"Structuromics": another step toward a holistic view of the cell.

Large-scale mapping of protein structures and their different states is crucial for gaining a mechanistic understanding of proteome function and regulation. In this issue of Cell, Cappelletti et al. achieve such a feat and identify hundreds of protein structural changes in response to outside stressors, providing a rich "structuromics" resource characterizing cellular adaptation.

New horizons in the stormy sea of multimodal single-cell data integration.

While measurements of RNA expression have dominated the world of single-cell analyses, new single-cell techniques increasingly allow collection of different data modalities, measuring different molecules, structural connections, and intermolecular interactions. Integrating the resulting multimodal single-cell datasets is a new bioinformatics challenge. Equally important, it is a new experimental design challenge for the bench scientist, who is not only choosing from a myriad of techniques for each data modality but also faces new challenges in experimental design. The ultimate goal is to design, execute, and analyze multimodal single-cell experiments that are more than just descriptive but enable the learning of new causal and mechanistic biology. This objective requires strict consideration of the goals behind the analysis, which might range from mapping the heterogeneity of a cellular population to assembling system-wide causal networks that can further our understanding of cellular functions and eventually lead to models of tissues and organs. We review steps and challenges toward this goal. Single-cell transcriptomics is now a mature technology, and methods to measure proteins, lipids, small-molecule metabolites, and other molecular phenotypes at the single-cell level are rapidly developing. Integrating these single-cell readouts so that each cell has measurements of multiple types of data, e.g., transcriptomes, proteomes, and metabolomes, is expected to allow identification of highly specific cellular subpopulations and to provide the basis for inferring causal biological mechanisms.

The Plant Microbiota: Systems-Level Insights and Perspectives.

Plants do not grow as axenic organisms in nature, but host a diverse community of microorganisms, termed the plant microbiota. There is an increasing awareness that the plant microbiota plays a role in plant growth and can provide protection from invading pathogens. Apart from intense research on crop plants, Arabidopsis is emerging as a valuable model system to investigate the drivers shaping stable bacterial communities on leaves and roots and as a tool to decipher the intricate relationship among the host and its colonizing microorganisms. Gnotobiotic experimental systems help establish causal relationships between plant and microbiota genotypes and phenotypes and test hypotheses on biotic and abiotic perturbations in a systematic way. We highlight major recent findings in plant microbiota research using comparative community profiling and omics analyses, and discuss these approaches in light of community establishment and beneficial traits like nutrient acquisition and plant health.

New Views of Old Proteins: Clarifying the Enigmatic Proteome.

All human diseases involve proteins, yet our current tools to characterize and quantify them are limited. To better elucidate proteins across space, time, and molecular composition, we provide a >10 years of projection for technologies to meet the challenges that protein biology presents. With a broad perspective, we discuss grand opportunities to transition the science of proteomics into a more propulsive enterprise. Extrapolating recent trends, we describe a next generation of approaches to define, quantify, and visualize the multiple dimensions of the proteome, thereby transforming our understanding and interactions with human disease in the coming decade.

Evaluation of determinants of the serological response to the quadrivalent split-inactivated influenza vaccine.

The seasonal influenza vaccine is only effective in half of the vaccinated population. To identify determinants of vaccine efficacy, we used data from > 1,300 vaccination events to predict the response to vaccination measured as seroconversion as well as hemagglutination inhibition (HAI) titer levels one year after. We evaluated the predictive capabilities of age, body mass index (BMI), sex, race, comorbidities, vaccination history, and baseline HAI titers, as well as vaccination month and vaccine dose in multiple linear regression models. The models predicted the categorical response for > 75% of the cases in all subsets with one exception. Prior vaccination, baseline titer level, and age were the major determinants of seroconversion, all of which had negative effects. Further, we identified a gender effect in older participants and an effect of vaccination month. BMI had a surprisingly small effect, likely due to its correlation with age. Comorbidities, vaccine dose, and race had negligible effects. Our models can generate a new seroconversion score that is corrected for the impact of these factors which can facilitate future biomarker identification.

New Proteomic Signatures to Distinguish Between Zika and Dengue Infections.

Distinguishing between Zika and dengue virus infections is critical for accurate treatment, but we still lack detailed understanding of their impact on their host. To identify new protein signatures of the two infections, we used next-generation proteomics to profile 122 serum samples from 62 Zika and dengue patients. We quantified >500 proteins and identified 13 proteins that were significantly differentially expressed (adjusted p-value < 0.05). These proteins typically function in infection and wound healing, with several also linked to pregnancy and brain function. We successfully validated expression differences with Carbonic Anhydrase 2 in both the original and an independent sample set. Three of the differentially expressed proteins, i.e., Fibrinogen Alpha, Platelet Factor 4 Variant 1, and Pro-Platelet Basic Protein, predicted Zika virus infection at a ∼70% true-positive and 6% false-positive rate. Further, we showed that intraindividual temporal changes in protein signatures can disambiguate diagnoses and serve as indicators for past infections. Taken together, we demonstrate that serum proteomics can provide new resources that serve to distinguish between different viral infections.

