Experimental analysis of co-evolution within protein complexes: the yeast exosome as a model

Comparative Structural Analysis of 20S Proteasome Ortholog Protein Complexes by Native Mass Spectrometry

Ortholog protein complexes are responsible for equivalent functions in different organisms. However, during evolution, each organism adapts to meet its physiological needs and the environmental challenges imposed by its niche. This selection pressure leads to structural diversity in protein complexes, which are often difficult to specify, especially in the absence of high-resolution structures. Here, we describe a multilevel experimental approach based on native mass spectrometry (MS) tools for elucidating the structural preservation and variations among highly related protein complexes. The 20S proteasome, an essential protein degradation machinery, served as our model system, wherein we examined five complexes isolated from different organisms. We show that throughout evolution, from the Thermoplasma acidophilum archaeal prokaryotic complex to the eukaryotic 20S proteasomes in yeast (Saccharomyces cerevisiae) and mammals (rat - Rattus norvegicus, rabbit - Oryctolagus cuniculus and human - HEK293 cells), the proteasome increased both in size and stability. Native MS structural signatures of the rat and rabbit 20S proteasomes, which heretofore lacked high-resolution, three-dimensional structures, highly resembled that of the human complex. Using cryoelectron microscopy single-particle analysis, we were able to obtain a high-resolution structure of the rat 20S proteasome, allowing us to validate the MS-based results. Our study also revealed that the yeast complex, and not those in mammals, was the largest in size and displayed the greatest degree of kinetic stability. Moreover, we also identified a new proteoform of the PSMA7 subunit that resides within the rat and rabbit complexes, which to our knowledge have not been previously described. Altogether, our strategy enables elucidation of the unique structural properties of protein complexes that are highly similar to one another, a framework that is valid not only to ortholog protein complexes, but also for other highly related protein assemblies.

Immunosignature Screening for Multiple Cancer Subtypes Based on Expression Rule

Liquid biopsy (i.e., fluid biopsy) involves a series of clinical examination approaches. Monitoring of cancer immunological status by the “immunosignature” of patients presents a novel method for tumor-associated liquid biopsy. The major work content and the core technological difficulties for the monitoring of cancer immunosignature are the recognition of cancer-related immune-activating antigens by high-throughput screening approaches. Currently, one key task of immunosignature-based liquid biopsy is the qualitative and quantitative identification of typical tumor-specific antigens. In this study, we reused two sets of peptide microarray data that detected the expression level of potential antigenic peptides derived from tumor tissues to avoid the detection differences induced by chip platforms. Several machine learning algorithms were applied on these two sets. First, the Monte Carlo Feature Selection (MCFS) method was used to analyze features in two sets. A feature list was obtained according to the MCFS results on each set. Second, incremental feature selection method incorporating one classification algorithm (support vector machine or random forest) followed to extract optimal features and construct optimal classifiers. On the other hand, the repeated incremental pruning to produce error reduction, a rule learning algorithm, was applied on key features yielded by the MCFS method to extract quantitative rules for accurate cancer immune monitoring and pathologic diagnosis. Finally, obtained key features and quantitative rules were extensively analyzed.

Marker-free genetic manipulations in yeast using CRISPR/CAS9 system

The budding yeast is currently one of the major model organisms for the study of a wide variety of biological processes. Genetic manipulation of yeast involves the extensive usage of selectable markers that can lead to undesired effects. Thus, marker-free genetic manipulation in yeast is highly desirable for gene/promoter replacement and various other applications. Here we combine the power of selectable markers followed by CRISPR/CAS9 genome editing for common genetic manipulations in yeast in a marker-free manner. We demonstrate our approach for whole gene and promoter replacements and for high-efficiency operator array integration. Our approach allows the utilization of many thousands of existing strains including library strains for the generation of significant genetic changes in yeast in a marker-free and cloning-free fashion.

Gene duplication and co-evolution of G1/S transcription factor specificity in fungi are essential for optimizing cell fitness

Transcriptional regulatory networks play a central role in optimizing cell survival. How DNA binding domains and cis-regulatory DNA binding sequences have co-evolved to allow the expansion of transcriptional networks and how this contributes to cellular fitness remains unclear. Here we experimentally explore how the complex G1/S transcriptional network evolved in the budding yeast Saccharomyces cerevisiae by examining different chimeric transcription factor (TF) complexes. Over 200 G1/S genes are regulated by either one of the two TF complexes, SBF and MBF, which bind to specific DNA binding sequences, SCB and MCB, respectively. The difference in size and complexity of the G1/S transcriptional network across yeast species makes it well suited to investigate how TF paralogs (SBF and MBF) and DNA binding sequences (SCB and MCB) co-evolved after gene duplication to rewire and expand the network of G1/S target genes. Our data suggests that whilst SBF is the likely ancestral regulatory complex, the ancestral DNA binding element is more MCB-like. G1/S network expansion took place by both cis- and trans- co-evolutionary changes in closely related but distinct regulatory sequences. Replacement of the endogenous SBF DNA-binding domain (DBD) with that from more distantly related fungi leads to a contraction of the SBF-regulated G1/S network in budding yeast, which also correlates with increased defects in cell growth, cell size, and proliferation.

Large oligomeric complex structures can be computationally assembled by efficiently combining docked interfaces

Macromolecular oligomeric assemblies are involved in many biochemical processes of living organisms. The benefits of such assemblies in crowded cellular environments include increased reaction rates, efficient feedback regulation, cooperativity and protective functions. However, an atom-level structural determination of large assemblies is challenging due to the size of the complex and the difference in binding affinities of the involved proteins. In this study, we propose a novel combinatorial greedy algorithm for assembling large oligomeric complexes from information on the approximate position of interaction interfaces of pairs of monomers in the complex. Prior information on complex symmetry is not required but rather the symmetry is inferred during assembly. We implement an efficient geometric score, the transformation match score, that bypasses the model ranking problems of state-of-the-art scoring functions by scoring the similarity between the inferred dimers of the same monomer simultaneously with different binding partners in a (sub)complex with a set of pregenerated docking poses. We compiled a diverse benchmark set of 308 homo and heteromeric complexes containing 6 to 60 monomers. To explore the applicability of the method, we considered 48 sets of parameters and selected those three sets of parameters, for which the algorithm can correctly reconstruct the maximum number, namely 252 complexes (81.8%) in, at least one of the respective three runs. The crossvalidation coverage, that is, the mean fraction of correctly reconstructed benchmark complexes during crossvalidation, was 78.1%, which demonstrates the ability of the presented method to correctly reconstruct topology of a large variety of biological complexes.