Metabolomics Insights in Early Childhood Caries.

Dental caries is characterized by a dysbiotic shift at the biofilm-tooth surface interface, yet comprehensive biochemical characterizations of the biofilm are scant. We used metabolomics to identify biochemical features of the supragingival biofilm associated with early childhood caries (ECC) prevalence and severity. The study's analytical sample comprised 289 children ages 3 to 5 (51% with ECC) who attended public preschools in North Carolina and were enrolled in a community-based cross-sectional study of early childhood oral health. Clinical examinations were conducted by calibrated examiners in community locations using International Caries Detection and Classification System (ICDAS) criteria. Supragingival plaque collected from the facial/buccal surfaces of all primary teeth in the upper-left quadrant was analyzed using ultra-performance liquid chromatography-tandem mass spectrometry. Associations between individual metabolites and 18 clinical traits (based on different ECC definitions and sets of tooth surfaces) were quantified using Brownian distance correlations (dCor) and linear regression modeling of log2-transformed values, applying a false discovery rate multiple testing correction. A tree-based pipeline optimization tool (TPOT)-machine learning process was used to identify the best-fitting ECC classification metabolite model. There were 503 named metabolites identified, including microbial, host, and exogenous biochemicals. Most significant ECC-metabolite associations were positive (i.e., upregulations/enrichments). The localized ECC case definition (ICDAS ≥1 caries experience within the surfaces from which plaque was collected) had the strongest correlation with the metabolome (dCor P = 8 × 10-3). Sixteen metabolites were significantly associated with ECC after multiple testing correction, including fucose (P = 3.0 × 10-6) and N-acetylneuraminate (p = 6.8 × 10-6) with higher ECC prevalence, as well as catechin (P = 4.7 × 10-6) and epicatechin (P = 2.9 × 10-6) with lower. Catechin, epicatechin, imidazole propionate, fucose, 9,10-DiHOME, and N-acetylneuraminate were among the top 15 metabolites in terms of ECC classification importance in the automated TPOT model. These supragingival biofilm metabolite findings provide novel insights in ECC biology and can serve as the basis for the development of measures of disease activity or risk assessment.

Electrophysiology-based stratification of pancreatic tumorigenicity by label-free single-cell impedance cytometry.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer lacking specific biomarkers that can be correlated to disease onset, promotion and progression. To assess whether tumor cell electrophysiology may serve as a marker for PDAC tumorigenicity, we use multi-frequency impedance cytometry at high throughput (∼350 cells/s) to measure the electrical phenotype of single PDAC tumor cells from xenografts, which are derived from primary pancreatic tumors versus those from liver metastases of different patients. A novel phase contrast metric based on variations in the high and low frequency impedance phase responses that is related to electrophysiology of the cell interior is found to be systematically altered as a function of tumorigenicity. PDAC cells of higher tumorigenicity exhibited lowered interior conductivity and enhanced permittivity, which is validated by the dielectrophoresis on the respective cell types. Using genetic analysis, we suggest the role of dysregulated Na+ transport and removal of Ca2+ ions from the cytoplasm on key oncogenic KRAS-driven processes that may be responsible for lowering of the interior cell conductivity. We envision that impedance cytometry can serve as a tool to quantify phenotypic heterogeneity for rapidly stratifying tumorigenicity. It can also aid in protocols for dielectrophoretic isolation of cells with a particular phenotype for prognostic studies on patient survival and to tailor therapy selection to specific patients.

How to increase our belief in discovered statistical interactions via large-scale association studies?

The understanding that differences in biological epistasis may impact disease risk, diagnosis, or disease management stands in wide contrast to the unavailability of widely accepted large-scale epistasis analysis protocols. Several choices in the analysis workflow will impact false-positive and false-negative rates. One of these choices relates to the exploitation of particular modelling or testing strategies. The strengths and limitations of these need to be well understood, as well as the contexts in which these hold. This will contribute to determining the potentially complementary value of epistasis detection workflows and is expected to increase replication success with biological relevance. In this contribution, we take a recently introduced regression-based epistasis detection tool as a leading example to review the key elements that need to be considered to fully appreciate the value of analytical epistasis detection performance assessments. We point out unresolved hurdles and give our perspectives towards overcoming these.

