Chemical cross-linking with mass spectrometry (XL-MS) provides structural information for proteins and protein complexes in the form of crosslinked residue proximity and distance constraints between reactive residues. Utilizing spatial information derived from cross-linked residues can therefore assist with structural modeling of proteins. Selection of computationally derived model structures of proteins remains a major challenge in structural biology. The comparison of site interactions resulting from XL-MS with protein structure contact maps can assist the selection of structural models.Online Versions of ContactMapper and CMScore are available via shinyapp.io.
Bacterial lineages that chronically infect cystic fibrosis (CF) patients genetically diversify during infection. However, the mechanisms driving diversification are unknown. By dissecting ten CF lung pairs and studying ∼12,000 regional isolates, we were able to investigate whether clonally related Pseudomonas aeruginosa inhabiting different lung regions evolve independently and differ functionally. Phylogenetic analysis of genome sequences showed that regional isolation of P. aeruginosa drives divergent evolution. We investigated the consequences of regional evolution by studying isolates from mildly and severely diseased lung regions and found evolved differences in bacterial nutritional requirements, host defense and antibiotic resistance, and virulence due to hyperactivity of the type 3 secretion system. These findings suggest that bacterial intermixing is limited in CF lungs and that regional selective pressures may markedly differ. The findings also may explain how specialized bacterial variants arise during infection and raise the possibility that pathogen diversification occurs in other chronic infections characterized by spatially heterogeneous conditions.
In pathogenic Gram-negative bacteria, interactions among membrane proteins are key mediators of host cell attachment, invasion, pathogenesis, and antibiotic resistance. Membrane protein interactions are highly dependent upon local properties and environment, warranting direct measurements on native protein complex structures as they exist in cells. Here we apply in vivo chemical cross-linking mass spectrometry, to reveal the first large-scale protein interaction network in Pseudomonas aeruginosa, an opportunistic human pathogen, by covalently linking interacting protein partners, thereby fixing protein complexes in vivo. A total of 626 cross-linked peptide pairs, including previously unknown interactions of many membrane proteins, are reported. These pairs not only define the existence of these interactions in cells but also provide linkage constraints for complex structure predictions. Structures of three membrane proteins, namely, SecD-SecF, OprF, and OprI are predicted using in vivo cross-linked sites. These findings improve understanding of membrane protein interactions and structures in cells.
Chemoresistance is a common mode of therapy failure for many cancers. Tumours develop resistance to chemotherapeutics through a variety of mechanisms, with proteins serving pivotal roles. Changes in protein conformations and interactions affect the cellular response to environmental conditions contributing to the development of new phenotypes. The ability to understand how protein interaction networks adapt to yield new function or alter phenotype is limited by the inability to determine structural and protein interaction changes on a proteomic scale. Here, chemical crosslinking and mass spectrometry were employed to quantify changes in protein structures and interactions in multidrug-resistant human carcinoma cells. Quantitative analysis of the largest crosslinking-derived, protein interaction network comprising 1,391 crosslinked peptides allows for ‘edgotype’ analysis in a cell model of chemoresistance. We detect consistent changes to protein interactions and structures, including those involving cytokeratins, topoisomerase-2-alpha, and post-translationally modified histones, which correlate with a chemoresistant phenotype.