Prenatal and early-life exposure to the Great Chinese Famine increased the risk of tuberculosis in adulthood across two generations.

Global food security is a major driver of population health, and food system collapse may have complex and long-lasting effects on health outcomes. We examined the effect of prenatal exposure to the Great Chinese Famine (1958-1962)-the largest famine in human history-on pulmonary tuberculosis (PTB) across consecutive generations in a major center of ongoing transmission in China. We analyzed >1 million PTB cases diagnosed between 2005 and 2018 in Sichuan Province using age-period-cohort analysis and mixed-effects metaregression to estimate the effect of the famine on PTB risk in the directly affected birth cohort (F1) and their likely offspring (F2). The analysis was repeated on certain sexually transmitted and blood-borne infections (STBBI) to explore potential mechanisms of the intergenerational effects. A substantial burden of active PTB in the exposed F1 cohort and their offspring was attributable to the Great Chinese Famine, with more than 12,000 famine-attributable active PTB cases (>1.23% of all cases reported between 2005 and 2018). An interquartile range increase in famine intensity resulted in a 6.53% (95% confidence interval [CI]: 1.19-12.14%) increase in the ratio of observed to expected incidence rate (incidence rate ratio, IRR) in the absence of famine in F1, and an 8.32% (95% CI: 0.59-16.6%) increase in F2 IRR. Increased risk of STBBI was also observed in F2. Prenatal and early-life exposure to malnutrition may increase the risk of active PTB in the exposed generation and their offspring, with the intergenerational effect potentially due to both within-household transmission and increases in host susceptibility.

A likelihood ratio test on temporal trends in age-period-cohort models with applications to the disparities of heart disease mortality among US populations and comparison with Japan.

In this article, we introduce the recently developed intrinsic estimator method in the age-period-cohort (APC) models in examining disease incidence and mortality data, further develop a likelihood ratio (L-R) test for testing differences in temporal trends across populations, and apply the methods to examining temporal trends in the age, period or calendar time, and birth cohort of the US heart disease mortality across racial and sex groups. The temporal trends are estimated with the intrinsic estimator method to address the model identification problem, in which multiple sets of parameter estimates yield the same fitted values for a given dataset, making it difficult to conduct comparison of and hypothesis testing on the temporal trends in the age, period, and cohort across populations. We employ a penalized profile log-likelihood approach in developing the L-R test to deal with the issues of multiple estimators and the diverging number of model parameters. The identification problem also induces overparametrization of the APC model, which requires a correction of the degree of freedom of the L-R test. Monte Carlo simulation studies demonstrate that the L-R test performs well in the Type I error calculation and is powerful to detect differences in the age or period trends. The L-R test further reveals disparities of heart disease mortality among the US populations and between the US and Japanese populations.

Construction and Validation of a Nomogram to Identify the Risk of Cavitation in Pulmonary Tuberculosis.

Background: The present study aimed to construct and validate a nomogram based on clinical metrics to identify CPTB. Patients and Methods: The present study retrospectively recruited pulmonary tuberculosis (PTB) patients admitted to Jiashan County First People's Hospital in China from November 2018 to September 2023. PTB patients were classified into the CPTB group and the non-CPTB group based on chest computed tomography findings, and were randomly allocated to the training set (70%) and the validation cohort (30%). The training set and validation set were used to establish and validate nomogram, respectively. Multivariate logistic regression analysis (MLSA) was used to identify the independent risk factors for CPTB in patients with PTB. Statistically significant variables in the MLSA were then used to construct a nomogram predicting CPTB in patients with PTB. The receiver operating characteristic (ROC) curve, calibration curve analysis (CCA), and decision curve analysis (DCA) were used for the evaluation of the nomogram. Results: A total of 293 PTB patients, including 208 in the training set (85 CPTB) and 85 in the validation set (33 CPTB\), were included in this study. Stepwise MLSA showed that sputum smear (≥2+), smoking(yes), glycosylated hemoglobin A1c(HbA1c), hemoglobin (HB), and systemic inflammatory response index (SIRI) were independent risk factors for the development of cavitation in patients with PTB. The nomogram identifying the high-risk CPTB patients was successfully established and showed a strong predictive capacity, with area under the curves (AUCs) of 0.875 (95% CI:0.806-0.909) and 0.848 (95% CI:0.751-0.946) in the training set and validation set respectively. In addition, the CCA and DCA corroborated the nomogram's high level of accuracy and clinical applicability within both the training and validation sets. Conclusion: The constructed nomogram, consisting of sputum smear positivity, smoking, HbA1C, HB, and SIRI, serves as a practical and effective tool for early identification and personalized management of CPTB.

