Current advances in systems and integrative biology

Systems biology has gained a tremendous amount of interest in the last few years. This is partly due to the realization that traditional approaches focusing only on a few molecules at a time cannot describe the impact of aberrant or modulated molecular environments across a whole system. Furthermore, a hypothesis-driven study aims to prove or disprove its postulations, whereas a hypothesis-free systems approach can yield an unbiased and novel testable hypothesis as an end-result. This latter approach foregoes assumptions which predict how a biological system should react to an altered microenvironment within a cellular context, across a tissue or impacting on distant organs. Additionally, re-use of existing data by systematic data mining and re-stratification, one of the cornerstones of integrative systems biology, is also gaining attention. While tremendous efforts using a systems methodology have already yielded excellent results, it is apparent that a lack of suitable analytic tools and purpose-built databases poses a major bottleneck in applying a systematic workflow. This review addresses the current approaches used in systems analysis and obstacles often encountered in large-scale data analysis and integration which tend to go unnoticed, but have a direct impact on the final outcome of a systems approach. Its wide applicability, ranging from basic research, disease descriptors, pharmacological studies, to personalized medicine, makes this emerging approach well suited to address biological and medical questions where conventional methods are not ideal

The integration of large biological and clinical datasets towards the understanding of human disease

As the cost of high-throughput techniques reduces, and new more powerful equipment is designed, more highly-dimensional biological data will be available – and a lot of data is already in the public domain. The aim of this thesis is to investigate three case studies with interesting opportunities for the integration of large molecular datasets, corresponding clinical data, and publicly available data. Genome-wide DNA methylation was studied with respect to hypertension. Genomic location data was used both to group individual methylation sites into meaningful functional groups such as promoter regions, and to report the results in a genomic context. Genome-wide SNP data was used to help rule out potential false positives where SNPs interfere with detection of DNA methylation. Left ventricular hypertrophy is an intermediate cardiovascular phenotype associated with the development of heart failure. This phenotype was studied as a continuous variable – left ventricular mass index (LVMI) – using multiple sample types, in the context of a large cohort, using datasets with different classes of biomolecules and varying genomic coverage. Two alternative analysis approaches were compared, and a linear model was generated showing that a signature of molecular and clinical markers in combination best describes LVMI. A multi-omics respiratory dataset was investigated, which includes high-throughput data for mRNA, miRNA, proteins, and metabolites and has measurements in two relevant sample types. Test statistics were performed on all datasets, identifying molecules dysregulated with asthma, COPD, and smoking. An asthma molecular interaction network was created with the significant molecules, and the links between them were formed using a variety of public data. Comparisons were made between asthma and COPD, and between asthma in smokers and non-smokers. Correlations with cell type counts may indicate cell type of origin in samples with multiple cell types like induced sputum

Studying Effects of Primary Care Physicians and Patients on the Trade-Off Between Charges for Primary Care and Specialty Care Using a Hierarchical Multivariate Two-Part Model

Objective. To examine effects of primary care physicians (PCPs) and patients on the association between charges for primary care and specialty care in a point-of-service (POS) health plan. Data Source. Claims from 1996 for 3,308 adult male POS plan members, each of whom was assigned to one of the 50 family practitioner-PCPs with the largest POS plan member-loads. Study Design. A hierarchical multivariate two-part model was fitted using a Gibbs sampler to estimate PCPs\u27 effects on patients\u27 annual charges for two types of services, primary care and specialty care, the associations among PCPs\u27 effects, and within-patient associations between charges for the two services. Adjusted Clinical Groups (ACGs) were used to adjust for case-mix. Principal Findings. PCPs with higher case-mix adjusted rates of specialist use were less likely to see their patients at least once during the year (estimated correlation: –.40; 95% CI: –.71, –.008) and provided fewer services to patients that they saw (estimated correlation: –.53; 95% CI: –.77, –.21). Ten of 11 PCPs whose case-mix adjusted effects on primary care charges were significantly less than or greater than zero (p \u3c .05) had estimated, case-mix adjusted effects on specialty care charges that were of opposite sign (but not significantly different than zero). After adjustment for ACG and PCP effects, the within-patient, estimated odds ratio for any use of primary care given any use of specialty care was .57 (95% CI: .45, .73). Conclusions. PCPs and patients contributed independently to a trade-off between utilization of primary care and specialty care. The trade-off appeared to partially offset significant differences in the amount of care provided by PCPs. These findings were possible because we employed a hierarchical multivariate model rather than separate univariate models

A Hierarchical Multivariate Two-Part Model for Profiling Providers\u27 Effects on Healthcare Charges

Procedures for analyzing and comparing healthcare providers\u27 effects on health services delivery and outcomes have been referred to as provider profiling. In a typical profiling procedure, patient-level responses are measured for clusters of patients treated by providers that in turn, can be regarded as statistically exchangeable. Thus, a hierarchical model naturally represents the structure of the data. When provider effects on multiple responses are profiled, a multivariate model rather than a series of univariate models, can capture associations among responses at both the provider and patient levels. When responses are in the form of charges for healthcare services and sampled patients include non-users of services, charge variables are a mix of zeros and highly-skewed positive values that present a modeling challenge. For analysis of regressor effects on charges for a single service, a frequently used approach is a two-part model (Duan, Manning, Morris, and Newhouse 1983) that combines logistic or probit regression on any use of the service and linear regression on the log of positive charges given use of the service. Here, we extend the two-part model to the case of charges for multiple services, using a log-linear model and a general multivariate log-normal model, and employ the resultant multivariate two-part model as the within-provider component of a hierarchical model. The log-linear likelihood is reparameterized as proposed by Fitzmaurice and Laird (1993), so that regressor effects on any use of each service are marginal with respect to any use of other services. The general multivariate log-normal likelihood is constructed in such a way that variances of log of positive charges for each service are provider-specific but correlations between log of positive charges for different services are uniform across providers. A data augmentation step is included in the Gibbs sampler used to fit the hierarchical model, in order to accommodate the fact that values of log of positive charges are undefined for unused service. We apply this hierarchical, multivariate, two-part model to analyze the effects of primary care physicians on their patients\u27 annual charges for two services, primary care and specialty care. Along the way, we also demonstrate an approach for incorporating prior information about the effects of patient morbidity on response variables, to improve the accuracy of provider profiles that are based on patient samples of limited size

