A Quantitative Proteome Map of the Human Body

Translational remodeling by RNA-binding proteins and noncoding RNAs

Responsible for generating the proteome that controls phenotype, translation is the ultimate convergence point for myriad upstream signals that influence gene expression. System-wide adaptive translational reprogramming has recently emerged as a pillar of cellular adaptation. As classic regulators of mRNA stability and translation efficiency, foundational studies established the concept of collaboration and competition between RNA-binding proteins (RBPs) and noncoding RNAs (ncRNAs) on individual mRNAs. Fresh conceptual innovations now highlight stress-activated, evolutionarily conserved RBP networks and ncRNAs that increase the translation efficiency of populations of transcripts encoding proteins that participate in a common cellular process. The discovery of post-transcriptional functions for long noncoding RNAs (lncRNAs) was particularly intriguing given their cell-type-specificity and historical definition as nuclear-functioning epigenetic regulators. The convergence of RBPs, lncRNAs, and microRNAs on functionally related mRNAs to enable adaptive protein synthesis is a newer biological paradigm that highlights their role as "translatome (protein output) remodelers" and reinvigorates the paradigm of "RNA operons." Together, these concepts modernize our understanding of cellular stress adaptation and strategies for therapeutic development. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications Translation > Translation Regulation Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

Biochemical and transcript level differences between the three human phosphofructokinases show optimisation of each isoform for specific metabolic niches

6-Phosphofructokinase-1-kinase (PFK) tetramers catalyse the phosphorylation of fructose 6-phosphate (F6P) to fructose 1,6-bisphosphate (F16BP). Vertebrates have three PFK isoforms (PFK-M, PFK-L, and PFK-P). This study is the first to compare the kinetics, structures, and transcript levels of recombinant human PFK isoforms. Under the conditions tested PFK-M has the highest affinities for F6P and ATP (K_0.5^ATP 152 µM; K_0.5^F6P 147 µM), PFK-P the lowest affinities (K_0.5^ATP 276 µM; K_0.5^F6P 1333 µM), and PFK-L demonstrates a mixed picture of high ATP affinity and low F6P affinity (K_0.5^ATP 160 µM; K_0.5^F6P 1360 µM). PFK-M is more resistant to ATP inhibition compared with PFK-L and PFK-P (respectively, 23%, 31%, 50% decreases in specificity constants). GTP is an alternate phospho donor. Interface 2, which regulates the inactive dimer to active tetramer equilibrium, differs between isoforms, resulting in varying tetrameric stability. Under the conditions tested PFK-M is less sensitive to fructose 2,6-bisphosphate (F26BP) allosteric modulation than PFK-L or PFK-P (allosteric constants [K_0.5^ATP+F26BP/K_0.5^ATP] 1.10, 0.92, 0.54, respectively). Structural analysis of two allosteric sites reveals one may be specialised for AMP/ADP and the other for smaller/flexible regulators (citrate or phosphoenolpyruvate). Correlations between PFK-L and PFK-P transcript levels indicate that simultaneous expression may expand metabolic capacity for F16BP production whilst preserving regulatory capabilities. Analysis of cancer samples reveals intriguing parallels between PFK-P and PKM2 (pyruvate kinase M2), and simultaneous increases in PFK-P and PFKFB3 (responsible for F26BP production) transcript levels, suggesting prioritisation of metabolic flexibility in cancers. Our results describe the kinetic and transcript level differences between the three PFK isoforms, explaining how each isoform may be optimised for distinct roles.

Risk of Metabolic Syndrome in Kidney Stone Formers: A Comparative Cohort Study with a Median Follow-Up of 19 Years

