The genome regulatory landscape of Atlantic salmon liver through smoltification

The anadromous Atlantic salmon undergo a preparatory physiological transformation before seawater entry, referred to as smoltification. Key molecular developmental processes involved in this life stage transition, such as remodeling of gill functions, are known to be synchronized and modulated by environmental cues like photoperiod. However, little is known about the photoperiod influence and genome regulatory processes driving other canonical aspects of smoltification such as the large-scale changes in lipid metabolism and energy homeostasis in the developing smolt liver. Here we generate transcriptome, DNA methylation, and chromatin accessibility data from salmon livers across smoltification under different photoperiod regimes. We find a systematic reduction of expression levels of genes with a metabolic function, such as lipid metabolism, and increased expression of energy related genes such as oxidative phosphorylation, during smolt development in freshwater. However, in contrast to similar studies of the gill, smolt liver gene expression prior to seawater transfer was not impacted by photoperiodic history. Integrated analyses of gene expression, chromatin accessibility, and transcription factor (TF) binding signatures highlight chromatin remodeling and TF dynamics underlying smolt gene regulatory changes. Differential peak accessibility patterns largely matched differential gene expression patterns during smoltification and we infer that ZNF682, KLFs, and NFY TFs are important in driving a liver metabolic shift from synthesis to break down of organic compounds in freshwater. Overall, chromatin accessibility and TFBS occupancy were highly correlated to changes in gene expression. On the other hand, we identified numerous differential methylation patterns across the genome, but associated genes were not functionally enriched or correlated to observed gene expression changes across smolt development. Taken together, this work highlights the relative importance of chromatin remodeling during smoltification and demonstrates that metabolic remodeling occurs as a preadaptation to life at sea that is not to a large extent driven by photoperiod history.

Extracting functionally accurate context-specific models of Atlantic salmon metabolism

Constraint-based models (CBMs) are used to study metabolic network structure and function in organisms ranging from microbes to multicellular eukaryotes. Published CBMs are usually generic rather than context-specific, meaning that they do not capture differences in reaction activities, which, in turn, determine metabolic capabilities, between cell types, tissues, environments, or other conditions. Only a subset of a CBM's metabolic reactions and capabilities are likely to be active in any given context, and several methods have therefore been developed to extract context-specific models from generic CBMs through integration of omics data. We tested the ability of six model extraction methods (MEMs) to create functionally accurate context-specific models of Atlantic salmon using a generic CBM (SALARECON) and liver transcriptomics data from contexts differing in water salinity (life stage) and dietary lipids. Three MEMs (iMAT, INIT, and GIMME) outperformed the others in terms of functional accuracy, which we defined as the extracted models' ability to perform context-specific metabolic tasks inferred directly from the data, and one MEM (GIMME) was faster than the others. Context-specific versions of SALARECON consistently outperformed the generic version, showing that context-specific modeling better captures salmon metabolism. Thus, we demonstrate that results from human studies also hold for a non-mammalian animal and major livestock species.

SALARECON connects the Atlantic salmon genome to growth and feed efficiency

Atlantic salmon (Salmo salar) is the most valuable farmed fish globally and there is much interest in optimizing its genetics and rearing conditions for growth and feed efficiency. Marine feed ingredients must be replaced to meet global demand, with challenges for fish health and sustainability. Metabolic models can address this by connecting genomes to metabolism, which converts nutrients in the feed to energy and biomass, but such models are currently not available for major aquaculture species such as salmon. We present SALARECON, a model focusing on energy, amino acid, and nucleotide metabolism that links the Atlantic salmon genome to metabolic fluxes and growth. It performs well in standardized tests and captures expected metabolic (in)capabilities. We show that it can explain observed hypoxic growth in terms of metabolic fluxes and apply it to aquaculture by simulating growth with commercial feed ingredients. Predicted limiting amino acids and feed efficiencies agree with data, and the model suggests that marine feed efficiency can be achieved by supplementing a few amino acids to plant- and insect-based feeds. SALARECON is a high-quality model that makes it possible to simulate Atlantic salmon metabolism and growth. It can be used to explain Atlantic salmon physiology and address key challenges in aquaculture such as development of sustainable feeds.