Structural impact of K63 ubiquitin on yeast translocating ribosomes under oxidative stress.

Subpopulations of ribosomes are responsible for fine tuning the control of protein synthesis in dynamic environments. K63 ubiquitination of ribosomes has emerged as a new posttranslational modification that regulates protein synthesis during cellular response to oxidative stress. K63 ubiquitin, a type of ubiquitin chain that functions independently of the proteasome, modifies several sites at the surface of the ribosome, however, we lack a molecular understanding on how this modification affects ribosome structure and function. Using cryoelectron microscopy (cryo-EM), we resolved the first three-dimensional (3D) structures of K63 ubiquitinated ribosomes from oxidatively stressed yeast cells at 3.5-3.2 Å resolution. We found that K63 ubiquitinated ribosomes are also present in a polysome arrangement, similar to that observed in yeast polysomes, which we determined using cryoelectron tomography (cryo-ET). We further showed that K63 ubiquitinated ribosomes are captured uniquely at the rotated pretranslocation stage of translation elongation. In contrast, cryo-EM structures of ribosomes from mutant cells lacking K63 ubiquitin resolved at 4.4-2.7 Å showed 80S ribosomes represented in multiple states of translation, suggesting that K63 ubiquitin regulates protein synthesis at a selective stage of elongation. Among the observed structural changes, ubiquitin mediates the destabilization of proteins in the 60S P-stalk and in the 40S beak, two binding regions of the eukaryotic elongation factor eEF2. These changes would impact eEF2 function, thus, inhibiting translocation. Our findings help uncover the molecular effects of K63 ubiquitination on ribosomes, providing a model of translation control during oxidative stress, which supports elongation halt at pretranslocation.

Ring Finger 149-Related Is an FGF/MAPK-Independent Regulator of Pharyngeal Muscle Fate Specification.

During embryonic development, cell-fate specification gives rise to dedicated lineages that underlie tissue formation. In olfactores, which comprise tunicates and vertebrates, the cardiopharyngeal field is formed by multipotent progenitors of both cardiac and branchiomeric muscles. The ascidian Ciona is a powerful model to study cardiopharyngeal fate specification with cellular resolution, as only two bilateral pairs of multipotent cardiopharyngeal progenitors give rise to the heart and to the pharyngeal muscles (also known as atrial siphon muscles, ASM). These progenitors are multilineage primed, in as much as they express a combination of early ASM- and heart-specific transcripts that become restricted to their corresponding precursors, following oriented and asymmetric divisions. Here, we identify the primed gene ring finger 149 related (Rnf149-r), which later becomes restricted to the heart progenitors, but appears to regulate pharyngeal muscle fate specification in the cardiopharyngeal lineage. CRISPR/Cas9-mediated loss of Rnf149-r function impairs atrial siphon muscle morphogenesis, and downregulates Tbx1/10 and Ebf, two key determinants of pharyngeal muscle fate, while upregulating heart-specific gene expression. These phenotypes are reminiscent of the loss of FGF/MAPK signaling in the cardiopharyngeal lineage, and an integrated analysis of lineage-specific bulk RNA-seq profiling of loss-of-function perturbations has identified a significant overlap between candidate FGF/MAPK and Rnf149-r target genes. However, functional interaction assays suggest that Rnf149-r does not directly modulate the activity of the FGF/MAPK/Ets1/2 pathway. Instead, we propose that Rnf149-r acts both in parallel to the FGF/MAPK signaling on shared targets, as well as on FGF/MAPK-independent targets through (a) separate pathway(s).

Prevaccination Glycan Markers of Response to an Influenza Vaccine Implicate the Complement Pathway.

A key to improving vaccine design and vaccination strategy is to understand the mechanism behind the variation of vaccine response with host factors. Glycosylation, a critical modulator of immunity, has no clear role in determining vaccine responses. To gain insight into the association between glycosylation and vaccine-induced antibody levels, we profiled the pre- and postvaccination serum protein glycomes of 160 Caucasian adults receiving the FLUZONE influenza vaccine during the 2019-2020 influenza season using lectin microarray technology. We found that prevaccination levels of Lewis A antigen (Lea) are significantly higher in nonresponders than responders. Glycoproteomic analysis showed that Lea-bearing proteins are enriched in complement activation pathways, suggesting a potential role of glycosylation in tuning the activities of complement proteins, which may be implicated in mounting vaccine responses. In addition, we observed a postvaccination increase in sialyl Lewis X antigen (sLex) and a decrease in high mannose glycans among high responders, which were not observed in nonresponders. These data suggest that the immune system may actively modulate glycosylation as part of its effort to establish effective protection postvaccination.