Data science approaches to pharmacogenetics.

Pharmacogenetic studies rely on applied statistics to evaluate genetic data describing natural variation in response to pharmacotherapeutics such as drugs and vaccines. In the beginning, these studies were based on candidate gene approaches that specifically focused on efficacy or adverse events correlated with variants of single genes. This hypothesis driven method required the researcher to have a priori knowledge of which genes or gene sets to investigate. According to rational design, the focus of these studies has been on drug metabolizing enzymes, drug transporters, and drug targets. As technology has progressed, these studies have transitioned to hypothesis-free explorations where markers across the entire genome can be measured in large scale, population based, genome-wide association studies (GWAS). This enables identification of novel genetic biomarkers, therapeutic targets, and analysis of gene-gene interactions, which may reveal molecular mechanisms of drug activities. Ultimately, the challenge is to utilize gene-drug associations to create dosing algorithms based individual genotypes, which will guide physicians and ensure they prescribe the correct dose of the correct drug the first time eliminating trial-and-error and adverse events. We review here basic concepts and applications of data science to the genetic analysis of pharmacologic outcomes.

Epiregulin (EREG) and human V-ATPase (TCIRG1): genetic variation, ethnicity and pulmonary tuberculosis susceptibility in Guinea-Bissau and The Gambia.

We analyzed two West African samples (Guinea-Bissau: n=289 cases and 322 controls; The Gambia: n=240 cases and 248 controls) to evaluate single-nucleotide polymorphisms (SNPs) in Epiregulin (EREG) and V-ATPase (T-cell immune regulator 1 (TCIRG1)) using single and multilocus analyses to determine whether previously described associations with pulmonary tuberculosis (PTB) in Vietnamese and Italians would replicate in African populations. We did not detect any significant single locus or haplotype associations in either sample. We also performed exploratory pairwise interaction analyses using Visualization of Statistical Epistasis Networks (ViSEN), a novel method to detect only interactions among multiple variables, to elucidate possible interaction effects between SNPs and demographic factors. Although we found no strong evidence of marginal effects, there were several significant pairwise interactions that were identified in either the Guinea-Bissau or the Gambian samples, two of which replicated across populations. Our results indicate that the effects of EREG and TCIRG1 variants on PTB susceptibility, to the extent that they exist, are dependent on gene-gene interactions in West African populations as detected with ViSEN. In addition, epistatic effects are likely to be influenced by inter- and intra-population differences in genetic or environmental context and/or the mycobacterial lineages causing disease.

Serial analysis of the gut and respiratory microbiome in cystic fibrosis in infancy: interaction between intestinal and respiratory tracts and impact of nutritional exposures.

UNLABELLED: Pulmonary damage caused by chronic colonization of the cystic fibrosis (CF) lung by microbial communities is the proximal cause of respiratory failure. While there has been an effort to document the microbiome of the CF lung in pediatric and adult patients, little is known regarding the developing microflora in infants. We examined the respiratory and intestinal microbiota development in infants with CF from birth to 21 months. Distinct genera dominated in the gut compared to those in the respiratory tract, yet some bacteria overlapped, demonstrating a core microbiota dominated by Veillonella and Streptococcus. Bacterial diversity increased significantly over time, with evidence of more rapidly acquired diversity in the respiratory tract. There was a high degree of concordance between the bacteria that were increasing or decreasing over time in both compartments; in particular, a significant proportion (14/16 genera) increasing in the gut were also increasing in the respiratory tract. For 7 genera, gut colonization presages their appearance in the respiratory tract. Clustering analysis of respiratory samples indicated profiles of bacteria associated with breast-feeding, and for gut samples, introduction of solid foods even after adjustment for the time at which the sample was collected. Furthermore, changes in diet also result in altered respiratory microflora, suggesting a link between nutrition and development of microbial communities in the respiratory tract. Our findings suggest that nutritional factors and gut colonization patterns are determinants of the microbial development of respiratory tract microbiota in infants with CF and present opportunities for early intervention in CF with altered dietary or probiotic strategies. IMPORTANCE: While efforts have been focused on assessing the microbiome of pediatric and adult cystic fibrosis (CF) patients to understand how chronic colonization by these microbes contributes to pulmonary damage, little is known regarding the earliest development of respiratory and gut microflora in infants with CF. Our findings suggest that colonization of the respiratory tract by microbes is presaged by colonization of the gut and demonstrated a role of nutrition in development of the respiratory microflora. Thus, targeted dietary or probiotic strategies may be an effective means to change the course of the colonization of the CF lung and thereby improve patient outcomes.

