Richard Bonneau

The mRNA-Bound Proteome and Its Global Occupancy Profile on Protein-Coding Transcripts

Protein-RNA interactions are fundamental to core biological processes, such as mRNA splicing, localization, degradation, and translation. We developed a photoreactive nucleotide-enhanced UV crosslinking and oligo(dT) purification approach to identify the mRNA-bound proteome using quantitative proteomics and to display the protein occupancy on mRNA transcripts by next-generation sequencing. Application to a human embryonic kidney cell line identified close to 800 proteins. To our knowledge, nearly one-third were not previously annotated as RNA binding, and about 15% were not predictable by computational methods to interact with RNA. Protein occupancy profiling provides a transcriptome-wide catalog of potential cis-regulatory regions on mammalian mRNAs and showed that large stretches in 3 UTRs can be contacted by the mRNA-bound proteome, with numerous putative binding sites in regions harboring disease-associated nucleotide polymorphisms. Our observations indicate the presence of a large number of mRNA binders with diverse molecular functions participating in combinatorial posttranscriptional gene-expression networks.

A validated regulatory network for Th17 cell specification.

Th17 cells have critical roles in mucosal defense and are major contributors to inflammatory disease. Their differentiation requires the nuclear hormone receptor RORγt working with multiple other essential transcription factors (TFs). We have used an iterative systems approach, combining genome-wide TF occupancy, expression profiling of TF mutants, and expression time series to delineate the Th17 global transcriptional regulatory network. We find that cooperatively bound BATF and IRF4 contribute to initial chromatin accessibility and, with STAT3, initiate a transcriptional program that is then globally tuned by the lineage-specifying TF RORγt, which plays a focal deterministic role at key loci. Integration of multiple data sets allowed inference of an accurate predictive model that we computationally and experimentally validated, identifying multiple new Th17 regulators, including Fosl2, a key determinant of cellular plasticity. This interconnected network can be used to investigate new therapeutic approaches to manipulate Th17 functions in the setting of inflammatory disease.

Innate immune detection of the type III secretion apparatus through the NLRC4 inflammasome.

The mammalian innate immune system uses Toll-like receptors (TLRs) and Nod-LRRs (NLRs) to detect microbial components during infection. Often these molecules work in concert; for example, the TLRs can stimulate the production of the proforms of the cytokines IL-1β and IL-18, whereas certain NLRs trigger their subsequent proteolytic processing via caspase 1. Gram-negative bacteria use type III secretion systems (T3SS) to deliver virulence factors to the cytosol of host cells, where they modulate cell physiology to favor the pathogen. We show here that NLRC4/Ipaf detects the basal body rod component of the T3SS apparatus (rod protein) from S. typhimurium (PrgJ), Burkholderia pseudomallei (BsaK), Escherichia coli (EprJ and EscI), Shigella flexneri (MxiI), and Pseudomonas aeruginosa (PscI). These rod proteins share a sequence motif that is essential for detection by NLRC4; a similar motif is found in flagellin that is also detected by NLRC4. S. typhimurium has two T3SS: Salmonella pathogenicity island-1 (SPI1), which encodes the rod protein PrgJ, and SPI2, which encodes the rod protein SsaI. Although PrgJ is detected by NLRC4, SsaI is not, and this evasion is required for virulence in mice. The detection of a conserved component of the T3SS apparatus enables innate immune responses to virulent bacteria through a single pathway, a strategy that is divergent from that used by plants in which multiple NB-LRR proteins are used to detect T3SS effectors or their effects on cells. Furthermore, the specific detection of the virulence machinery permits the discrimination between pathogenic and nonpathogenic bacteria.

A Predictive Model for Transcriptional Control of Physiology in a Free Living Cell

The environment significantly influences the dynamic expression and assembly of all components encoded in the genome of an organism into functional biological networks. We have constructed a model for this process in Halobacterium salinarum NRC-1 through the data-driven discovery of regulatory and functional interrelationships among approximately 80% of its genes and key abiotic factors in its hypersaline environment. Using relative changes in 72 transcription factors and 9 environmental factors (EFs) this model accurately predicts dynamic transcriptional responses of all these genes in 147 newly collected experiments representing completely novel genetic backgrounds and environments-suggesting a remarkable degree of network completeness. Using this model we have constructed and tested hypotheses critical to this organisms interaction with its changing hypersaline environment. This study supports the claim that the high degree of connectivity within biological and EF networks will enable the construction of similar models for any organism from relatively modest numbers of experiments.

