Differential methylation region detection via an array-adaptive normalized kernel-weighted model.

A differentially methylated region (DMR) is a genomic region that has significantly different methylation patterns between biological conditions. Identifying DMRs between different biological conditions is critical for developing disease biomarkers. Although methods for detecting DMRs in microarray data have been introduced, developing methods with high precision, recall, and accuracy in determining the true length of DMRs remains a challenge. In this study, we propose a normalized kernel-weighted model to account for similar methylation profiles using the relative probe distance from "nearby" CpG sites. We also extend this model by proposing an array-adaptive version in attempt to account for the differences in probe spacing between Illumina's Infinium 450K and EPIC bead array respectively. We also study the asymptotic results of our proposed statistic. We compare our approach with a popular DMR detection method via simulation studies under large and small treatment effect settings. We also discuss the susceptibility of our method in detecting the true length of the DMRs under these two settings. Lastly, we demonstrate the biological usefulness of our method when combined with pathway analysis methods on oral cancer data. We have created an R package called idDMR, downloadable from GitHub repository with link: https://github.com/DanielAlhassan/idDMR, that allows for the convenient implementation of our array-adaptive DMR method.

Mesenchymal stem cells in autoimmune disease: A systematic review and meta-analysis of pre-clinical studies.

Mesenchymal Stem Cells (MSCs) are of interest in the clinic because of their immunomodulation capabilities, capacity to act upstream of inflammation, and ability to sense metabolic environments. In standard physiologic conditions, they play a role in maintaining the homeostasis of tissues and organs; however, there is evidence that they can contribute to some autoimmune diseases. Gaining a deeper understanding of the factors that transition MSCs from their physiological function to a pathological role in their native environment, and elucidating mechanisms that reduce their therapeutic relevance in regenerative medicine, is essential. We conducted a Systematic Review and Meta-Analysis of human MSCs in preclinical studies of autoimmune disease, evaluating 60 studies that included 845 patient samples and 571 control samples. MSCs from any tissue source were included, and the study was limited to four autoimmune diseases: multiple sclerosis, rheumatoid arthritis, systemic sclerosis, and lupus. We developed a novel Risk of Bias tool to determine study quality for in vitro studies. Using the International Society for Cell & Gene Therapy's criteria to define an MSC, most studies reported no difference in morphology, adhesion, cell surface markers, or differentiation into bone, fat, or cartilage when comparing control and autoimmune MSCs. However, there were reported differences in proliferation. Additionally, 308 biomolecules were differentially expressed, and the abilities to migrate, invade, and form capillaries were decreased. The findings from this study could help to explain the pathogenic mechanisms of autoimmune disease and potentially lead to improved MSC-based therapeutic applications.

Testing differentially methylated regions through functional principal component analysis.

DNA methylation is an epigenetic modification that plays an important role in many biological processes and diseases. Several statistical methods have been proposed to test for DNA methylation differences between conditions at individual cytosine sites, followed by a post hoc aggregation procedure to explore regional differences. While there are benefits to analyzing CpGs individually, there are both biological and statistical reasons to test entire genomic regions for differential methylation. Variability in methylation levels measured by Next-Generation Sequencing (NGS) is often observed across CpG sites in a genomic region. Evaluating meaningful changes in regional level methylation profiles between conditions over noisy site-level measurements is often difficult to implement with parametric models. To overcome these limitations, this study develops a nonparametric approach to detect predefined differentially methylated regions (DMR) based on functional principal component analysis (FPCA). The performance of this approach is compared with two alternative methods (GIFT and M3D), using real and simulated data.

Heterogeneity in Blood Biomarker Trajectories After Mild TBI Revealed by Unsupervised Learning.

Concussions, also known as mild traumatic brain injury (mTBI), are a growing health challenge. Approximately four million concussions are diagnosed annually in the United States. Concussion is a heterogeneous disorder in causation, symptoms, and outcome making precision medicine approaches to this disorder important. Persistent disabling symptoms sometimes delay recovery in a difficult to predict subset of mTBI patients. Despite abundant data, clinicians need better tools to assess and predict recovery. Data-driven decision support holds promise for accurate clinical prediction tools for mTBI due to its ability to identify hidden correlations in complex datasets. We apply a Locality-Sensitive Hashing model enhanced by varied statistical methods to cluster blood biomarker level trajectories acquired over multiple time points. Additional features derived from demographics, injury context, neurocognitive assessment, and postural stability assessment are extracted using an autoencoder to augment the model. The data, obtained from FITBIR, consisted of 301 concussed subjects (athletes and cadets). Clustering identified 11 different biomarker trajectories. Two of the trajectories (rising GFAP and rising NF-L) were associated with a greater risk of loss of consciousness or post-traumatic amnesia at onset. The ability to cluster blood biomarker trajectories enhances the possibilities for precision medicine approaches to mTBI.

