"A system biology" approach to bioinformatics and functional genomics in complex human diseases: arthritis

Human and other annotated genome sequences have facilitated generation of vast amounts of correlative data, from human/animal genetics, normal and disease-affected tissues from complex diseases such as arthritis using gene/protein chips and SNP analysis. These data sets include genes/proteins whose functions are partially known at the cellular level or may be completely unknown (e.g. ESTs). Thus, genomic research has transformed molecular biology from "data poor" to "data rich" science, allowing further division into subpopulations of subcellular fractions, which are often given an "-omic" suffix. These disciplines have to converge at a systemic level to examine the structure and dynamics of cellular and organismal function. The challenge of characterizing ESTs linked to complex diseases is like interpreting sharp images on a blurred background and therefore requires a multidimensional screen for functional genomics ("functionomics") in tissues, mice and zebra fish model, which intertwines various approaches and readouts to study development and homeostasis of a system. In summary, the post-genomic era of functionomics will facilitate to narrow the bridge between correlative data and causative data by quaint hypothesis-driven research using a system approach integrating "intercoms" of interacting and interdependent disciplines forming a unified whole as described in this review for Arthritis.     

Mixture models for assessing differential expression in complex tissues using microarray data

MOTIVATION: The use of DNA microarrays has become quite popular in many scientific and medical disciplines, such as in cancer research. One common goal of these studies is to determine which genes are differentially expressed between cancer and healthy tissue, or more generally, between two experimental conditions. A major complication in the molecular profiling of tumors using gene expression data is that the data represent a combination of tumor and normal cells. Much of the methodology developed for assessing differential expression with microarray data has assumed that tissue samples are homogeneous. RESULTS: In this paper, we outline a general framework for determining differential expression in the presence of mixed cell populations. We consider study designs in which paired tissues and unpaired tissues are available. A hierarchical mixture model is used for modeling the data; a combination of methods of moments procedures and the expectation-maximization algorithm are used to estimate the model parameters. The finite-sample properties of the methods are assessed in simulation studies; they are applied to two microarray datasets from cancer studies. Commands in the R language can be downloaded from the URL http://www.sph.umich.edu/~ghoshd/COMPBIO/COMPMIX/.     

System-wide analysis of hepatotoxicological responses: tissomics is key.

BACKGROUND: Combining diverse data streams across different levels of biological observation, such as molecular, cellular, and clinical chemistry responses, support a system-wide diagnostic approach. Recent progress in slide-based cytometry contributes to the development of tissomics, a high-throughput and high-content phenotyping methodology that provides data-rich profiles of cellular heterogeneity in tissues enabling correlative statistical treatments over multiple scales of biological hierarchies. METHODS: Phenotypical data are covariants that can be used as biomarkers to identify relevant candidate genes by associating initiating molecular events with phenotypical changes and adverse outcomes. We introduce a procedure of combined statistical and analytical tools to identify and visualize such associations for nonpooled entities. The new utility is applied to a time-controlled, low-dose toxicological study including a control and two xenobiotic compounds. RESULTS: An integrated analysis identified specific molecular and phenotypical biomarkers, which support the classification of animals in the absence of any visual indicators from pathology readings. DISCUSSION: The introduction of controlled perturbations to tissues provides a prototypical setting to develop a sensitive, systems-based analysis methodology suitable for a broader range of biomedical applications.     

Vascular mechanics in the clinic

The bioengineer has more to contribute to medicine than he/she ever has in the past. The successful contribution must be based on such experiences as described by Donald McDonald in his collaboration with John Womersley. Clinician and engineer must come to know the other's problems, their weaknesses and their strengths. They must be prepared to compromise, but to know where compromise is warranted, and where it is not. The clinician must be prepared to change if he/she is to gain help from the engineer. Blind acceptance of old concepts (of "hypertension", and of cuff sphygmomanometric accuracy, etc.) needs enlightenment, while acceptance of physiological reality such as wave reflection needs emerge. The clinician's vocabulary will need to change. This chapter opens with a discussion of a time where knowledge of engineering, physics, physiology and medicine was meagre. These disciplines were small, but they did interconnect through the work of renaissance (and later) scientists. With increase in knowledge, the disciplines enlarged, and grew apart from each other. The challenge of today is to bring these closer together so that there may be some connection, some overlap, and so that the crevices between the disciplines are not so deep, and not such a deterrent to those who wish to engage in interdisciplinary activity.     