Nitrogen fixation: A poorly understood process along the freshwater-marine continuum

N2 fixation is a major component of the global N cycle and has been extensively studied in open-ocean and terrestrial ecosystems. Yet rates and ecological dynamics remain virtually unknown for the inland and coastal aquatic ecosystems (lakes, wetlands, rivers, streams, and estuaries) that connect terrestrial and marine biomes. This is due to the diversity of these habitats as well as the traditional paradigm that N2 fixation rates were low to nonexistent, and therefore not important, in these ecosystems. We identify three major research themes to advance understanding of aquatic N2 fixation: (1) the biological diversity of diazotrophs and variability of N2 fixation rates, (2) the ecological stoichiometry of N2 fixation, and (3) the upscaling of N2 fixation rates from genes to ecosystems. Coordinating research across these areas will advance limnology and oceanography by fully integrating N2 fixation into ecological dynamics of aquatic ecosystems from local to global scales

HLA gene expression is altered in whole blood and placenta from women who later developed preeclampsia

Preeclampsia is a multi-system disease that significantly contributes to maternal and fetal morbidity and mortality. In this study, we used a non-biased microarray approach to identify dysregulated genes in maternal whole blood samples which may be associated with the development of preeclampsia. Whole blood samples were obtained at 28 weeks of gestation from 5 women who later developed preeclampsia (cases) and 10 matched women with normotensive pregnancies (controls). Placenta samples were obtained from an independent cohort of 19 women with preeclampsia matched with 19 women with normotensive pregnancies. We studied gene expression profiles using Illumina microarray in blood and validated changes in gene expression in whole blood and placenta tissue by qPCR. We found a transcriptional profile differentiating cases from controls; 236 genes were significantly dysregulated in blood from women who developed preeclampsia. Functional annotation of microarray results indicated that most of the genes found to be dysregulated were involved in inflammatory pathways. Whilst general trends were preserved, only HLA-A was validated in whole blood samples from cases using qPCR (2.30 ± 0.9 fold change) whereas in placental tissue HLA-DRB1 expression was found to be significantly increased in samples from women with preeclampsia (5.88 ± 2.24 fold change). We have identified that HLA-A is up-regulated in the circulation of women who went on to develop preeclampsia. In placenta of women with preeclampsia we identified that HLA-DRB1 is up-regulated. Our data provide further evidence for involvement of the HLA gene family in the pathogenesis of preeclampsia

Differential expression of microRNA-206 and its target genes in pre-eclampsia

Objectives: Pre-eclampsia is a multi-system disease that significantly contributes to maternal and fetal morbidity and mortality. In this study, we used a non-biased microarray approach to identify novel circulating miRNAs in maternal plasma that may be associated with pre-eclampsia. Methods: Plasma samples were obtained at 16 and 28 weeks of gestation from 18 women who later developed pre-eclampsia (cases) and 18 matched women with normotensive pregnancies (controls). We studied miRNA expression profiles in plasma and subsequently confirmed miRNA and target gene expression in placenta samples. Placental samples were obtained from an independent cohort of 19 women with pre-eclampsia matched with 19 women with normotensive pregnancies. Results: From the microarray, we identified 1 miRNA that was significantly differentially expressed between cases and controls at 16 weeks of gestation and 6 miRNAs that were significantly differentially expressed at 28 weeks. Following qPCR validation only one, miR-206, was found to be significantly increased in 28 week samples in women who later developed pre-eclampsia (1.4 fold change ± 0.2). The trend for increase in miR-206 expression was mirrored within placental tissue from women with pre-eclampsia. In parallel, IGF-1, a target gene of miR-206, was also found to be down-regulated (0.41 ± 0.04) in placental tissue from women with pre-eclampsia. miR-206 expression was also detectable in myometrium tissue and trophoblast cell lines. Conclusions: Our pilot study has identified miRNA-206 as a novel factor up-regulated in pre-eclampsia within the maternal circulation and in placental tissue

An experimental test of green management information system effects on carrier selection: weigh station and tollbooth bypass technology adoption

In a highly competitive price-driven industry, carriers are continuously searching for opportunities to differentiate their offerings, minimize operational costs, and appeal to shippers. At the same time, environmental sustainability has evolved from being trendy jargon into a requirement for competitive supply chain management. It is at the intersection of these two modern topics that the current study identifies a new carrier selection attribute based on a specialized type of green management information system. We apply social exchange theory to hypothesize carrier price and green technology adoption effects on shipper purchase intent. The hypothesized direct and interaction effects are tested by way of a vignette-based experiment, with a sample of full-time working professionals. The supported hypotheses collectively suggest that the adoption of weigh station and tollbooth bypass technology, as a type of environmentally sustainable information system, positively affects transportation carrier selection and attenuates the negative effect of a carrier’s price on shippers’ purchase intentions. These research findings offer unique theoretical, practical, and policy implications surrounding the trucking carrier selection decision