Dynamics of cellular level function and regulation derived from murine expression array data

Circadian hormone control in a human-on-a-chip: In vitro biology's ignored component?

Organs-on-Chips (OoCs) are poised to reshape dramatically the study of biology by replicating in vivo the function of individual and coupled human organs. Such microphysiological systems (MPS) have already recreated complex physiological responses necessary to simulate human organ function not evident in two-dimensional in vitro biological experiments. OoC researchers hope to streamline pharmaceutical development, accelerate toxicology studies, limit animal testing, and provide new insights beyond the capability of current biological models. However, to develop a physiologically accurate Human-on-a-Chip, i.e., an MPS homunculus that functions as an interconnected, whole-body, model organ system, one must couple individual OoCs with proper fluidic and metabolic scaling. This will enable the study of the effects of organ-organ interactions on the metabolism of drugs and toxins. Critical to these efforts will be the recapitulation of the complex physiological signals that regulate the endocrine, metabolic, and digestive systems. To date, with the exception of research focused on reproductive organs on chips, most OoC research ignores homuncular endocrine regulation, in particular the circadian rhythms that modulate the function of all organ systems. We outline the importance of cyclic endocrine regulation and the role that it may play in the development of MPS homunculi for the pharmacology, toxicology, and systems biology communities. Moreover, we discuss the critical end-organ hormone interactions that are most relevant for a typical coupled-OoC system, and the possible research applications of a missing endocrine system MicroFormulator (MES-µF) that could impose biological rhythms on in vitro models. By linking OoCs together through chemical messenger systems, advanced physiological phenomena relevant to pharmacokinetics and pharmacodynamics studies can be replicated. The concept of a MES-µF could be applied to other standard cell-culture systems such as well plates, thereby extending the concept of circadian hormonal regulation to much of in vitro biology. Impact statement Historically, cyclic endocrine modulation has been largely ignored within in vitro cell culture, in part because cultured cells typically have their media changed every day or two, precluding hourly adjustment of hormone concentrations to simulate circadian rhythms. As the Organ-on-Chip (OoC) community strives for greater physiological realism, the contribution of hormonal oscillations toward regulation of organ systems has been examined only in the context of reproductive organs, and circadian variation of the breadth of other hormones on most organs remains unaddressed. We illustrate the importance of cyclic endocrine modulation and the role that it plays within individual organ systems. The study of cyclic endocrine modulation within OoC systems will help advance OoC research to the point where it can reliably replicate in vitro key regulatory components of human physiology. This will help translate OoC work into pharmaceutical applications and connect the OoC community with the greater pharmacology and physiology communities.

Learning transcriptional regulation on a genome scale: a theoretical analysis based on gene expression data

The recent advent of high-throughput microarray data has enabled the global analysis of the transcriptome, driving the development and application of computational approaches to study transcriptional regulation on the genome scale, by reconstructing in silico the regulatory interactions of the gene network. Although there are many in-depth reviews of such 'reverse-engineering' methodologies, most have focused on the practical aspect of data mining, and few on the biological problem and the biological relevance of the methodology. Therefore, in this review, from a biological perspective, we used a set of yeast microarray data as a working example, to evaluate the fundamental assumptions implicit in associating transcription factor (TF)-target gene expression levels and estimating TFs' activity, and further explore cooperative models. Finally we confirm that the detailed transcription mechanism is overly-complex for expression data alone to reveal, nevertheless, future network reconstruction studies could benefit from the incorporation of context-specific information, the modeling of multiple layers of regulation (e.g. micro-RNA), or the development of approaches for context-dependent analysis, to uncover the mechanisms of gene regulation.

The challenges for molecular nutrition research 4: the "nutritional systems biology level"

Nutritional systems biology may be defined as the ultimate goal of molecular nutrition research, where all relevant aspects of regulation of metabolism in health and disease states at all levels of its complexity are taken into account to describe the molecular physiology of nutritional processes. The complexity spans from intracellular to inter-organ dynamics, and involves iterations between mathematical modelling and analysis employing all profiling methods and other biological read-outs. On the basis of such dynamic models we should be enabled to better understand how the nutritional status and nutritional challenges affect human metabolism and health. Although the achievement of this proposition may currently sound unrealistic, many initiatives in theoretical biology and biomedical sciences work on parts of the solution. This review provides examples and some recommendations for the molecular nutrition research arena to move onto the systems level.

Understanding biological functions through molecular networks

The completion of genome sequences and subsequent high-throughput mapping of molecular networks have allowed us to study biology from the network perspective. Experimental, statistical and mathematical modeling approaches have been employed to study the structure, function and dynamics of molecular networks, and begin to reveal important links of various network properties to the functions of the biological systems. In agreement with these functional links, evolutionary selection of a network is apparently based on the function, rather than directly on the structure of the network. Dynamic modularity is one of the prominent features of molecular networks. Taking advantage of such a feature may simplify network-based biological studies through construction of process-specific modular networks and provide functional and mechanistic insights linking genotypic variations to complex traits or diseases, which is likely to be a key approach in the next wave of understanding complex human diseases. With the development of ready-to-use network analysis and modeling tools the networks approaches will be infused into everyday biological research in the near future.

