Skip to main content

The dawn of a new era in cell signalling research

Dramatic changes in our thinking of how cells organise and utilise their signal transduction networks are currently arising. These changes have by and large not yet reached the majority of the scientific community and university teaching. Even in the latest editions of top cell biology books the cell signalling machinery is typically depicted as an assembly of fairly unorganised protein molecules, for example diffusing more or less freely in the cytosol. According to current textbook wisdom, upon activation of a signalling pathway its components stochastically meet to generate transient assemblies in the form of signalling 'cascades' or protein complexes with up to 10 or so components. These in turn appear to be linked together into a giant 'floating signalling network' of several thousand proteins which nobody really understands.

We are now beginning to appreciate that this image is far from the truth. It is in fact hindering us in designing more appropriate experiments to understand cell signalling in general and the role of specific components in particular.

Similarly, attempts to describe cellular signalling events with mathematical equations that are based on solution phase diffusion chemistry by self-declared 'systems biologists' [1] are commonly doomed to failure.

A number of recent publications [2–6] and conferences (e.g. the 2009 Seefeld Meeting of the Protein Modules Consortium; http://www.proteinmodules.org/) provide some insight into how we can advance our research field in the future. To give but a few examples:

We must take into serious consideration that signalling mostly occurs in protein assemblies that may be highly organised but are at least specifically localised to distinct, functionally defined subcellular compartments. These complexes are often of considerable size and probably contain vast numbers of components in some cases.

We must take into account that many of the utilised proteins are being produced (translated) in restricted subcellular locations and that they may not diffuse much before they meet most of their interaction partners.

We need to investigate more carefully in which cases signalling enzyme - substrate interactions are primarily driven by highly specific recognition motifs and in which by close proximity of the interacting components [or by a combination of both].

Some signalling proteins appear to be quite scarce, with only a few molecules present per cell, while others can be found in several distinct pools with many thousand copies in each pool. Local signal transduction component ratios within distinct cellular sites therefore deserve much more detailed investigation than is currently undertaken. In this context it should be pointed out that many standard over-expression experiments are rather likely to produce substantial artefacts: the resulting inappropriate amounts and localisations of signalling components will often lead to signal spill-over and/or disrupt the functionality of native complexes.

The distinct signals elicited by different concentrations within the sometimes 3-log-wide physiological concentration range of cytokines and other regulatory factors remains largely unexplored. Too many signalling experiments still rely for convenience on 'super-natural' concentrations of stimuli.

Our knowledge on the temporal features of many signalling events, including oscillations of signals in subcellular compartments of individual cells and waves of signals migrating through tissues is still minimal, especially when physiological concentrations of stimuli are being considered.

We need to explore whether multi-component signalling 'machines' like transmembrane-receptor kinases are indeed pleiomorphic entities [3] which generate 'fuzzy signals' or whether we simply do not understand yet their more sophisticated ways of generating reasonably discrete signalling outputs.

Molecular highways for the directional transport of signalling proteins, as well as meeting points for some molecular signalling components that have not yet been integrated into their destination complexes, are only barely known.

We still have a very limited understanding of how the rather substantial unstructured portion of the proteome, which is often linked to disease development, i.e. not a 'dirty dozen' but the 'dirty thousand', contribute to the organisation and function of cell signalling networks and pathologies [7–10].

I firmly believe that the best is yet to come for the field of biological signal transduction research, but despite impressive technical advances, e.g. HTP-proteomics, -transcriptomics and -genomics, one must still tread carefully when devising conceptual frameworks in which these vast amounts of data are funnelled. If we get these wrong, all those HTP-data will not amount to much in terms of generating robust and realistic information. Moreover, we will require not only fundamental shifts in our currently prevailing concepts but also an arsenal of novel tools in bioinformatics and the 'wet lab'.

Equally important, changing our ways of investigating cellular signalling components, pathways and networks will require abandoning some of the much used current methods, though they may have served us seemingly well in the past. The already mentioned over-expression studies are one example.

On the ultrastructural level, protein crystallography studies will need to be combined more often with analyses by NMR and other solution phase methods to prevent misconceptions from arising. Global folding effects and site-specific long-range effects of introduced point mutations will need to receive more attention. The same is true for the common terminal tagging of proteins, which can lead to mislocalisation or partial unfolding of proteins. In cases where both termini have functional roles, careful tagging in internal loops may be the only option. On the cellular level, monitoring individual cells within a population in real time will become increasingly important.

Eventually, we will even need to think about how different individuals may vary in their signalling components and networks due to their unique genetic compositions. Possibly the most obvious example for this 'personalised' signalling heterogeneity is the tremendous diversity of molecular defects in signalling proteins and networks of human cancers [11–16], but this also applies for some physiological signals transmitted in genetically different individuals.