Blood biomarkers for mild traumatic brain injury: a selective review of unresolved issues.

BACKGROUND: The use of blood biomarkers after mild traumatic brain injury (mTBI) has been widely studied. We have identified eight unresolved issues related to the use of five commonly investigated blood biomarkers: neurofilament light chain, ubiquitin carboxy-terminal hydrolase-L1, tau, S100B, and glial acidic fibrillary protein. We conducted a focused literature review of unresolved issues in three areas: mode of entry into and exit from the blood, kinetics of blood biomarkers in the blood, and predictive capacity of the blood biomarkers after mTBI. FINDINGS: Although a disruption of the blood brain barrier has been demonstrated in mild and severe traumatic brain injury, biomarkers can enter the blood through pathways that do not require a breach in this barrier. A definitive accounting for the pathways that biomarkers follow from the brain to the blood after mTBI has not been performed. Although preliminary investigations of blood biomarkers kinetics after TBI are available, our current knowledge is incomplete and definitive studies are needed. Optimal sampling times for biomarkers after mTBI have not been established. Kinetic models of blood biomarkers can be informative, but more precise estimates of kinetic parameters are needed. Confounding factors for blood biomarker levels have been identified, but corrections for these factors are not routinely made. Little evidence has emerged to date to suggest that blood biomarker levels correlate with clinical measures of mTBI severity. The significance of elevated biomarker levels thirty or more days following mTBI is uncertain. Blood biomarkers have shown a modest but not definitive ability to distinguish concussed from non-concussed subjects, to detect sub-concussive hits to the head, and to predict recovery from mTBI. Blood biomarkers have performed best at distinguishing CT scan positive from CT scan negative subjects after mTBI.

A Kinetic Model for Blood Biomarker Levels After Mild Traumatic Brain Injury.

Traumatic brain injury (TBI) imposes a significant economic and social burden. The diagnosis and prognosis of mild TBI, also called concussion, is challenging. Concussions are common among contact sport athletes. After a blow to the head, it is often difficult to determine who has had a concussion, who should be withheld from play, if a concussed athlete is ready to return to the field, and which concussed athlete will develop a post-concussion syndrome. Biomarkers can be detected in the cerebrospinal fluid and blood after traumatic brain injury and their levels may have prognostic value. Despite significant investigation, questions remain as to the trajectories of blood biomarker levels over time after mild TBI. Modeling the kinetic behavior of these biomarkers could be informative. We propose a one-compartment kinetic model for S100B, UCH-L1, NF-L, GFAP, and tau biomarker levels after mild TBI based on accepted pharmacokinetic models for oral drug absorption. We approximated model parameters using previously published studies. Since parameter estimates were approximate, we did uncertainty and sensitivity analyses. Using estimated kinetic parameters for each biomarker, we applied the model to an available post-concussion biomarker dataset of UCH-L1, GFAP, tau, and NF-L biomarkers levels. We have demonstrated the feasibility of modeling blood biomarker levels after mild TBI with a one compartment kinetic model. More work is needed to better establish model parameters and to understand the implications of the model for diagnostic use of these blood biomarkers for mild TBI.

Exploratory Analysis of Concussion Recovery Trajectories using Multi-modal Assessments and Serum Biomarkers.

Clinicians need better tools to assess severity, prognosis, and recovery from mild Traumatic Brain Injury (mTBI), which can cause long term impairment. To enable better mTBI outcome prediction, an initial step is to analyze the trajectory of recovery metrics over time. This study provides an assessment of recovery trajectories of mTBI while incorporating heterogeneity of individual responses. We analyze the trajectories over multiple discrete time points from baseline to 6 months post injury using a combination of neurocognitive and postural stability assessments and serum biomarkers. The data, obtained from FITBIR, consists of concussed subjects and a matched control group, to allow for comparison in prognostic assessment. Outcomes derived from this exploratory analysis will aid future studies in developing a mTBI recovery timeline model.Clinical relevance- This study further informs clinicians as to the recovery trajectory of clinical measures and biomarkers after mTBI to support return to play decisions. GFAP biomarker and measures related to balance, memory, orientation, and concentration were significantly different than controls early after mTBI.

