Pathway dynamics can delineate the sources of transcriptional noise in gene expression

Single-cell expression profiling opens up new vistas on cellular processes. Extensive cell-to-cell variability at the transcriptomic and proteomic level has been one of the stand-out observations. Because most experimental analyses are destructive we only have access to snapshot data of cellular states. This loss of temporal information presents significant challenges for inferring dynamics, as well as causes of cell-to-cell variability. In particular, we typically cannot separate dynamic variability from within cells ('intrinsic noise') from variability across the population ('extrinsic noise'). Here, we make this non-identifiability mathematically precise, allowing us to identify new experimental set-ups that can assist in resolving this non-identifiability. We show that multiple generic reporters from the same biochemical pathways (e.g. mRNA and protein) can infer magnitudes of intrinsic and extrinsic transcriptional noise, identifying sources of heterogeneity. Stochastic simulations support our theory, and demonstrate that 'pathway-reporters' compare favourably to the well-known, but often difficult to implement, dual-reporter method.

P38 and JNK have opposing effects on persistence of in vivo leukocyte migration in zebrafish

The recruitment and migration of macrophages and neutrophils is an important process during the early stages of the innate immune system in response to acute injury. Transgenic pu.1:EGFP zebrafish permit the acquisition of leukocyte migration trajectories during inflammation. Currently, these high-quality live-imaging data are mainly analysed using general statistics, for example, cell velocity. Here, we present a spatio-temporal analysis of the cell dynamics using transition matrices, which provide information of the type of cell migration. We find evidence that leukocytes exhibit types of migratory behaviour, which differ from previously described random walk processes. Dimethyl sulfoxide treatment decreased the level of persistence at early time points after wounding and ablated temporal dependencies observed in untreated embryos. We then use pharmacological inhibition of p38 and c-Jun N-terminal kinase mitogen-activated protein kinases to determine their effects on in vivo leukocyte migration patterns and discuss how they modify the characteristics of the cell migration process. In particular, we find that their respective inhibition leads to decreased and increased levels of persistent motion in leukocytes following wounding. This example shows the high level of information content, which can be gained from live-imaging data if appropriate statistical tools are used.

What the papers say: text mining for genomics and systems biology

Keeping up with the rapidly growing literature has become virtually impossible for most scientists. This can have dire consequences. First, we may waste research time and resources on reinventing the wheel simply because we can no longer maintain a reliable grasp on the published literature. Second, and perhaps more detrimental, judicious (or serendipitous) combination of knowledge from different scientific disciplines, which would require following disparate and distinct research literatures, is rapidly becoming impossible for even the most ardent readers of research publications. Text mining - the automated extraction of information from (electronically) published sources - could potentially fulfil an important role - but only if we know how to harness its strengths and overcome its weaknesses. As we do not expect that the rate at which scientific results are published will decrease, text mining tools are now becoming essential in order to cope with, and derive maximum benefit from, this information explosion. In genomics, this is particularly pressing as more and more rare disease-causing variants are found and need to be understood. Not being conversant with this technology may put scientists and biomedical regulators at a severe disadvantage. In this review, we introduce the basic concepts underlying modern text mining and its applications in genomics and systems biology. We hope that this review will serve three purposes: (i) to provide a timely and useful overview of the current status of this field, including a survey of present challenges; (ii) to enable researchers to decide how and when to apply text mining tools in their own research; and (iii) to highlight how the research communities in genomics and systems biology can help to make text mining from biomedical abstracts and texts more straightforward.

Evolving proteins at Darwin's bicentennary

A report of the Biochemical Society/Wellcome Trust meeting 'Protein Evolution - Sequences, Structures and Systems', Hinxton, UK, 26-27 January 2009.

A report of the Biochemical Society/Wellcome Trust meeting 'Protein Evolution - Sequences, Structures and Systems', Hinxton, UK, 26-27 January 2009.

Network motifs: structure does not determine function

Background

A number of publications have recently examined the occurrence and properties of the feed-forward motif in a variety of networks, including those that are of interest in genome biology, such as gene networks. The present work looks in some detail at the dynamics of the bi-fan motif, using systems of ordinary differential equations to model the populations of transcription factors, mRNA and protein, with the aim of extending our understanding of what appear to be important building blocks of gene network structure.

Results

We develop an ordinary differential equation model of the bi-fan motif and analyse variants of the motif corresponding to its behaviour under various conditions. In particular, we examine the effects of different steady and pulsed inputs to five variants of the bifan motif, based on evidence in the literature of bifan motifs found in Saccharomyces cerevisiae (commonly known as baker's yeast). Using this model, we characterize the dynamical behaviour of the bi-fan motif for a wide range of biologically plausible parameters and configurations. We find that there is no characteristic behaviour for the motif, and with the correct choice of parameters and of internal structure, very different, indeed even opposite behaviours may be obtained.

Conclusion

Even with this relatively simple model, the bi-fan motif can exhibit a wide range of dynamical responses. This suggests that it is difficult to gain significant insights into biological function simply by considering the connection architecture of a gene network, or its decomposition into simple structural motifs. It is necessary to supplement such structural information by kinetic parameters, or dynamic time series experimental data, both of which are currently difficult to obtain.