Inflammation biomarkers in vaginal fluid and preterm delivery.

STUDY QUESTION: Which inflammation biomarkers detected in the vaginal fluid are most informative for identifying preterm delivery (PTD) risk? SUMMARY ANSWER: Elevated interleukin (IL)-6 at mid-trimester was associated with increased odds of spontaneous PTD at <35 weeks and with PTD plus histologic chorioamnionitis (HCA), and had the greatest sensitivity for detecting these two PTD subtypes. WHAT IS KNOWN ALREADY: Maternal and/or fetal inflammation play a role in some preterm deliveries, therefore inflammation biomarkers might help to identify women at greater risk. STUDY DESIGN, SIZE, DURATION: We examined 1115 women from the Pregnancy Outcomes and Community Health Study, a cohort study conducted from September 1998 through June 2004, for whom data were available on mid-pregnancy inflammatory biomarkers. PARTICIPANTS/MATERIALS, SETTING, METHODS: At enrollment at 16-27 weeks gestation, vaginal fluid samples were collected from a swab and 15 eluted biomarkers were measured using the Meso Scale Discovery multiplex electrochemiluminescence platform. Associations of biomarkers with PTD were examined, according to clinical circumstance, week at delivery and presence/absence of HCA. Weighted logistic regression was used to determine odds ratios (OR) and 95% confidence intervals (CI) adjusted for race. Sensitivity and specificity were compared between individual and multiple biomarkers, identified by a bootstrapping method. MAIN RESULTS AND THE ROLE OF CHANCE: Elevated IL-6 (>75th percentile) displayed the strongest association with spontaneous PTD <35 weeks (OR 2.3; CI 1.3-4.0) and PTD with HCA (OR 2.8; CI 1.4-6.0). The sensitivity of IL-6 to detect spontaneous PTD <35 weeks or PTD with HCA was 0.43 and 0.51, respectively, while specificity was 0.74 and 0.75, respectively. IL-6 plus IL1ß, IL-6r, tumor necrosis factor-alpha or granulocyte-macrophage colony-stimulating factor increased specificity (range 0.84-0.88), but decreased sensitivity (range 0.28-0.34) to detect both PTD subtypes. Results were similar when a combination of IL-6 and bacterial vaginosis (BV) was explored. Thus, the use of multiple biomarkers did not detect PTD subtypes with a greater sensitivity than IL-6 alone, and IL-6 is a specific but non-sensitive marker for the detection of spontaneous PTD. LIMITATIONS, REASONS FOR CAUTION: Our ability to find small effect size associations between PTD and inflammation biomarkers (OR <2.0) might have been limited by the modest number of less common PTD subtypes in our population (e.g. spontaneous delivery <35 weeks, PTD accompanied by HCA) and by relatively higher variability for some cytokines, for example tumor necrosis factor-α, IL-12p70, IL-10 and granulocyte-macrophage colony-stimulating factor, that are less stable and commonly undetectable or detectable at low levels in human vaginal secretions. WIDER IMPLICATIONS OF THE FINDINGS: Larger studies are needed to further explore a role of inflammation biomarkers in combination with other risk factors, including specific BV-associated organisms, for the prediction of PTD subtypes. STUDY FUNDING/COMPETING INTEREST(S): This work was supported by the National Institute of Child Health and Human Development, National Institute of Nursing, March of Dimes Foundation, Thrasher Research Foundation and Centers for Disease Control and Prevention. The authors have no conflicts of interest.

Gene expression in archived newborn blood spots distinguishes infants who will later develop cerebral palsy from matched controls.