Atlantic salmon aquaculture generates billions of euros annually, but faces challenges of sustainability. Salmon are carnivores by nature, and fish oil and fish meal have become scarce resources in fish feed production. Novel, sustainable feedstuffs are being trialed hand in hand with studies of the genetics of growth and feed efficiency. This calls for a mathematical-biological framework to integrate data with understanding of the effects of novel feeds on salmon physiology and its interplay with genetics. We have developed the SALARECON model of the core salmon metabolic reaction network, linking its genome to metabolic fluxes and growth. Computational analyses show good agreement with observed growth, amino acid limitations, and feed efficiencies, illustrating the potential for in silico studies of potential feed mixtures. In particular, in silico screening of possible diets will enable more efficient animal experiments with improved knowledge gain. We have adopted best practices for test-driven development, virtual experiments to assay metabolic capabilities, revision control, and FAIR data and model management. This facilitates fast, collaborative, reliable development of the model for future applications in sustainable production biology.

Diet and Life Stage-Associated Lipidome Remodeling in Atlantic Salmon

Salmon is an important source of long-chain highly unsaturated fatty acids (LC-HUFAs) such as 22:6n-3 [docosahexaenoic acid (DHA)]. In the present study, we conducted two identical experiments on salmon in freshwater (FW) and seawater (SW) stages, with a diet switch from fish oil (high in LC-HUFA) to vegetable oil (low in LC-HUFA) and vice versa. Our aim was to investigate the diet and life stage-specific features of lipid uptake (gut), processing (liver), and deposition (muscle). The lipid composition changed much faster in the gut of SW fish relative to FW fish, suggesting that the former had a higher rate of lipid absorption and transport. SW fish also had higher expression of phospholipid synthesis and lipoprotein formation genes in the gut, whereas FW fish had higher expression of lipid synthesis genes in the liver. All phospholipids except PC-44:12 and PE-44:12 were less abundant in SW, suggesting that SW fish have a higher requirement for DHA.

Targeted mutagenesis of ∆5 and ∆6 fatty acyl desaturases induce dysregulation of lipid metabolism in Atlantic salmon (Salmo salar)

Background

With declining wild fish populations, farmed salmon has gained popularity as a source for healthy long-chain highly unsaturated fatty acids (LC-HUFA). However, the introduction of plant oil in farmed salmon feeds has reduced the content of these beneficial LC-HUFA. The synthetic capability for LC-HUFAs depends upon the dietary precursor fatty acids and the genetic potential, thus there is a need for in-depth understanding of LC-HUFA synthetic genes and their interactions with other genes involved in lipid metabolism. Several key genes of LC-HUFA synthesis in salmon belong to the fatty acid desaturases 2 (fads2) family. The present study applied whole transcriptome analysis on two CRISPR-mutated salmon strains (crispants), 1) Δ6abc/5^Mt with mutations in Δ5fads2, Δ6fads2-a, Δ6fads2-b and Δ6fads2-c genes, and 2) Δ6bc^Mt with mutations in Δ6fads2-b and Δ6fads2-c genes. Our purpose is to evaluate the genetic effect fads2 mutations have on other lipid metabolism pathways in fish, as well as to investigate mosaicism in a commercial species with a very long embryonal period.

Results

Both Δ6abc/5^Mt and Δ6bc^Mt crispants demonstrated high percentage of indels within all intended target genes, though different indel types and percentage were observed between individuals. The Δ6abc/5^Mt fish displayed several disruptive indels which resulted in over 100 differentially expressed genes (DEGs) enriched in lipid metabolism pathways in liver. This includes up-regulation of srebp1 genes which are known key transcription regulators of lipid metabolism as well as a number of down-stream genes involved in fatty acid de-novo synthesis, fatty acid β-oxidation and lipogenesis. Both elovl5 and elovl2 genes were not changed, suggesting that the genes were not targeted by Srebp1. The mutation of Δ6bc^Mt surprisingly resulted in over 3000 DEGs which were enriched in factors encoding genes involved in mRNA regulation and stability.

Conclusions

CRISPR-Cas9 can efficiently mutate multiple fads2 genes simultaneously in salmon. The results of the present study have provided new information on the transcriptional regulations of lipid metabolism genes after reduction of LC-HUFA synthesis pathways in salmon.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-020-07218-1.

