Association of intestinal alkaline phosphatase with necrotizing enterocolitis among premature infants

Importance: Necrotizing enterocolitis (NEC) in preterm infants is an often-fatal gastrointestinal tract emergency. A robust NEC biomarker that is not confounded by sepsis could improve bedside management, lead to lower morbidity and mortality, and permit patient selection in randomized clinical trials of possible therapeutic approaches. Objective: To evaluate whether aberrant intestinal alkaline phosphatase (IAP) biochemistry in infant stool is a molecular biomarker for NEC and not associated with sepsis. Design, Setting, and Participants: This multicenter diagnostic study enrolled 136 premature infants (gestational age, \u3c37 weeks) in 2 hospitals in Louisiana and 1 hospital in Missouri. Data were collected and analyzed from May 2015 to November 2018. Exposures: Infant stool samples were collected between 24 and 40 or more weeks postconceptual age. Enrolled infants underwent abdominal radiography at physician and hospital site discretion. Main Outcomes and Measures: Enzyme activity and relative abundance of IAP were measured using fluorometric detection and immunoassays, respectively. After measurements were performed, biochemical data were evaluated against clinical entries from infants\u27 hospital stay. Results: Of 136 infants, 68 (50.0%) were male infants, median (interquartile range [IQR]) birth weight was 1050 (790-1350) g, and median (IQR) gestational age was 28.4 (26.0-30.9) weeks. A total of 25 infants (18.4%) were diagnosed with severe NEC, 19 (14.0%) were suspected of having NEC, and 92 (66.9%) did not have NEC; 26 patients (19.1%) were diagnosed with late-onset sepsis, and 14 (10.3%) had other non-gastrointestinal tract infections. For severe NEC, suspected NEC, and no NEC samples, median (IQR) fecal IAP content, relative to the amount of IAP in human small intestinal lysate, was 99.0% (51.0%-187.8%) (95% CI, 54.0%-163.0%), 123.0% (31.0%-224.0%) (95% CI, 31.0%-224.0%), and 4.8% (2.4%-9.8%) (95% CI, 3.4%-5.9%), respectively. For severe NEC, suspected NEC, and no NEC samples, median (IQR) enzyme activity was 183 (56-507) μmol/min/g (95% CI, 63-478 μmol/min/g) of stool protein, 355 (172-608) μmol/min/g (95% CI, 172-608 μmol/min/g) of stool protein, and 613 (210-1465) μmol/min/g (95% CI, 386-723 μmol/min/g) of stool protein, respectively. Mean (SE) area under the receiver operating characteristic curve values for IAP content measurements were 0.97 (0.02) (95% CI, 0.93-1.00; P \u3c .001) at time of severe NEC, 0.97 (0.02) (95% CI, 0.93-1.00; P \u3c .001) at time of suspected NEC, 0.52 (0.07) (95% CI, 0.38-0.66; P = .75) at time of sepsis, and 0.58 (0.08) (95% CI, 0.42-0.75; P = .06) at time of other non-gastrointestinal tract infections. Mean (SE) area under the receiver operating characteristic curve values for IAP activity were 0.76 (0.06) (95% CI, 0.64-0.86; P \u3c .001), 0.62 (0.07) (95% CI, 0.48-0.77; P = .13), 0.52 (0.07) (95% CI, 0.39-0.67; P = .68), and 0.57 (0.08) (95% CI, 0.39-0.69; P = .66), respectively. Conclusions and Relevance: In this diagnostic study, high amounts of IAP protein in stool and low IAP enzyme activity were associated with diagnosis of NEC and may serve as useful biomarkers for NEC. Our findings indicated that IAP biochemistry was uniquely able to distinguish NEC from sepsis

Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads

Background: Comprehensive annotation and quantification of transcriptomes are outstanding problems in functional genomics. While high throughput mRNA sequencing (RNA-Seq) has emerged as a powerful tool for addressing these problems, its success is dependent upon the availability and quality of reference genome sequences, thus limiting the organisms to which it can be applied. Results: Here, we describe Rnnotator, an automated software pipeline that generates transcript models by de novo assembly of RNA-Seq data without the need for a reference genome. We have applied the Rnnotator assembly pipeline to two yeast transcriptomes and compared the results to the reference gene catalogs of these organisms. The contigs produced by Rnnotator are highly accurate (95percent) and reconstruct full-length genes for the majority of the existing gene models (54.3percent). Furthermore, our analyses revealed many novel transcribed regions that are absent from well annotated genomes, suggesting Rnnotator serves as a complementary approach to analysis based on a reference genome for comprehensive transcriptomics. Conclusions: These results demonstrate that the Rnnotator pipeline is able to reconstruct full-length transcripts in the absence of a complete reference genome

Uniform Approximation Is More Appropriate for Wilcoxon Rank-Sum Test in Gene Set Analysis

Gene set analysis is widely used to facilitate biological interpretations in the analyses of differential expression from high throughput profiling data. Wilcoxon Rank-Sum (WRS) test is one of the commonly used methods in gene set enrichment analysis. It compares the ranks of genes in a gene set against those of genes outside the gene set. This method is easy to implement and it eliminates the dichotomization of genes into significant and non-significant in a competitive hypothesis testing. Due to the large number of genes being examined, it is impractical to calculate the exact null distribution for the WRS test. Therefore, the normal distribution is commonly used as an approximation. However, as we demonstrate in this paper, the normal approximation is problematic when a gene set with relative small number of genes is tested against the large number of genes in the complementary set. In this situation, a uniform approximation is substantially more powerful, more accurate, and less intensive in computation. We demonstrate the advantage of the uniform approximations in Gene Ontology (GO) term analysis using simulations and real data sets