Evaluation of standard and semantically-augmented distance metrics for neurology patients.

BACKGROUND: Patient distances can be calculated based on signs and symptoms derived from an ontological hierarchy. There is controversy as to whether patient distance metrics that consider the semantic similarity between concepts can outperform standard patient distance metrics that are agnostic to concept similarity. The choice of distance metric can dominate the performance of classification or clustering algorithms. Our objective was to determine if semantically augmented distance metrics would outperform standard metrics on machine learning tasks. METHODS: We converted the neurological findings from 382 published neurology cases into sets of concepts with corresponding machine-readable codes. We calculated patient distances by four different metrics (cosine distance, a semantically augmented cosine distance, Jaccard distance, and a semantically augmented bipartite distance). Semantic augmentation for two of the metrics depended on concept similarities from a hierarchical neuro-ontology. For machine learning algorithms, we used the patient diagnosis as the ground truth label and patient findings as machine learning features. We assessed classification accuracy for four classifiers and cluster quality for two clustering algorithms for each of the distance metrics. RESULTS: Inter-patient distances were smaller when the distance metric was semantically augmented. Classification accuracy and cluster quality were not significantly different by distance metric. CONCLUSION: Although semantic augmentation reduced inter-patient distances, we did not find improved classification accuracy or improved cluster quality with semantically augmented patient distance metrics when applied to a dataset of neurology patients. Further work is needed to assess the utility of semantically augmented patient distances.

Student Preclass Preparation by Both Reading the Textbook and Watching Videos Online Improves Exam Performance in a Partially Flipped Course.

The flipped classroom has the potential to improve student performance. Because flipping involves both preclass preparation and problem solving in the classroom, the means by which increased learning occurs and whether the method of delivering content matters is of interest. In a partially flipped cell biology course, students were assigned online videos before the flipped class and textbook reading before lectures. Low-stakes assessments were used to incentivize both types of preclass preparation. We hypothesized that more students would watch the videos than read the textbook and that both types of preparation would positively affect exam performance. A multiple linear regression analysis showed that both reading and video viewing had a significant positive impact on exam score, and this model was predictive of exam scores. In contrast to our expectations, most students prepared by both watching videos and reading the textbook and did not exhibit a pattern of solely watching videos. This analysis supports previous findings that engagement with material outside class is partly responsible for the improved outcomes in a flipped classroom and shows that both reading and watching videos are effective at delivering content outside class.

Neuroimaging abnormalities in clade C HIV are independent of Tat genetic diversity.

Controversy remains regarding the neurotoxicity of clade C human immunodeficiency virus (HIV-C). When examined in preclinical studies, a cysteine to serine substitution in the C31 dicysteine motif of the HIV-C Tat protein (C31S) results in less severe brain injury compared to other viral clades. By contrast, patient cohort studies identify significant neuropsychological impairment among HIV-C individuals independent of Tat variability. The present study clarified this discrepancy by examining neuroimaging markers of brain integrity among HIV-C individuals with and without the Tat substitution. Thirty-seven HIV-C individuals with the Tat C31S substitution, 109 HIV-C individuals without the Tat substitution (C31C), and 34 HIV- controls underwent 3T structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). Volumes were determined for the caudate, putamen, thalamus, corpus callosum, total gray matter, and total white matter. DTI metrics included fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD). Tracts of interest included the anterior thalamic radiation (ATR), cingulum bundle (CING), uncinate fasciculus (UNC), and corpus callosum (CC). HIV+ individuals exhibited smaller volumes in subcortical gray matter, total gray matter and total white matter compared to HIV- controls. HIV+ individuals also exhibited DTI abnormalities across multiple tracts compared to HIV- controls. By contrast, neither volumetric nor diffusion indices differed significantly between the Tat C31S and C31C groups. Tat C31S status is not a sufficient biomarker of HIV-related brain integrity in patient populations. Clinical attention directed at brain health is warranted for all HIV+ individuals, independent of Tat C31S or clade C status.

Assessing Freshman Engineering Students' Understanding of Ethical Behavior.

