Use of pleiotropy to model genetic interactions in a population

Systems-level genetic studies in humans and model systems increasingly involve both high-resolution genotyping and multi-dimensional quantitative phenotyping. We present a novel method to infer and interpret genetic interactions that exploits the complementary information in multiple phenotypes. We applied this approach to a population of yeast strains with randomly assorted perturbations of five genes involved in mating. We quantified pheromone response at the molecular level and overall mating efficiency. These phenotypes were jointly analyzed to derive a network of genetic interactions that mapped mating-pathway relationships. To determine the distinct biological processes driving the phenotypic complementarity, we analyzed patterns of gene expression to find that the pheromone response phenotype is specific to cellular fusion, whereas mating efficiency was a combined measure of cellular fusion, cell cycle arrest, and modifications in cellular metabolism. We applied our novel method to global gene expression patterns to derive an expression-specific interaction network and demonstrate applicability to global transcript data. Our approach provides a basis for interpretation of genetic interactions and the generation of specific hypotheses from populations assayed for multiple phenotypes.

Parallel advances in genotype and phenotype measurement technologies are yielding large-scale, multidimensional datasets that can potentially decipher the genetic etiology of complex traits. Understanding these data will require methods that combine the experimental power of molecular biology and the quantitative power of statistical genetics. In this work, we describe a novel approach that uses the complementary information encoded by multiple phenotypes in conjunction with genetic data to map genetic interaction networks in terms of quantitative variant-to-variant and variant-to-phenotype influences. We tested this method using a population of yeast strains with random combinations of five genetic mutations and derived an interaction network using molecular and colony-level assays of mating phenotypes. Distinct biological processes that underlie the two phenotypes were identified with gene expression analysis, validating the method's ability to exploit complementary biological information in multiple phenotypes. Our method generates data-driven models and testable hypotheses of how the genetic variation in a population combines to affect complex traits. It is designed to be flexible and scalable for application to populations with extensive genetic diversity.

Correction: quantifying and analyzing the network basis of genetic complexity

[This corrects the article on p. e1002583 in vol. 8.].

Quantifying and analyzing the network basis of genetic complexity

Genotype-to-phenotype maps exhibit complexity. This genetic complexity is mentioned frequently in the literature, but a consistent and quantitative definition is lacking. Here, we derive such a definition and investigate its consequences for model genetic systems. The definition equates genetic complexity with a surplus of genotypic diversity over phenotypic diversity. Applying this definition to ensembles of Boolean network models, we found that the in-degree distribution and the number of periodic attractors produced determine the relative complexity of different topology classes. We found evidence that networks that are difficult to control, or that exhibit a hierarchical structure, are genetically complex. We analyzed the complexity of the cell cycle network of Sacchoromyces cerevisiae and pinpointed genes and interactions that are most important for its high genetic complexity. The rigorous definition of genetic complexity is a tool for unraveling the structure and properties of genotype-to-phenotype maps by enabling the quantitative comparison of the relative complexities of different genetic systems. The definition also allows the identification of specific network elements and subnetworks that have the greatest effects on genetic complexity. Moreover, it suggests ways to engineer biological systems with desired genetic properties.

Predicting the effects of copy-number variation in double and triple mutant combinations

The study of genetic interactions is a powerful tool in inferring structure and function of biological networks. To date, genetic interaction studies have been dominated by pair-wise gene deletion screens. However, classical genetic analysis and natural genetic variation involve diverse gene forms ranging from null alleles to copy number variants. Moreover, genetic variation is typically multifactorial. Addressing multiple combinatorial genetic variations ranging in gene activity is therefore of critical value. We approach this problem using genetic network modeling that quantitatively encodes how genes influence the activity of one another and phenotype outcomes. A network model was initially inferred from linear decomposition of gene expression data. We used this network to predict the effects of combining multi-copy and deletion mutations of specific gene pairs and a gene triplet. Predicted expression patterns across hundreds of genes were experimentally validated. Prediction success was critically dependent on how a multi-copy gene interacted with other genes in the network model. This strategy provides a template for the inference, prediction, and testing of genetically complex hypotheses involving diverse genetic variation.