BACKGROUND: Gene expression in archived newborn blood spots remaining from newborn screening may reflect pathophysiological disturbances useful in understanding the etiology of cerebral palsy (CP). METHODS: We quantified the expression of gene sets representing four physiological pathways hypothesized to contribute to CP in archived unfrozen residual newborn blood spot specimens from 53 children with CP and 53 age-, gender-, and gestational age-matched controls. We selected four empirical and three canonical gene sets representing the inflammatory, hypoxic, coagulative, and thyroidal pathways and examined mRNA expression using an 8 × 60,000 oligonucleotide microarray. The log2 fold change of gene expression between matched cases and controls was analyzed using the generally applicable gene set enrichment method. RESULTS: The empirical inflammatory and empirical hypoxic gene sets were significantly downregulated in term-born CP cases (n = 33) as compared with matched controls (P = 0.0007 and 0.0009, respectively), whereas both gene sets were significantly upregulated (P =0.0055 and 0.0223, respectively) in preterm-born CP cases (n = 20). The empirical thyroidal gene set was significantly upregulated in preterm-born CP cases (P = 0.0023). CONCLUSION: The newborn blood spot transcriptome can serve as a platform for investigating distinctive gene expression patterns in children who later develop CP.

Tear proteomic analysis of young glasses, orthokeratology, and soft contact lens wearers.

Contact lens-related ocular surface complications occur more often in teenagers and young adults. The purpose of this study was to determine changes in tear proteome of young patients wearing glasses (GL), orthokeratology lenses (OK), and soft contact lenses (SCL). Twenty-two young subjects (10-26 years of age) who were established GL, OK, and SCL wearers were recruited. Proteomic data were collected using a data-independent acquisition-parallel accumulation serial fragmentation workflow. In total, 3406 protein groups were identified, the highest number of proteins identified in Schirmer strip tears to date. Eight protein groups showed higher abundance, and 11 protein groups showed lower abundance in the SCL group compared to the OK group. In addition, the abundance of 82 proteins significantly differed in children compared to young adult GL wearers, among which 67 proteins were higher, and 15 proteins were lower in children. These 82 proteins were involved in inflammation, immune, and glycoprotein metabolic biological processes. In summary, this work identified over 3000 proteins in Schirmer Strip tears. The results indicated that tear proteomes were altered by orthokeratology and soft contact wear and age, which warrants further larger-scale study on the ocular surface responses of teenagers and young adults separately to contact lens wear. SIGNIFICANCE: In this work, we examined the tear proteomes of young patients wearing glasses, orthokeratology lenses, and soft contact lenses using a data-independent acquisition-parallel accumulation serial fragmentation (diaPASEF) workflow and identified 3406 protein groups in Schirmer strip tears. Nineteen protein groups showed significant abundance changes between orthokeratology and soft contact lens wearers. Moreover, eighty-two protein groups significantly differed in abundance in children and young adult glasses wearers. As a pilot study, this work provides a deep coverage of tear proteome and suggests the need to investigate ocular responses to contact lens wear separately for children and young adults.

Detecting genetic interactions for quantitative traits with U-statistics.

The genetic etiology of complex human diseases has been commonly viewed as a process that involves multiple genetic variants, environmental factors, as well as their interactions. Statistical approaches, such as the multifactor dimensionality reduction (MDR) and generalized MDR (GMDR), have recently been proposed to test the joint association of multiple genetic variants with either dichotomous or continuous traits. In this study, we propose a novel Forward U-Test to evaluate the combined effect of multiple loci on quantitative traits with consideration of gene-gene/gene-environment interactions. In this new approach, a U-Statistic-based forward algorithm is first used to select potential disease-susceptibility loci and then a weighted U-statistic is used to test the joint association of the selected loci with the disease. Through a simulation study, we found the Forward U-Test outperformed GMDR in terms of greater power. Aside from that, our approach is less computationally intensive, making it feasible for high-dimensional gene-gene/gene-environment research. We illustrate our method with a real data application to nicotine dependence (ND), using three independent datasets from the Study of Addiction: Genetics and Environment. Our gene-gene interaction analysis of 155 SNPs in 67 candidate genes identified two SNPs, rs16969968 within gene CHRNA5 and rs1122530 within gene NTRK2, jointly associated with the level of ND (P-value = 5.31e-7). The association, which involves essential interaction, is replicated in two independent datasets with P-values of 1.08e-5 and 0.02, respectively. Our finding suggests that joint action may exist between the two gene products.