Automated prediction of CASP‐5 structures using the Robetta server

Robetta is a fully automated protein structure prediction server that uses the Rosetta fragment‐insertion method. It combines template‐based and de novo structure prediction methods in an attempt to produce high quality models that cover every residue of a submitted sequence. The first step in the procedure is the automatic detection of the locations of domains and selection of the appropriate modeling protocol for each domain. For domains matched to a homolog with an experimentally characterized structure by PSI‐BLAST or Pcons2, Robetta uses a new alignment method, called K*Sync, to align the query sequence onto the parent structure. It then models the variable regions by allowing them to explore conformational space with fragments in fashion similar to the de novo protocol, but in the context of the template. When no structural homolog is available, domains are modeled with the Rosetta de novo protocol, which allows the full length of the domain to explore conformational space via fragment‐insertion, producing a large decoy ensemble from which the final models are selected. The Robetta server produced quite reasonable predictions for targets in the recent CASP‐5 and CAFASP‐3 experiments, some of which were at the level of the best human predictions. Proteins 2003;53:524–533.

Rosetta in CASP4: Progress in ab initio protein structure prediction

Rosetta ab initio protein structure predictions in CASP4 were considerably more consistent and more accurate than previous ab initio structure predictions. Large segments were correctly predicted (>50 residues superimposed within an RMSD of 6.5 Å) for 16 of the 21 domains under 300 residues for which models were submitted. Models with the global fold largely correct were produced for several targets with new folds, and for several difficult fold recognition targets, the Rosetta models were more accurate than those produced with traditional fold recognition models. These promising results suggest that Rosetta may soon be able to contribute to the interpretation of genome sequence information. Proteins 2001;Suppl 5:119–126.

De Novo Prediction of Three-dimensional Structures for Major Protein Families

We use the Rosetta de novo structure prediction method to produce three-dimensional structure models for all Pfam-A sequence families with average length under 150 residues and no link to any protein of known structure. To estimate the reliability of the predictions, the method was calibrated on 131 proteins of known structure. For approximately 60% of the proteins one of the top five models was correctly predicted for 50 or more residues, and for approximately 35%, the correct SCOP superfamily was identified in a structure-based search of the Protein Data Bank using one of the models. This performance is consistent with results from the fourth critical assessment of structure prediction (CASP4). Correct and incorrect predictions could be partially distinguished using a confidence function based on a combination of simulation convergence, protein length and the similarity of a given structure prediction to known protein structures. While the limited accuracy and reliability of the method precludes definitive conclusions, the Pfam models provide the only tertiary structure information available for the 12% of publicly available sequences represented by these large protein families.

A conserved surface on Toll-like receptor 5 recognizes bacterial flagellin

The molecular basis for Toll-like receptor (TLR) recognition of microbial ligands is unknown. We demonstrate that mouse and human TLR5 discriminate between different flagellins, and we use this difference to map the flagellin recognition site on TLR5 to 228 amino acids of the extracellular domain. Through molecular modeling of the TLR5 ectodomain, we identify two conserved surface-exposed regions. Mutagenesis studies demonstrate that naturally occurring amino acid variation in TLR5 residue 268 is responsible for human and mouse discrimination between flagellin molecules. Mutations within one conserved surface identify residues D295 and D367 as important for flagellin recognition. These studies localize flagellin recognition to a conserved surface on the modeled TLR5 structure, providing detailed analysis of the interaction of a TLR with its ligand. These findings suggest that ligand binding at the β sheets results in TLR activation and provide a new framework for understanding TLR–agonist interactions.

A Protein Domain-Based Interactome Network for C. elegans Early Embryogenesis

Many protein-protein interactions are mediated through independently folding modular domains. Proteome-wide efforts to model protein-protein interaction or interactome networks have largely ignored this modular organization of proteins. We developed an experimental strategy to efficiently identify interaction domains and generated a domain-based interactome network for proteins involved in C. elegans early-embryonic cell divisions. Minimal interacting regions were identified for over 200 proteins, providing important information on their domain organization. Furthermore, our approach increased the sensitivity of the two-hybrid system, resulting in a more complete interactome network. This interactome modeling strategy revealed insights into C. elegans centrosome function and is applicable to other biological processes in this and other organisms.

An improved protein decoy set for testing energy functions for protein structure prediction.

We have improved the original Rosetta centroid/backbone decoy set by increasing the number of proteins and frequency of near native models and by building on sidechains and minimizing clashes. The new set consists of 1,400 model structures for 78 different and diverse protein targets and provides a challenging set for the testing and evaluation of scoring functions. We evaluated the extent to which a variety of all‐atom energy functions could identify the native and close‐to‐native structures in the new decoy sets. Of various implicit solvent models, we found that a solvent‐accessible surface area–based solvation provided the best enrichment and discrimination of close‐to‐native decoys. The combination of this solvation treatment with Lennard Jones terms and the original Rosetta energy provided better enrichment and discrimination than any of the individual terms. The results also highlight the differences in accuracy of NMR and X‐ray crystal structures: a large energy gap was observed between native and non‐native conformations for X‐ray structures but not for NMR structures. Proteins 2003.