Exploiting Interdata Relationships in Next-generation Proteomics Analysis.

Mass spectrometry based proteomics and other technologies have matured to enable routine quantitative, system-wide analysis of concentrations, modifications, and interactions of proteins, mRNAs, and other molecules. These studies have allowed us to move toward a new field concerned with mining information from the combination of these orthogonal data sets, perhaps called "integromics." We highlight examples of recent studies and tools that aim at relating proteomic information to mRNAs, genetic associations, and changes in small molecules and lipids. We argue that productive data integration differs from parallel acquisition and interpretation and should move toward quantitative modeling of the relationships between the data. These relationships might be expressed by temporal information retrieved from time series experiments, rate equations to model synthesis and degradation, or networks of causal, evolutionary, physical, and other interactions. We outline steps and considerations toward such integromic studies to exploit the synergy between data sets.

A protein-centric approach for exome variant aggregation enables sensitive association analysis with clinical outcomes.

Somatic mutations are early drivers of tumorigenesis and tumor progression. However, the mutations typically occur at variable positions across different individuals, resulting in the data being too sparse to test meaningful associations between variants and phenotypes. To overcome this challenge, we devised a novel approach called Gene-to-Protein-to-Disease (GPD) which accumulates variants into new sequence units as the degree of genetic assault on structural or functional units of each protein. The variant frequencies in the sequence units were highly reproducible between two large cancer cohorts. Survival analysis identified 232 sequence units in which somatic mutations had deleterious effects on overall survival, including consensus driver mutations obtained from multiple calling algorithms. By contrast, around 76% of the survival predictive units had been undetected by conventional gene-level analysis. We demonstrate the ability of these signatures to separate patient groups according to overall survival, therefore, providing novel prognostic tools for various cancers. GPD also identified sequence units with somatic mutations whose impact on survival was modified by the occupancy of germline variants in the surrounding regions. The findings indicate that a patient's genetic predisposition interacts with the effect of somatic mutations on survival outcomes in some cancers.

Site-Specific K63 Ubiquitinomics Provides Insights into Translation Regulation under Stress.

During oxidative stress, K63-linked polyubiquitin chains modify a variety of proteins including ribosomes. Knowledge of the precise sites of K63 ubiquitin is key to understand its function during the response to stress. To identify the sites of K63 ubiquitin, we developed a new mass spectrometry based method that quantified >1100 K63 ubiquitination sites in yeast that responded to oxidative stress induced by H2O2. We determined that under stress, K63 ubiquitin-modified proteins were involved in several cellular functions including ion transport, protein trafficking, and translation. The most abundant ubiquitin sites localized to the head of the 40S subunit of the ribosome, modified assembled polysomes, and affected the binding of translation factors. The results suggested a new pathway of post-initiation control of translation during oxidative stress and illustrated the importance of high-resolution mapping of noncanonical ubiquitination events.

EBprotV2: A Perseus Plugin for Differential Protein Abundance Analysis of Labeling-Based Quantitative Proteomics Data.

We present EBprotV2, a Perseus plugin for peptide-ratio-based differential protein abundance analysis in labeling-based proteomics experiments. The original version of EBprot models the distribution of log-transformed peptide-level ratios as a Gaussian mixture of differentially abundant proteins and nondifferentially abundant proteins and computes the probability score of differential abundance for each protein based on the reproducible magnitude of peptide ratios. However, the fully parametric model can be inflexible, and its R implementation is time-consuming for data sets containing a large number of peptides (e.g., >100 000). The new tool built in the C++ language is not only faster in computation time but also equipped with a flexible semiparametric model that handles skewed ratio distributions better. We have also developed a Perseus plugin for EBprotV2 for easy access to the tool. In addition, the tool now offers a new submodule (MakeGrpData) to transform label-free peptide intensity data into peptide ratio data for group comparisons and performs differential abundance analysis using mixture modeling. This approach is especially useful when the label-free data have many missing peptide intensity data points.

Phenazines Regulate Nap-Dependent Denitrification in Pseudomonas aeruginosa Biofilms.