The neuropathology of fatal cerebral malaria in malawian children.

We examined the brains of 50 Malawian children who satisfied the clinical definition of cerebral malaria (CM) during life; 37 children had sequestration of infected red blood cells (iRBCs) and no other cause of death, and 13 had a nonmalarial cause of death with no cerebral sequestration. For comparison, 18 patients with coma and no parasitemia were included. We subdivided the 37 CM cases into two groups based on the cerebral microvasculature pathology: iRBC sequestration only (CM1) or sequestration with intravascular and perivascular pathology (CM2). We characterized and quantified the axonal and myelin damage, blood-brain barrier (BBB) disruption, and cellular immune responses and correlated these changes with iRBC sequestration and microvascular pathology. Axonal and myelin damage was associated with ring hemorrhages and vascular thrombosis in the cerebral and cerebellar white matter and brainstem of the CM2 cases. Diffuse axonal and myelin damage were present in CM1 and CM2 cases in areas of prominent iRBC sequestration. Disruption of the BBB was associated with ring hemorrhages and vascular thrombosis in CM2 cases and with sequestration in both CM1 and CM2 groups. Monocytes with phagocytosed hemozoin accumulated within microvessels containing iRBCs in CM2 cases but were not present in the adjacent neuropil. These findings are consistent with a link between iRBC sequestration and intravascular and perivascular pathology in fatal pediatric CM, resulting in myelin damage, axonal injury, and breakdown of the BBB.

MA-SNP--A new genotype calling method for oligonucleotide SNP arrays modeling the batch effect with a normal mixture model.

Genome-wide association studies hold great promise in identifying disease-susceptibility variants and understanding the genetic etiology of complex diseases. Microarray technology enables the genotyping of millions of single nucleotide polymorphisms. Many factors in microarray studies, such as probe selection, sample quality, and experimental process and batch, have substantial effect on the genotype calling accuracy, which is crucial for downstream analyses. Failure to account for the variability of these sources may lead to inaccurate genotype calls and false positive and false negative findings. In this study, we develop a SNP-specific genotype calling algorithm based on the probe intensity composite representation (PICR) model, while using a normal mixture model to account for the variability of batch effect on the genotype calls. We demonstrate our method with SNP array data in a few studies, including the HapMap project, the coronary heart disease and the UK Blood Service Control studies by the Wellcome Trust Case-Control Consortium, and a methylation profiling study. Our single array based approach outperforms PICR and is comparable to the best multi-array genotype calling methods.

Comprehensive spectral libraries for various rabbit eye tissue proteomes.

Rabbits have been widely used for studying ocular physiology and pathology due to their relatively large eye size and similar structures with human eyes. Various rabbit ocular disease models, such as dry eye, age-related macular degeneration, and glaucoma, have been established. Despite the growing application of proteomics in vision research using rabbit ocular models, there is no spectral assay library for rabbit eye proteome publicly available. Here, we generated spectral assay libraries for rabbit eye compartments, including conjunctiva, cornea, iris, retina, sclera, vitreous humor, and tears using fractionated samples and ion mobility separation enabling deep proteome coverage. The rabbit eye spectral assay library includes 9,830 protein groups and 113,593 peptides. We present the data as a freely available community resource for proteomic studies in the vision field. Instrument data and spectral libraries are available via ProteomeXchange with identifier PXD031194.

Daily versus three-times-weekly azithromycin in Chinese patients with non-cystic fibrosis bronchiectasis: protocol for a prospective, open-label and randomised controlled trial.