Replacing soybean meal with rapeseed meal and faba beans in a growing-finishing pig diet: Effect on growth performance, meat quality and metabolite changes

Rapeseed meal and faba beans (RSM/FB) can serve as an alternative to imported soybean meal (SBM). In this study, forty Norwegian crossbred ([Landrace x Yorkshire] x Duroc) growing-finishing pigs (108.7 ± 4.2 kg final BW) were fed a diet with either SBM or RSM/FB as protein sources. RSM/FB increased feed conversion ratio (P = .04) in the finishing period, reduced lightness (P = .04) and yellowness (P = .004) of meat, changed amounts of individual fatty acids, but not of total SFA, MUFA and PUFA. Importantly, RSM/FB reduced the glucose level (P < .05) in meat. Lower pyroglutamic acid (P = .06) in RSM/FB indicate lower oxidative stress in pre-rigor muscle cell. Increased abundance of free amino acids, sweet tasting metabolites, reduced warmed-over flavor and flavor attributes indicated desirable properties of RSM/FB meat. To conclude, RSM/FB in pig diet supported growth performance and carcass quality comparable to SBM and had a positive effect on meat quality.

Comparative transcriptomics reveals domestication-associated features of Atlantic salmon lipid metabolism

Domestication of animals imposes strong targeted selection for desired traits but can also result in unintended selection due to new domestic environments. Atlantic salmon (Salmo salmar) was domesticated in the 1970s and has subsequently been selected for faster growth in systematic breeding programmes. More recently, salmon aquaculture has replaced fish oils (FOs) with vegetable oils (VOs) in feed, radically changing the levels of essential long-chain polyunsaturated fatty acids (LC-PUFAs). Our aim here was to study the impact of domestication on metabolism and explore the hypothesis that the shift to VO diets has unintentionally selected for a domestication-specific lipid metabolism. We conducted a 96-day feeding trial of domesticated and wild salmon fed diets based on FOs, VOs or phospholipids, and compared transcriptomes and fatty acids in tissues involved in lipid absorption (pyloric caeca) and lipid turnover and synthesis (liver). Domesticated salmon had faster growth and higher gene expression in glucose and lipid metabolism compared to wild fish, possibly linked to differences in regulation of circadian rhythm pathways. Only the domesticated salmon increased expression of LC-PUFA synthesis genes when given VOs. This transcriptome response difference was mirrored at the physiological level, with domesticated salmon having higher LC-PUFA levels but lower 18:3n-3 and 18:2n-6 levels. In line with this, the VO diet decreased growth rate in wild but not domesticated salmon. Our study revealed a clear impact of domestication on transcriptomic regulation linked to metabolism and suggests that unintentional selection in the domestic environment has resulted in evolution of stronger compensatory mechanisms to a diet low in LC-PUFAs.

Liver slice culture as a model for lipid metabolism in fish

Hepatic lipid metabolism is traditionally investigated in vitro using hepatocyte monocultures lacking the complex three-dimensional structure and interacting cell types essential liver function. Precision cut liver slice (PCLS) culture represents an alternative in vitro system, which benefits from retention of tissue architecture. Here, we present the first comprehensive evaluation of the PCLS method in fish (Atlantic salmon, Salmo salar L.) and validate it in the context of lipid metabolism using feeding trials, extensive transcriptomic data, and fatty acid measurements. We observe an initial period of post-slicing global transcriptome adjustment, which plateaued after 3 days in major metabolic pathways and stabilized through 9 days. PCLS fed alpha-linolenic acid (ALA) and insulin responded in a liver-like manner, increasing lipid biosynthesis gene expression. We identify interactions between insulin and ALA, where two PUFA biosynthesis genes that were induced by insulin or ALA alone, were highly down-regulated when insulin and ALA were combined. We also find that transcriptomic profiles of liver slices are exceedingly more similar to whole liver than hepatocyte monocultures, both for lipid metabolism and liver marker genes. PCLS culture opens new avenues for high throughput experimentation on the effect of “novel feed composition” and represent a promising new strategy for studying genotype-specific molecular features of metabolism.