The power of correlative microscopy: multi-modal, multi-scale, multi-dimensional

Correlative microscopy is a sophisticated approach that combines the capabilities of typically separate, but powerful microscopy platforms: often including, but not limited, to conventional light, confocal and super-resolution microscopy, atomic force microscopy, transmission and scanning electron microscopy, magnetic resonance imaging and micro/nano CT (computed tomography). When targeting rare or specific events within large populations or tissues, correlative microscopy is increasingly being recognized as the method of choice. Furthermore, this multi-modal assimilation of technologies provides complementary and often unique information, such as internal and external spatial, structural, biochemical and biophysical details from the same targeted sample. The development of a continuous stream of cutting-edge applications, probes, preparation methodologies, hardware and software developments will enable realization of the full potential of correlative microscopy.     

Distributed structure-searchable toxicity (DSSTox) public database network: a proposal.

The ability to assess the potential genotoxicity, carcinogenicity, or other toxicity of pharmaceutical or industrial chemicals based on chemical structure information is a highly coveted and shared goal of varied academic, commercial, and government regulatory groups. These diverse interests often employ different approaches and have different criteria and use for toxicity assessments, but they share a need for unrestricted access to existing public toxicity data linked with chemical structure information. Currently, there exists no central repository of toxicity information, commercial or public, that adequately meets the data requirements for flexible analogue searching, Structure-Activity Relationship (SAR) model development, or building of chemical relational databases (CRD). The distributed structure-searchable toxicity (DSSTox) public database network is being proposed as a community-supported, web-based effort to address these shared needs of the SAR and toxicology communities. The DSSTox project has the following major elements: (1) to adopt and encourage the use of a common standard file format (structure data file (SDF)) for public toxicity databases that includes chemical structure, text and property information, and that can easily be imported into available CRD applications; (2) to implement a distributed source approach, managed by a DSSTox Central Website, that will enable decentralized, free public access to structure-toxicity data files, and that will effectively link knowledgeable toxicity data sources with potential users of these data from other disciplines (such as chemistry, modeling, and computer science); and (3) to engage public/commercial/academic/industry groups in contributing to and expanding this community-wide, public data sharing and distribution effort. The DSSTox project's overall aims are to effect the closer association of chemical structure information with existing toxicity data, and to promote and facilitate structure-based exploration of these data within a common chemistry-based framework that spans toxicological disciplines.     

Determining Causality and Controlling Disease is Based on Collaborative Research involving Multidisciplinary Approaches

Understanding the causes of infectious disease to facilitate better control requires observational and experimental studies. Often these must be conducted at many scales such as at the molecular, cellular, organism, and population level. Studies need to consider both intrinsic and extrinsic factors affecting the pathogen/host interaction. They also require a combination of study methods covered by disciplines such as pathology, epidemiology, microbiology, and ecology. Therefore, it is important that disciplines work together when designing and conducting studies. Finally, we need to integrate and interpret data across levels and disciplines to better formulate control strategies. This requires another group of specialists with broad cross-disciplinary training in epidemiology and an ability to readily work with others.     

The fatty acid transport function of fatty acid-binding proteins

The intracellular fatty acid-binding proteins (FABPs) comprise a family of 14-15 kDa proteins which bind long-chain fatty acids. A role for FABPs in fatty acid transport has been hypothesized for several decades, and the accumulated indirect and correlative evidence is largely supportive of this proposed function. In recent years, a number of experimental approaches which more directly examine the transport function of FABPs have been taken. These include molecular level in vitro modeling of fatty acid transfer mechanisms, whole cell studies of fatty acid uptake and intracellular transfer following genetic manipulation of FABP type and amount, and an examination of cells and tissues from animals engineered to lack expression of specific FABPs. Collectively, data from these studies have provided strong support for defining the FABPs as fatty acid transport proteins. Further studies are necessary to elucidate the fundamental mechanisms by which cellular fatty acid trafficking is modulated by the FABPs.     

Multi-dimensional correlative imaging of subcellular events: combining the strengths of light and electron microscopy

To genuinely understand how complex biological structures function, we must integrate knowledge of their dynamic behavior and of their molecular machinery. The combined use of light or laser microscopy and electron microscopy has become increasingly important to our understanding of the structure and function of cells and tissues at the molecular level. Such a combination of two or more different microscopy techniques, preferably with different spatial- and temporal-resolution limits, is often referred to as ‘correlative microscopy’. Correlative imaging allows researchers to gain additional novel structure–function information, and such information provides a greater degree of confidence about the structures of interest because observations from one method can be compared to those from the other method(s). This is the strength of correlative (or ‘combined’) microscopy, especially when it is combined with combinatorial or non-combinatorial labeling approaches. In this topical review, we provide a brief historical perspective of correlative microscopy and an in-depth overview of correlative sample-preparation and imaging methods presently available, including future perspectives on the trend towards integrative microscopy and microanalysis.     