Academic dishonesty, including cheating and plagiarism, is on the rise in colleges, particularly among engineering students. While students decide to engage in these behaviors for many different reasons, academic integrity training can help improve their understanding of ethical decision making. The two studies outlined in this paper assess the effectiveness of an online module in increasing academic integrity among first semester engineering students. Study 1 tested the effectiveness of an academic honesty tutorial by using a between groups design with a Time 1- and Time 2-test. An academic honesty quiz assessed participants' knowledge at both time points. Study 2, which incorporated an improved version of the module and quiz, utilized a between groups design with three assessment time points. The additional Time 3-test allowed researchers to test for retention of information. Results were analyzed using ANCOVA and t tests. In Study 1, the experimental group exhibited significant improvement on the plagiarism items, but not the total score. However, at Time 2 there was no significant difference between groups after controlling for Time 1 scores. In Study 2, between- and within-group analyses suggest there was a significant improvement in total scores, but not plagiarism scores, after exposure to the tutorial. Overall, the academic integrity module impacted participants as evidenced by changes in total score and on specific plagiarism items. Although future implementation of the tutorial and quiz would benefit from modifications to reduce ceiling effects and improve assessment of knowledge, the results suggest such tutorial may be one valuable element in a systems approach to improving the academic integrity of engineering students.

Cellular oxidative damage is more sensitive to biosynthetic rate than to metabolic rate: A test of the theoretical model on hornworms (Manduca sexta larvae).

We develop a theoretical model from an energetic viewpoint for unraveling the entangled effects of metabolic and biosynthetic rates on oxidative cellular damage accumulation during animal's growth, and test the model by experiments in hornworms. The theoretical consideration suggests that most of the cellular damages caused by the oxidative metabolism can be repaired by the efficient maintenance mechanisms, if the energy required by repair is unlimited. However, during growth a considerable amount of energy is allocated to the biosynthesis, which entails tradeoffs with the requirements of repair. Thus, the model predicts that cellular damage is more influenced by the biosynthetic rate than the metabolic rate. To test the prediction, we induced broad variations in metabolic and biosynthetic rates in hornworms, and assayed the lipid peroxidation and protein carbonyl. We found that the increase in the cellular damage was mainly caused by the increase in biosynthetic rate, and the variations in metabolic rate had negligible effect. The oxidative stress hypothesis of aging suggests that high metabolism leads to high cellular damage and short lifespan. However, some empirical studies showed that varying biosynthetic rate, rather than metabolic rate, changes animal's lifespan. The conflicts between the empirical evidence and the hypothesis are reconciled by this study.

Ensemble statistical and subspace clustering model for analysis of autism spectrum disorder phenotypes.

Heterogeneity in Autism Spectrum Disorder (ASD) is complex including variability in behavioral phenotype as well as clinical, physiologic, and pathologic parameters. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) now diagnoses ASD using a 2-dimensional model based social communication deficits and fixated interests and repetitive behaviors. Sorting out heterogeneity is crucial for study of etiology, diagnosis, treatment and prognosis. In this paper, we present an ensemble model for analyzing ASD phenotypes using several machine learning techniques and a k-dimensional subspace clustering algorithm. Our ensemble also incorporates statistical methods at several stages of analysis. We apply this model to a sample of 208 probands drawn from the Simon Simplex Collection Missouri Site patients. The results provide useful evidence that is helpful in elucidating the phenotype complexity within ASD. Our model can be extended to other disorders that exhibit a diverse range of heterogeneity.

Cytokinins secreted by Agrobacterium promote transformation by repressing a plant myb transcription factor.

Agrobacterium-mediated transformation is the most widely used technique for generating transgenic plants. However, many crops remain recalcitrant. We found that an Arabidopsis myb family transcription factor (MTF1) inhibited plant transformation susceptibility. Mutating MTF1 increased attachment of several Agrobacterium strains to roots and increased both stable and transient transformation in both susceptible and transformation-resistant Arabidopsis ecotypes. Cytokinins from Agrobacterium tumefaciens decreased the expression of MTF1 through activation of the cytokinin response regulator ARR3. Mutating AHK3 and AHK4, genes that encode cytokinin-responsive kinases, increased the expression of MTF1 and impaired plant transformation. Mutant mtf1 plants also had increased expression of AT14A, which encodes a putative transmembrane receptor for cell adhesion molecules. Plants overexpressing AT14A exhibited increased susceptibility to transformation, whereas at14a mutant plants exhibited decreased attachment of bacteria to roots and decreased transformation, suggesting that AT14A may serve as an anchor point for Agrobacteria. Thus, by promoting bacterial attachment and transformation of resistant plants and increasing such processes in susceptible plants, treating roots with cytokinins may help engineer crops with improved features or yield.