Transcriptional development of phospholipid and lipoprotein metabolism in different intestinal regions of Atlantic salmon (Salmo salar) fry

Background

It has been suggested that the high phospholipid (PL) requirement in Atlantic salmon (Salmo salar) fry is due to insufficient intestinal de-novo synthesis causing low lipoprotein (LP) production and reduced transport capacity of dietary lipids. However, in-depth ontogenetic analysis of intestinal PL and LP synthesis with the development of salmon has yet to be performed. Therefore, in this paper we used RNA-Seq technology to investigate the expression of genes involved in PL synthesis and LP formation throughout early developmental stages and associate insufficient expression of synthesis pathways in salmon fry with its higher dietary PL requirement. There was a special focus on the understanding homologous genes, especially those from salmonid-specific fourth vertebrate whole-genome duplication (Ss4R), and their contribution to salmonid specific features of regulation of PL metabolic pathways. Salmon fry were sampled at 0.16 g (1 day before first-feeding), 2.5 and 10 g stages of development and transcriptomic analysis was applied separately on stomach, pyloric caeca and hindgut of the fish.

Results

In general, we found up-regulated pathways involved in synthesis of phosphatidylcholine (PtdCho), phosphatidylethanolamine (PtdEtn), and LP in pyloric caeca of salmon between 0.16 and 10 g. Thirteen differentially expressed genes (q < 0.05) in these pathways were highly up-regulated in 2.5 g salmon compared to 0.16 g, while only five more differentially expressed (q < 0.05) genes were found when the fish grew up to 10 g. Different homologous genes were found dominating in stomach, pyloric caeca and hindgut. However, the expression of dominating genes in pathways of PL and LP synthesis were much higher in pyloric caeca than stomach and hindgut. Salmon-specific homologous genes (Ss4R) had similar expression during development, while other homologs had more diverged expression.

Conclusions

The up-regulation of the de-novo PtdCho and PtdEtn pathways confirm that salmon have decreasing requirement for dietary PL as the fish develops. The similar expressions between Ss4R homologous genes suggest that the functional divergence of these genes was incomplete compared to homologs derived from other genome duplication. The results of the present study have provided new information on the molecular mechanisms of phospholipid synthesis and lipoprotein formation in fish.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4651-8) contains supplementary material, which is available to authorized users.

Life-stage-associated remodelling of lipid metabolism regulation in Atlantic salmon

Atlantic salmon migrates from rivers to sea to feed, grow and develop gonads before returning to spawn in freshwater. The transition to marine habitats is associated with dramatic changes in the environment, including water salinity, exposure to pathogens and shift in dietary lipid availability. Many changes in physiology and metabolism occur across this life-stage transition, but little is known about the molecular nature of these changes. Here, we use a long-term feeding experiment to study transcriptional regulation of lipid metabolism in Atlantic salmon gut and liver in both fresh- and saltwater. We find that lipid metabolism becomes significantly less plastic to differences in dietary lipid composition when salmon transitions to saltwater and experiences increased dietary lipid availability. Expression of genes in liver relating to lipogenesis and lipid transport decreases overall and becomes less responsive to diet, while genes for lipid uptake in gut become more highly expressed. Finally, analyses of evolutionary consequences of the salmonid-specific whole-genome duplication on lipid metabolism reveal several pathways with significantly different (p < .05) duplicate retention or duplicate regulatory conservation. We also find a limited number of cases where the whole-genome duplication has resulted in an increased gene dosage. In conclusion, we find variable and pathway-specific effects of the salmonid genome duplication on lipid metabolism genes. A clear life-stage-associated shift in lipid metabolism regulation is evident, and we hypothesize this to be, at least partly, driven by nondietary factors such as the preparatory remodelling of gene regulation and physiology prior to sea migration.