INTRODUCTION: Non-cystic fibrosis bronchiectasis (NCFB) brought a heavy healthcare burden worldwide. Macrolide maintenance therapy was proved to be helpful in reducing exacerbation of NCFB. However, the optimal dosing regimens of macrolides have not been determined, and its efficacy in Chinese NCFB population has not been validated. This protocol describes a head-to-head clinical trial designed to compare the efficacy of two dosing regimens of azithromycin in Chinese NCFB population. METHODS AND ANALYSIS: This prospective, open-label and randomised controlled trial will be conducted in the First People's Hospital of Jiashan, China. Eligible patients with high-resolution CT defined NCFB will be randomly divided into three groups, which will receive either 250 mg daily azithromycin, or 500 mg three-times-weekly azithromycin or no treatment for 6 months. They will be followed up for another 6 months without treatment. The primary outcome is the mean rate of protocol-defined pulmonary exacerbation at 6 months. ETHICS AND DISSEMINATION: Ethical approval was obtained from the First People's Hospital of Jiashan Ethics Committee. The findings will be disseminated in peer-reviewed publications. TRIAL REGISTRATION NUMBER: ChiCTR2100052906.

Differentiating the pathologies of cerebral malaria by postmortem parasite counts.

To study the pathogenesis of fatal cerebral malaria, we conducted autopsies in 31 children with this clinical diagnosis. We found that 23% of the children had actually died from other causes. The remaining patients had parasites sequestered in cerebral capillaries, and 75% of those had additional intra- and perivascular pathology. Retinopathy was the only clinical sign distinguishing malarial from nonmalarial coma. These data have implications for treating malaria patients, designing clinical trials and assessing malaria-specific disease associations.

Hybridization modeling of oligonucleotide SNP arrays for accurate DNA copy number estimation.

Affymetrix SNP arrays have been widely used for single-nucleotide polymorphism (SNP) genotype calling and DNA copy number variation inference. Although numerous methods have achieved high accuracy in these fields, most studies have paid little attention to the modeling of hybridization of probes to off-target allele sequences, which can affect the accuracy greatly. In this study, we address this issue and demonstrate that hybridization with mismatch nucleotides (HWMMN) occurs in all SNP probe-sets and has a critical effect on the estimation of allelic concentrations (ACs). We study sequence binding through binding free energy and then binding affinity, and develop a probe intensity composite representation (PICR) model. The PICR model allows the estimation of ACs at a given SNP through statistical regression. Furthermore, we demonstrate with cell-line data of known true copy numbers that the PICR model can achieve reasonable accuracy in copy number estimation at a single SNP locus, by using the ratio of the estimated AC of each sample to that of the reference sample, and can reveal subtle genotype structure of SNPs at abnormal loci. We also demonstrate with HapMap data that the PICR model yields accurate SNP genotype calls consistently across samples, laboratories and even across array platforms.

Actual causes of death in Chaoyang District of Beijing, China, 2007.

OBJECTIVES: To identify and quantify major external (non-genetic) factors that contribute to death in Chaoyang District of Beijing, China in 2007. METHODS: The death registration data reported to the Center of Disease Control and Prevention of Chaoyang District of Beijing, China, during the year 2007, were obtained. The analysis was conducted in 2009 using the health risk factors identified by the World Health Report 2002 and the population attributable fractions of mortality from Global burden of disease and risk factors. The estimates of actual causes of death attributable to each risk factor were calculated by multiplying the population attributable fractions of mortality by the corresponding number of deaths of the subgroup or total population. RESULTS: The five leading actual causes of death in Chaoyang District of Beijing, China in 2007 were high blood pressure (2159 deaths, 18%), smoking (990, 8%), low fruit and vegetable consumption (968, 8%), high cholesterol (891, 7%), and physical inactivity (629, 5%). The pattern and ordering of these leading causes vary with sex and age specific subgroups. CONCLUSIONS: More than half of the total number of deaths in Chaoyang District in 2007 could be attributed to a few major preventable risk factors. Although the study focused on only one district of Beijing in one single year, and is by no means comprehensive, its findings suggest that public health policies and programmes in China should address these public health concerns by focusing on these largely preventable risk factors for primary prevention.

Variation of rhizosphere microbial community in continuous mono-maize seed production.

