The Hexosamine Biosynthesis Pathway: Regulation and Function.

The hexosamine biosynthesis pathway (HBP) produces uridine diphosphate-N-acetyl glucosamine, UDP-GlcNAc, which is a key metabolite that is used for N- or O-linked glycosylation, a co- or post-translational modification, respectively, that modulates protein activity and expression. The production of hexosamines can occur via de novo or salvage mechanisms that are catalyzed by metabolic enzymes. Nutrients including glutamine, glucose, acetyl-CoA, and UTP are utilized by the HBP. Together with availability of these nutrients, signaling molecules that respond to environmental signals, such as mTOR, AMPK, and stress-regulated transcription factors, modulate the HBP. This review discusses the regulation of GFAT, the key enzyme of the de novo HBP, as well as other metabolic enzymes that catalyze the reactions to produce UDP-GlcNAc. We also examine the contribution of the salvage mechanisms in the HBP and how dietary supplementation of the salvage metabolites glucosamine and N-acetylglucosamine could reprogram metabolism and have therapeutic potential. We elaborate on how UDP-GlcNAc is utilized for N-glycosylation of membrane and secretory proteins and how the HBP is reprogrammed during nutrient fluctuations to maintain proteostasis. We also consider how O-GlcNAcylation is coupled to nutrient availability and how this modification modulates cell signaling. We summarize how deregulation of protein N-glycosylation and O-GlcNAcylation can lead to diseases including cancer, diabetes, immunodeficiencies, and congenital disorders of glycosylation. We review the current pharmacological strategies to inhibit GFAT and other enzymes involved in the HBP or glycosylation and how engineered prodrugs could have better therapeutic efficacy for the treatment of diseases related to HBP deregulation.

Build a better bootstrap and the RAWR shall beat a random path to your door: phylogenetic support estimation revisited.

MOTIVATION: The standard bootstrap method is used throughout science and engineering to perform general-purpose non-parametric resampling and re-estimation. Among the most widely cited and widely used such applications is the phylogenetic bootstrap method, which Felsenstein proposed in 1985 as a means to place statistical confidence intervals on an estimated phylogeny (or estimate 'phylogenetic support'). A key simplifying assumption of the bootstrap method is that input data are independent and identically distributed (i.i.d.). However, the i.i.d. assumption is an over-simplification for biomolecular sequence analysis, as Felsenstein noted. RESULTS: In this study, we introduce a new sequence-aware non-parametric resampling technique, which we refer to as RAWR ('RAndom Walk Resampling'). RAWR consists of random walks that synthesize and extend the standard bootstrap method and the 'mirrored inputs' idea of Landan and Graur. We apply RAWR to the task of phylogenetic support estimation. RAWR's performance is compared to the state-of-the-art using synthetic and empirical data that span a range of dataset sizes and evolutionary divergence. We show that RAWR support estimates offer comparable or typically superior type I and type II error compared to phylogenetic bootstrap support. We also conduct a re-analysis of large-scale genomic sequence data from a recent study of Darwin's finches. Our findings clarify phylogenetic uncertainty in a charismatic clade that serves as an important model for complex adaptive evolution. AVAILABILITY AND IMPLEMENTATION: Data and software are publicly available under open-source software and open data licenses at: https://gitlab.msu.edu/liulab/RAWR-study-datasets-and-scripts.

Prediction of tumor location in prostate cancer tissue using a machine learning system on gene expression data.

