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Abstract The ongoing evolution of SARS-CoV-2 into more easily transmissible and infectious variants has provided unprecedented insight into mutations enabling immune escape. Understanding how these mutations affect the dynamics of antibody-antigen interactions is crucial to the development of broadly protective antibodies and vaccines. Here we report the characterization of a potent neutralizing antibody (N3-1) identified from a COVID-19 patient during the first disease wave. Cryogenic electron microscopy revealed a quaternary binding mode that enables direct interactions with all three receptor-binding domains of the spike protein trimer, resulting in extraordinary avidity and potent neutralization of all major variants of concern until the emergence of Omicron. Structure-based rational design of N3-1 mutants improved binding to all Omicron variants but only partially restored neutralization of the conformationally distinct Omicron BA.1. This study provides new insights into immune evasion through changes in spike protein dynamics and highlights considerations for future conformationally biased multivalent vaccine designs.

Abstract Cilia are hairlike protrusions that project from the surface of eukaryotic cells and play key roles in cell signaling and motility. Ciliary motility is regulated by the conserved nexin-dynein regulatory complex (N-DRC), which links adjacent doublet microtubules and regulates and coordinates the activity of outer doublet complexes. Despite its critical role in cilia motility, the assembly and molecular basis of the regulatory mechanism are poorly understood. Here, using cryo-electron microscopy in conjunction with biochemical cross-linking and integrative modeling, we localize 12 DRC subunits in the N-DRC structure of Tetrahymena thermophila. We also find that the CCDC96/113 complex is in close contact with the DRC9/10 in the linker region. In addition, we reveal that the N-DRC is associated with a network of coiled-coil proteins that most likely mediates N-DRC regulatory activity.

Abstract Background Proteins often assemble into higher-order complexes to perform their biological functions. Such protein–protein interactions (PPI) are often experimentally measured for pairs of proteins and summarized in a weighted PPI network, to which community detection algorithms can be applied to define the various higher-order protein complexes. Current methods include unsupervised and supervised approaches, often assuming that protein complexes manifest only as dense subgraphs. Utilizing supervised approaches, the focus is not on how to find them in a network, but only on learning which subgraphs correspond to complexes, currently solved using heuristics. However, learning to walk trajectories on a network to identify protein complexes leads naturally to a reinforcement learning (RL) approach, a strategy not extensively explored for community detection. Here, we develop and evaluate a reinforcement learning pipeline for community detection on weighted protein–protein interaction networks to detect new protein complexes. The algorithm is trained to calculate the value of different subgraphs encountered while walking on the network to reconstruct known complexes. A distributed prediction algorithm then scales the RL pipeline to search for novel protein complexes on large PPI networks. Results The reinforcement learning pipeline is applied to a human PPI network consisting of 8k proteins and 60k PPI, which results in 1,157 protein complexes. The method demonstrated competitive accuracy with improved speed compared to previous algorithms. We highlight protein complexes such as C4orf19, C18orf21, and KIAA1522 which are currently minimally characterized. Additionally, the results suggest TMC04 be a putative additional subunit of the KICSTOR complex and confirm the involvement of C15orf41 in a higher-order complex with HIRA, CDAN1, ASF1A, and by 3D structural modeling. Conclusions Reinforcement learning offers several distinct advantages for community detection, including scalability and knowledge of the walk trajectories defining those communities. Applied to currently available human protein interaction networks, this method had comparable accuracy with other algorithms and notable savings in computational time, and in turn, led to clear predictions of protein function and interactions for several uncharacterized human proteins.

Abstract Cilia are ubiquitous eukaryotic organelles responsible for cellular motility and sensory functions. The ciliary axoneme is a microtubule-based cytoskeleton consisting of two central singlets and nine outer doublet microtubules. Cryo-electron microscopy-based studies have revealed a complex network inside the lumen of both tubules composed of microtubule-inner proteins (MIPs). However, the functions of most MIPs remain unknown. Here, we present single-particle cryo-EM-based analyses of the Tetrahymena thermophila native doublet microtubule and identify 42 MIPs. These data shed light on the evolutionarily conserved and diversified roles of MIPs. In addition, we identified MIPs potentially responsible for the assembly and stability of the doublet outer junction. Knockout of the evolutionarily conserved outer junction component CFAP77 moderately diminishes Tetrahymena swimming speed and beat frequency, indicating the important role of CFAP77 and outer junction stability in cilia beating generation and/or regulation.

