Contextual genetics

Complex phenotypes result from multiple inputs from genetic and environmental sources, with intricate subsystems mediating the influence of both sources on the phenotype. Experiments that attempt to describe the influence of a particular gene involve partial isolation of the sub-system in which that gene is an element from other components of the total system influencing the phenotype. Any interactions that exist between the controlled variables and the processes downstream of the gene in the normally operating total system become undetectable; therefore, the results of the experiment can be restricted to the particular configuration of the controlled variables. The inescapable price of the precision of knowledge generated by experiment is a reduction in the generalizability of the results beyond the constrained circumstances of the particular experimental situation. Integrative research, permitting the influence of related subsystems, is required to provide a comprehensive assessment of the influence of a gene.     

Contextual Risk Factors for Low Birth Weight: A Multilevel Analysis

Background

Low birth weight (LBW) remains to be a leading cause of neonatal death and a major contributor to infant and under-five mortality. Its prevalence has not declined in the last decade in sub-Saharan Africa (SSA) and Asia. Some individual level factors have been identified as risk factors for LBW but knowledge is limited on contextual risk factors for LBW especially in SSA.

Methods

Contextual risk factors for LBW in Ghana were identified by performing multivariable multilevel logistic regression analysis of 6,900 mothers dwelling in 412 communities that participated in the 2003 and 2008 Demographic and Health Surveys in Ghana.

Results

Contextual-level factors were significantly associated with LBW: Being a rural dweller increased the likelihood of having a LBW infant by 43% (OR 1.43; 95% CI 1.01–2.01; P-value <0.05) while living in poverty-concentrated communities increased the risk of having a LBW infant twofold (OR 2.16; 95% CI 1.29–3.61; P-value <0.01). In neighbourhoods with a high coverage of safe water supply the odds of having a LBW infant reduced by 28% (OR 0.74; 95% CI 0.57–0.96; P-value <0.05).

Conclusion

This study showed contextual risk factors to have independent effects on the prevalence of LBW infants. Being a rural dweller, living in a community with a high concentration of poverty and a low coverage of safe water supply were found to increase the prevalence of LBW infants. Implementing appropriate community-based intervention programmes will likely reduce the occurrence of LBW infants.     

Contextual modulation of behavioral choice

We review the influence of context on behavioral choice. Context can refer to external (environmental) factors such as the season or presence of predators and it can also refer to the internal or behavioral state of an animal. Usually, animals make decisions in the midst of other ongoing behaviors. We discuss recent findings on the impact of both types of contexts, focusing on how context gets encoded at the intersection between the sensory and motor systems, emphasizing the role of neuromodulators. We also review recent technological advances that have made feasible the exploration of neural correlates of decision making in freely moving, behaving animals.     

Contextual modulation outside of awareness

Contextual effects are ubiquitous in vision and reveal fundamental principles of sensory coding. Here, we demonstrate that an oriented surround grating can affect the perceived orientation of a central test grating even when backward masking of the surround prevents its orientation from being consciously perceived. The effect survives introduction of a gap between test and surround of over a degree even under masking, suggesting either that contextual information can effectively propagate across early visual cortex in the absence of awareness of the signaled context or that it can proceed undetected to higher processing levels at which such horizontal propagation may not be necessary. The effect under masking also shows partial interocular transfer, demonstrating processing of orientation by binocular neurons in visual cortex in the absence of conscious orientation perception. This pattern of results is consistent with the suggestion that simultaneous orientation contrast is mediated at multiple levels of the visual processing hierarchy, and it supports the view that propagation of signals to and, possibly, back from higher visual areas is necessary for conscious perception.     

Generalizing genetical genomics: getting added value from environmental perturbation

Genetical genomics is a useful approach for studying the effect of genetic perturbations on biological systems at the molecular level. However, molecular networks depend on the environmental conditions and, thus, a comprehensive understanding of biological systems requires studying them across multiple environments. We propose a generalization of genetical genomics, which combines genetic and sensibly chosen environmental perturbations, to study the plasticity of molecular networks. This strategy forms a crucial step toward understanding why individuals respond differently to drugs, toxins, pathogens, nutrients and other environmental influences. Here we outline a strategy for selecting and allocating individuals to particular treatments, and we discuss the promises and pitfalls of the generalized genetical genomics approach.     

Contextual equilibrium effects in DNA molecules

The thermodynamic parameters of DNA triplex formation between oligonucleotides and double-stranded DNA segments containing adenine runs (A-tracts) were investigated to explore equilibrium structural effects exerted by flanking segments upon the A-tracts. Results obtained from isothermal titration calorimetry, temperature-dependent circular dichroism (CD), and UV melting experiments indicate that A-tracts, considered as a uniquely robust and inflexible DNA motif, can be structurally perturbed by neighboring sequences in a way that significantly affects the propensity of this motif to interact with triplex-forming oligonucleotides. These contextual equilibrium effects, which depend upon the composition and location of the flanking sequences, are likely to apply not only to the interaction of A-tracts with single-stranded DNA molecules but also to interactions with drugs and proteins. As such, the current results refine the guidelines for the design of triplex-forming oligonucleotides used for antigene strategies. More generally, they substantiate the notion that significant data might be encoded by structural DNA parameters.     