High-throughput tracking of single yeast cells in a microfluidic imaging matrix

Time-lapse live cell imaging is a powerful tool for studying signaling network dynamics and complexity and is uniquely suited to single cell studies of response dynamics, noise, and heritable differences. Although conventional imaging formats have the temporal and spatial resolution needed for such studies, they do not provide the simultaneous advantages of cell tracking, experimental throughput, and precise chemical control. This is particularly problematic for system-level studies using non-adherent model organisms such as yeast, where the motion of cells complicates tracking and where large-scale analysis under a variety of genetic and chemical perturbations is desired. We present here a high-throughput microfluidic imaging system capable of tracking single cells over multiple generations in 128 simultaneous experiments with programmable and precise chemical control. High-resolution imaging and robust cell tracking are achieved through immobilization of yeast cells using a combination of mechanical clamping and polymerization in an agarose gel. The channel and valve architecture of our device allows for the formation of a matrix of 128 integrated agarose gel pads, each allowing for an independent imaging experiment with fully programmable medium exchange via diffusion. We demonstrate our system in the combinatorial and quantitative analysis of the yeast pheromone signaling response across 8 genotypes and 16 conditions, and show that lineage-dependent effects contribute to observed variability at stimulation conditions near the critical threshold for cellular decision making.

A systems-biology approach to modular genetic complexity

Multiple high-throughput genetic interaction studies have provided substantial evidence of modularity in genetic interaction networks. However, the correspondence between these network modules and specific pathways of information flow is often ambiguous. Genetic interaction and molecular interaction analyses have not generated large-scale maps comprising multiple clearly delineated linear pathways. We seek to clarify the situation by discerning the difference between genetic modules and classical pathways. We review a method to optimize the discovery of biologically meaningful genetic modules based on a previously described context-dependent information measure to obtain maximally informative networks. We compare the results of this method with the established measures of network clustering and find that it balances global and local clustering information in networks. We further discuss the consequences for genetic interaction networks and propose a framework for the analysis of genetic modularity.

Maximal extraction of biological information from genetic interaction data

Extraction of all the biological information inherent in large-scale genetic interaction datasets remains a significant challenge for systems biology. The core problem is essentially that of classification of the relationships among phenotypes of mutant strains into biologically informative "rules" of gene interaction. Geneticists have determined such classifications based on insights from biological examples, but it is not clear that there is a systematic, unsupervised way to extract this information. In this paper we describe such a method that depends on maximizing a previously described context-dependent information measure to obtain maximally informative biological networks. We have successfully validated this method on two examples from yeast by demonstrating that more biological information is obtained when analysis is guided by this information measure. The context-dependent information measure is a function only of phenotype data and a set of interaction rules, involving no prior biological knowledge. Analysis of the resulting networks reveals that the most biologically informative networks are those with the greatest context-dependent information scores. We propose that these high-complexity networks reveal genetic architecture at a modular level, in contrast to classical genetic interaction rules that order genes in pathways. We suggest that our analysis represents a powerful, data-driven, and general approach to genetic interaction analysis, with particular potential in the study of mammalian systems in which interactions are complex and gene annotation data are sparse.

Network motif analysis of a multi-mode genetic-interaction network

Statistical and computational methods for the extraction of biological information from dense multi-mode genetic-interaction networks were developed and implemented in open-source software.

Different modes of genetic interaction indicate different functional relationships between genes. The extraction of biological information from dense multi-mode genetic-interaction networks demands appropriate statistical and computational methods. We developed such methods and implemented them in open-source software. Motifs extracted from multi-mode genetic-interaction networks form functional subnetworks, highlight genes dominating these subnetworks, and reveal genetic reflections of the underlying biochemical system.

Prediction of phenotype and gene expression for combinations of mutations

Molecular interactions provide paths for information flows. Genetic interactions reveal active information flows and reflect their functional consequences. We integrated these complementary data types to model the transcription network controlling cell differentiation in yeast. Genetic interactions were inferred from linear decomposition of gene expression data and were used to direct the construction of a molecular interaction network mediating these genetic effects. This network included both known and novel regulatory influences, and predicted genetic interactions. For corresponding combinations of mutations, the network model predicted quantitative gene expression profiles and precise phenotypic effects. Multiple predictions were tested and verified.