Soil microbe is crucial to a healthy soil, therefore its diversities and abundances under different conditions are still need fully understand.The aims of the study were to characterize the community structure and diversity of microbe in the rhizosphere soil after continuous maize seed production, and the relationship between the disease incidence of four diseases and the variation of the rhizosphere microbe. The results showed that different fungal and bacterial species were predominant in different cropping year, and long-term maize seed production had a huge impact on structure and diversity of soil microbial. Ascomycota and Mortierellomycota were the dominant fungal phyla and Mortierella and Ascomycetes represented for a large proportion of genus. A relative increase of Fusarium and Gibberella and a relative decrease of Mortierella, Chrysosporium, Podospora, and Chaetomium were observed with the increase of cropping year. Pathogenic Fusarium, Curvularia, Curvularia-lunata, Cladosporium, Gibberella-baccata, and Plectosphaerellaceae were over-presented and varied at different continuous cropping year, led to different maize disease incidence. Proteobacteria and Actinobacteria ranked in the top two of all bacterial phyla, and genus Pseudarthrobacter, Roseiflexus and RB41 dominated top 3. Haliangium and Streptomyces decreased with the continuous cropping year and mono-cropping of maize seed production increased disease incidence with the increase of cropping year, while the major disease was different. Continuous cropping of maize seed production induced the decrease of protective microbe and biocontrol genera, while pathogenic pathogen increased, and maize are in danger of pathogen invasion. Field management show great effects on soil microbial community.

Mapping haplotype-haplotype interactions with adaptive LASSO.

BACKGROUND: The genetic etiology of complex diseases in human has been commonly viewed as a complex process involving both genetic and environmental factors functioning in a complicated manner. Quite often the interactions among genetic variants play major roles in determining the susceptibility of an individual to a particular disease. Statistical methods for modeling interactions underlying complex diseases between single genetic variants (e.g. single nucleotide polymorphisms or SNPs) have been extensively studied. Recently, haplotype-based analysis has gained its popularity among genetic association studies. When multiple sequence or haplotype interactions are involved in determining an individual's susceptibility to a disease, it presents daunting challenges in statistical modeling and testing of the interaction effects, largely due to the complicated higher order epistatic complexity. RESULTS: In this article, we propose a new strategy in modeling haplotype-haplotype interactions under the penalized logistic regression framework with adaptive L1-penalty. We consider interactions of sequence variants between haplotype blocks. The adaptive L1-penalty allows simultaneous effect estimation and variable selection in a single model. We propose a new parameter estimation method which estimates and selects parameters by the modified Gauss-Seidel method nested within the EM algorithm. Simulation studies show that it has low false positive rate and reasonable power in detecting haplotype interactions. The method is applied to test haplotype interactions involved in mother and offspring genome in a small for gestational age (SGA) neonates data set, and significant interactions between different genomes are detected. CONCLUSIONS: As demonstrated by the simulation studies and real data analysis, the approach developed provides an efficient tool for the modeling and testing of haplotype interactions. The implementation of the method in R codes can be freely downloaded from http://www.stt.msu.edu/~cui/software.html.

A regularized regression approach for dissecting genetic conflicts that increase disease risk in pregnancy.

Human diseases developed during pregnancy could be caused by the direct effects of both maternal and fetal genes, and/or by the indirect effects caused by genetic conflicts. Genetic conflicts exist when the effects of fetal genes are opposed by the effects of maternal genes, or when there is a conflict between the maternal and paternal genes within the fetal genome. The two types of genetic conflicts involve the functions of different genes in different genomes and are genetically distinct. Differentiating and further dissecting the two sets of genetic conflict effects that increase disease risk during pregnancy present statistical challenges, and have been traditionally pursued as two separate endeavors. In this article, we develop a unified framework to model and test the two sets of genetic conflicts via a regularized regression approach. Our model is developed considering real situations in which the paternal information is often completely missing; an assumption that fails most of the current family-based studies. A mixture model-based penalized logistic regression is proposed for data sampled from a natural population. We develop a variable selection procedure to select significant genetic features. Simulation studies show that the model has high power and good false positive control under reasonable sample sizes and disease allele frequency. A case study of small for gestational age (SGA) is provided to show the utility of the proposed approach. Our model provides a powerful tool for dissecting genetic conflicts that increase disease risk during pregnancy, and offers a testable framework for the genetic conflict hypothesis previously proposed.