Contextual constraints on synonymous codon choice

We have studied the statistical constraints on synonymous codon choice to evaluate various proposals regarding the origin of the bias in synonymous codon usage observed by Fiers et al. (1975), Air et al. (1976), Grantham et al. (1980) and others. We have determined the statistical dependence of the degenerate third base on either of its nearest neighbors in mitochondrial, prokaryotic, and eukaryotic coding sequences. We noted an increasing dependence of the third base on its nearest neighbors in moving from mitochrondria to prokaryotes to eukaryotes.A statistical model assuming random equiprobable selection of synonymous codons was found grossly adequate for the mitochondria, but totally indequate for prokaryotes and eukaryotes. A model assuming selection of synonymous codons reflecting a genomic strategy, i.e. the genome hypothesis of Grantham et al. (1980), gave a good approximation of the mitochondrial sequences. A statistical model which exactly maintains codon frequency, but allows the position of corresponding synonymous codons to vary was only grossly adequate for prokaryotes and totally inadequate for eukaryotes. The results of these simulations are consistent with the measures on experimental sequences and suggest that a “frequency constraint” model such as that of Grantham et al. (1980) may be an adequate explanation of the codon usage in mitochondria. However, in addition to this frequency constraint, there may be constraints on synonymous codon choice in prokaryotes due to codon context. Furthermore, any proposal to explain codon usage in eukaryotes must involve a constraint on the context of a codon in the sequence.     

Contextual specificity in peptide-mediated protein interactions

Most biological processes are regulated through complex networks of transient protein interactions where a globular domain in one protein recognizes a linear peptide from another, creating a relatively small contact interface. Although sufficient to ensure binding, these linear motifs alone are usually too short to achieve the high specificity observed, and additional contacts are often encoded in the residues surrounding the motif (i.e. the context). Here, we systematically identified all instances of peptide-mediated protein interactions of known three-dimensional structure and used them to investigate the individual contribution of motif and context to the global binding energy. We found that, on average, the context is responsible for roughly 20% of the binding and plays a crucial role in determining interaction specificity, by either improving the affinity with the native partner or impeding non-native interactions. We also studied and quantified the topological and energetic variability of interaction interfaces, finding a much higher heterogeneity in the context residues than in the consensus binding motifs. Our analysis partially reveals the molecular mechanisms responsible for the dynamic nature of peptide-mediated interactions, and suggests a global evolutionary mechanism to maximise the binding specificity. Finally, we investigated the viability of non-native interactions and highlight cases of potential cross-reaction that might compensate for individual protein failure and establish backup circuits to increase the robustness of cell networks.     

Contextual models and the non-Newtonian paradigm

Biological systems exhibit a wide range of contextual effects, and this often makes it difficult to construct valid mathematical models of their behaviour. In particular, mathematical paradigms built upon the successes of Newtonian physics make assumptions about the nature of biological systems that are unlikely to hold true. After discussing two of the key assumptions underlying the Newtonian paradigm, we discuss two key aspects of the formalism that extended it, Quantum Theory (QT). We draw attention to the similarities between biological and quantum systems, motivating the development of a similar formalism that can be applied to the modelling of biological processes.     

Conditioning-Based Modeling of Contextual Genomic Regulation

A more complete understanding of the alterations in cellular regulatory and control mechanisms that occur in the various forms of cancer has been one of the central targets of the genomic and proteomic methods that allow surveys of the abundance and/or state of cellular macromolecules. This preference is driven both by the intractability of cancer to generic therapies, assumed to be due to the highly varied molecular etiologies observed in cancer, and by the opportunity to discern and dissect the regulatory and control interactions presented by the highly diverse assortment of perturbations of regulation and control that arise in cancer. Exploiting the opportunities for inference on the regulatory and control connections offered by these revealing system perturbations is fraught with the practical problems that arise from the way biological systems operate. Two classes of regulatory action in biological systems are particularly inimical to inference, convergent regulation, where a variety of regulatory actions result in a common set of control responses (crosstalk), and divergent regulation, where a single regulatory action produces entirely different sets of control responses, depending on cellular context (conditioning). We have constructed a coarse mathematical model of the propagation of regulatory influence in such distributed, context-sensitive regulatory networks that allows a quantitative estimation of the amount of crosstalk and conditioning associated with a candidate regulatory gene taken from a set of genes that have been profiled over a series of samples where the candidate's activity varies.     