Control of signaling in a MAP-kinase pathway by an RNA-binding protein

Signaling-protein mRNAs tend to have long untranslated regions (UTRs) containing binding sites for RNA-binding proteins regulating gene expression. Here we show that a PUF-family RNA-binding protein, Mpt5, represses the yeast MAP-kinase pathway controlling differentiation to the filamentous form. Mpt5 represses the protein levels of two pathway components, the Ste7 MAP-kinase kinase and the Tec1 transcriptional activator, and negatively regulates the kinase activity of the Kss1 MAP kinase. Moreover, Mpt5 specifically inhibits the output of the pathway in the absence of stimuli, and thereby prevents inappropriate cell differentiation. The results provide an example of what may be a genome-scale level of regulation at the interface of signaling networks and protein-RNA binding networks.

Antisense transcription controls cell fate in Saccharomyces cerevisiae

Entry into meiosis is a key developmental decision. We show here that meiotic entry in Saccharomyces cerevisiae is controlled by antisense-mediated regulation of IME4, a gene required for initiating meiosis. In MAT a/alpha diploids the antisense IME4 transcript is repressed by binding of the a1/alpha2 heterodimer at a conserved site located downstream of the IME4 coding sequence. MAT a/alpha diploids that produce IME4 antisense transcript have diminished sense transcription and fail to initiate meiosis. Haploids that produce the sense transcript have diminished antisense transcription and manifest several diploid phenotypes. Our data are consistent with transcription interference as a regulatory mechanism at the IME4 locus that determines cell fate.

SBEAMS-Microarray: database software supporting genomic expression analyses for systems biology

Background

The biological information in genomic expression data can be understood, and computationally extracted, in the context of systems of interacting molecules. The automation of this information extraction requires high throughput management and analysis of genomic expression data, and integration of these data with other data types.

Results

SBEAMS-Microarray, a module of the open-source Systems Biology Experiment Analysis Management System (SBEAMS), enables MIAME-compliant storage, management, analysis, and integration of high-throughput genomic expression data. It is interoperable with the Cytoscape network integration, visualization, analysis, and modeling software platform.

Conclusion

SBEAMS-Microarray provides end-to-end support for genomic expression analyses for network-based systems biology research.

Disentangling information flow in the Ras-cAMP signaling network

The perturbation of signal-transduction molecules elicits genomic-expression effects that are typically neither restricted to a small set of genes nor uniform. Instead there are broad, varied, and complex changes in expression across the genome. These observations suggest that signal transduction is not mediated by isolated pathways of information flow to distinct groups of genes in the genome. Rather, multiple entangled paths of information flow influence overlapping sets of genes. Using the Ras-cAMP pathway in Saccharomyces cerevisiae as a model system, we perturbed key pathway elements and collected genomic-expression data. Singular value decomposition was applied to separate the genome-wide transcriptional response into weighted expression components exhibited by overlapping groups of genes. Molecular interaction data were integrated to connect gene groups to perturbed signaling elements. The resulting series of linked subnetworks maps multiple putative pathways of information flow through a dense signaling network, and provides a set of testable hypotheses for complex gene-expression effects across the genome.

Tools enabling the elucidation of molecular pathways active in human disease: application to Hepatitis C virus infection

BACKGROUND

The extraction of biological knowledge from genome-scale data sets requires its analysis in the context of additional biological information. The importance of integrating experimental data sets with molecular interaction networks has been recognized and applied to the study of model organisms, but its systematic application to the study of human disease has lagged behind due to the lack of tools for performing such integration.

RESULTS

We have developed techniques and software tools for simplifying and streamlining the process of integration of diverse experimental data types in molecular networks, as well as for the analysis of these networks. We applied these techniques to extract, from genomic expression data from Hepatitis C virus-infected liver tissue, potentially useful hypotheses related to the onset of this disease. Our integration of the expression data with large-scale molecular interaction networks and subsequent analyses identified molecular pathways that appear to be induced or repressed in the response to Hepatitis C viral infection.

CONCLUSION

The methods and tools we have implemented allow for the efficient dynamic integration and analysis of diverse data in a major human disease system. This integrated data set in turn enabled simple analyses to yield hypotheses related to the response to Hepatitis C viral infection.

Derivation of genetic interaction networks from quantitative phenotype data

Genetic interaction networks were derived from quantitative phenotype data by analyzing agar-invasion phenotypes of mutant yeast strains, which showed specific modes of genetic interaction with specific biological processes.