Microbes in biofilms face the challenge of substrate limitation. In particular, oxygen often becomes limited for cells in Pseudomonas aeruginosa biofilms growing in the laboratory or during host colonization. Previously we found that phenazines, antibiotics produced by P. aeruginosa, balance the intracellular redox state of cells in biofilms. Here, we show that genes involved in denitrification are induced in phenazine-null (Δphz) mutant biofilms grown under an aerobic atmosphere, even in the absence of nitrate. This finding suggests that resident cells employ a bet-hedging strategy to anticipate the potential availability of nitrate and counterbalance their highly reduced redox state. Consistent with our previous characterization of aerobically grown colonies supplemented with nitrate, we found that the pathway that is induced in Δphz mutant colonies combines the nitrate reductase activity of the periplasmic enzyme Nap with the downstream reduction of nitrite to nitrogen gas catalyzed by the enzymes Nir, Nor, and Nos. This regulatory relationship differs from the denitrification pathway that functions under anaerobic growth, with nitrate as the terminal electron acceptor, which depends on the membrane-associated nitrate reductase Nar. We identified the sequences in the promoter regions of the nap and nir operons that are required for the effects of phenazines on expression. We also show that specific phenazines have differential effects on nap gene expression. Finally, we provide evidence that individual steps of the denitrification pathway are catalyzed at different depths within aerobically grown biofilms, suggesting metabolic cross-feeding between community subpopulations.IMPORTANCE An understanding of the unique physiology of cells in biofilms is critical to our ability to treat fungal and bacterial infections. Colony biofilms of the opportunistic pathogen Pseudomonas aeruginosa grown under an aerobic atmosphere but without nitrate express a denitrification pathway that differs from that used for anaerobic growth. We report that the components of this pathway are induced by electron acceptor limitation and that they are differentially expressed over the biofilm depth. These observations suggest that (i) P. aeruginosa exhibits "bet hedging," in that it expends energy and resources to prepare for nitrate availability when other electron acceptors are absent, and (ii) cells in distinct biofilm microniches may be able to exchange substrates to catalyze full denitrification.

Insights into the regulation of protein abundance from proteomic and transcriptomic analyses.

Recent advances in next-generation DNA sequencing and proteomics provide an unprecedented ability to survey mRNA and protein abundances. Such proteome-wide surveys are illuminating the extent to which different aspects of gene expression help to regulate cellular protein abundances. Current data demonstrate a substantial role for regulatory processes occurring after mRNA is made - that is, post-transcriptional, translational and protein degradation regulation - in controlling steady-state protein abundances. Intriguing observations are also emerging in relation to cells following perturbation, single-cell studies and the apparent evolutionary conservation of protein and mRNA abundances. Here, we summarize current understanding of the major factors regulating protein expression.

Differential dynamics of the mammalian mRNA and protein expression response to misfolding stress.

The relative importance of regulation at the mRNA versus protein level is subject to ongoing debate. To address this question in a dynamic system, we mapped proteomic and transcriptomic changes in mammalian cells responding to stress induced by dithiothreitol over 30 h. Specifically, we estimated the kinetic parameters for the synthesis and degradation of RNA and proteins, and deconvoluted the response patterns into common and unique to each regulatory level using a new statistical tool. Overall, the two regulatory levels were equally important, but differed in their impact on molecule concentrations. Both mRNA and protein changes peaked between two and eight hours, but mRNA expression fold changes were much smaller than those of the proteins. mRNA concentrations shifted in a transient, pulse-like pattern and returned to values close to pre-treatment levels by the end of the experiment. In contrast, protein concentrations switched only once and established a new steady state, consistent with the dominant role of protein regulation during misfolding stress. Finally, we generated hypotheses on specific regulatory modes for some genes.

The Arabidopsis leaf transcriptome reveals distinct but also overlapping responses to colonization by phyllosphere commensals and pathogen infection with impact on plant health.

Plants are colonized by a variety of bacteria, most of which are not pathogenic. Currently, the plant responses to phyllosphere commensals or to pathogen infection in the presence of commensals are not well understood. Here, we examined the transcriptional response of Arabidopsis thaliana leaves to colonization by common commensal bacteria in a gnotobiotic system using RNA sequencing and conducted plant mutant assays. Arabidopsis responded differently to the model bacteria Sphingomonas melonis Fr1 (S.Fr1) and Methylobacterium extorquens PA1 (M.PA1). Whereas M.PA1 only marginally affected the expression of plant genes (< 10), S.Fr1 colonization changed the expression of almost 400 genes. For the latter, genes related to defense responses were activated and partly overlapped with those elicited by the pathogen Pseudomonas syringae DC3000 (Pst). As S.Fr1 is able to mediate plant protective activity against Pst, we tested plant immunity mutants and found that the pattern-recognition co-receptor mutant bak1/bkk1 showed attenuated S.Fr1-dependent plant protection. The experiments demonstrate that the plant responds differently to members of its natural phyllosphere microbiota. A subset of commensals trigger expression of defense-related genes and thereby may contribute to plant health upon pathogen encounter.