Entropy-based information gain approaches to detect and to characterize gene-gene and gene-environment interactions/correlations of complex diseases.

For complex diseases, the relationship between genotypes, environment factors, and phenotype is usually complex and nonlinear. Our understanding of the genetic architecture of diseases has considerably increased over the last years. However, both conceptually and methodologically, detecting gene-gene and gene-environment interactions remains a challenge, despite the existence of a number of efficient methods. One method that offers great promises but has not yet been widely applied to genomic data is the entropy-based approach of information theory. In this article, we first develop entropy-based test statistics to identify two-way and higher order gene-gene and gene-environment interactions. We then apply these methods to a bladder cancer data set and thereby test their power and identify strengths and weaknesses. For two-way interactions, we propose an information gain (IG) approach based on mutual information. For three-ways and higher order interactions, an interaction IG approach is used. In both cases, we develop one-dimensional test statistics to analyze sparse data. Compared to the naive chi-square test, the test statistics we develop have similar or higher power and is robust. Applying it to the bladder cancer data set allowed to investigate the complex interactions between DNA repair gene single nucleotide polymorphisms, smoking status, and bladder cancer susceptibility. Although not yet widely applied, entropy-based approaches appear as a useful tool for detecting gene-gene and gene-environment interactions. The test statistics we develop add to a growing body methodologies that will gradually shed light on the complex architecture of common diseases.

Neuromuscular excitability changes in the vastus medialis following anterior cruciate ligament reconstruction.

PURPOSE: Quadriceps weakness following anterior cruciate ligament reconstruction (ACLR) is prevalent despite intensive rehabilitation. Diminished neuromuscular excitability is one potential factor that may limit muscular recovery following injury or surgery. The H-reflex provides a measure of alpha motorneuron (neuromuscular) excitability in the sensory-motor pathway of the respective muscle and nerve. To date the vastus medialis (VM) and soleus (SOL) H-reflexes have been examined primarily in control subjects with induced knee joint effusion. This prospective, randomized clinical trial evaluated the affect of ACLR, utilizing hamsting (HS) or bone-patellar tendon-bone (BTB) autograft, on VM and SOL H-reflex latency and amplitude in twenty subjects. METHODS: Preoperatively bilateral VM and SOL H-reflex tests were conducted. VM and SOL H-reflexes were subsequently conducted on the involved lower extremity at 1 and 3 months post surgery. At each test session subjects completed visual analog scales and knee girth was measured. RESULTS: The VM H-reflex amplitude increased in the HS group at 3 months compared to 1-month post surgery (p<.05). Significant changes over time were also noted in the visual analog pain and functional scales and the mid-patella girth. CONCLUSIONS: The increased VM H-reflex amplitude at 3 months following HS autograft ACLR demonstrates an increase in VM neuromuscular excitability. Increased VM neuromuscular excitability was not evident in patients following BTB reconstruction. The increased neuromuscular excitability, observed only in the HS group, warrants consideration when selecting graft type for patients with extensive preoperative quadriceps dysfunction.

Integrated analysis of genetic and proteomic data identifies biomarkers associated with adverse events following smallpox vaccination.

