What are the kinetic and thermodynamic rules for RNA dynamics and RNA/protein interactions in vivo?

The genomic explosion over the past two decades has revolutionized biology, revealing cellular pathways, control points, and interconnections that were previously inaccessible and unimaginable. Nevertheless, current genomic approaches are limited, and complementary approaches are urgently needed to achieve our ultimate goal of a predictive quantitative model of cell function, regulation, and disease. For example, current research into the control of RNA processing has focused on the identification of the complete set of RNAs interacting with each protein and conversely the complete set of proteins interacting with each RNA. However, the fulfillment of this genomic goal would not translate into quantitative models of cellular function; we need to know when, where, and how much, and how interactions affect one another, and we need the understanding to predict these interactions. Our overarching goal is to quantitatively predict RNA/protein interactions in the cell—i.e., the amount of each complex present in cells and how these change with cellular conditions and in disease states. Major advances often arise when disciplines and approaches merge to allow new questions to be asked and answered. A key conceptual advance, which we refer to as Quantitative Cellular Biochemistry (QCB), involves carrying out the same types of experiments that biochemists have done in test tubes for nearly a century—but in high-throughput, quantitatively, and in cells, essentially merging the power of biochemistry and genomics. 

More on RNA/Protein Interactions & RNA In Vivo

One of the greatest challenges of modern-day biology is transferring the rich biochemical and structural knowledge from in vitro studies into the context of the cellular milieu. Nowhere is this more critical than for RNA molecules.

Deciphering the regulatory networks of gene expression is a grand challenge in modern biology. Post-transcriptional processes are integral to the control of gene expression, with hundreds of RNA-binding proteins regulating every step from RNA processing, transport, and translation to localization and decay. Thus, RNA-protein associations are a critical component of the overall challenge of understanding gene regulation.

Over a decade ago, Keene put forward the RNA regulon hypothesis, proposing that functionally related mRNAs are regulated as a group by virtue of binding to the same RBP. Shortly after, clear and strong evidence for this model came from our observation that five yeast PUF-family proteins bound large and distinct sets of mRNAs that followed functional and cytotopic themes. Further, knock-out of the Puf3 protein exclusively affected the expression of its mRNA target set, and slowed growth in glycerol, thereby linking the knockout phenotype to Puf3’s target set of nuclear-encoded mitochondrial mRNAs.

Since Keene’s proposal, our lab and others have provided information on in vivo RNA-protein interactions for hundreds of proteins from yeast to humans. Each RNA interacts with multiple RNA binding proteins, and, at least in some cases, we can trace evolutionary changes in these proteins and their RNA targets, indicating important roles of RNA/protein interactions in the rewiring of the gene expression program through evolution.

We are now advancing to the next challenges in this field, as descriptions that provide a predictive understanding of gene expression will require thermodynamic and dynamic models. Thus, we are using high-throughput approaches developed by the Greenleaf lab (Stanford Genetics) to obtain the foundational thermodynamic and kinetic measurements that are needed to build such robust models.

Most generally, thermodynamic values universally affect occupancies in vivo and are modulated by additional cellular features, including the amount of available protein, RNA structure, competing and collaborating proteins, co-localization versus compartmentalization. We will leverage the thermodynamic information we are obtaining to identify what cellular features alter RNA properties and RNA/protein interactions. While in its infancy, this work will provide fundamental information about molecular interactions within the cell. Ultimately this work will lead to deeper understanding of cellular RNA behavior and gene expression and will lead to an ability to predict cellular interactions and regulation, and thereby better understand and treat disease and better engineer biological systems.

Some leading papers from the lab:
1. Ganser, L.R., Kelly, M.L., Herschlag, D., Al-Hashimi, H.M. (2019) Nat. Rev. Mol. Cell Biol. 20, 474-489. The Roles of Structural Dynamics in the Cellular Functions of RNAs. PMID: 31182864. (Medline) (PDF File) (Supporting Info)
2. Jarmoskaite, I., Denny, S.K., Vaidyanathan, P.P., Becker, W.R., Andreasson, J.O.L., Layton, C.J., Kappel, K., Shivashankar, V., Sreenivasan, R., Das, R., Greenleaf, W.J., Herschlag, D. (2019) Molec. Cell 74, 966-981. A Quantitative and Predictive Model for RNA Binding by Human Pumilio Proteins. PMCID: PMC6645366. (Medline) (bioRxiv) (PDF File) (Supporting Info)
3. Vaidyanathan, P.P., AlSadhan, I., Merriman, D.K., Al-Hashimi, H.M., Herschlag, D. (2017) RNA 23, 611-618. Pseudoridine and N-6 Methyladenosine Modifications Weaken PUF Protein/RNA Interactions. PMCID: PMC5393172. (Medline) (PDF File) (Supporting Info)
4. Bisaria, N., Jarmoskaite, I., Herschlag, D. (2017) Cell Syst. 4, 21-29. Lessons from Enzyme Kinetics Reveal Specificity Principles for RNA-Guided Nucleases in RNA Interference and CRISPR-Based Genome Editing. PMCID: PMC5308874. (Medline) (PDF File)
5. Hogan, G., Brown, P., Herschlag, D. (2015) PLoS Biol. 11, e1002307. Evolutionary Conservation and Diversification of Puf RNA Binding Proteins and their mRNA Targets. PMICD: PMC4654594. (Medline) (PDF File) (Supporting Info)
6. Hogan, D., Riordan, D., Gerber, A., Herschlag, D., Brown, P.O. (2008) PLoS Biology 6, 2297-2313. Diverse RNA-Binding Proteins Interact with Functionally Related Sets of RNAs, Suggesting an Extensive Regulatory System. PMCID: PMC2573929. (Medline) (PDF File) 
7. Gerber, A.P., Herschlag, D., Brown, P.O. (2004) PLoS Biology 2, 342-354. Extensive Association of Functionality and Cytotopically Related mRNAs with Puf-family RNA-binding Proteins in Yeast. PMCID: PMC368173. (Medline) (PDF File) (Supporting Info)
8. Arava, Y., Wang, Y., Storey, J.D., Liu, C.L., Brown, P., Herschlag, D. (2003) Proc. Natl. Acad. Sci. U.S.A. 100, 3889-3894. Genome-wide Analysis of mRNA Translation Profiles in Saccharmyces cerevisiae. PMCID: PMC153018. (Medline) (PDF File) 
9. Wang, Y., Liu, C.L., Storey, J.D., Tibshirani, R.J., Herschlag, D., Brown, P.O. (2002) Proc. Natl. Acad. Sci. U.S.A. 99, 5860-5865. Precision and Functional Specificity in mRNA Decay. PMCID: PMC122867. (Medline) (PDF File) 
10. Herschlag, D. (1995) J. Biol. Chem. 270, 20871-20874. RNA Chaperones and the RNA Folding Problem. PMID: 754662. (Medline) (PDF File)
11. Herschlag, D., Johnson, F.B. (1993) Genes & Development 7, 173-179. Synergism in Transcriptional Activation: A Kinetic View. PMID: 8436289. (Medline) (PDF File)