High-throughput analyses of hnRNP H1 dissects its multi-functional aspect.

hnRNPs are polyvalent RNA binding proteins that have been implicated in a range of regulatory roles including splicing, mRNA decay, translation, and miRNA metabolism. A variety of genome wide studies have taken advantage of methods like CLIP and RIP to identify the targets and binding sites of RNA binding proteins. However, due to the complex nature of RNA-binding proteins, these studies are incomplete without assays that characterize the impact of RBP binding on mRNA target expression. Here we used a suite of high-throughput approaches (RIP-Seq, iCLIP, RNA-Seq and shotgun proteomics) to provide a comprehensive view of hnRNP H1s ensemble of targets and its role in splicing, mRNA decay, and translation. The combination of RIP-Seq and iCLIP allowed us to identify a set of 1,086 high confidence target transcripts. Binding site motif analysis of these targets suggests the TGGG tetramer as a prevalent component of hnRNP H1 binding motif, with particular enrichment around intronic hnRNP H1 sites. Our analysis of the target transcripts and binding sites indicates that hnRNP H1s involvement in splicing is 2-fold: it directly affects a substantial number of splicing events, but also regulates the expression of major components of the splicing machinery and other RBPs with known roles in splicing regulation. The identified mRNA targets displayed function enrichment in MAPK signaling and ubiquitin mediated proteolysis, which might be main routes by which hnRNP H1 promotes tumorigenesis.

Statistical approach to protein quantification.

A major goal in proteomics is the comprehensive and accurate description of a proteome. This task includes not only the identification of proteins in a sample, but also the accurate quantification of their abundance. Although mass spectrometry typically provides information on peptide identity and abundance in a sample, it does not directly measure the concentration of the corresponding proteins. Specifically, most mass-spectrometry-based approaches (e.g. shotgun proteomics or selected reaction monitoring) allow one to quantify peptides using chromatographic peak intensities or spectral counting information. Ultimately, based on these measurements, one wants to infer the concentrations of the corresponding proteins. Inferring properties of the proteins based on experimental peptide evidence is often a complex problem because of the ambiguity of peptide assignments and different chemical properties of the peptides that affect the observed concentrations. We present SCAMPI, a novel generic and statistically sound framework for computing protein abundance scores based on quantified peptides. In contrast to most previous approaches, our model explicitly includes information from shared peptides to improve protein quantitation, especially in eukaryotes with many homologous sequences. The model accounts for uncertainty in the input data, leading to statistical prediction intervals for the protein scores. Furthermore, peptides with extreme abundances can be reassessed and classified as either regular data points or actual outliers. We used the proposed model with several datasets and compared its performance to that of other, previously used approaches for protein quantification in bottom-up mass spectrometry.

PECA: a novel statistical tool for deconvoluting time-dependent gene expression regulation.

Protein expression varies as a result of intricate regulation of synthesis and degradation of messenger RNAs (mRNA) and proteins. Studies of dynamic regulation typically rely on time-course data sets of mRNA and protein expression, yet there are no statistical methods that integrate these multiomics data and deconvolute individual regulatory processes of gene expression control underlying the observed concentration changes. To address this challenge, we developed Protein Expression Control Analysis (PECA), a method to quantitatively dissect protein expression variation into the contributions of mRNA synthesis/degradation and protein synthesis/degradation, termed RNA-level and protein-level regulation respectively. PECA computes the rate ratios of synthesis versus degradation as the statistical summary of expression control during a given time interval at each molecular level and computes the probability that the rate ratio changed between adjacent time intervals, indicating regulation change at the time point. Along with the associated false-discovery rates, PECA gives the complete description of dynamic expression control, that is, which proteins were up- or down-regulated at each molecular level and each time point. Using PECA, we analyzed two yeast data sets monitoring the cellular response to hyperosmotic and oxidative stress. The rate ratio profiles reported by PECA highlighted a large magnitude of RNA-level up-regulation of stress response genes in the early response and concordant protein-level regulation with time delay. However, the contributions of RNA- and protein-level regulation and their temporal patterns were different between the two data sets. We also observed several cases where protein-level regulation counterbalanced transcriptomic changes in the early stress response to maintain the stability of protein concentrations, suggesting that proteostasis is a proteome-wide phenomenon mediated by post-transcriptional regulation.