Complex clinical outcomes, such as adverse reaction to vaccination, arise from the concerted interactions among the myriad components of a biological system. Therefore, comprehensive etiological models can be developed only through the integrated study of multiple types of experimental data. In this study, we apply this paradigm to high-dimensional genetic and proteomic data collected to elucidate the mechanisms underlying the development of adverse events (AEs) in patients after smallpox vaccination. As vaccination was successful in all of the patients under study, the AE outcomes reported likely represent the result of interactions among immune system components that result in excessive or prolonged immune stimulation. In this study, we examined 1442 genetic variables (single nucleotide polymorphisms) and 108 proteomic variables (serum cytokine concentrations) to model AE risk. To accomplish this daunting analytical task, we employed the Random Forests (RF) method to filter the most important attributes, then we used the selected attributes to build a final decision tree model. This strategy is well suited to integrated analysis, as relevant attributes may be selected from categorical or continuous data. Importantly, RF is a natural approach for studying the type of gene-gene, gene-protein and protein-protein interactions we hypothesize to be involved in the development of clinical AEs. RF importance scores for particular attributes take interactions into account, and there may be interactions across data types. Combining information from previous studies on AEs related to smallpox vaccination with the genetic and proteomic attributes identified by RF, we built a comprehensive model of AE development that includes the cytokines intercellular adhesion molecule-1 (ICAM-1 or CD54), interleukin-10 (IL-10), and colony stimulating factor-3 (CSF-3 or G-CSF) and a genetic polymorphism in the cytokine gene interleukin-4 (IL4). The biological factors included in the model support our hypothesized mechanism for the development of AEs involving prolonged stimulation of inflammatory pathways and an imbalance of normal tissue damage repair pathways. This study shows the utility of RF for such analytical tasks, while both enhancing and reinforcing our working model of AE development after smallpox vaccination.

Male-female differences in the genetic regulation of t-PA and PAI-1 levels in a Ghanaian population.

Tissue-type plasminogen activator (t-PA) and plasminogen activator inhibitor-1 (PAI-1) directly influence thrombus formation and degradation, and have been identified as risk factors for thromboembolic disease. Prior studies investigated determinants of t-PA and PAI-1 expression, but mainly in Caucasian subjects. The aim of this study was to identify the contributions of genetic and other factors to inter-individual variation in plasma levels of t-PA and PAI-1 in a large-scale population-based sample from urban West Africa. t-PA, PAI-1 and several demographic, anthropometric, and metabolic parameters were measured in 992 residents of Sunyani, the capital of the Brong-Ahafo region of Ghana. In addition, nine gene polymorphisms associated with components of the renin-angiotensin and fibrinolytic systems were determined. We found that BMI, systolic and diastolic blood pressure, total cholesterol, glucose, and triglycerides were all significant predictors of t-PA and PAI-1 in both females and males. In addition, a significant relationship was found between the PAI-1 4G/5G (rs1799768) polymorphism on PAI-1 levels in females, the TPA I/D (rs4646972) polymorphism on t-PA and PAI-1 in males, the renin (rs3730103) polymorphism on t-PA and PAI-1 in males, the ethanolamine kinase 2 (rs1917542) polymorphism on PAI-1 in males, and the renin (rs1464816) polymorphism on t-PA in females and on PAI-1 in males. This study of urban West Africans shows that t-PA and PAI-1 levels are determined by both genetic loci of the fibrinolytic and renin-angiotensin systems and other factors often associated with cardiovascular disease, and that genetic factors differ between males and females.

The Pathway Less Traveled: Moving from Candidate Genes to Candidate Pathways in the Analysis of Genome-Wide Data from Large Scale Pharmacogenetic Association Studies.

The candidate gene approach to pharmacogenetics is hypothesis driven, and anchored in biological plausibility. Whole genome scanning is hypothesis generating, and it may lead to new biology. While both approaches are important, the scientific community is rapidly reallocating resources toward the latter. We propose a step-wise approach to large-scale pharmacogenetic association studies that begins with candidate genes, then uses a pathway-based intermediate step, to inform subsequent analyses of data generated through whole genome scanning. Novel computational strategies are explored in the context of two clinically relevant examples, cholesterol synthesis and lipid signaling.

Evaporative cooling feature selection for genotypic data involving interactions.