Background: Kidney stone formers (SF) are more likely to develop diabetes mellitus (DM), but there is no study examining risk of metabolic syndrome (MetS) in this population. We aimed to describe the risk of MetS in SF compared to non-SF. Methods and Materials: SF referred to a tertiary referral metabolic centre in Southern England from 1990 to 2007, comparator patients were age, sex, and period (first stone) matched with 3:1 ratio from the same primary care database. SF with no documentation or previous MetS were excluded. Ethical approval was obtained and MetS was defined using the modified Association of American Clinical Endocrinologists (AACE) criteria. Analysis with cox proportional hazard regression. Results: In total, 828 SF were included after 1000 records were screened for inclusion, with 2484 age and sex matched non-SF comparators. Median follow-up was 19 years (interquartile range—IQR: 15–22) for both stone formers and stone-free comparators. SF were at significantly increased risk of developing MetS (hazard ratio—HR: 1.77; 95% confidence interval—CI: 1.55–2.03, p < 0.001). This effect was robust to adjustment for pre-existing components (HR: 1.91; 95% CI: 1.66–2.19, p < 0.001). Conclusions: Kidney stone formers are at increased risk of developing metabolic syndrome. Given the pathophysiological mechanism, the stone is likely a ‘symptom’ of an underlying metabolic abnormality, whether covert or overt. This has implications the risk of further stone events and cardiovascular disease.

SARS-CoV-2 Viremia is Associated with Distinct Proteomic Pathways and Predicts COVID-19 Outcomes

BACKGROUND

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) plasma viremia has been associated with severe disease and death in coronavirus disease 2019 (COVID-19) in small-scale cohort studies. The mechanisms behind this association remain elusive.

METHODS

We evaluated the relationship between SARS-CoV-2 viremia, disease outcome, inflammatory and proteomic profiles in a cohort of COVID-19 emergency department participants. SARS-CoV-2 viral load was measured using qRT-PCR based platform. Proteomic data were generated with Proximity Extension Assay (PEA) using the Olink platform.

RESULTS

Three hundred participants with nucleic acid test-confirmed COVID-19 were included in this study. Levels of plasma SARS-CoV-2 viremia at the time of presentation predicted adverse disease outcomes, with an adjusted odds ratio (aOR) of 10.6 (95% confidence interval [CI] 4.4, 25.5, P<0.001) for severe disease (mechanical ventilation and/or 28-day mortality) and aOR of 3.9 (95%CI 1.5, 10.1, P=0.006) for 28-day mortality. Proteomic analyses revealed prominent proteomic pathways associated with SARS-CoV-2 viremia, including upregulation of SARS-CoV-2 entry factors (ACE2, CTSL, FURIN), heightened markers of tissue damage to the lungs, gastrointestinal tract, endothelium/vasculature and alterations in coagulation pathways.

CONCLUSIONS

These results highlight the cascade of vascular and tissue damage associated with SARS-CoV-2 plasma viremia that underlies its ability to predict COVID-19 disease outcomes.

Imaging the future: the emerging era of single-cell spatial proteomics

The proteome of a human cell is partitioned within organelles, such as the nucleus, and other subcellular compartments, such as the cytoplasm, forming a myriad of membrane-bound and membrane-free ultrastructures. This compartmentalization allows discrete biochemical processes to occur efficiently in isolation, with relevant proteins localized to appropriate niches to fulfill their biological function(s). Proper delivery and dynamic exchange of proteins between compartments underlie the regulation of many cellular processes, such as cell signaling, division, and programmed cell death. To this end, cells deploy dedicated trafficking mechanisms to ensure correct protein localization, as mis-localization can result in pathology. In addition to trafficking, variation in the expression, modification, and physical associations of proteins within and between cells can result in biological heterogeneity, motivating the need for single-cell measurements. In this review, we introduce diverse platform technologies developed for subcellular proteomics and high-throughput systems biology, with the aim of providing mechanistic insights into fundamental cell biological processes underlying healthy and diseased states, and valuable public data resources. In contrast to the rapidly advancing field of single-cell genomics, the single-cell spatial proteomics toolbox remains in its infancy, but is poised to make considerable advances in the coming years.

Comparative Multiplexed Interactomics of SARS-CoV-2 and Homologous Coronavirus Nonstructural Proteins Identifies Unique and Shared Host-Cell Dependencies