Intake patterns of specific alcoholic beverages by prostate cancer status

Background: Previous studies have shown that different alcoholic beverage types impact prostate cancer (PCa) clinical outcomes differently. However, intake patterns of specific alcoholic beverages for PCa status are understudied. The study?s objective is to evaluate intake patterns of total alcohol and the three types of beverage (beer, wine, and spirits) by the PCa risk and aggressiveness status. Method: This is a cross-sectional study using 10,029 men (4676 non-PCa men and 5353 PCa patients) with European ancestry from the PCa consortium. Associations between PCa status and alcohol intake patterns (infrequent, light/moderate, and heavy) were tested using multinomial logistic regressions. Results: Intake frequency patterns of total alcohol were similar for non-PCa men and PCa patients after adjusting for demographic and other factors. However, PCa patients were more likely to drink wine (light/moderate, OR = 1.11, p = 0.018) and spirits (light/moderate, OR = 1.14, p = 0.003; and heavy, OR = 1.34, p = 0.04) than non-PCa men. Patients with aggressive PCa drank more beer than patients with non-aggressive PCa (heavy, OR = 1.48, p = 0.013). Interestingly, heavy wine intake was inversely associated with PCa aggressiveness (OR = 0.56, p = 0.009). Conclusions: The intake patterns of some alcoholic beverage types differed by PCa status. Our findings can provide valuable information for developing custom alcohol interventions for PCa patients

KLK3 SNP-SNP interactions for prediction of prostate cancer aggressiveness

Risk classification for prostate cancer (PCa) aggressiveness and underlying mechanisms remain inadequate. Interactions between single nucleotide polymorphisms (SNPs) may provide a solution to fill these gaps. To identify SNP-SNP interactions in the four pathways (the angiogenesis-, mitochondria-, miRNA-, and androgen metabolism-related pathways) associated with PCa aggressiveness, we tested 8587 SNPs for 20,729 cases from the PCa consortium. We identified 3 KLK3 SNPs, and 1083 (P-9) and 3145 (P-5) SNP-SNP interaction pairs significantly associated with PCa aggressiveness. These SNP pairs associated with PCa aggressiveness were more significant than each of their constituent SNP individual effects. The majority (98.6%) of the 3145 pairs involved KLK3. The 3 most common gene-gene interactions were KLK3-COL4A1:COL4A2, KLK3-CDH13, and KLK3-TGFBR3. Predictions from the SNP interaction-based polygenic risk score based on 24 SNP pairs are promising. The prevalence of PCa aggressiveness was 49.8%, 21.9%, and 7.0% for the PCa cases from our cohort with the top 1%, middle 50%, and bottom 1% risk profiles. Potential biological functions of the identified KLK3 SNP-SNP interactions were supported by gene expression and protein-protein interaction results. Our findings suggest KLK3 SNP interactions may play an important role in PCa aggressiveness.</p

SNP interaction pattern identifier (SIPI): an intensive search for SNP-SNP interaction patterns.

MOTIVATION: Testing SNP-SNP interactions is considered as a key for overcoming bottlenecks of genetic association studies. However, related statistical methods for testing SNP-SNP interactions are underdeveloped. RESULTS: We propose the SNP Interaction Pattern Identifier (SIPI), which tests 45 biologically meaningful interaction patterns for a binary outcome. SIPI takes non-hierarchical models, inheritance modes and mode coding direction into consideration. The simulation results show that SIPI has higher power than MDR (Multifactor Dimensionality Reduction), AA_Full, Geno_Full (full interaction model with additive or genotypic mode) and SNPassoc in detecting interactions. Applying SIPI to the prostate cancer PRACTICAL consortium data with approximately 21 000 patients, the four SNP pairs in EGFR-EGFR , EGFR-MMP16 and EGFR-CSF1 were found to be associated with prostate cancer aggressiveness with the exact or similar pattern in the discovery and validation sets. A similar match for external validation of SNP-SNP interaction studies is suggested. We demonstrated that SIPI not only searches for more meaningful interaction patterns but can also overcome the unstable nature of interaction patterns. AVAILABILITY AND IMPLEMENTATION: The SIPI software is freely available at http://publichealth.lsuhsc.edu/LinSoftware/ . CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.This study was supported by the National Cancer Institute (R01CA128813, PI: Park, JY and R21CA202417, PI: Lin, HY)

D-optimal designs for weighted polynomial regression

By utilizing the equivalence theorem and Descartes's rule of signs, we construct D-optimal designs for a weighted polynomial regression model of degree k, with specific weight function w(x)=1/(a2-x2)[delta], on the compact interval [-1,1]. The main result shows that in most cases, the number of support points of the D-optimal design is k+1, while in other cases, the D-optimal design has k+2 support points.Approximate design Descartes's rule of signs Equivalence theorem Weighted polynomial regression

D-optimal designs for polynomial regression models through origin

In this article we consider D-optimal designs for polynomial regression models with low-degree terms being missed, by applying the theory of canonical moments. It turns out that the optimal design places equal weight on each of the zeros of some Jacobi polynomial when the number of unknown parameters in the model is even. The procedure and examples of finding the optimal supports and weights are given when the number of unknown parameters in the model is odd.Polynomial regression Jacobi polynomials Canonical moments Hankel determinant Regression through the origin