Nonlinear cooperation of p53-ING1-induced bax expression and protein S-nitrosylation in GSNO-induced thymocyte apoptosis: a quantitative approach with cross-platform validation.

Increasing evidence has been gathered for p53-dependent apoptosis, but it is still unclear how p53 initiates apoptosis by employing its transcriptional program. Pair-wise interactions of p53 with expression of other genes fail to predict p53 levels or rate of apoptosis. A more sophisticated approach, using neural networks, permits prediction of interaction among three or more genes (p53, bax, and ING1). These interactions are decidedly nonlinear. Careful measurements and advanced mathematical treatments will permit us not only to understand how expression of pro- and anti-apoptotic genes is regulated, but also to integrate cross-platform and cross-experimental data for the validation of predicted interactions.

Gene-centric genomewide association study via entropy.

Genes are the functional units in most organisms. Compared to genetic variants located outside genes, genic variants are more likely to affect disease risk. The development of the human HapMap project provides an unprecedented opportunity for genetic association studies at the genomewide level for elucidating disease etiology. Currently, most association studies at the single-nucleotide polymorphism (SNP) or the haplotype level rely on the linkage information between SNP markers and disease variants, with which association findings are difficult to replicate. Moreover, variants in genes might not be sufficiently covered by currently available methods. In this article, we present a gene-centric approach via entropy statistics for a genomewide association study to identify disease genes. The new entropy-based approach considers genic variants within one gene simultaneously and is developed on the basis of a joint genotype distribution among genetic variants for an association test. A grouping algorithm based on a penalized entropy measure is proposed to reduce the dimension of the test statistic. Type I error rates and power of the entropy test are evaluated through extensive simulation studies. The results indicate that the entropy test has stable power under different disease models with a reasonable sample size. Compared to single SNP-based analysis, the gene-centric approach has greater power, especially when there is more than one disease variant in a gene. As the genomewide genic SNPs become available, our entropy-based gene-centric approach would provide a robust and computationally efficient way for gene-based genomewide association study.

High fat feeding and dietary L-arginine supplementation differentially regulate gene expression in rat white adipose tissue.

Dietary L-arginine (Arg) supplementation reduces white-fat gain in diet-induced obese rats but the underlying mechanisms are unknown. This study tested the hypothesis that Arg treatment affects expression of genes related to lipid metabolism in adipose tissue. Four-week-old male Sprague-Dawley rats were fed a low-fat (LF) or high-fat (HF) diet for 15 weeks. Thereafter, lean or obese rats continued to be fed their same respective diets and received drinking water containing 1.51% Arg-HCl or 2.55% L: -alanine (isonitrogenous control). After 12 weeks of Arg supplementation, rats were euthanized to obtain retroperitoneal adipose tissue for analyzing global changes in gene expression by microarray. The results were confirmed by RT-PCR analysis. HF feeding decreased mRNA levels for lipogenic enzymes, AMP-activated protein kinase, glucose transporters, heme oxygenase 3, glutathione synthetase, superoxide dismutase 3, peroxiredoxin 5, glutathione peroxidase 3, and stress-induced protein, while increasing expression of carboxypeptidase-A, peroxisome proliferator activated receptor (PPAR)-alpha, caspase 2, caveolin 3, and diacylglycerol kinase. In contrast, Arg supplementation reduced mRNA levels for fatty acid binding protein 1, glycogenin, protein phosphates 1B, caspases 1 and 2, and hepatic lipase, but increased expression of PPARgamma, heme oxygenase 3, glutathione synthetase, insulin-like growth factor II, sphingosine-1-phosphate receptor, and stress-induced protein. Biochemical analysis revealed oxidative stress in white adipose tissue of HF-fed rats, which was prevented by Arg supplementation. Collectively, these results indicate that HF diet and Arg supplementation differentially regulate gene expression to affect energy-substrate oxidation, redox state, fat accretion, and adipocyte differentiation in adipose tissue. Our findings provide a molecular mechanism to explain a beneficial effect of Arg on ameliorating diet-induced obesity in mammals.