Abstract A major goal in structural biology is to understand protein assemblies in their biologically relevant states. Here, we investigate whether AlphaFold2 structure predictions match native protein conformations. We chemically cross-linked proteins in situ within intact Tetrahymena thermophila cilia and native ciliary extracts, identifying 1,225 intramolecular cross-links within the 100 best-sampled proteins, providing a benchmark of distance restraints obeyed by proteins in their native assemblies. The corresponding structure predictions were highly concordant, positioning 86.2% of cross-linked residues within Cɑ-to-Cɑ distances of 30 Å, consistent with the cross-linker length. 43% of proteins showed no violations. Most inconsistencies occurred in low-confidence regions or between domains. Overall, AlphaFold2 predictions with lower predicted aligned error corresponded to more correct native structures. However, we observe cases where rigid body domains are oriented incorrectly, as for ciliary protein BBC118, suggesting that combining structure prediction with experimental information will better reveal biologically relevant conformations.

Yeast expression of human G-protein-coupled receptors (GPCRs) can be used as a biosensor platform for the detection of pharmaceuticals. Cannabinoid receptor type 1 (CB1R) is of particular interest, given the cornucopia of natural and synthetic cannabinoids being explored as therapeutics. We show for the first time that engineering the N-terminus of CB1R allows for efficient signal transduction in yeast, and that engineering the sterol composition of the yeast membrane modulates its performance. Using an engineered cannabinoid biosensor, we demonstrate that large libraries of synthetic cannabinoids and terpenes can be quickly screened to elucidate known and novel structure–activity relationships. The biosensor strains offer a ready platform for evaluating the activity of new synthetic cannabinoids, monitoring drugs of abuse, and developing therapeutic molecules.

Hybrid vigor or heterosis has been widely applied in agriculture and extensively studied using genetic and gene expression approaches. However, the biochemical mechanism underlying heterosis remains elusive. One theory suggests that a decrease in protein aggregation may occur in hybrids due to the presence of protein variants between parental alleles, but it has not been experimentally tested. Here, we report comparative analysis of soluble and insoluble proteomes in Arabidopsis intraspecific and interspecific hybrids or allotetraploids formed between A. thaliana and A. arenosa. Both allotetraploids and intraspecific hybrids displayed nonadditive expression (unequal to the sum of the two parents) of the proteins, most of which were involved in biotic and abiotic stress responses. In the allotetraploids, homoeolog-expression bias was not observed among all proteins examined but accounted for 17-20% of the nonadditively expressed proteins, consistent with the transcriptome results. Among expression-biased homoeologs, there were more A. thaliana-biased than A. arenosa-biased homoeologs. Analysis of the insoluble and soluble proteomes revealed more soluble proteins in the hybrids than their parents but not in the allotetraploids. Most proteins in ribosomal biosynthesis and in the thylakoid lumen, membrane, and stroma were in the soluble fractions, indicating a role of protein stability in photosynthetic activities for promoting growth. Thus, nonadditive expression of stress-responsive proteins and increased solubility of photosynthetic proteins may contribute to heterosis in Arabidopsis hybrids and allotetraploids and possibly hybrid crops.

Summary: During eukaryotic transcription, RNA polymerase II undergoes dynamic post-translational modifications on the C-terminal domain (CTD) of the largest subunit, generating an information-rich PTM landscape that transcriptional regulators bind. The phosphorylation of Ser5 and Ser2 of CTD heptad occurs spatiotemporally with the transcriptional stages, recruiting different transcriptional regulators to Pol II. To delineate the protein interactomes at different transcriptional stages, we reconstructed phosphorylation patterns of the CTD at Ser5 and Ser2 in vitro. Our results showed that distinct protein interactomes are recruited to RNA polymerase II at different stages of transcription by the phosphorylation of Ser2 and Ser5 of the CTD heptads. In particular, we characterized calcium homeostasis endoplasmic reticulum protein (CHERP) as a regulator bound by phospho-Ser2 heptad. Pol II association with CHERP recruits an accessory splicing complex whose loss results in broad changes in alternative splicing events. Our results shed light on the PTM-coded recruitment process that coordinates transcription.