We have generalized the derivation of genetic-interaction networks from quantitative phenotype data. Familiar and unfamiliar modes of genetic interaction were identified and defined. A network was derived from agar-invasion phenotypes of mutant yeast. Mutations showed specific modes of genetic interaction with specific biological processes. Mutations formed cliques of significant mutual information in their large-scale patterns of genetic interaction. These local and global interaction patterns reflect the effects of gene perturbations on biological processes and pathways.

Defects arising from whole-genome duplications in Saccharomyces cerevisiae

Comparisons among closely related species have led to the proposal that the duplications found in many extant genomes are the remnants of an ancient polyploidization event, rather than a result of successive duplications of individual chromosomal segments. If this interpretation is correct, it would support Ohno's proposal that polyploidization drives evolution by generating the genetic material necessary for the creation of new genes. Paradoxically, analysis of contemporary polyploids suggests that increased ploidy is an inherently unstable state. To shed light on this apparent contradiction and to determine the effects of nascent duplications of the entire genome, we generated isogenic polyploid strains of the budding yeast Saccharomyces cerevisiae. Our data show that an increase in ploidy results in a marked decrease in a cell's ability to survive during stationary phase in growth medium. Tetraploid cells die rapidly, whereas isogenic haploids remain viable for weeks. Unlike haploid cells, which arrest growth as unbudded cells, tetraploid cells continue to bud and form mitotic spindles in stationary phase. The stationary-phase death of tetraploids can be prevented by mutations or conditions that result in growth arrest. These data show that whole-genome duplications are accompanied by defects that affect viability and subsequent survival of the new organism.

MOLECULAR NETWORKS IN MODEL SYSTEMS

Model organisms, especially the budding yeast, are leading systems in the transformation of biology into an information science. With the availability of genome sequences and genome-scale data generation technologies, the extraction of biological insight from complex integrated molecular networks has become a major area of research. Here I examine key concepts and review research developments. I propose specific areas of research effort to drive network analysis in directions that will promote modeling with increasing predictive power.

System-based proteomic analysis of the interferon response in human liver cells

BACKGROUND

Interferons (IFNs) play a critical role in the host antiviral defense and are an essential component of current therapies against hepatitis C virus (HCV), a major cause of liver disease worldwide. To examine liver-specific responses to IFN and begin to elucidate the mechanisms of IFN inhibition of virus replication, we performed a global quantitative proteomic analysis in a human hepatoma cell line (Huh7) in the presence and absence of IFN treatment using the isotope-coded affinity tag (ICAT) method and tandem mass spectrometry (MS/MS).

RESULTS

In three subcellular fractions from the Huh7 cells treated with IFN (400 IU/ml, 16 h) or mock-treated, we identified more than 1,364 proteins at a threshold that corresponds to less than 5% false-positive error rate. Among these, 54 were induced by IFN and 24 were repressed by more than two-fold, respectively. These IFN-regulated proteins represented multiple cellular functions including antiviral defense, immune response, cell metabolism, signal transduction, cell growth and cellular organization. To analyze this proteomics dataset, we utilized several systems-biology data-mining tools, including Gene Ontology via the GoMiner program and the Cytoscape bioinformatics platform.

CONCLUSIONS

Integration of the quantitative proteomics with global protein interaction data using the Cytoscape platform led to the identification of several novel and liver-specific key regulatory components of the IFN response, which may be important in regulating the interplay between HCV, interferon and the host response to virus infection.

Control of yeast filamentous-form growth by modules in an integrated molecular network

On solid growth media with limiting nitrogen source, diploid budding-yeast cells differentiate from the yeast form to a filamentous, adhesive, and invasive form. Genomic profiles of mRNA levels in Saccharomyces cerevisiae yeast-form and filamentous-form cells were compared. Disparate data types, including genes implicated by expression change, filamentation genes known previously through a phenotype, protein-protein interaction data, and protein-metabolite interaction data were integrated as the nodes and edges of a filamentation-network graph. Application of a network-clustering method revealed 47 clusters in the data. The correspondence of the clusters to modules is supported by significant coordinated expression change among cluster co-member genes, and the quantitative identification of collective functions controlling cell properties. The modular abstraction of the filamentation network enables the association of filamentous-form cell properties with the activation or repression of specific biological processes, and suggests hypotheses. A module-derived hypothesis was tested. It was found that the 26S proteasome regulates filamentous-form growth.