Implicit Versus Explicit Local Contextual Processing

We investigated the effects of implicit local contextual processing using behavioral and electrophysiological measures. EEG recording blocks consisted of targets preceded by either randomized sequences of standards or by sequences including a predictive sequence signaling the occurrence of a target event. Subjects performed two sessions: in the first the regularity of the predictive sequence was implicit, while in the second this regularity was made explicit. Subjects pressed a button in response to targets. Both the implicit and explicit sessions showed shorter reaction times and peak P3b latencies for predicted versus random targets, although to a greater extent in the explicit session. In both sessions the middle and last most-informative stimuli of the three-standard predictive sequence induced a significant larger P3b compared with randomized standards. The findings show that local contextual information is processed implicitly, but that this modulation was significantly greater when subjects were explicitly instructed to attend to target-predictive contextual information. The findings suggest that top-down attentional networks have a role in modulating the extent to which contextual information is utilized.     

Generalizing Double and Triple Sampling for Repeated Surveys and Partial Verification

Population composition is often estimated by double sampling in which the value of a covariate is noted on each of a large number of randomly selected units and the value of the covariate and the exact class to which the unit belongs is noted for a smaller sample. The cross‐classified sample can be used to estimate the classification rates and these, in turn, can be used in conjunction with the estimated distribution of the covariate to obtain an improved estimate of the population composition over that obtained by direct observation of the identity of the individuals in a small sample. There are two approaches to this problem characterized by the way in which the classification rates are defined. The simplest approach uses estimates of the probability P(i | j) that the unit is actually in class i given that the covariate is in class j. The more complicated approach uses estimates of the probability Pi | j) that the covariate falls in class j given that the unit is actually in class i. The latter approach involves estimating more parameters than the former but avoids the necessity for the two samples to be drawn from the same population. We show the two approaches can be combined when there are multiple surveys. For example, one might conduct a disease survey for several years; in each year the accurate and/or error‐prone techniques may be applied to samples. The sensitivities and specificities of the error‐prone test are assumed constant across surveys. Generalizations allow for more than one error‐prone classifier and partial verification (estimation of misclassification rates by application of the accurate technique to fixed subsamples from each error‐prone category). The general approach is illustrated by considering a repeated survey for malaria.     

Generalizing Levins metapopulation model in explicit space: models of intermediate complexity

A recent study [Harding and McNamara, 2002. A unifying framework for metapopulation dynamics. Am. Nat. 160, 173-185] presented a unifying framework for the classic Levins metapopulation model by incorporating several realistic biological processes, such as the Allee effect, the Rescue effect and the Anti-rescue effect, via appropriate modifications of the two basic functions of colonization and extinction rates. Here we embed these model extensions on a spatially explicit framework. We consider population dynamics on a regular grid, each site of which represents a patch that is either occupied or empty, and with spatial coupling by neighborhood dispersal. While broad qualitative similarities exist between the spatially explicit models and their spatially implicit (mean-field) counterparts, there are also important differences that result from the details of local processes. Because of localized dispersal, spatial correlation develops among the dynamics of neighboring populations that decays with distance between patches. The extent of this correlation at equilibrium differs among the metapopulation types, depending on which processes prevail in the colonization and extinction dynamics. These differences among dynamical processes become manifest in the spatial pattern and distribution of “clusters” of occupied patches. Moreover, metapopulation dynamics along a smooth gradient of habitat availability show significant differences in the spatial pattern at the range limit. The relevance of these results to the dynamics of disease spread in metapopulations is discussed.     

Generalizing matrix structure affects the identification of least-cost paths and patch connectivity

Understanding and assessing landscape connectivity is often a primary goal when studying patchy or spatially structured populations. It is commonly accepted that the matrix plays a role in determining connectivity; however, it is not clear how the process of assessing connectivity is impacted by different ways in which the matrix may be represented, particularly if matrix structure is generalized to expedite analysis. We conducted a controlled experiment using computer simulations to evaluate the impact of increasing levels of matrix generalization on connectivity assessment using a constant arrangement of habitat patches. We varied matrix generalization for six simulated landscape patterns by adjusting the number of classes and level of pattern grain in 16 ways to yield sets of landscapes for which the matrix ranged from not generalized (i.e., heterogeneous) to completely generalized (i.e., homogeneous) while habitat placement was held constant. Least-cost paths were calculated for each landscape, and a spatial interaction model (SIM) was implemented to model the counts of patch inflows (immigration) and pairwise exchange. Applying a SIM allowed us to generate absolute outputs and explicitly compare the effect of changes to matrix generalization on connectivity. We found that both assessment of connections (i.e., measured distance and spatial delineation of least-cost paths) and patch inflows/pairwise exchange were highly sensitive to matrix generalization and that effects were inconsistent and unpredictable across the range of matrix representations, especially when estimating connectivity for individual patches. We conclude that matrix pattern may have an underappreciated effect on connectivity and that least-cost path delineation and connectivity assessment may be very sensitive to generalizations of the matrix. We suggest that sensitivity analysis of the matrix representation should be performed when conducting connectivity analyses.