Empirical multiscale networks of cellular regulation

Grouping genes by similarity of expression across multiple cellular conditions enables the identification of cellular modules. The known functions of genes enable the characterization of the aggregate biological functions of these modules. In this paper, we use a high-throughput approach to identify the effective mutual regulatory interactions between modules composed of mouse genes from the Alliance for Cell Signaling (AfCS) murine B-lymphocyte database which tracks the response of ∼15,000 genes following chemokine perturbation. This analysis reveals principles of cellular organization that we discuss along four conceptual axes. (1) Regulatory implications: the derived collection of influences between any two modules quantifies intuitive as well as unexpected regulatory interactions. (2) Behavior across scales: trends across global networks of varying resolution (composed of various numbers of modules) reveal principles of assembly of high-level behaviors from smaller components. (3) Temporal behavior: tracking the mutual module influences over different time intervals provides features of regulation dynamics such as duration, persistence, and periodicity. (4) Gene Ontology correspondence: the association of modules to known biological roles of individual genes describes the organization of functions within coexpressed modules of various sizes. We present key specific results in each of these four areas, as well as derive general principles of cellular organization. At the coarsest scale, the entire transcriptional network contains five divisions: two divisions devoted to ATP production/biosynthesis and DNA replication that activate all other divisions, an “extracellular interaction” division that represses all other divisions, and two divisions (proliferation/differentiation and membrane infrastructure) that activate and repress other divisions in specific ways consistent with cell cycle control.

In a eukaryotic organism such as the mouse, the complete transcriptional network contains ∼15,000 genes and up to 225 million regulatory relationships between pairs of genes. Determining all of these relationships is currently intractable using traditional experimental techniques, and, thus, a comprehensive description of the entire mouse transcriptional network is elusive. Alternatively, one can apply the limited amount of experimental data to determine the entire transcriptional network at a less detailed, higher level. This is analogous to considering a map of the world resolved to the kilometer rather than to the millimeter. Here, we derive from mouse microarray data several high-scale transcriptional networks by determining the mutual effective regulatory influences of large modules of genes. In particular, global transcriptional networks containing 12 to 72 modules are derived, and analysis of these multiscale networks reveals properties of the transcriptional network that are universal at all scales (e.g., maintenance of homeostasis) and properties that vary as a function of scale (e.g., the fractions of module pairs that exert mutual regulation). In addition, we describe how cellular functions associated with large modules (those containing many genes) are composed of more specific functions associated with smaller modules.

A rapid genome-scale response of the transcriptional oscillator to perturbation reveals a period-doubling path to phenotypic change

Perturbation of the gated-synchrony system in yeast with phenelzine, an antidepressant drug used in the treatment of affective disorders in humans, leads to a rapid lengthening in the period of the genome-wide transcriptional oscillation. The effect is a concerted, genome-scale change in expression that is first seen in genes maximally expressed in the late-reductive phase of the cycle, doubling the length of the reductive phase within two cycles after treatment. Clustering of genes based on their temporal patterns of expression yielded just three super clusters whose trajectories through time could then be mapped into a simple 3D figure. In contrast to transcripts in the late-reductive phase, most transcripts do not show transients in expression relative to others in their temporal cluster but change their period in a concerted fashion. Mapping the trajectories of the transcripts into low-dimensional surfaces that can be represented by simple systems of differential equations provides a readily testable model of the dynamic architecture of phenotype. In this system, period doubling may be a preferred pathway for phenotypic change. As a practical matter, low-amplitude, genome-wide oscillations, a ubiquitous but often unrecognized attribute of phenotype, could be a source of seemingly intractable biological noise in microarray studies.

Evolutionary Conservation of Expression Profiles Between Human and Mouse Orthologous Genes

Mouse models are often used to study human genes because it is believed that the expression and function are similar for the majority of orthologous genes between the two species. However, recent comparisons of microarray data from thousands of orthologous human and mouse genes suggested rapid evolution of gene expression profiles under minimal or no selective constraint. These findings appear to contradict non-array-based observations from many individual genes and imply the uselessness of mouse models for studying human genes. Because absolute levels of gene expression are not comparable between species when the data are generated by species-specific microarrays, use of relative mRNA abundance among tissues (RA) is preferred to that of absolute expression signals. We thus reanalyze human and mouse genome-wide gene expression data generated by oligonucleotide microarrays. We show that the mean correlation coefficient among expression profiles detected by different probe sets of the same gene is only 0.38 for humans and 0.28 for mice, indicating that current measures of expression divergence are flawed because the large estimation error (discrepancy in expression signal detected by different probe sets of the same gene) is mistakenly included in the between-species divergence. When this error is subtracted, 84% of human-mouse orthologous gene pairs show significantly lower expression divergence than that of random gene pairs. In contrast to a previous finding, but consistent with the common sense, expression profiles of orthologous tissues between species are more similar to each other than to those of nonorthologous tissues. Furthermore, the evolutionary rate of expression divergence and that of coding sequence divergence are found to be weakly, but significantly positively correlated, when RA and the Euclidean distance are used to measure expression-profile divergence. These results highlight the importance of proper consideration of various estimation errors in comparing the microarray data between species.

Mammalian Toll-like receptors: to immunity and beyond

Toll-like receptors (TLRs) constitute an archetypal pattern recognition system. Their sophisticated biology underpins the ability of innate immunity to discriminate between highly diverse microbial pathogens and self. However, the remarkable progress made in describing this biology has also revealed new immunological systems and processes previously hidden to investigators. In particular, TLRs appear to have a fundamental role in the generation of clonal adaptive immune responses, non-infectious disease pathogenesis and even in the maintenance of normal mammalian homeostasis. Although an understanding of TLRs has answered some fundamental questions at the host-pathogen interface, further issues, particularly regarding therapeutic modulation of these receptors, have yet to be resolved.