI and AD pathogenesis. We previously identified B cell and major histocompatibility complex class II (MHCII)-associated invariant chain peptide (CLIP)-positive B cell expansion after TBI. We also showed that antagonizing CLIP binding to the antigen presenting groove of MHCII after TBI acutely reduced CLIP + splenic B cells and was neuroprotective. The current study investigated the chronic effects of antagonizing CLIP in the 5xFAD Alzheimer's mouse model, with and without TBI. METHODS: 12-week-old male wild type (WT) and 5xFAD mice were administered either CLIP antagonist peptide (CAP) or vehicle, once at 30 min after either sham or a lateral fluid percussion injury (FPI). Analyses included flow cytometric analysis of immune cells in dural meninges and spleen, histopathological analysis of the brain, magnetic resonance diffusion tensor imaging, cerebrovascular analysis, and assessment of motor and neurobehavioral function over the ensuing 6 months. RESULTS: 9-month-old 5xFAD mice had significantly more CLIP + B cells in the meninges compared to age-matched WT mice. A one-time treatment with CAP significantly reduced this population in 5xFAD mice. Importantly, CAP also improved some of the immune, histopathological, and neurobehavioral impairments in 5xFAD mice over the ensuing six months. Although FPI did not further elevate meningeal CLIP + B cells, it did negate the ability of CAP to reduce meningeal CLIP + B cells in the 5xFAD mice. FPI at 3 months of age exacerbated some aspects of AD pathology in 5xFAD mice, including further reducing hippocampal neurogenesis, increasing plaque deposition in CA3, altering microgliosis, and disrupting the cerebrovascular structure. CAP treatment after injury ameliorated some but not all of these FPI effects.

Morpholino-driven blockade of Dkk-1 in osteosarcoma inhibits bone damage and tumour expansion by multiple mechanisms.

BACKGROUND: Osteosarcoma (OS) is the most common primary bone malignancy. Chemotherapy plays an essential role in OS treatment, potentially doubling 5-year event-free survival if tumour necrosis can be stimulated. The canonical Wnt inhibitor Dickkopf-1 (Dkk-1) enhances OS survival in part through upregulation of aldehyde-dehydrogenase-1A1 which neutralises reactive oxygen species originating from nutritional stress and chemotherapeutic challenge. METHODS: A vivo morpholino (DkkMo) was employed to block the expression of Dkk-1 in OS cells. Cell mitosis, gene expression and bone destruction were measured in vitro and in vivo in the presence and absence of doxorubicin (DRB). RESULTS: DkkMo reduced the expression of Dkk-1 and Aldh1a1, reduced expansion of OS tumours, preserved bone volume and architecture and stimulated tumour necrosis. This was observed in the presence or absence of DRB. CONCLUSION: These results indicate that administration of DkkMo with or without chemotherapeutics can substantially improve OS outcome with respect to tumour expansion and osteolytic corruption of bone in experimental OS model.

Sex differences in stroke outcome correspond to rapid and severe changes in gut permeability in adult Sprague-Dawley rats.