MOTIVATION: The development of genome-wide capabilities for genotyping has led to the practical problem of identifying the minimum subset of genetic variants relevant to the classification of a phenotype. This challenge is especially difficult in the presence of attribute interactions, noise and small sample size. METHODS: Analogous to the physical mechanism of evaporation, we introduce an evaporative cooling (EC) feature selection algorithm that seeks to obtain a subset of attributes with the optimum information temperature (i.e. the least noise). EC uses an attribute quality measure analogous to thermodynamic free energy that combines Relief-F and mutual information to evaporate (i.e. remove) noise features, leaving behind a subset of attributes that contain DNA sequence variations associated with a given phenotype. RESULTS: EC is able to identify functional sequence variations that involve interactions (epistasis) between other sequence variations that influence their association with the phenotype. This ability is demonstrated on simulated genotypic data with attribute interactions and on real genotypic data from individuals who experienced adverse events following smallpox vaccination. The EC formalism allows us to combine information entropy, energy and temperature into a single information free energy attribute quality measure that balances interaction and main effects. AVAILABILITY: Open source software, written in Java, is freely available upon request.

Gender-specific correlations of plasminogen activator inhibitor-1 and tissue plasminogen activator levels with cardiovascular disease-related traits.

BACKGROUND: The purpose of this study was to examine the correlations between plasma levels of plasminogen activator inhibitor-1 (PAI-1) and tissue plasminogen activator (t-PA) and cardiovascular disease-related traits in a general population and whether these correlations differed between females and males. METHODS: Plasma PAI-1 and t-PA antigen levels and C-reactive protein (CRP), HDL-cholesterol, triglycerides, total cholesterol, systolic blood pressure, diastolic blood pressure, urinary albumin excretion, and glucose were measured in the population-based PREVEND study in Groningen, the Netherlands (n = 2527). RESULTS: Except for CRP and total cholesterol levels, all traits were significantly different between gender (P < 0.001). PAI-1 levels were correlated with all measured cardiovascular disease-related traits (P < 0.01) in both females and males. Except for urinary albumin excretion, similar results, albeit less significant, were found for t-PA levels. Age-adjusted correlations between PAI-1 and CRP, triglycerides, total cholesterol, systolic blood pressure, and diastolic blood pressure differed significantly between females and males (P < 0.01). Many of the gender differences were predominantly present between premenopausal females and males. CONCLUSION: PAI-1 and t-PA levels were correlated with cardiovascular disease-related traits in subjects obtained from the general population and several of these correlations differed across gender. The correlations found in the present study suggest the presence of coordinated patterns of cardiovascular risk factors and indicate which traits might influence PAI-1 and t-PA levels and thereby provide a framework and potential tool for therapeutic intervention to reduce thromboembolic events in the general population.

Hybrid grammar-based approach to nonlinear dynamical system identification from biological time series.

We introduce a grammar-based hybrid approach to reverse engineering nonlinear ordinary differential equation models from observed time series. This hybrid approach combines a genetic algorithm to search the space of model architectures with a Kalman filter to estimate the model parameters. Domain-specific knowledge is used in a context-free grammar to restrict the search space for the functional form of the target model. We find that the hybrid approach outperforms a pure evolutionary algorithm method, and we observe features in the evolution of the dynamical models that correspond with the emergence of favorable model components. We apply the hybrid method to both artificially generated time series and experimentally observed protein levels from subjects who received the smallpox vaccine. From the observed data, we infer a cytokine protein interaction network for an individual's response to the smallpox vaccine.

A novel method to identify gene-gene effects in nuclear families: the MDR-PDT.