BACKGROUND: Finding the tumor location in the prostate is an essential pathological step for prostate cancer diagnosis and treatment. The location of the tumor - the laterality - can be unilateral (the tumor is affecting one side of the prostate), or bilateral on both sides. Nevertheless, the tumor can be overestimated or underestimated by standard screening methods. In this work, a combination of efficient machine learning methods for feature selection and classification are proposed to analyze gene activity and select them as relevant biomarkers for different laterality samples. RESULTS: A data set that consists of 450 samples was used in this study. The samples were divided into three laterality classes (left, right, bilateral). The aim of this work is to understand the genomic activity in each class and find relevant genes as indicators for each class with nearly 99% accuracy. The system identified groups of differentially expressed genes (RTN1, HLA-DMB, MRI1) that are able to differentiate samples among the three classes. CONCLUSION: The proposed method was able to detect sets of genes that can identify different laterality classes. The resulting genes are found to be strongly correlated with disease progression. HLA-DMB and EIF4G2, which are detected in the set of genes can detect the left laterality, were reported earlier to be in the same pathway called Allograft rejection SuperPath.

A Hierarchical Machine Learning Model to Discover Gleason Grade-Specific Biomarkers in Prostate Cancer.

(1) Background:One of the most common cancers that affect North American men and men worldwide is prostate cancer. The Gleason score is a pathological grading system to examine the potential aggressiveness of the disease in the prostate tissue. Advancements in computing and next-generation sequencing technology now allow us to study the genomic profiles of patients in association with their different Gleason scores more accurately and effectively. (2) Methods: In this study, we used a novel machine learning method to analyse gene expression of prostate tumours with different Gleason scores, and identify potential genetic biomarkers for each Gleason group. We obtained a publicly-available RNA-Seq dataset of a cohort of 104 prostate cancer patients from the National Center for Biotechnology Information's (NCBI) Gene Expression Omnibus (GEO) repository, and categorised patients based on their Gleason scores to create a hierarchy of disease progression. A hierarchical model with standard classifiers in different Gleason groups, also known as nodes, was developed to identify and predict nodes based on their mRNA or gene expression. In each node, patient samples were analysed via class imbalance and hybrid feature selection techniques to build the prediction model. The outcome from analysis of each node was a set of genes that could differentiate each Gleason group from the remaining groups. To validate the proposed method, the set of identified genes were used to classify a second dataset of 499 prostate cancer patients collected from cBioportal. (3) Results: The overall accuracy of applying this novel method to the first dataset was 93.3%; the method was further validated to have 87% accuracy using the second dataset. This method also identified genes that were not previously reported as potential biomarkers for specific Gleason groups. In particular, PIAS3 was identified as a potential biomarker for Gleason score 4 + 3 = 7, and UBE2V2 for Gleason score 6. (4) Insight: Previous reports show that the genes predicted by this newly proposed method strongly correlate with prostate cancer development and progression. Furthermore, pathway analysis shows that both PIAS3 and UBE2V2 share similar protein interaction pathways, the JAK/STAT signaling process.

Contribution of TLR4 signaling in intermittent hypoxia-mediated atherosclerosis progression.

BACKGROUND: Intermittent hypoxia (IH), a typical character of obstructive sleep apnea (OSA), is related to atherogenesis. However, the role of IH on atherosclerosis (AS) progression and the mechanisms involved remains poorly understood. METHODS: In the present study, high-fat fed ApoE-/- mice were treated with recombinant shRNA-TLR4 lentivirus and exposed to IH. Atherosclerotic lesions on the en face aorta and cross-sections of aortic root were examined by Oil-Red O staining. The content of lipids and collagen of aortic root plaques were detected by Oil-Red O staining and Sirius red staining, respectively. The TLR4, NF-κB p65, α-SMA and MOMA-2 expression in aorta and IL-6 and TNF-α expression in the mice serum were also detected. RESULTS: Compared with the Sham group, the IH treated group further increased atherosclerotic plaque loads and plaque vulnerability in the aortic sinus. Along with increased TLR4 expression, enhanced NF-κB activation, inflammatory activity and aggravated dyslipidemia were observed in the IH treated group. TLR4 interference partly inhibited IH-mediated AS progression with decreased inflammation and improved cholesterol levels. Similarly, in endothelial cells, hypoxia/reoxygenation exposure has been shown to promote TLR4 expression and activation of proinflammatory TLR4/NF-κB signaling, while TLR4 interference inhibited these effects. CONCLUSIONS: We found that the IH accelerated growth and vulnerability of atherosclerotic plaque, which probably acted by triggering the activation of proinflammatory TLR4/NF-κB signaling. These findings may suggest that IH is a risk factor for vulnerable plaque and provide a new insight into the treatment of OSA-induced AS progression.