BACKGROUND: Sex differences in experimental stroke outcomes are well documented, such that adult males have a greater infarct volume, increased stroke-induced mortality, and more severe sensory-motor impairment. Based on recent evidence that the gut is an early responder to stroke, the present study tested the hypothesis that sex differences in stroke severity will be accompanied by rapid and greater permeability of the gut-blood barrier and gut dysbiosis in males as compared to females. METHOD: Male and female Sprague-Dawley rats (5-7 months of age) were subject to endothelin (ET)-1-induced middle cerebral artery occlusion (MCAo). Sensory-motor tests were conducted pre- and 2 days after MCAo. Gut permeability was assessed in serum samples using biomarkers of gut permeability as well as functional assays using size-graded dextrans. Histological analysis of the gut was performed with H&E staining, periodic acid-Schiff for mucus, and immunohistochemistry for the tight junction protein, ZO-1. Fecal samples obtained pre- and post-stroke were analyzed for bacterial taxa and short-chain fatty acids (SCFAs). RESULTS: After stroke, males displayed greater mortality, worse sensory-motor deficit, and higher serum levels of proinflammatory cytokines IL-17A, MCP-1, and IL-5 as compared to females. MCAo-induced gut permeability was rapid and severe in males as indicated by dextran extravasation from the gut to the blood in the hyperacute (< 2 h) and early acute (2 days) phase of stroke. This was accompanied by dysmorphology of the gut villi and dysregulation of the tight junction protein ZO-1 in the acute phase. Fecal 16s sequencing showed no differences in bacterial diversity in the acute phase of stroke. Predictive modeling indicated that markers of gut permeability were associated with acute sensory-motor impairment and infarct volume. CONCLUSIONS: These data show that extensive leakiness of the gut barrier is associated with severe post-stroke disability and suggest that reinforcing this barrier may improve stroke outcomes.

SECNVs: A Simulator of Copy Number Variants and Whole-Exome Sequences From Reference Genomes.

Copy number variants are duplications and deletions of the genome that play an important role in phenotypic changes and human disease. Many software applications have been developed to detect copy number variants using either whole-genome sequencing or whole-exome sequencing data. However, there is poor agreement in the results from these applications. Simulated datasets containing copy number variants allow comprehensive comparisons of the operating characteristics of existing and novel copy number variant detection methods. Several software applications have been developed to simulate copy number variants and other structural variants in whole-genome sequencing data. However, none of the applications reliably simulate copy number variants in whole-exome sequencing data. We have developed and tested Simulator of Exome Copy Number Variants (SECNVs), a fast, robust and customizable software application for simulating copy number variants and whole-exome sequences from a reference genome. SECNVs is easy to install, implements a wide range of commands to customize simulations, can output multiple samples at once, and incorporates a pipeline to output rearranged genomes, short reads and BAM files in a single command. Variants generated by SECNVs are detected with high sensitivity and precision by tools commonly used to detect copy number variants. SECNVs is publicly available at https://github.com/YJulyXing/SECNVs.

Author Correction: RNA-seq of serial kidney biopsies obtained during progression of chronic kidney disease from dogs with X-linked hereditary nephropathy.

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

RNA-seq of serial kidney biopsies obtained during progression of chronic kidney disease from dogs with X-linked hereditary nephropathy.

Dogs with X-linked hereditary nephropathy (XLHN) have a glomerular basement membrane defect that leads to progressive juvenile-onset renal failure. Their disease is analogous to Alport syndrome in humans, and they also serve as a good model of progressive chronic kidney disease (CKD). However, the gene expression profile that affects progression in this disease has only been partially characterized. To help fill this gap, we used RNA sequencing to identify differentially expressed genes (DEGs), over-represented pathways, and upstream regulators that contribute to kidney disease progression. Total RNA from kidney biopsies was isolated at 3 clinical time points from 3 males with rapidly-progressing CKD, 3 males with slowly-progressing CKD, and 2 age-matched controls. We identified 70 DEGs by comparing rapid and slow groups at specific time points. Based on time course analysis, 1,947 DEGs were identified over the 3 time points revealing upregulation of inflammatory pathways: integrin signaling, T cell activation, and chemokine and cytokine signaling pathways. T cell infiltration was verified by immunohistochemistry. TGF-ß1 was identified as the primary upstream regulator. These results provide new insights into the underlying molecular mechanisms of disease progression in XLHN, and the identified DEGs can be potential biomarkers and therapeutic targets translatable to all CKDs.

Evaluation of Sterilized Artificial Diets for Mass Rearing the Lucilia sericata (Diptera: Calliphoridae).