It is now well recognized that gene-gene and gene-environment interactions are important in complex diseases, and statistical methods to detect interactions are becoming widespread. Traditional parametric approaches are limited in their ability to detect high-order interactions and handle sparse data, and standard stepwise procedures may miss interactions that occur in the absence of detectable main effects. To address these limitations, the multifactor dimensionality reduction (MDR) method [Ritchie et al., 2001: Am J Hum Genet 69:138-147] was developed. The MDR is well-suited for examining high-order interactions and detecting interactions without main effects. The MDR was originally designed to analyze balanced case-control data. The analysis can use family data, but requires a single matched pair be selected from each family. This may be a discordant sib pair, or may be constructed from triad data when parents are available. To take advantage of additional affected and unaffected siblings requires a test statistic that measures the association of genotype with disease in general nuclear families. We have developed a novel test, the MDR-PDT, by merging the MDR method with the genotype-Pedigree Disequilibrium Test (geno-PDT)[Martin et al., 2003: Genet Epidemiol 25:203-213]. MDR-PDT allows identification of single-locus effects or joint effects of multiple loci in families of diverse structure. We present simulations to demonstrate the validity of the test and evaluate its power. To examine its applicability to real data, we applied the MDR-PDT to data from candidate genes for Alzheimer disease (AD) in a large family dataset. These results show the utility of the MDR-PDT for understanding the genetics of complex diseases.

Analysis of the RELN gene as a genetic risk factor for autism.

Several genome-wide screens have indicated the presence of an autism susceptibility locus within the distal long arm of chromosome 7 (7q). Mapping at 7q22 within this region is the candidate gene reelin (RELN). RELN encodes a signaling protein that plays a pivotal role in the migration of several neuronal cell types and in the development of neural connections. Given these neurodevelopmental functions, recent reports that RELN influences genetic risk for autism are of significant interest. The total data set consists of 218 Caucasian families collected by our group, 85 Caucasian families collected by AGRE, and 68 Caucasian families collected at Tufts University were tested for genetic association of RELN variants to autism. Markers included five single-nucleotide polymorphisms (SNPs) and a repeat in the 5'-untranslated region (5'-UTR). Tests for association in Duke and AGRE families were also performed on four additional SNPs in the genes PSMC2 and ORC5L, which flank RELN. Family-based association analyses (PDT, Geno-PDT, and FBAT) were used to test for association of single-locus markers and multilocus haplotypes with autism. The most significant association identified from this combined data set was for the 5'-UTR repeat (PDT P-value=0.002). These analyses show the potential of RELN as an important contributor to genetic risk in autism.

Practical means for the study of electron correlation in atoms.

Electron correlation is basic to the understanding of a diverse range of physical and chemical phenomena, yet, there have been no direct measurements of the correlated motion of electrons. Measurement of the correlated momenta of atomic electrons is possible via electron-impact double ionization provided that the ionizing collisions are both impulsive and binary, and the three-body scattering mechanism is known. The results reported here satisfy these conditions, and a practical means for the study of atomic electron correlation through measurement of two-electron momentum densities is presented.

Multifactor-dimensionality reduction shows a two-locus interaction associated with Type 2 diabetes mellitus.

AIMS/HYPOTHESIS: Type 2 diabetes mellitus is a complex genetic disease, which results from interactions between multiple genes and environmental factors without any single factor having strong independent effects. This study was done to identify gene to gene interactions which could be associated with the risk of Type 2 diabetes. METHODS: We genotyped 23 different loci in the 15 candidate genes of Type 2 diabetes in 504 unrelated Type 2 diabetic patients and 133 non-diabetic control subjects. We analysed gene to gene interactions among 23 polymorphic loci using the multifactor-dimensionality reduction (MDR) method, which has been shown to be effective for detecting and characterising gene to gene interactions in case-control studies with relatively small samples. RESULTS: The MDR analysis showed a significant gene to gene interaction between the Ala55Val polymorphism in the uncoupling protein 2 gene ( UCP2) and the 161C>T polymorphism in the exon 6 of peroxisome proliferator-activated receptor gamma ( PPARgamma) gene. This interaction showed the maximum consistency and minimum prediction error among all gene to gene interaction models evaluated. Moreover, the combination of the UCP2 55 Ala/Val heterozygote and the PPARgamma 161 C/C homozygote was associated with a reduced risk of Type 2 diabetes (odds ratio: 0.51, 95% CI: 0.34 to 0.77, p=0.0016). CONCLUSIONS/INTERPRETATION: Using the MDR method, we showed a two-locus interaction between the UCP2 and PPARgamma genes among 23 loci in the candidate genes of Type 2 diabetes. The determination of such genotype combinations contributing to Type 2 diabetes mellitus could provide a new tool for identifying high-risk individuals.