Living with observational data in biological anthropology

The establishment of cause and effect relationships is a fundamental objective of scientific research. Many lines of evidence can be used to make cause–effect inferences. When statistical data are involved, alternative explanations for the statistical relationship need to be ruled out. These include chance (apparent patterns due to random factors), confounding effects (a relationship between two variables because they are each associated with an unmeasured third variable), and sampling bias (effects due to preexisting properties of compared groups). The gold standard for managing these issues is a controlled randomized experiment. In disciplines such as biological anthropology, where controlled experiments are not possible for many research questions, causal inferences are made from observational data. Methods that statisticians recommend for this difficult objective have not been widely adopted in the biological anthropology literature. Issues involved in using statistics to make valid causal inferences from observational data are discussed.     

Beyond Correlation in the Detection of Climate Change Impacts: Testing a Mechanistic Hypothesis for Climatic Influence on Sockeye Salmon (Oncorhynchus nerka) Productivity

Detecting the biological impacts of climate change is a current focus of ecological research and has important applications in conservation and resource management. Owing to a lack of suitable control systems, measuring correlations between time series of biological attributes and hypothesized environmental covariates is a common method for detecting such impacts. These correlative approaches are particularly common in studies of exploited fish species because rich biological time-series data are often available. However, the utility of species-environment relationships for identifying or predicting biological responses to climate change has been questioned because strong correlations often deteriorate as new data are collected. Specifically stating and critically evaluating the mechanistic relationship(s) linking an environmental driver to a biological response may help to address this problem. Using nearly 60 years of data on sockeye salmon from the Kvichak River, Alaska we tested a mechanistic hypothesis linking water temperatures experienced during freshwater rearing to population productivity by modeling a series of intermediate, deterministic relationships and evaluating temporal trends in biological and environmental time-series. We found that warming waters during freshwater rearing have profoundly altered patterns of growth and life history in this population complex yet there has been no significant correlation between water temperature and metrics of productivity commonly used in fisheries management. These findings demonstrate that pairing correlative approaches with careful consideration of the mechanistic links between populations and their environments can help to both avoid spurious correlations and identify biologically important, but not statistically significant relationships, and ultimately producing more robust conclusions about the biological impacts of climate change.     

Normalization of microRNA expression levels in quantitative RT-PCR assays: identification of suitable reference RNA targets in normal and cancerous human solid tissues

Proper normalization is a critical but often an underappreciated aspect of quantitative gene expression analysis. This study describes the identification and characterization of appropriate reference RNA targets for the normalization of microRNA (miRNA) quantitative RT-PCR data. miRNA microarray data from dozens of normal and disease human tissues revealed ubiquitous and stably expressed normalization candidates for evaluation by qRT-PCR. miR-191 and miR-103, among others, were found to be highly consistent in their expression across 13 normal tissues and five pair of distinct tumor/normal adjacent tissues. These miRNAs were statistically superior to the most commonly used reference RNAs used in miRNA qRT-PCR experiments, such as 5S rRNA, U6 snRNA, or total RNA. The most stable normalizers were also highly conserved across flash-frozen and formalin-fixed paraffin-embedded lung cancer tumor/NAT sample sets, resulting in the confirmation of one well-documented oncomir (let-7a), as well as the identification of novel oncomirs. These findings constitute the first report describing the rigorous normalization of miRNA qRT-PCR data and have important implications for proper experimental design and accurate data interpretation.     

Changes in Data Sharing and Data Reuse Practices and Perceptions among Scientists Worldwide

The incorporation of data sharing into the research lifecycle is an important part of modern scholarly debate. In this study, the DataONE Usability and Assessment working group addresses two primary goals: To examine the current state of data sharing and reuse perceptions and practices among research scientists as they compare to the 2009/2010 baseline study, and to examine differences in practices and perceptions across age groups, geographic regions, and subject disciplines. We distributed surveys to a multinational sample of scientific researchers at two different time periods (October 2009 to July 2010 and October 2013 to March 2014) to observe current states of data sharing and to see what, if any, changes have occurred in the past 3–4 years. We also looked at differences across age, geographic, and discipline-based groups as they currently exist in the 2013/2014 survey. Results point to increased acceptance of and willingness to engage in data sharing, as well as an increase in actual data sharing behaviors. However, there is also increased perceived risk associated with data sharing, and specific barriers to data sharing persist. There are also differences across age groups, with younger respondents feeling more favorably toward data sharing and reuse, yet making less of their data available than older respondents. Geographic differences exist as well, which can in part be understood in terms of collectivist and individualist cultural differences. An examination of subject disciplines shows that the constraints and enablers of data sharing and reuse manifest differently across disciplines. Implications of these findings include the continued need to build infrastructure that promotes data sharing while recognizing the needs of different research communities. Moving into the future, organizations such as DataONE will continue to assess, monitor, educate, and provide the infrastructure necessary to support such complex grand science challenges.     