The impact of six sterilized diets (blood-yeast agar diet, decomposed beef liver diet, powdered beef liver diet, powdered fish diet, milk-based diet, and a chemically defined diet) on Lucilia sericata (Meigen) larvae reared at three densities (10 larvae, 20 larvae, and 40 larvae on 20 g diet) was determined in comparison to fresh beef liver as a control. Specifically, the effects of these diets on the following traits of L. sericata were measured: 1) pupal weight, 2) pupation percentage, 3) eclosion percentage, as well as 4) adult longevity. The experiment included two trials with five technical replicates in each. Lucilia sericata did not successfully develop on the powdered fish, milk-based, or chemically defined diets. Overall, the liver-based diets (decomposed and powdered) resulted in the most similar fly development to the fresh beef liver. Larvae reared on blood-yeast agar diet resulted in a significantly (increased 20.56% ± 8.09%) greater pupation rate than those reared on the decomposed and powdered beef liver diets. Pupae from larvae fed the fresh beef liver were significantly larger (6.27 ± 1.01 mg, 4.05 ± 0.94 mg larger, respectively) than those reared on the blood-yeast agar diet, decomposed beef liver, and powdered beef liver diets. Overall, results revealed larvae reared on sterilized liver-based diets resulted in traits similar to those raised on fresh beef liver. Owing to low costs the sterile liver-based diets could be produced and used with limited infrastructure and economic incomes.

Transitioning to Patient-Centered Medical Homes: Associations With Appointment Availability.

INTRODUCTION: Recent implementation of the Patient-Centered Medical Home (PCMH) in military primary care has gained significant traction and attention from leadership and policy makers. The study objective was to measure the rate of change in appointment availability before and after primary care clinics were certified as a medical home. Access to care is one core tenet of the medical home and appointment availability is an important indicator of access. MATERIALS AND METHODS: This was a retrospective, longitudinal observational study involving 21 U.S. Navy primary care clinics from 2011 to 2014. Appointment availability, as measured by third next available appointment, was constructed for 21 primary care clinics over a 29-month time period (14 months precertification, certification month, 14 months postcertification). A mixed-effects model with linear splines was applied where third next available appointment was the dependent variable. Main interest independent variables include time (precertification and postcertification). Remaining independent variables include categories pertaining to clinic characteristics, ancillary services, and nonemergent primary care treatable emergency department visits. RESULTS: Appointment availability improved slightly postcertification. Although there were statistically significant differences in appointment availability pre- and postcertification, the differences were so small that patients may not actually experience noticeable improvements. CONCLUSION: Although slight improvements in appointment availability following medical home certification exist, adoption of the medical home model in the military setting may not have all the potential outcomes expected on the basis of prior findings in civilian settings. This study demonstrated that improvements in appointment availability following medical home certification exist, but are quite small. Patients, as a result, are unlikely to notice any improvements. Additional research should test other expected benefits of PCMH in military settings. At that point, military policy makers can decide which aspects of PCMH practices merit sustaining.

Military Deployments and Mental Health Utilization Among Spouses of Active Duty Service Members.

Since the onset of the wars in Iraq and Afghanistan attention has increased on the importance of mental health with military service members. An integral component, although far less studied, are the ties between mental health and military spouses. Military deployments place considerable stress on military families. This study analyzed the mental health utilization of military spouses of active duty service members assigned to an aircraft carrier between 2011 and 2014. A negative binomial generalized estimating equation was used to examine the rate of change in mental health utilization over time against various deployment phases. Associations emerged between select deployment phases (i.e., deployment 1, between deployments, deployment 2) with increases in mental health utilization ranging between 12% and 20% for military spouses. This study demonstrated, for military spouses, the in between deployment phase has associations with mental health utilization rates similar to actual deployed periods. As a result, military leaders should continue to monitor the well-being of their service members' families throughout all deployment phases.

Predicting "heart age" using electrocardiography.