Identification of androgen-coregulated protein networks from the microsomes of human prostate cancer cells

A large-scale proteomic approach has been used to define cellular processes that are responsive to androgen treatment in LNCaP prostate cancer cells. The results provide evidence for the role of androgenic hormones in coordinating the expression of critical components involved in distinct cellular processes

Background

Androgens play a critical role in the development of prostate cancer-dysregulation of androgen-regulated growth pathways can led to hormone-refractory prostate cancer. A comprehensive understanding of androgen-regulated cellular processes has not been achieved to date. To this end, we have applied a large-scale proteomic approach to define cellular processes that are responsive to androgen treatment in LNCaP prostate cancer cells.

Results

Using isotope-coded affinity tags and mass spectrometry we identified and quantified the relative abundance levels of 1,064 proteins and found that distinct cellular processes were coregulated by androgen while others were essentially unaffected. Subsequent pharmacological perturbation of the cellular process for energy generation confirmed that androgen starvation had a profound effect on this pathway.

Conclusions

Our results provide evidence for the role of androgenic hormones in coordinating the expression of critical components involved in distinct cellular processes and further establish a foundation for the comprehensive reconstruction of androgen-regulated protein networks and pathways in prostate cancer cells.

Inventories to insights

"In the long course of cell life on this earth it remained, for our age, for our generation, to receive the full ownership of our inheritance. We have entered the cell, the Mansion of our birth and started the inventory of our acquired wealth." (Albert Claude, Nobel lecture, 1974).

Modular organization of cellular networks

We investigated the organization of interacting proteins and protein complexes into networks of modules. A network-clustering method was developed to identify modules. This method of network-structure determination was validated by clustering known signaling-protein modules and by identifying module rudiments in exclusively high-throughput protein-interaction data with high error frequencies and low coverage. The signaling network controlling the yeast developmental transition to a filamentous form was clustered. Abstraction of a modular network-structure model identified module-organizer proteins and module-connector proteins. The functions of these proteins suggest that they are important for module function and intermodule communication.

Transcriptome profiling to identify genes involved in peroxisome assembly and function

Yeast cells were induced to proliferate peroxisomes, and microarray transcriptional profiling was used to identify PEX genes encoding peroxins involved in peroxisome assembly and genes involved in peroxisome function. Clustering algorithms identified 224 genes with expression profiles similar to those of genes encoding peroxisomal proteins and genes involved in peroxisome biogenesis. Several previously uncharacterized genes were identified, two of which, YPL112c and YOR084w, encode proteins of the peroxisomal membrane and matrix, respectively. Ypl112p, renamed Pex25p, is a novel peroxin required for the regulation of peroxisome size and maintenance. These studies demonstrate the utility of comparative gene profiling as an alternative to functional assays to identify genes with roles in peroxisome biogenesis.

A new approach to decoding life: systems biology

Systems biology studies biological systems by systematically perturbing them (biologically, genetically, or chemically); monitoring the gene, protein, and informational pathway responses; integrating these data; and ultimately, formulating mathematical models that describe the structure of the system and its response to individual perturbations. The emergence of systems biology is described, as are several examples of specific systems approaches.

Effectors of a developmental mitogen-activated protein kinase cascade revealed by expression signatures of signaling mutants

Despite the importance of mitogen-activated protein kinase (MAPK) signaling in eukaryotic biology, the mechanisms by which signaling yields phenotypic changes are poorly understood. We have combined transcriptional profiling with genetics to determine how the Kss1 MAPK signaling pathway controls dimorphic development in Saccharomyces cerevisiae. This analysis identified dozens of transcripts that are regulated by the pathway, whereas previous work had identified only a single downstream target, FLO11. One of the MAPK-regulated genes is PGU1, which encodes a secreted enzyme that hydrolyzes polygalacturonic acid, a structural barrier to microbial invasion present in the natural plant substrate of S. cerevisiae. A third key transcriptional target is the G(1) cyclin gene CLN1, a morphogenetic regulator that we show to be essential for pseudohyphal growth. In contrast, the homologous CLN2 cyclin gene is dispensable for development. Thus, the Kss1 MAPK cascade programs development by coordinately modulating a cell adhesion factor, a secreted host-destroying activity, and a specialized subunit of the Cdc28 cyclin-dependent kinase.