The Evolution of Integration of Biological Systems: An Evolutionary Perspective through Studies on Cells, Tissues, and Organs

Biologists are integrating studies of morphology, development,physiology,and other disciplines in order to understand howspecies and lineages diversify and cope with their environments.An evolutionary perspective in such studies, including thoseof cells, tissues, and organs, is potentially useful for thestructure and analysis of such problems. Evolutionary biologyis the study of the history of evolution and the elucidationof its mechanisms. Comparative biology is the comparison ofa trait or traits in selected taxa, and may be, but need notbe, evolutionary in approach. A phylogenetic hypothesis is necessaryfor reconstruction of pattern in morphology, ecology, behavior,and other areas. Acquaintance with evolutionary and phylogeneticperspectives can guide selection of taxa for study and opennew approaches to analysis of data. Such an approach is notalways appropriate to problems in biology, but it could be utilizedbeneficially more frequently than is currently practiced. Studiesof cells, tissues, and organs may contribute to the constructionof new phylogenetic hypotheses and to analysis of patterns andmechanisms of change when pursued from an evolutionary perspective     

Receptor sites for atrial natriuretic factors in brain and associated structures: an overview

1. Recent data have clearly shown the existence of specific receptor binding sites for atrial natriuretic factors (ANF) or polypeptides in mammalian brain tissues. 2. Ligand selectivity pattern and coupling to cGMP production suggest that brain ANF sites are similar to high-affinity/low-capacity sites found in various peripheral tissues (kidney, adrenal gland, blood vessels). These brain ANF sites possibly are of the B-ANP subtype. 3. High densities of ANF binding sites are found especially in areas of the central nervous system associated with the control of various cardiovascular parameters (such as the subfornical organ and area postrema). However, high densities of sites are also present in other regions such as the hippocampus, cerebellum, and thalamus in the brain of certain mammalian species, suggesting that brain ANF could act as a neuromodulator of noncardiovascular functions. 4. The density of brain ANF binding sites is modified in certain animal models of cardiovascular disorders and during postnatal ontogeny, demonstrating the plasticity of these sites in the central nervous system (CNS). 5. Specific ANF binding sites are also found in various other CNS-associated tissues such as the eye, pituitary gland, and adrenal medulla. In these tissues ANF appears to act as a modulator of fluid production and hormone release. 6. Thus, ANF-like peptides and ANF receptor sites are present in brain and various peripheral tissues, demonstrating the existence of a family of brain/heart peptides.     

Research Data in Core Journals in Biology,Chemistry, Mathematics,and Physics

This study takes a stratified random sample of articles published in 2014 from the top 10 journals in the disciplines of biology, chemistry, mathematics, and physics, as ranked by impact factor. Sampled articles were examined for their reporting of original data or reuse of prior data, and were coded for whether the data was publicly shared or otherwise made available to readers. Other characteristics such as the sharing of software code used for analysis and use of data citation and DOIs for data were examined. The study finds that data sharing practices are still relatively rare in these disciplines’ top journals, but that the disciplines have markedly different practices. Biology top journals share original data at the highest rate, and physics top journals share at the lowest rate. Overall, the study finds that within the top journals, only 13% of articles with original data published in 2014 make the data available to others.     

Paradigms in tick evolution

The study of tick evolution may be classified into disciplines such as taxonomy and systematics, biogeography, evolution and development (evo-devo), ecology, and hematophagy. These disciplines overlap and impact each other to various extents. Advances in one field may lead to paradigm shifts in our understanding of tick evolution not apparent to other fields. The current study considers paradigm shifts that occurred, are in the process, or may occur in future for the disciplines that study tick evolution. Some disciplines have undergone significant changes, while others may still be developing their own paradigms. Integration of these various disciplines is essential to come to a holistic view of tick evolution; however, maturation of paradigms may be necessary before this vision can be attained.     

Microarrays as validation strategies in clinical samples: tissue and protein microarrays

The widespread use of DNA microarrays has led to the discovery of many genes whose expression profile may have significant clinical relevance. The translation of this data to the bedside requires that gene expression be validated as protein expression, and that annotated clinical samples be available for correlative and quantitative studies to assess clinical context and usefulness of putative biomarkers. We review two microarray platforms developed to facilitate the clinical validation of candidate biomarkers: tissue microarrays and reverse-phase protein microarrays. Tissue microarrays are arrays of core biopsies obtained from paraffin-embedded tissues, which can be assayed for histologically-specific protein expression by immunohistochemistry. Reverse-phase protein microarrays consist of arrays of cell lysates or, more recently, plasma or serum samples, which can be assayed for protein quantity and for the presence of post-translational modifications such as phosphorylation. Although these platforms are limited by the availability of validated antibodies, both enable the preservation of precious clinical samples as well as experimental standardization in a high-throughput manner proper to microarray technologies. While tissue microarrays are rapidly becoming a mainstay of translational research, reverse-phase protein microarrays require further technical refinements and validation prior to their widespread adoption by research laboratories.