Knowledge of a patient's cardiac age, or "heart age", could prove useful to both patients and physicians for better encouraging lifestyle changes potentially beneficial for cardiovascular health. This may be particularly true for patients who exhibit symptoms but who test negative for cardiac pathology. We developed a statistical model, using a Bayesian approach, that predicts an individual's heart age based on his/her electrocardiogram (ECG). The model is tailored to healthy individuals, with no known risk factors, who are at least 20 years old and for whom a resting ~5 min 12-lead ECG has been obtained. We evaluated the model using a database of ECGs from 776 such individuals. Secondarily, we also applied the model to other groups of individuals who had received 5-min ECGs, including 221 with risk factors for cardiac disease, 441 with overt cardiac disease diagnosed by clinical imaging tests, and a smaller group of highly endurance-trained athletes. Model-related heart age predictions in healthy non-athletes tended to center around body age, whereas about three-fourths of the subjects with risk factors and nearly all patients with proven heart diseases had higher predicted heart ages than true body ages. The model also predicted somewhat higher heart ages than body ages in a majority of highly endurance-trained athletes, potentially consistent with possible fibrotic or other anomalies recently noted in such individuals.

Diet complexity and estrogen receptor ß status affect the composition of the murine intestinal microbiota.

Intestinal microbial dysbiosis contributes to the dysmetabolism of luminal factors, including steroid hormones (sterones) that affect the development of chronic gastrointestinal inflammation and the incidence of sterone-responsive cancers of the breast, prostate, and colon. Little is known, however, about the role of specific host sterone nucleoreceptors, including estrogen receptor ß (ERß), in microbiota maintenance. Herein, we test the hypothesis that ERß status affects microbiota composition and determine if such compositionally distinct microbiota respond differently to changes in diet complexity that favor Proteobacteria enrichment. To this end, conventionally raised female ERß(+/+) and ERß(-/-) C57BL/6J mice (mean age of 27 weeks) were initially reared on 8604, a complex diet containing estrogenic isoflavones, and then fed AIN-76, an isoflavone-free semisynthetic diet, for 2 weeks. 16S rRNA gene surveys revealed that the fecal microbiota of 8604-fed mice and AIN-76-fed mice differed, as expected. The relative diversity of Proteobacteria, especially the Alphaproteobacteria and Gammaproteobacteria, increased significantly following the transition to AIN-76. Distinct patterns for beneficial Lactobacillales were exclusive to and highly abundant among 8604-fed mice, whereas several Proteobacteria were exclusive to AIN-76-fed mice. Interestingly, representative orders of the phyla Proteobacteria, Bacteroidetes, and Firmicutes, including the Lactobacillales, also differed as a function of murine ERß status. Overall, these interactions suggest that sterone nucleoreceptor status and diet complexity may play important roles in microbiota maintenance. Furthermore, we envision that this model for gastrointestinal dysbiosis may be used to identify novel probiotics, prebiotics, nutritional strategies, and pharmaceuticals for the prevention and resolution of Proteobacteria-rich dysbiosis.

Normalization and missing value imputation for label-free LC-MS analysis.

Shotgun proteomic data are affected by a variety of known and unknown systematic biases as well as high proportions of missing values. Typically, normalization is performed in an attempt to remove systematic biases from the data before statistical inference, sometimes followed by missing value imputation to obtain a complete matrix of intensities. Here we discuss several approaches to normalization and dealing with missing values, some initially developed for microarray data and some developed specifically for mass spectrometry-based data.

DanteR: an extensible R-based tool for quantitative analysis of -omics data.

MOTIVATION: The size and complex nature of mass spectrometry-based proteomics datasets motivate development of specialized software for statistical data analysis and exploration. We present DanteR, a graphical R package that features extensive statistical and diagnostic functions for quantitative proteomics data analysis, including normalization, imputation, hypothesis testing, interactive visualization and peptide-to-protein rollup. More importantly, users can easily extend the existing functionality by including their own algorithms under the Add-On tab. AVAILABILITY: DanteR and its associated user guide are available for download free of charge at http://omics.pnl.gov/software/. We have an updated binary source for the DanteR package up on our website together with a vignettes document. For Windows, a single click automatically installs DanteR along with the R programming environment. For Linux and Mac OS X, users must install R and then follow instructions on the DanteR website for package installation. CONTACT: rds@pnnl.gov.

Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data.

MOTIVATION: Protein abundance in quantitative proteomics is often based on observed spectral features derived from liquid chromatography mass spectrometry (LC-MS) or LC-MS/MS experiments. Peak intensities are largely non-normal in distribution. Furthermore, LC-MS-based proteomics data frequently have large proportions of missing peak intensities due to censoring mechanisms on low-abundance spectral features. Recognizing that the observed peak intensities detected with the LC-MS method are all positive, skewed and often left-censored, we propose using survival methodology to carry out differential expression analysis of proteins. Various standard statistical techniques including non-parametric tests such as the Kolmogorov-Smirnov and Wilcoxon-Mann-Whitney rank sum tests, and the parametric survival model and accelerated failure time-model with log-normal, log-logistic and Weibull distributions were used to detect any differentially expressed proteins. The statistical operating characteristics of each method are explored using both real and simulated datasets. RESULTS: Survival methods generally have greater statistical power than standard differential expression methods when the proportion of missing protein level data is 5% or more. In particular, the AFT models we consider consistently achieve greater statistical power than standard testing procedures, with the discrepancy widening with increasing missingness in the proportions. AVAILABILITY: The testing procedures discussed in this article can all be performed using readily available software such as R. The R codes are provided as supplemental materials. CONTACT: ctekwe@stat.tamu.edu.

A hybrid approach to protein differential expression in mass spectrometry-based proteomics.

MOTIVATION: Quantitative mass spectrometry-based proteomics involves statistical inference on protein abundance, based on the intensities of each protein's associated spectral peaks. However, typical MS-based proteomics datasets have substantial proportions of missing observations, due at least in part to censoring of low intensities. This complicates intensity-based differential expression analysis. RESULTS: We outline a statistical method for protein differential expression, based on a simple Binomial likelihood. By modeling peak intensities as binary, in terms of 'presence/absence,' we enable the selection of proteins not typically amenable to quantitative analysis; e.g. 'one-state' proteins that are present in one condition but absent in another. In addition, we present an analysis protocol that combines quantitative and presence/absence analysis of a given dataset in a principled way, resulting in a single list of selected proteins with a single-associated false discovery rate. AVAILABILITY: All R code available here: http://www.stat.tamu.edu/~adabney/share/xuan_code.zip.

A statistical method for assessing peptide identification confidence in accurate mass and time tag proteomics.

Current algorithms for quantifying peptide identification confidence in the accurate mass and time (AMT) tag approach assume that the AMT tags themselves have been correctly identified. However, there is uncertainty in the identification of AMT tags, because this is based on matching LC-MS/MS fragmentation spectra to peptide sequences. In this paper, we incorporate confidence measures for the AMT tag identifications into the calculation of probabilities for correct matches to an AMT tag database, resulting in a more accurate overall measure of identification confidence for the AMT tag approach. The method is referenced as Statistical Tools for AMT Tag Confidence (STAC). STAC additionally provides a uniqueness probability (UP) to help distinguish between multiple matches to an AMT tag and a method to calculate an overall false discovery rate (FDR). STAC is freely available for download, as both a command line and a Windows graphical application.

Liquid Chromatography Mass Spectrometry-Based Proteomics: Biological and Technological Aspects.

Mass spectrometry-based proteomics has become the tool of choice for identifying and quantifying the proteome of an organism. Though recent years have seen a tremendous improvement in instrument performance and the computational tools used, significant challenges remain, and there are many opportunities for statisticians to make important contributions. In the most widely used "bottom-up" approach to proteomics, complex mixtures of proteins are first subjected to enzymatic cleavage, the resulting peptide products are separated based on chemical or physical properties and analyzed using a mass spectrometer. The two fundamental challenges in the analysis of bottom-up MS-based proteomics are: (1) Identifying the proteins that are present in a sample, and (2) Quantifying the abundance levels of the identified proteins. Both of these challenges require knowledge of the biological and technological context that gives rise to observed data, as well as the application of sound statistical principles for estimation and inference. We present an overview of bottom-up proteomics and outline the key statistical issues that arise in protein identification and quantification.