Stable Core Gut Microbiota across the Freshwater-to-Saltwater Transition for Farmed Atlantic Salmon

Gut microbiota associations through habitat transitions are fundamentally important yet poorly understood. One such habitat transition is the migration from freshwater to saltwater for anadromous fish, such as salmon. The aim of the current work was therefore to determine the freshwater-to-saltwater transition impact on the gut microbiota in farmed Atlantic salmon, with dietary interventions resembling freshwater and saltwater diets with respect to fatty acid composition. Using deep 16S rRNA gene sequencing and quantitative PCR, we found that the freshwater-to-saltwater transition had a major association with the microbiota composition and quantity, while diet did not show significant associations with the microbiota. In saltwater there was a 100-fold increase in bacterial quantity, with a relative increase of Firmicutes and a relative decrease of both Actinobacteria and Proteobacteria Irrespective of an overall shift in microbiota composition from freshwater to saltwater, we identified three core clostridia and one Lactobacillus-affiliated phylotype with wide geographic distribution that were highly prevalent and co-occurring. Taken together, our results support the importance of the dominating bacteria in the salmon gut, with the freshwater microbiota being immature. Due to the low number of potentially host-associated bacterial species in the salmon gut, we believe that farmed salmon can represent an important model for future understanding of host-bacterium interactions in aquatic environments.IMPORTANCE Little is known about factors affecting the interindividual distribution of gut bacteria in aquatic environments. We have shown that there is a core of four highly prevalent and co-occurring bacteria irrespective of feed and freshwater-to-saltwater transition. The potential host interactions of the core bacteria, however, need to be elucidated further.

Functional Annotation of All Salmonid Genomes (FAASG): an international initiative supporting future salmonid research, conservation and aquaculture

We describe an emerging initiative - the 'Functional Annotation of All Salmonid Genomes' (FAASG), which will leverage the extensive trait diversity that has evolved since a whole genome duplication event in the salmonid ancestor, to develop an integrative understanding of the functional genomic basis of phenotypic variation. The outcomes of FAASG will have diverse applications, ranging from improved understanding of genome evolution, to improving the efficiency and sustainability of aquaculture production, supporting the future of fundamental and applied research in an iconic fish lineage of major societal importance.

Towards causally cohesive genotype-phenotype modelling for characterization of the soft-tissue mechanics of the heart in normal and pathological geometries

A scientific understanding of individual variation is key to personalized medicine, integrating genotypic and phenotypic information via computational physiology. Genetic effects are often context-dependent, differing between genetic backgrounds or physiological states such as disease. Here, we analyse in silico genotype-phenotype maps (GP map) for a soft-tissue mechanics model of the passive inflation phase of the heartbeat, contrasting the effects of microstructural and other low-level parameters assumed to be genetically influenced, under normal, concentrically hypertrophic and eccentrically hypertrophic geometries. For a large number of parameter scenarios, representing mock genetic variation in low-level parameters, we computed phenotypes describing the deformation of the heart during inflation. The GP map was characterized by variance decompositions for each phenotype with respect to each parameter. As hypothesized, the concentric geometry allowed more low-level parameters to contribute to variation in shape phenotypes. In addition, the relative importance of overall stiffness and fibre stiffness differed between geometries. Otherwise, the GP map was largely similar for the different heart geometries, with little genetic interaction between the parameters included in this study. We argue that personalized medicine can benefit from a combination of causally cohesive genotype-phenotype modelling, and strategic phenotyping that captures effect modifiers not explicitly included in the mechanistic model.

A call for virtual experiments: accelerating the scientific process

Experimentation is fundamental to the scientific method, whether for exploration, description or explanation. We argue that promoting the reuse of virtual experiments (the in silico analogues of wet-lab or field experiments) would vastly improve the usefulness and relevance of computational models, encouraging critical scrutiny of models and serving as a common language between modellers and experimentalists. We review the benefits of reusable virtual experiments: in specifying, assaying, and comparing the behavioural repertoires of models; as prerequisites for reproducible research; to guide model reuse and composition; and for quality assurance in the translational application of models. A key step towards achieving this is that models and experimental protocols should be represented separately, but annotated so as to facilitate the linking of models to experiments and data. Lastly, we outline how the rigorous, streamlined confrontation between experimental datasets and candidate models would enable a "continuous integration" of biological knowledge, transforming our approach to systems biology.