Ploidy regulation of gene expression

Microarray-based gene expression analysis identified genes showing ploidy-dependent expression in isogenic Saccharomyces cerevisiae strains that varied in ploidy from haploid to tetraploid. These genes were induced or repressed in proportion to the number of chromosome sets, regardless of the mating type. Ploidy-dependent repression of some G1 cyclins can explain the greater cell size associated with higher ploidies, and suggests ploidy-dependent modifications of cell cycle progression. Moreover, ploidy regulation of the FLO11 gene had direct consequences for yeast development.

Pathways for homologous recombination between chromosomal direct repeats in Salmonella typhimurium

Homologous recombination pathways probably evolved primarily to accomplish chromosomal repair and the formation of and resolution of duplications by sister-chromosome exchanges. Various DNA lesions initiate these events. Classical recombination assays, involving bacterial sex, focus attention on double-strand ends of DNA. Sexual exchanges, initiated at these ends, depend on the RecBCD pathway. In the absence of RecBCD function, mutation of the sbcB and sbcC genes activates the apparently cryptic RecF pathway. To provide a more general view of recombination, we describe an assay in which endogenous DNA damage initiates recombination between chromosomal direct repeats. The repeats flank markers conferring lactose utilization (Lac+) and ampicillin resistance (ApR); recombination generates Lac-ApS segregants. In this assay, the RecF pathway is not cryptic; it plays a major role without sbcBC mutations. Others have proposed that single-strand gaps are the natural substrate for RecF-dependent recombination. Supporting this view, recombination stimulated by a double-strand break (DSB) in a chromosomal repeat depended on RecB function, not RecF function. Without RecBCD function, sbcBC mutations modified the RecF pathway and allowed it to catalyze DSB-stimulated recombination. Sexual recombination assays overestimate the importance of RecBCD and DSBs, and underestimate the importance of the RecF pathway.

A search for a general phenomenon of adaptive mutability

The most prominent systems for the study of adaptive mutability depend on the specialized activities of genetic elements like bacteriophage Mu and the F plasmid. Searching for general adaptive mutability, we have investigated the behavior of Salmonella typhimurium strains with chromosomal lacZ mutations. We have studied 30 revertible nonsense, missense, frameshift, and insertion alleles. One-third of the mutants produced > or = 10 late revertant colonies (appearing three to seven days after plating on selective medium). For the prolific mutants, the number of late revertants showed rank correlation with the residual beta-galactosidase activity; for the same mutants, revertant number showed no correlation with the nonselective reversion rate (from fluctuation tests). Leaky mutants, which grew slowly on selective medium, produced late revertants whereas tight nongrowing mutants generally did not produce late revertants. However, the number of late revertants was not proportional to residual growth. Using total residual growth and the nonselective reversion rate, the expected number of late revertants was calculated. For several leaky mutants, the observed revertant number exceeded the expected number. We suggest that excess late revertants from these mutants arise from general adaptive mutability available to any chromosomal gene.

Evidence that F plasmid transfer replication underlies apparent adaptive mutation

An Escherichia coli K12 strain, FC40, has been used extensively in the analysis of adaptive mutability. This strain carries a revertible mutant lac allele on an F plasmid and accumulates Lac+ (lactose utilizing) revertants, but not unselected mutants, when placed on selective medium. These adaptive mutations are a subset of spontaneous types and their formation depends on the RecABC functions. Data presented here suggest that this phenomenon depends on transfer functions of the F factor. Fertility inhibition eliminates RecA-dependent adaptive reversion. Thus, "adaptive" revertants may form during replication from the transfer origin, whereas loci in the nonreplicating chromosome show little mutation.

Transformation of mouse cells by wild-type mouse c-Src

Previous studies in which chicken and human c-Src were overexpressed in chicken and rodent cells have indicated that overexpression of wild-type c-Src can not induce complete neoplastic transformation. However, studies with v-Src mutants have demonstrated that species-specific differences can play a significant role in transforming activity. Here we show that, in contrast to chicken c-Src, overexpressed mouse c-Src can induce significant anchorage-independent growth and tumorigenicity when transfected into NIH3T3 mouse cells. The biochemical cause for this difference is unknown. In particular, the protein-tyrosine kinase activities of chicken and mouse c-Src appear to be similar. This result is consistent with the hypothesis that v-Src-induced transformation results from perturbation of signalling pathways modulated by c-Src and highlights the need for caution in controlling for potential species-specific differences in studies of c-Src function.