The extent and importance of intragenic recombination

We have studied the recombination rate behaviour of a set of 140 genes which were investigated for their potential importance in inflammatory disease. Each gene was extensively sequenced in 24 individuals of African descent and 23 individuals of European descent, and the recombination process was studied separately in the two population samples. The results obtained from the two populations were highly correlated, suggesting that demographic bias does not affect our population genetic estimation procedure. We found evidence that levels of recombination correlate with levels of nucleotide diversity. High marker density allowed us to study recombination rate variation on a very fine spatial scale. We found that about 40 per cent of genes showed evidence of uniform recombination, while approximately 12 per cent of genes carried distinct signatures of recombination hotspots. On studying the locations of these hotspots, we found that they are not always confined to introns but can also stretch across exons. An investigation of the protein products of these genes suggested that recombination hotspots can sometimes separate exons belonging to different protein domains; however, this occurs much less frequently than might be expected based on evolutionary studies into the origins of recombination. This suggests that evolutionary analysis of the recombination process is greatly aided by considering nucleotide sequences and protein products jointly.

Comparative analysis of the Saccharomyces cerevisiae and Caenorhabditis elegans protein interaction networks

Background

Protein interaction networks aim to summarize the complex interplay of proteins in an organism. Early studies suggested that the position of a protein in the network determines its evolutionary rate but there has been considerable disagreement as to what extent other factors, such as protein abundance, modify this reported dependence.

Results

We compare the genomes of Saccharomyces cerevisiae and Caenorhabditis elegans with those of closely related species to elucidate the recent evolutionary history of their respective protein interaction networks. Interaction and expression data are studied in the light of a detailed phylogenetic analysis. The underlying network structure is incorporated explicitly into the statistical analysis. The increased phylogenetic resolution, paired with high-quality interaction data, allows us to resolve the way in which protein interaction network structure and abundance of proteins affect the evolutionary rate. We find that expression levels are better predictors of the evolutionary rate than a protein's connectivity. Detailed analysis of the two organisms also shows that the evolutionary rates of interacting proteins are not sufficiently similar to be mutually predictive.

Conclusion

It appears that meaningful inferences about the evolution of protein interaction networks require comparative analysis of reasonably closely related species. The signature of protein evolution is shaped by a protein's abundance in the organism and its function and the biological process it is involved in. Its position in the interaction networks and its connectivity may modulate this but they appear to have only minor influence on a protein's evolutionary rate.

RNA replication kinetics, genetic polymorphism and selection in the case of the hepatitis C virus

We show in a simple theoretical quasispecies model that the replication dynamics of hepatitis C virus and a related model-system, the bovine viral diarrhoea virus, result in an effective reduction of RNA templates in infected cells. Viral fitness does not translate directly into RNA sequence replication efficiency, and hence the abundance of the viral master sequences diminishes over time. Our results suggest that genes not involved in RNA replication accumulate mutations over time because they do not undergo selection during this phase. The selection of viral RNA occurs not only during replication but also during the ensuing stages of the viral life cycle: (i) envelopment of viral RNA and (ii) successful infection of other cells, which also requires functionality of non-replicative genes. In particular, viral fitness requires the ability of the genome to encode structural proteins which do not encounter selective pressure during RNA replication. We conclude by discussing the potential value of antiviral drugs which inhibit selection on parts of the viral genome.

Genealogical and evolutionary inference with the human Y chromosome

Population genetics has emerged as a powerful tool for unraveling human history. In addition to the study of mitochondrial and autosomal DNA, attention has recently focused on Y-chromosome variation. Ambiguities and inaccuracies in data analysis, however, pose an important obstacle to further development of the field. Here we review the methods available for genealogical inference using Y-chromosome data. Approaches can be divided into those that do and those that do not use an explicit population model in genealogical inference. We describe the strengths and weaknesses of these model-based and model-free approaches, as well as difficulties associated with the mutation process that affect both methods. In the case of genealogical inference using microsatellite loci, we use coalescent simulations to show that relatively simple generalizations of the mutation process can greatly increase the accuracy of genealogical inference. Because model-free and model-based approaches have different biases and limitations, we conclude that there is considerable benefit in the continued use of both types of approaches.

Ecology. Species-area relations in tropical forests

A power law called the species-area relationship describes the finding that the number of species is proportional to the size of the area in which they are found, raised to an exponent (usually, a number between 0.2 and 0.3). In their Perspective, May and Stumpf discuss new results from a survey of five tropical forest census areas containing a total of a million trees. They explain how this large data set can be used to fine-tune the existing power law so that it provides a better prediction of species diversity in small census samples.

Mapping the parameters of prion-induced neuropathology

We present a theoretical framework that enables us to dissect out the parametric dependencies of the pathogenesis of prion diseases. We are able to determine the influence of both host-dependent factors (connectivity, cell density, protein synthesis rate, and cell death) and strain-dependent factors (cell tropism, virulence, and replication rate). We use a model based on a linked system of differential equations on a lattice to explore how the regional distribution of central nervous system pathology in Creutzfeldt-Jakob disease, Gerstmann-Sträussler-Scheinker syndrome, and fatal familial insomnia relates to each of these factors. The model then is used to make qualitative predictions about the pathology for two possible hypothetical triggers of neuronal loss in prion diseases. Pathological progression in overexpressing mouse models has been shown to depend on the site of initial infection. The model allows us to compare the pathologies resulting from different inoculation routes.