A computational pipeline for quantification of mouse myocardial stiffness parameters

The mouse is an important model for theoretical-experimental cardiac research, and biophysically based whole organ models of the mouse heart are now within reach. However, the passive material properties of mouse myocardium have not been much studied. We present an experimental setup and associated computational pipeline to quantify these stiffness properties. A mouse heart was excised and the left ventricle experimentally inflated from 0 to 1.44kPa in eleven steps, and the resulting deformation was estimated by echocardiography and speckle tracking. An in silico counterpart to this experiment was built using finite element methods and data on ventricular tissue microstructure from diffusion tensor MRI. This model assumed a hyperelastic, transversely isotropic material law to describe the force-deformation relationship, and was simulated for many parameter scenarios, covering the relevant range of parameter space. To identify well-fitting parameter scenarios, we compared experimental and simulated outcomes across the whole range of pressures, based partly on gross phenotypes (volume, elastic energy, and short- and long-axis diameter), and partly on node positions in the geometrical mesh. This identified a narrow region of experimentally compatible values of the material parameters. Estimation turned out to be more precise when based on changes in gross phenotypes, compared to the prevailing practice of using displacements of the material points. We conclude that the presented experimental setup and computational pipeline is a viable method that deserves wider application.

Effect of regulatory architecture on broad versus narrow sense heritability

Additive genetic variance (VA ) and total genetic variance (VG ) are core concepts in biomedical, evolutionary and production-biology genetics. What determines the large variation in reported VA /VG ratios from line-cross experiments is not well understood. Here we report how the VA /VG ratio, and thus the ratio between narrow and broad sense heritability (h(2) /H(2) ), varies as a function of the regulatory architecture underlying genotype-to-phenotype (GP) maps. We studied five dynamic models (of the cAMP pathway, the glycolysis, the circadian rhythms, the cell cycle, and heart cell dynamics). We assumed genetic variation to be reflected in model parameters and extracted phenotypes summarizing the system dynamics. Even when imposing purely linear genotype to parameter maps and no environmental variation, we observed quite low VA /VG ratios. In particular, systems with positive feedback and cyclic dynamics gave more non-monotone genotype-phenotype maps and much lower VA /VG ratios than those without. The results show that some regulatory architectures consistently maintain a transparent genotype-to-phenotype relationship, whereas other architectures generate more subtle patterns. Our approach can be used to elucidate these relationships across a whole range of biological systems in a systematic fashion.

Bridging the genotype-phenotype gap: what does it take?

The genotype-phenotype map (GP map) concept applies to any time point in the ontogeny of a living system. It is the outcome of very complex dynamics that include environmental effects, and bridging the genotype-phenotype gap is synonymous with understanding these dynamics. The context for this understanding is physiology, and the disciplinary goals of physiology do indeed demand the physiological community to seek this understanding. We claim that this task is beyond reach without use of mathematical models that bind together genetic and phenotypic data in a causally cohesive way. We provide illustrations of such causally cohesive genotype-phenotype models where the phenotypes span from gene expression profiles to development of whole organs. Bridging the genotype-phenotype gap also demands that large-scale biological ('omics') data and associated bioinformatics resources be more effectively integrated with computational physiology than is currently the case. A third major element is the need for developing a phenomics technology way beyond current state of the art, and we advocate the establishment of a Human Phenome Programme solidly grounded on biophysically based mathematical descriptions of human physiology.

Parameters in dynamic models of complex traits are containers of missing heritability

Polymorphisms identified in genome-wide association studies of human traits rarely explain more than a small proportion of the heritable variation, and improving this situation within the current paradigm appears daunting. Given a well-validated dynamic model of a complex physiological trait, a substantial part of the underlying genetic variation must manifest as variation in model parameters. These parameters are themselves phenotypic traits. By linking whole-cell phenotypic variation to genetic variation in a computational model of a single heart cell, incorporating genotype-to-parameter maps, we show that genome-wide association studies on parameters reveal much more genetic variation than when using higher-level cellular phenotypes. The results suggest that letting such studies be guided by computational physiology may facilitate a causal understanding of the genotype-to-phenotype map of complex traits, with strong implications for the development of phenomics technology.

Despite an ever-increasing number of genome locations reported to be associated with complex human diseases or quantitative traits, only a small proportion of phenotypic variations in a typical quantitative trait can be explained by the discovered variants. We argue that this problem can partly be resolved by combining the statistical methods of quantitative genetics with computational biology. We demonstrate this for the in silico genotype-to-phenotype map of a model heart cell in conjunction with publically accessible genomic data. We show that genome wide association studies (GWAS) on model parameters identify more causal variants and can build better prediction models for the higher-level phenotypes than by performing GWAS on the higher-level phenotypes themselves. Since model parameters are in principle measurable physiological phenotypes, our findings suggest that development of future phenotyping technologies could be guided by mathematical models of the biological systems being targeted.