These are exciting times for young signalling researchers. They will be able to make rapid progress by building on more than three decades of pioneering signal transduction research - if they dare to leave some of the old misconceptions and false dogmas behind. These have often arisen from the 'primitive' tools available at that time and the apparent urge of the human brain to build simple linear models with a small number of components to explain functional relationships.

References

  1. de Chadarevian S: Interview with Sydney Brenner. Stud Hist Philos Biol Biomed Sci. 2009, 40: 65-71. 10.1016/j.shpsc.2008.12.008.

    Article  Google Scholar 

  2. Gibson TJ: Cell regulation: determined to signal discrete cooperation. Trends Biochem Sci. 2009, 34: 471-482. 10.1016/j.tibs.2009.06.007.

    Article  CAS  PubMed  Google Scholar 

  3. Mayer BJ, Blinov ML, Loew LM: Molecular machines or pleiomorphic ensembles: signaling complexes revisited. J Biol. 2009, 8: 81-10.1186/jbiol185.

    Article  PubMed Central  PubMed  Google Scholar 

  4. Holt CE, Bullock SL: Subcellular mRNA localization in animal cells and why it matters. Science. 2009, 326: 1212-1216. 10.1126/science.1176488.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  5. Scott JD, Pawson T: Cell signaling in space and time: where proteins come together and when they're apart. Science. 2009, 326: 1220-1224. 10.1126/science.1175668.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  6. Jørgensen C, Linding R: Simplistic pathways or complex networks?. Curr Opin Genet Dev. 2010, 20: 15-22. 10.1016/j.gde.2009.12.003.

    Article  PubMed  Google Scholar 

  7. Csizmok V, Dosztanyi Z, Simon I, Tompa P: Towards proteomic approaches for the identification of structural disorder. Curr Protein Pept Sci. 2007, 8: 173-179. 10.2174/138920307780363479.

    Article  CAS  PubMed  Google Scholar 

  8. Dunker AK, Silman I, Uversky VN, Sussman JL: Function and structure of inherently disordered proteins. Curr Opin Struct Biol. 2008, 18: 756-764. 10.1016/j.sbi.2008.10.002.

    Article  CAS  PubMed  Google Scholar 

  9. Fuxreiter M, Tompa P, Simon I, Uversky VN, Hansen JC, Asturias FJ: Malleable machines take shape in eukaryotic transcriptional regulation. Nat Chem Biol. 2008, 4: 728-737. 10.1038/nchembio.127.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  10. Midic U, Oldfield CJ, Dunker AK, Obradovic Z, Uversky VN: Unfoldomics of human genetic diseases: illustrative examples of ordered and intrinsically disordered members of the human diseasome. Protein Pept Lett. 2009, 16: 1533-1547. 10.2174/092986609789839377.

    Article  CAS  PubMed  Google Scholar 

  11. Bild AH, Yao G, Chang JT, Wang Q, Potti A, Chasse D, Joshi MB, Harpole D, Lancaster JM, Berchuck A, et al: Oncogenic pathway signatures in human cancers as a guide to targeted therapie. Nature. 2006, 439: 353-357. 10.1038/nature04296.

    Article  CAS  PubMed  Google Scholar 

  12. Bild AH, Potti A, Nevins JR: Linking oncogenic pathways with therapeutic opportunities. Nat Rev Cancer. 2006, 6: 735-741. 10.1038/nrc1976.

    Article  CAS  PubMed  Google Scholar 

  13. Emaduddin M, Bicknell DC, Bodmer WF, Feller SM: Cell growth, global phosphotyrosine elevation, and c-Met phosphorylation through Src family kinases in colorectal cancer cells. Proc Natl Acad Sci USA. 2008, 105: 2358-2362. 10.1073/pnas.0712176105.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  14. Aleksic T, Feller SM: Gamma-secretase inhibition combined with platinum compounds enhances cell death in a large subset of colorectal cancer cells. Cell Commun Signal. 2008, 6: 8-10.1186/1478-811X-6-8.

    Article  PubMed Central  PubMed  Google Scholar 

  15. Chang JT, Carvalho C, Mori S, Bild AH, Gatza ML, Wang Q, Lucas JE, Potti A, Febbo PG, West M, Nevins JR: A genomic strategy to elucidate modules of oncogenic pathway signaling networks. Mol Cell. 2009, 34: 104-114. 10.1016/j.molcel.2009.02.030.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  16. Kress TR, Raabe T, Feller SM: High Erk activity suppresses expression of the cell cycle inhibitor p27Kip1 in colorectal cancer cells. Cell Commun Signal. 8: 1-10.1186/1478-811X-8-1.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stephan M Feller.

Rights and permissions

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article

Feller, S.M. The dawn of a new era in cell signalling research. Cell Commun Signal 8, 7 (2010). https://doi.org/10.1186/1478-811X-8-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/1478-811X-8-7