SBC seminars 2011

The work in my group is focused on the evolution and dynamics of protein interactions and transcriptional regulatory interactions. In these two areas, we analyse large datasets using computational and mathematical methods.

In the first part of my presentation, I will talk about our work on the evolution and assembly of protein complexes. We surveyed three-dimensional structures of protein complexes to identify interface size as a determining principle of both evolutionary and assembly pathways (Levy et al., Nature, 2008), and to quantify conformational change in assembly (Marsh & Teichmann, Structure, 2011). In recent work, we show for the first time, the importance of promiscuous protein interactions in determining a protein’s surface residue composition (Levy et al., in preparation). These new insights have implications for structure prediction and protein engineering.

In the second part of my presentation, I will talk about our work on dissecting the distribution of gene expression levels in mammalian cell populations, revealing two distinct mRNA abundance classes (Hebenstreit et al., Mol Sys Biol, 2011). We provide evidence, including correlation of the two mRNA abundance classes with epigenetic modifications (Hebenstreit et al., Nucleic Acids Res, 2011), supporting the notion that these two classes correspond to functional versus non-functional proteins in cells. These findings now help interpret mRNA and protein abundance data in the form of microarrays, RNA-sequencing and proteomics.

References

Levy, E.D., Boeri-Erba, E., Robinson, C.V. & Teichmann, S.A. (2008) Assembly reflects evolution of protein complexes. Nature, 453, 1262-5.

Marsh, J. & Teichmann, S.A. (2011) Relative solvent accessible surface area predicts protein conformational changes upon binding. Structure, 19(6):859-67.

Hebenstreit, D., Fang, M., Gu, M. Charoensawan, V., van Oudenaarden, A. & Teichmann, S.A. (2011) RNA sequencing reveals two major classes of gene expression levels in metazoan cells. Mol Sys Biol, 7:497.

Hebenstreit, D., Gu, M., Haider, S., Turner, D., Lio, P. & Teichmann S.A. (2011) EpiChIP: gene-by-gene quantification of epigenetic modification levels. Nucleic Acids Res, 39, e27.

A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for ~75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. Extensive and unbiased mapping of the genetic landscape also provides a key for interpretation of chemical-genetic interactions and drug target identification.

See The genetic landscape of a cell. Costanzo M et al. Science. (2010)

Background: Whole genome duplication (WGD) is a special case of gene duplication, observed rarely in animals, whereby all genes duplicate simultaneously through polyploidisation. Two rounds of WGD (2R-WGD) occurred at the base of vertebrates, giving rise to an enormous wave of genetic novelty, but a systematic analysis of functional consequences of this event has not yet been performed.

Results: We show that 2R-WGD affected an overwhelming majority (74%) of signalling genes, in particular developmental pathways involving receptor tyrosine kinases, Wnt and transforming growth factor-b ligands, G protein-coupled receptors and the apoptosis pathway. 2R-retained genes, in contrast to tandem duplicates, were enriched in protein interaction domains and multifunctional signalling modules of Ras and mitogen-activated protein kinase cascades. 2R-WGD had a fundamental impact on the cell-cycle machinery, redefined molecular building blocks of the neuronal synapse, and was formative for vertebrate brains. We investigated 2R-associated nodes in the context of the human signalling network, as well as in an inferred ancestral pre-2R (AP2R) network, and found that hubs (particularly involving negative regulation) were preferentially retained, with high connectivity driving retention. Finally, microarrays and proteomics demonstrated a trend for gradual paralog expression divergence independent of the duplication mechanism, but inferred ancestral expression states suggested preferential subfunctionalisation among 2R-ohnologs (2ROs).

Conclusions: The 2R event left an indelible imprint on vertebrate signalling and the cell cycle. We show that 2R- WGD preferentially retained genes are associated with higher organismal complexity (for example, locomotion, nervous system, morphogenesis), while genes associated with basic cellular functions (for example, translation, replication, splicing, recombination; with the notable exception of cell cycle) tended to be excluded. 2R-WGD set the stage for the emergence of key vertebrate functional novelties (such as complex brains, circulatory system, heart, bone, cartilage, musculature and adipose tissue). A full explanation of the impact of 2R on evolution, function and the flow of information in vertebrate signalling networks is likely to have practical consequences for regenerative medicine, stem cell therapies and cancer treatment.

Advancements in the field of DNA sequencing are changing the scientific horizon and promising an era of increased use of massive parallel sequencing tools. Although platforms are improving at the rate of Moore's Law, thereby reducing the sequencing costs by a factor of two or three each year, we find ourselves at a point in history where individual human genomes are starting to appear. Current parallel, state-of-the-art systems are providing significantly improved throughput over Sanger systems with massive deciphering of genetic sequences shedding light on novel biological functions, phenotypic differences etc. At Science for Life Laboratory, Stockholm that hosts the national center for massive DNA sequencing (SNISS) we have improved the sample preparation for the major next generation sequencing platforms for more efficient and sensitive use of the technology. This lecture will discuss some of these improvements and also demonstrate the use of the platform to perform large scale sequence analysis in several different areas.

Recent experiments have revealed unexpected plasticity and dynamic nature of the human proteome [1]. The paradigm that the time evolution of a biological system can be described by abundance variation of relatively few “regulated” proteins has been shuttered. Instead, understanding is growing that the whole proteome is regulated, and no protein remains unaffected when the system undergoes transition from one state to another.

This finding underlines the importance of systems biology analysis of expression proteomics data. Systems biology shifts the analytical focus from thousands of proteins to hundreds of signaling pathways, thus reducing the number of entities to be analyzed. Application of these methods required the development of novel systems biology tools, such as the pathway search engine (PSE [2-4]). These tools can only be effective when they are quantitative, i.e. predict not only the activated pathway, but also the relative degree of its activation. Introducing the quantitative aspect in systems biology is one of the greatest challenges this field is facing today, since the final goal of pathway analysis, which is the creation of a quantitative predicting model of the biological process under investigation.

Yet pathway analysis is not the only method of complexity reduction. Two more, complementary approaches will be discussed: correlation analysis and mapping on amino acid and elemental compositions.

REFERENCES

[1] Cohen, A. A.; Geva-Zatorsky, N.; E. Eden, M. Frenkel-Morgenstern, I. Issaeva, A. Sigal, R. Milo, C. Cohen-Saidon, Y. Liron, Z. Kam, L. Cohen, T. Danon, N. Perzov, U. Alon, Dynamic Proteomics of Individual Cancer Cells in Response to a Drug, Science, 2008, 322, 1511–1516.

[2] Zubarev, R. A.; Nielsen, M. L.; Savitski, M. M.; Kel-Margoulis, O.; Wingender, E.; Kel, A. Identification of dominant signaling pathways from proteomics expression data, J. Proteomics, 2008, 1, 89-96.

[3] Ståhl, S.; Fung, Y.M.E.; Adams, C. M.; Lengqvist, J.; Mörk, B.; Stenerlöw, B.; Lewensohn, R.; Lehtiö, J.; Zubarev, R. A.; Viktorsson, K. Proteomics and Pathway Analysis Identifies JNK-signaling as Critical for High-LET Radiation-induced Apoptosis in Non-Small Lung Cancer Cells, Mol. Cell Proteomics, 2009, 8, 1117-1129.

[4] Marin-Vicente, C.; Zubarev, R. A. Search engine for proteomics, Fact or Fiction? G.I.T. Lab J, 2009, 11-12, 10-11.