The Differences and Similarities Between Two-Sample T-Test and Paired T-Test.

In clinical research, comparisons of the results from experimental and control groups are often encountered. The two-sample t-test (also called independent samples t-test) and the paired t-test are probably the most widely used tests in statistics for the comparison of mean values between two samples. However, confusion exists with regard to the use of the two test methods, resulting in their inappropriate use. In this paper, we discuss the differences and similarities between these two t-tests. Three examples are used to illustrate the calculation procedures of the two-sample t-test and paired t-test.

Sample Size Calculations for Comparing Groups with Continuous Outcomes.

Sample size justification is required for all clinical studies. However, to many biomedical and clinical researchers, power and sample size analysis seems like a magic trick of statisticians. In this note, we discuss power and sample size calculations and show that biomedical and clinical investigators play a significant role in making such analyses possible and meaningful. Thus, power analysis is really an interactive process and scientific researchers and statisticians are equal partners in the research enterprise.

Inconsistency Between Univariate and Multiple Logistic Regressions.

Logistic regression is a popular statistical method in studying the effects of covariates on binary outcomes. It has been widely used in both clinical trials and observational studies. However, the results from the univariate regression and from the multiple logistic regression tend to be conflicting. A covariate may show very strong effect on the outcome in the multiple regression but not in the univariate regression, and vice versa. These facts have not been well appreciated in biomedical research. Misuse of logistic regression is very prevalent in medical publications. In this paper, we study the inconsistency between the univariate and multiple logistic regressions and give advice in the model section in multiple logistic regression analysis.

Continuous EEG monitoring enhances multimodal outcome prediction in hypoxic-ischemic brain injury.

OBJECTIVE: Hypoxic brain injury is the largest contributor to disability and mortality after cardiac arrest. We aim to identify electroencephalogram (EEG) characteristics that can predict outcome on cardiac arrest patients treated with targeted temperature management (TTM). METHODS: We retrospectively examined clinical, EEG, functional outcome at discharge, and in-hospital mortality for 373 adult subjects with return of spontaneous circulation after cardiac arrest. Poor outcome was defined as a Cerebral Performance Category score of 3-5. Pure suppression-burst (SB) was defined as SB not associated with status epilepticus (SE), seizures, or generalized periodic discharges. RESULTS: In-hospital mortality was 68.6% (N=256). Presence of both unreactive EEG background and SE was associated with a positive predictive value (PPV) of 100% (95% confidence interval: 0.96-1) and a false-positive rate (FPR) of 0% (95% CI: 0-0.11) for poor functional outcome. A prediction model including demographics data, admission exam, presence of status epilepticus, pure SB, and lack of EEG reactivity had an area under the curve of 0.92 (95% CI: 0.87-0.95) for poor functional outcome prediction, and 0.96 (95% CI: 0.94-0.98) for in-hospital mortality. Presence of pure SB (N=87) was confounded by anesthetics use in 83.9% of the cases, and was not an independent predictor of poor functional outcome, having a FPR of 23% (95% CI: 0.19-0.28). CONCLUSIONS: An unreactive EEG background and SE predicted poor functional outcome and in-hospital mortality in cardiac arrest patients undergoing TTM. Prognostic value of pure SB is confounded by use of sedative agents, and its use on prognostication decisions should be made with caution.

Two Paradoxes in Linear Regression Analysis.

Regression is one of the favorite tools in applied statistics. However, misuse and misinterpretation of results from regression analysis are common in biomedical research. In this paper we use statistical theory and simulation studies to clarify some paradoxes around this popular statistical method. In particular, we show that a widely used model selection procedure employed in many publications in top medical journals is wrong. Formal procedures based on solid statistical theory should be used in model selection.