The yeast Saccharomyces cerevisiae is powerful for studying human G protein-coupled receptors as they can be coupled to its mating pathway. Here the authors engineer baker’s yeast to produce human sterols and show that vertebrate G protein coupled receptors are more sensitive in this membrane environment.

Summary: Co-fractionation/mass spectrometry (CF/MS) is a flexible and powerful method to detect physical associations of proteins. CF/MS can be applied to any tissue or organism without the need for protein-specific antibodies or epitope tags. Here, we outline two alternate protocols for MS preparation of samples (containing low or high salt) and a computational pipeline (cfmsflow) that together allow the successful application of this approach. These protocols are based on CF/MS of over 16 diverse organisms including plants and animals.For complete details on the use and execution of this protocol, please refer to McWhite et al. (2020).

Characterization of protein complexes, i.e. sets of proteins assembling into a single larger physical entity, is important, as such assemblies play many essential roles in cells such as gene regulation. From networks of protein-protein interactions, potential protein complexes can be identified computationally through the application of community detection methods, which flag groups of entities interacting with each other in certain patterns. Most community detection algorithms tend to be unsupervised and assume that communities are dense network subgraphs, which is not always true, as protein complexes can exhibit diverse network topologies. The few existing supervised machine learning methods are serial and can potentially be improved in terms of accuracy and scalability by using better-suited machine learning models and parallel algorithms. Here, we present Super.Complex, a distributed, supervised AutoML-based pipeline for overlapping community detection in weighted networks. We also propose three new evaluation measures for the outstanding issue of comparing sets of learned and known communities satisfactorily. Super.Complex learns a community fitness function from known communities using an AutoML method and applies this fitness function to detect new communities. A heuristic local search algorithm finds maximally scoring communities, and a parallel implementation can be run on a computer cluster for scaling to large networks. On a yeast protein-interaction network, Super.Complex outperforms 6 other supervised and 4 unsupervised methods. Application of Super.Complex to a human protein-interaction network with ~8k nodes and ~60k edges yields 1,028 protein complexes, with 234 complexes linked to SARS-CoV-2, the COVID-19 virus, with 111 uncharacterized proteins present in 103 learned complexes. Super.Complex is generalizable with the ability to improve results by incorporating domain-specific features. Learned community characteristics can also be transferred from existing applications to detect communities in a new application with no known communities. Code and interactive visualizations of learned human protein complexes are freely available at: https://sites.google.com/view/supercomplex/super-complex-v3-0.

Abstract Background Encoding arbitrary digital information in DNA has attracted attention as a potential avenue for large scale and long term data storage. However, in order to enable DNA data storage technologies there needs to be improvements in data storage fidelity (tolerance to mutation), the facility of writing and reading the data (biases and systematic error arising from synthesis and sequencing), and overall scalability. Results To this end, we have developed and implemented an encoding scheme that is suitable for detecting and correcting errors that may arise during storage, writing, and reading, such as those arising from nucleotide substitutions, insertions, and deletions. We propose a scheme for parallelized long term storage of encoded sequences that relies on overlaps rather than the address blocks found in previously published work. Using computer simulations, we illustrate the encoding, sequencing, decoding, and recovery of encoded information, ultimately demonstrating the possibility of a successful round-trip read/write. These demonstrations show that in theory a precise control over error tolerance is possible. Even after simulated degradation of DNA, recovery of original data is possible owing to the error correction capabilities built into the encoding strategy. A secondary advantage of our method is that the statistical characteristics (such as repetitiveness and GC-composition) of encoded sequences can also be tailored without sacrificing the overall ability to store large amounts of data. Finally, the combination of the overlap-based partitioning of data with the LZMA compression that is integral to encoding means that the entire sequence must be present for successful decoding. This feature enables inordinately strong encryptions. As a potential application, an encrypted pathogen genome could be distributed and carried by cells without danger of being expressed, and could not even be read out in the absence of the entire DNA consortium. Conclusions We have developed a method for DNA encoding, using a significantly different fundamental approach from existing work, which often performs better than alternatives and allows for a great deal of freedom and flexibility of application.