Human coronaviruses (hCoVs) have become a threat to global health and society, as evident from the SARS outbreak in 2002 caused by SARS-CoV-1 and the most recent COVID-19 pandemic caused by SARS-CoV-2. Despite a high sequence similarity between SARS-CoV-1 and -2, each strain has a distinctive virulence. A better understanding of the basic molecular mechanisms mediating changes in virulence is needed. Here, we profile the virus-host protein-protein interactions of two hCoV nonstructural proteins (nsps) that are critical for virus replication. We use tandem mass tag-multiplexed quantitative proteomics to sensitively compare and contrast the interactomes of nsp2 and nsp4 from three betacoronavirus strains: SARS-CoV-1, SARS-CoV-2, and hCoV-OC43-an endemic strain associated with the common cold. This approach enables the identification of both unique and shared host cell protein binding partners and the ability to further compare the enrichment of common interactions across homologues from related strains. We identify common nsp2 interactors involved in endoplasmic reticulum (ER) Ca2+ signaling and mitochondria biogenesis. We also identify nsp4 interactors unique to each strain, such as E3 ubiquitin ligase complexes for SARS-CoV-1 and ER homeostasis factors for SARS-CoV-2. Common nsp4 interactors include N-linked glycosylation machinery, unfolded protein response associated proteins, and antiviral innate immune signaling factors. Both nsp2 and nsp4 interactors are strongly enriched in proteins localized at mitochondria-associated ER membranes suggesting a new functional role for modulating host processes, such as calcium homeostasis, at these organelle contact sites. Our results shed light on the role these hCoV proteins play in the infection cycle, as well as host factors that may mediate the divergent pathogenesis of OC43 from SARS strains. Our mass spectrometry workflow enables rapid and robust comparisons of multiple bait proteins, which can be applied to additional viral proteins. Furthermore, the identified common interactions may present new targets for exploration by host-directed antiviral therapeutics.

Tools for the Recognition of Sorting Signals and the Prediction of Subcellular Localization of Proteins From Their Amino Acid Sequences

At the time of translation, nascent proteins are thought to be sorted into their final subcellular localization sites, based on the part of their amino acid sequences (i.e., sorting or targeting signals). Thus, it is interesting to computationally recognize these signals from the amino acid sequences of any given proteins and to predict their final subcellular localization with such information, supplemented with additional information (e.g., k-mer frequency). This field has a long history and many prediction tools have been released. Even in this era of proteomic atlas at the single-cell level, researchers continue to develop new algorithms, aiming at accessing the impact of disease-causing mutations/cell type-specific alternative splicing, for example. In this article, we overview the entire field and discuss its future direction.

Enhanced Validation of Antibodies Enables the Discovery of Missing Proteins

The localization of proteins at a tissue- or cell-type-specific level is tightly linked to the protein function. To better understand each protein’s role in cellular systems, spatial information constitutes an important complement to quantitative data. The standard methods for determining the spatial distribution of proteins in single cells of complex tissue samples make use of antibodies. For a stringent analysis of the human proteome, we used orthogonal methods and independent antibodies to validate 5981 antibodies that show the expression of 3775 human proteins across all major human tissues. This enhanced validation uncovered 56 proteins corresponding to the group of “missing proteins” and 171 proteins of unknown function. The presented strategy will facilitate further discussions around criteria for evidence of protein existence based on immunohistochemistry and serves as a useful guide to identify candidate proteins for integrative studies with quantitative proteomics methods.

Comparative proteomics analysis of dietary restriction in Drosophila

To explore the underlying mechanism of dietary restriction (DR) induced lifespan extension in fruit flies at protein level, we performed proteome sequencing in Drosophila at day 7 (young) and day 42 (old) under DR and ad libitum (AL) conditions. A total of 18629 unique peptides were identified in Uniprot, corresponding to 3,662 proteins. Among them, 383 and 409 differentially expressed proteins (DEPs) were identified from comparison between DR vs AL at day 7 and 42, respectively. Bioinformatics analysis revealed that membrane-related processes, post-transcriptional processes, spliceosome and reproduction related processes, were highlighted significantly. In addition, expression of proteins involved in pathways such as spliceosomes, oxidative phosphorylation, lysosomes, ubiquitination, and riboflavin metabolism was relatively higher during DR. A relatively large number of DEPs were found to participate in longevity and age-related disease pathways. We identified 20 proteins that were consistently regulated during DR and some of which are known to be involved in ageing, such as mTORC1, antioxidant, DNA damage repair and autophagy. In the integration analysis, we found 15 genes that were stably regulated by DR at both transcriptional as well as translational levels. Our results provided a useful dataset for further investigations on the mechanism of DR and aging.