Genotype-Phenotype Map Characteristics of an In silico Heart Cell

Understanding the causal chain from genotypic to phenotypic variation is a tremendous challenge with huge implications for personalized medicine. Here we argue that linking computational physiology to genetic concepts, methodology, and data provides a new framework for this endeavor. We exemplify this causally cohesive genotype–phenotype (cGP) modeling approach using a detailed mathematical model of a heart cell. In silico genetic variation is mapped to parametric variation, which propagates through the physiological model to generate multivariate phenotypes for the action potential and calcium transient under regular pacing, and ion currents under voltage clamping. The resulting genotype-to-phenotype map is characterized using standard quantitative genetic methods and novel applications of high-dimensional data analysis. These analyses reveal many well-known genetic phenomena like intralocus dominance, interlocus epistasis, and varying degrees of phenotypic correlation. In particular, we observe penetrance features such as the masking/release of genetic variation, so that without any change in the regulatory anatomy of the model, traits may appear monogenic, oligogenic, or polygenic depending on which genotypic variation is actually present in the data. The results suggest that a cGP modeling approach may pave the way for a computational physiological genomics capable of generating biological insight about the genotype–phenotype relation in ways that statistical-genetic approaches cannot.

Order-preserving principles underlying genotype-phenotype maps ensure high additive proportions of genetic variance

In quantitative genetics, the degree of resemblance between parents and offspring is described in terms of the additive variance (V(A)) relative to genetic (V(G)) and phenotypic (V(P)) variance. For populations with extreme allele frequencies, high V(A)/V(G) can be explained without considering properties of the genotype-phenotype (GP) map. We show that randomly generated GP maps in populations with intermediate allele frequencies generate far lower V(A)/V(G) values than empirically observed. The main reason is that order-breaking behaviour is ubiquitous in random GP maps. Rearrangement of genotypic values to introduce order-preservation for one or more loci causes a dramatic increase in V(A)/V(G). This suggests the existence of order-preserving design principles in the regulatory machinery underlying GP maps. We illustrate this feature by showing how the ubiquitously observed monotonicity of dose-response relationships gives much higher V(A)/V(G) values than a unimodal dose-response relationship in simple gene network models.

Hierarchical cluster-based partial least squares regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models

Background

Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function.

Results

Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops.

Conclusions

HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems.

Rapid Advance of Spring Arrival Dates in Long-Distance Migratory Birds

Several bird species have advanced the timing of their spring migration in response to recent climate change. European short-distance migrants, wintering in temperate areas, have been assumed to be more affected by change in the European climate than long-distance migrants wintering in the tropics. However, we show that long-distance migrants have advanced their spring arrival in Scandinavia more than short-distance migrants. By analyzing a long-term data set from southern Italy, we show that long-distance migrants also pass through the Mediterranean region earlier. We argue that this may reflect a climate-driven evolutionary change in the timing of spring migration.

Using the satellite-derived NDVI to assess ecological responses to environmental change

Assessing how environmental changes affect the distribution and dynamics of vegetation and animal populations is becoming increasingly important for terrestrial ecologists to enable better predictions of the effects of global warming, biodiversity reduction or habitat degradation. The ability to predict ecological responses has often been hampered by our rather limited understanding of trophic interactions. Indeed, it has proven difficult to discern direct and indirect effects of environmental change on animal populations owing to limited information about vegetation at large temporal and spatial scales. The rapidly increasing use of the Normalized Difference Vegetation Index (NDVI) in ecological studies has recently changed this situation. Here, we review the use of the NDVI in recent ecological studies and outline its possible key role in future research of environmental change in an ecosystem context.

Living in synchrony on Greenland coasts?

Theory indicates that correlated weather may synchronize populations, but the extent to which this holds for non-identical, nonlinear systems is uncertain. Post and Forchhammer claim to have shown climate-induced synchrony for musk oxen and caribou that are separated by the Greenland ice sheet. However, logical and mathematical errors undermine their finding. Whether or not large-scale weather can be a major synchronizing factor across species remains an open question.