Abstract Background Post-translational modification (PTM) of proteins is central to many cellular processes across all domains of life, but despite decades of study and a wealth of genomic and proteomic data the biological function of many PTMs remains unknown. This is especially true for prokaryotic PTM systems, many of which have only recently been recognized and studied in depth. It is increasingly apparent that a deep sampling of abundance across a wide range of environmental stresses, growth conditions, and PTM types, rather than simply cataloging targets for a handful of modifications, is critical to understanding the complex pathways that govern PTM deposition and downstream effects. Results We utilized a deeply-sampled dataset of MS/MS proteomic analysis covering 9 timepoints spanning the Escherichia coli growth cycle and an unbiased PTM search strategy to construct a temporal map of abundance for all PTMs within a 400 Da window of mass shifts. Using this map, we are able to identify novel targets and temporal patterns for N-terminal N α acetylation, C-terminal glutamylation, and asparagine deamidation. Furthermore, we identify a possible relationship between N-terminal N α acetylation and regulation of protein degradation in stationary phase, pointing to a previously unrecognized biological function for this poorly-understood PTM. Conclusions Unbiased detection of PTM in MS/MS proteomics data facilitates the discovery of novel modification types and previously unobserved dynamic changes in modification across growth timepoints.

Characterizing genetic interactions is crucial to understanding cellular and organismal response to gene-level perturbations. Such knowledge can inform the selection of candidate disease therapy targets, yet experimentally determining whether genes interact is technically nontrivial and time-consuming. High-fidelity prediction of different classes of genetic interactions in multiple organisms would substantially alleviate this experimental burden. Under the hypothesis that functionally related genes tend to share common genetic interaction partners, we evaluate a computational approach to predict genetic interactions in Homo sapiens, Drosophila melanogaster, and Saccharomyces cerevisiae. By leveraging knowledge of functional relationships between genes, we cross-validate predictions on known genetic interactions and observe high predictive power of multiple classes of genetic interactions in all three organisms. Additionally, our method suggests high-confidence candidate interaction pairs that can be directly experimentally tested. A web application is provided for users to query genes for predicted novel genetic interaction partners. Finally, by subsampling the known yeast genetic interaction network, we found that novel genetic interactions are predictable even when knowledge of currently known interactions is minimal.

Longevity of antibody responses has been attributed to persistence of plasma cells in mice. Here the authors provide human data in support of this model by immunoglobulin sequencing bone marrow sections from two human donors over 6.5 years to show temporal stability of plasma cell clonotypes, but not other B cells.

Summary: RNA-binding proteins (RBPs) play essential roles in biology and are frequently associated with human disease. Although recent studies have systematically identified individual RNA-binding proteins, their higher-order assembly into ribonucleoprotein (RNP) complexes has not been systematically investigated. Here, we describe a proteomics method for systematic identification of RNP complexes in human cells. We identify 1,428 protein complexes that associate with RNA, indicating that more than 20% of known human protein complexes contain RNA. To explore the role of RNA in the assembly of each complex, we identify complexes that dissociate, change composition, or form stable protein-only complexes in the absence of RNA. We use our method to systematically identify cell-type-specific RNA-associated proteins in mouse embryonic stem cells and finally, distribute our resource, rna.MAP, in an easy-to-use online interface (rna.proteincomplexes.org). Our system thus provides a methodology for explorations across human tissues, disease states, and throughout all domains of life. : Ribonucleoprotein (RNP) complexes carry out many essential biological processes. Mallam et al. developed differential fractionation (DIF-FRAC), a proteomics method to systematically discover RNP complexes. Using their method, they discovered previously unknown RNP complexes, classified complexes by their RNA-dependent stability, and identified previously unknown roles for RNA in known protein complexes. Keywords: ribonucleoprotein complex, RNP, RNA-binding protein, RBP, proteomics, DIF-FRAC, protein complexes, biochemical fractionation, mass spectrometry, interactome