An introspective comparison of random forest-based classifiers for the analysis of cluster-correlated data by way of RF++.

Many mass spectrometry-based studies, as well as other biological experiments produce cluster-correlated data. Failure to account for correlation among observations may result in a classification algorithm overfitting the training data and producing overoptimistic estimated error rates and may make subsequent classifications unreliable. Current common practice for dealing with replicated data is to average each subject replicate sample set, reducing the dataset size and incurring loss of information. In this manuscript we compare three approaches to dealing with cluster-correlated data: unmodified Breiman's Random Forest (URF), forest grown using subject-level averages (SLA), and RF++ with subject-level bootstrapping (SLB). RF++, a novel Random Forest-based algorithm implemented in C++, handles cluster-correlated data through a modification of the original resampling algorithm and accommodates subject-level classification. Subject-level bootstrapping is an alternative sampling method that obviates the need to average or otherwise reduce each set of replicates to a single independent sample. Our experiments show nearly identical median classification and variable selection accuracy for SLB forests and URF forests when applied to both simulated and real datasets. However, the run-time estimated error rate was severely underestimated for URF forests. Predictably, SLA forests were found to be more severely affected by the reduction in sample size which led to poorer classification and variable selection accuracy. Perhaps most importantly our results suggest that it is reasonable to utilize URF for the analysis of cluster-correlated data. Two caveats should be noted: first, correct classification error rates must be obtained using a separate test dataset, and second, an additional post-processing step is required to obtain subject-level classifications. RF++ is shown to be an effective alternative for classifying both clustered and non-clustered data. Source code and stand-alone compiled versions of command-line and easy-to-use graphical user interface (GUI) versions of RF++ for Windows and Linux as well as a user manual (Supplementary File S2) are available for download at: http://sourceforge.org/projects/rfpp/ under the GNU public license.

Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition.

MOTIVATION: LC-MS allows for the identification and quantification of proteins from biological samples. As with any high-throughput technology, systematic biases are often observed in LC-MS data, making normalization an important preprocessing step. Normalization models need to be flexible enough to capture biases of arbitrary complexity, while avoiding overfitting that would invalidate downstream statistical inference. Careful normalization of MS peak intensities would enable greater accuracy and precision in quantitative comparisons of protein abundance levels. RESULTS: We propose an algorithm, called EigenMS, that uses singular value decomposition to capture and remove biases from LC-MS peak intensity measurements. EigenMS is an adaptation of the surrogate variable analysis (SVA) algorithm of Leek and Storey, with the adaptations including (i) the handling of the widespread missing measurements that are typical in LC-MS, and (ii) a novel approach to preventing overfitting that facilitates the incorporation of EigenMS into an existing proteomics analysis pipeline. EigenMS is demonstrated using both large-scale calibration measurements and simulations to perform well relative to existing alternatives. AVAILABILITY: The software has been made available in the open source proteomics platform DAnTE (Polpitiya et al., 2008)) (http://omics.pnl.gov/software/), as well as in standalone software available at SourceForge (http://sourceforge.net).

A statistical framework for protein quantitation in bottom-up MS-based proteomics.

MOTIVATION: Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level. RESULTS: We present a statistical model that carefully accounts for informative missingness in peak intensities and allows unbiased, model-based, protein-level estimation and inference. The model is applicable to both label-based and label-free quantitation experiments. We also provide automated, model-based, algorithms for filtering of proteins and peptides as well as imputation of missing values. Two LC/MS datasets are used to illustrate the methods. In simulation studies, our methods are shown to achieve substantially more discoveries than standard alternatives. AVAILABILITY: The software has been made available in the open-source proteomics platform DAnTE (http://omics.pnl.gov/software/).