DNMT1: an emerging target in the treatment of invasive urinary bladder cancer.

OBJECTIVES: More than 14,000 people die from invasive urothelial carcinoma (iUC) of the urinary bladder each year in the USA, and more effective therapies are needed. Naturally occurring canine iUC very closely resembles the disease in humans and serves as a highly relevant translational model for novel therapy of human iUC. Work was undertaken to identify new targets for anticancer therapy in dogs with the goal of translating successful therapeutic strategies into humans with iUC. MATERIALS AND METHODS: Microarray expression analyses were conducted on mRNA extracted from canine normal bladder (n = 4) and iUC tissues (n = 4) using Genome Array 1.0 and analyzed by GeneSpring GX 11, with the stringency of P < 0.02 and a ≥ 2-fold change. The genes thus identified were further analyzed for functional and pathway analysis using Protein ANalysis THrough Evolutionary Relationships (PANTHER) Classification System. In selecting genes for further study, consideration was given for evidence of a role of the gene in human iUC. From these analyses, DNA methyltransferase 1 (DNMT1) was selected for further study. Immunohistochemistry (IHC) of canine normal bladder and iUC tissues was performed to confirm the microarray expression analyses. The effects of targeting DNMT1 in vitro was assessed through MTT assay and Western blot of canine iUC cells treated with 5-azacitidine (5-azaC) and trichostatin A (TSA). RESULTS: DNMT1 was expressed in 0 of 6 normal canine bladder samples and in 10 of 22 (45%) canine iUC samples. The proliferation of canine iUC cells was inhibited by 5-azaC (at concentrations ≥ 5 µm) and by TSA (at concentrations ≥ 0.1 µm). Western blot results were supportive of DNMT1-related effects having a role in the antiproliferative activity. CONCLUSIONS: Microarray expression analyses on canine tissues identified DNMT1 as a potentially "targetable" gene. Expression of DNMT1 in canine iUC was confirmed by IHC, and in vitro studies confirmed that drugs that inhibit DNMT1 have antiproliferative effects. These findings are similar to those recently reported in human iUC and are also in line with results of a preclinical (prehuman) trial of 5-azaC in dogs with naturally occurring iUC. DNMT1 has excellent potential as a target for iUC therapy in humans.

Effect of DLK1 and RTL1 but not MEG3 or MEG8 on muscle gene expression in Callipyge lambs.

Callipyge sheep exhibit extreme postnatal muscle hypertrophy in the loin and hindquarters as a result of a single nucleotide polymorphism (SNP) in the imprinted DLK1-DIO3 domain on ovine chromosome 18. The callipyge SNP up-regulates the expression of surrounding transcripts when inherited in cis without altering their allele-specific imprinting status. The callipyge phenotype exhibits polar overdominant inheritance since only paternal heterozygous animals have muscle hypertrophy. Two studies were conducted profiling gene expression in lamb muscles to determine the down-stream effects of over-expression of paternal allele-specific DLK1 and RTL1 as well as maternal allele-specific MEG3, RTL1AS and MEG8, using Affymetrix bovine expression arrays. A total of 375 transcripts were differentially expressed in callipyge muscle and 25 transcripts were subsequently validated by quantitative PCR. The muscle-specific expression patterns of most genes were similar to DLK1 and included genes that are transcriptional repressors or affect feedback mechanisms in beta-adrenergic and growth factor signaling pathways. One gene, phosphodiesterase 7A had an expression pattern similar to RTL1 expression indicating a biological activity for RTL1 in muscle. Only transcripts that localize to the DLK1-DIO3 domain were affected by inheritance of a maternal callipyge allele. Callipyge sheep are a unique model to study over expression of both paternal allele-specific genes and maternal allele-specific non-coding RNA with an accessible and nonlethal phenotype. This study has identified a number of genes that are regulated by DLK1 and RTL1 expression and exert control on postnatal skeletal muscle growth. The genes identified in this model are primary candidates for naturally regulating postnatal muscle growth in all meat animal species, and may serve as targets to ameliorate muscle atrophy conditions including myopathic diseases and age-related sarcopenia.