Our analysis examines the conservation of multiprotein complexes among metazoa through use of high resolution biochemical fractionation and precision mass spectrometry applied to soluble cell extracts from 5 representative model organisms Caenorhabditis elegans, Drosophila melanogaster, Mus musculus, Strongylocentrotus purpuratus, and Homo sapiens. The interaction network obtained from the data was validated globally in 4 distant species (Xenopus laevis, Nematostella vectensis, Dictyostelium discoideum, Saccharomyces cerevisiae) and locally by targeted affinity-purification experiments. Here we provide details of our massive set of supporting biochemical fractionation data available via ProteomeXchange (http://www.ebi.ac.uk/pride/archive/projects/PXD002319-http://www.ebi.ac.uk/pride/archive/projects/PXD002328), PPIs via BioGRID (185267); and interaction network projections via (http://metazoa.med.utoronto.ca) made fully accessible to allow further exploration. The datasets here are related to the research article on metazoan macromolecular complexes in Nature [1]. Keywords: Proteomics, Metazoa, Protein complexes, Biochemical, Fractionation

Summary: Multi-protein complexes are necessary for nearly all cellular processes, and understanding their structure is required for elucidating their function. Current high-resolution strategies in structural biology are effective but lag behind other fields (e.g., genomics and proteomics) due to their reliance on purified samples rather than heterogeneous mixtures. Here, we present a method combining single-particle analysis by electron microscopy with protein identification by mass spectrometry to structurally characterize macromolecular complexes from human cell extract. We identify HSP60 through two-dimensional classification and obtain three-dimensional structures of native proteasomes directly from ab initio classification of a heterogeneous mixture of protein complexes. In addition, we reveal an ∼1-MDa-size structure of unknown composition and reference our proteomics data to suggest possible identities. Our study shows the power of using a shotgun approach to electron microscopy (shotgun EM) when coupled with mass spectrometry as a tool to uncover the structures of macromolecular machines. : Verbeke et al. demonstrate a shotgun approach to macromolecular structure determination by combining single-particle electron microscopy with mass spectrometry to reconstruct multiple three-dimensional models in a single experiment. This approach provides a method for investigating the structure and function of cellular machinery in parallel. Keywords: electron microscopy, structural biology, cellular fractionation, mass spectrometry, deep classification, heterogeneity analysis, protein complexes

ABSTRACT Maintaining control over inflammatory processes represents a paradox for viral pathogens. Although many viruses induce host inflammatory responses to facilitate infection, control is necessary to avoid overactivation. One way is through the manipulation of proinflammatory chemokine levels, both host and viral. Murine cytomegalovirus (MCMV), a model betaherpesvirus, encodes a viral C-C chemokine, MCK2, which promotes host inflammatory responses and incorporates into virions to facilitate viral dissemination. Here, we show that the activity of M48, the conserved MCMV deubiquitinating enzyme (DUB), regulates MCK2 levels during infection. Inactivation of M48 DUB activity results in viral attenuation and exacerbates virally induced, MCK2-dependent inflammatory responses. M48 DUB activity also influences MCK2 incorporation into virions. Importantly, attenuation of DUB-mutant virus acute replication in vitro and in vivo is largely ameliorated by targeted deletion of MCK2. Thus, uncontrolled MCK2 levels appear to mediate DUB-mutant virus attenuation in specific tissues or cell types. This demonstrates that MCMV M48 DUB activity plays a previously unappreciated role in controlling MCK2 levels, thereby managing MCK2-dependent processes. These findings reveal a novel intrinsic control mechanism of virally induced inflammation and support the identification of betaherpesvirus DUBs as possible new targets for antiviral therapies. IMPORTANCE Human cytomegalovirus infections represent a tremendous burden not only to those afflicted but also to health care systems worldwide. As cytomegalovirus infections are a leading cause of nongenetic sensory loss and neurodevelopmental delay, it is imperative that valuable model systems exist in order that we might understand what viral factors contribute to replication and pathogenesis. Currently, the only approved drug treatments against CMV infection are nucleoside analogues, to which some strains have become resistant. Understanding unique viral enzymatic contributions to infections will allow the development of novel pharmacological therapies. Here, we show that M48, the conserved MCMV deubiquitinase, is critical for MCMV replication in mice and demonstrate that attenuation is due to deregulated production of a viral proinflammatory chemokine. The deubiquitinases of both human and murine CMV represent structurally unique DUBs and are therefore attractive targets for pharmacological intervention. Continued research into the substrates of these DUBs will lend additional insight into their potential as targets.

Recently, using the frog Xenopus laevis as a model system, we showed that the transcription factor Rfx2 coordinates many genes involved in ciliogenesis and cell movement in multiciliated cells (Chung et al., 2014). To our knowledge, it was the first paper to utilize the genomic resources, including genome sequences and interim gene annotations, from the ongoing X. laevis genome project. For researchers who are interested in the application of genomics and systems biology approaches in Xenopus studies, here we provide additional details about our dataset (NCBI GEO accession number GSE50593) and describe how we analyzed RNA-seq and ChIP-seq data to identify direct targets of Rfx2.

ABSTRACT Antimicrobial-resistant bacteria pose a serious threat in the clinic. This is particularly true for opportunistic pathogens that possess high intrinsic resistance. Though many studies have focused on understanding the acquisition of bacterial resistance upon exposure to antimicrobials, the mechanisms controlling intrinsic resistance are not well understood. In this study, we subjected the model opportunistic superbug Pseudomonas aeruginosa to 14 antimicrobials under highly controlled conditions and assessed its response using expression- and fitness-based genomic approaches. Our results reveal that gene expression changes and mutant fitness in response to sub-MIC antimicrobials do not correlate on a genomewide scale, indicating that gene expression is not a good predictor of fitness determinants. In general, fewer fitness determinants were identified for antiseptics and disinfectants than for antibiotics. Analysis of gene expression and fitness data together allowed the prediction of antagonistic interactions between antimicrobials and insight into the molecular mechanisms controlling these interactions. IMPORTANCE Infections involving multidrug-resistant pathogens are difficult to treat because the therapeutic options are limited. These infections impose a significant financial burden on infected patients and on health care systems. Despite years of antimicrobial resistance research, we lack a comprehensive understanding of the intrinsic mechanisms controlling antimicrobial resistance. This work uses two fine-scale genomic approaches to identify genetic loci important for antimicrobial resistance of the opportunistic pathogen Pseudomonas aeruginosa. Our results reveal that antibiotics have more resistance determinants than antiseptics/disinfectants and that gene expression upon exposure to antimicrobials is not a good predictor of these resistance determinants. In addition, we show that when used together, genomewide gene expression and fitness profiling can provide mechanistic insights into multidrug resistance mechanisms.

ABSTRACT The Gram-negative bacterium Pseudomonas aeruginosa is a common cause of chronic airway infections in individuals with the heritable disease cystic fibrosis (CF). After prolonged colonization of the CF lung, P. aeruginosa becomes highly resistant to host clearance and antibiotic treatment; therefore, understanding how this bacterium evolves during chronic infection is important for identifying beneficial adaptations that could be targeted therapeutically. To identify potential adaptive traits of P. aeruginosa during chronic infection, we carried out global transcriptomic profiling of chronological clonal isolates obtained from 3 individuals with CF. Isolates were collected sequentially over periods ranging from 3 months to 8 years, representing up to 39,000 in vivo generations. We identified 24 genes that were commonly regulated by all 3 P. aeruginosa lineages, including several genes encoding traits previously shown to be important for in vivo growth. Our results reveal that parallel evolution occurs in the CF lung and that at least a proportion of the traits identified are beneficial for P. aeruginosa chronic colonization of the CF lung. IMPORTANCE Deadly diseases like AIDS, malaria, and tuberculosis are the result of long-term chronic infections. Pathogens that cause chronic infections adapt to the host environment, avoiding the immune response and resisting antimicrobial agents. Studies of pathogen adaptation are therefore important for understanding how the efficacy of current therapeutics may change upon prolonged infection. One notorious chronic pathogen is Pseudomonas aeruginosa, a bacterium that causes long-term infections in individuals with the heritable disease cystic fibrosis (CF). We used gene expression profiles to identify 24 genes that commonly changed expression over time in 3 P. aeruginosa lineages, indicating that these changes occur in parallel in the lungs of individuals with CF. Several of these genes have previously been shown to encode traits critical for in vivo-relevant processes, suggesting that they are likely beneficial adaptations important for chronic colonization of the CF lung.