Objective Antibodies against posttranslationally modified proteins are a hallmark of rheumatoid arthritis (RA), but the emergence and pathogenicity of these autoantibodies are still incompletely understood. The aim of this study was to analyze the antigen specificities and mutation patterns of monoclonal antibodies (mAb) derived from RA synovial plasma cells and address the question of antigen cross-reactivity. Methods IgG-secreting cells were isolated from RA synovial fluid, and the variable regions of the immunoglobulins were sequenced (n = 182) and expressed in full-length mAb (n = 93) and also as germline-reverted versions. The patterns of reactivity with 53,019 citrullinated peptides and 49,211 carbamylated peptides and the potential of the mAb to promote osteoclastogenesis were investigated. Results Four unrelated anti-citrullinated protein autoantibodies (ACPAs), of which one was clonally expanded, were identified and found to be highly somatically mutated in the synovial fluid of a patient with RA. The ACPAs recognized >3,000 unique peptides modified by either citrullination or carbamylation. This highly multireactive autoantibody feature was replicated for Ig sequences derived from B cells from the peripheral blood of other RA patients. The plasma cell-derived mAb were found to target distinct amino acid motifs and partially overlapping protein targets. They also conveyed different effector functions as revealed in an osteoclast activation assay. Conclusion These findings suggest that the high level of cross-reactivity among RA autoreactive B cells is the result of different antigen encounters, possibly at different sites and at different time points. This is consistent with the notion that RA is initiated in one context, such as in the mucosal organs, and thereafter targets other sites, such as the joints.

The availability of proteomics resources hosting protein and peptide standards, as well as the data describing their analytical performances, will continue to enhance our current capabilities to develop targeted proteomics methods for quantitative biology. This study describes the analysis of a resource of 26,840 individually purified recombinant protein fragments corresponding to more than 16,000 human protein-coding genes. The resource was screened to identify proteotypic peptides suitable for targeted proteomics efforts, and we report LC-MS/MS assay coordinates for more than 25,000 proteotypic peptides, corresponding to more than 10,000 unique proteins. Additionally, peptide formation and digestion kinetics were, for a subset of the standards, monitored using a time-course protocol involving parallel digestion of isotope-labeled recombinant protein standards and endogenous human plasma proteins. We show that the strategy by adding isotope-labeled recombinant proteins before trypsin digestion enables short digestion protocols (<= 60 min) with robust quantitative precision. In a proof-of-concept study, we quantified 23 proteins in human plasma using assay parameters defined in our study and used the standards to describe distinct clusters of individuals linked to different levels of LPA, APOE, SERPINAS, and TFRC. In summary, we describe the use and utility of a resource of recombinant proteins to identify proteotypic peptides useful for targeted proteomics assay development.

Few studies have investigated the blood proteome of inflammatory bowel disease (IBD). We characterized the serum abundance of proteins encoded at 163 known IBD risk loci and tested these proteins for their biomarker discovery potential. Based on the Human Protein Atlas (HPA) antibody availability, 218 proteins from genes mapping at 163 IBD risk loci were selected. Targeted serum protein profiles from 49 Crohns disease (CD) patients, 51 ulcerative colitis (UC) patients, and 50 sex- and age-matched healthy individuals were obtained using multiplexed antibody suspension bead array assays. Differences in relative serum abundance levels between disease groups and controls were examined. Replication was attempted for CD-UC comparisons (including disease subtypes) by including 64 additional patients (33 CD and 31 UC). Antibodies targeting a potentially novel risk protein were validated by paired antibodies, Western blot, immuno-capture mass spectrometry, and epitope mapping. By univariate analysis, 13 proteins mostly related to neutrophil, T-cell, and B-cell activation and function were differentially expressed in IBD patients vs healthy controls, 3 in CD patients vs healthy controls and 2 in UC patients vs healthy controls (q < 0.01). Multivariate analyses further differentiated disease groups from healthy controls and CD subtypes from UC (P < 0.05). Extended characterization of an antibody targeting a novel, discriminative serum marker, the laccase (multicopper oxidoreductase) domain containing 1 (LACC1) protein, provided evidence for antibody on-target specificity. Using affinity proteomics, we identified a set of IBD-associated serum proteins encoded at IBD risk loci. These candidate proteins hold the potential to be exploited as diagnostic biomarkers of IBD.

A natural way to benchmark the performance of an analytical experimental setup is to use samples of known measured analytes are peptides and not the actual proteins one of the inherent problems of interpreting data is that the composition and see to what degree one can correctly infer the content of such a sample from the data. For shotgun proteomics, themselves. As some proteins share proteolytic peptides, there might be more than one possible causative set of proteins resulting in a given set of peptides and there is a need for mechanisms that infer proteins from lists of detected peptides. A weakness of commercially available samples of known content is that they consist of proteins that are deliberately selected for producing tryptic peptides that are unique to a single protein. Unfortunately, such samples do not expose any complications in protein inference. Hence, for a realistic benchmark of protein inference procedures, there is a need for samples of known content where the present proteins share peptides with known absent proteins. Here, we present such a standard, that is based on E. coli expressed human protein fragments. To illustrate the application of this standard, we benchmark a set of different protein inference procedures on the data. We observe that inference procedures excluding shared peptides provide more accurate estimates of errors compared to methods that include information from shared peptides, while still giving a reasonable performance in terms of the number of identified proteins. We also demonstrate that using a sample of known protein content without proteins with shared tryptic peptides can give a false sense of accuracy for many protein inference methods.

Cyanobacteria must balance separate demands for energy generation, carbon assimilation, and biomass synthesis. We used shotgun proteomics to investigate proteome allocation strategies in the model cyanobacterium Synechocystis sp. PCC 6803 as it adapted to light and inorganic carbon (C-i) limitation. When partitioning the proteome into seven functional sectors, we find that sector sizes change linearly with growth rate. The sector encompassing ribosomes is significantly smaller than in E. coli, which may explain the lower maximum growth rate in Synechocystis. Limitation of light dramatically affects multiple proteome sectors, whereas the effect of C-i limitation is weak. Carbon assimilation proteins respond more strongly to changes in light intensity than to C-i. A coarse-grained cell economy model generally explains proteome trends. However, deviations from model predictions suggest that the large proteome sectors for carbon and light assimilation are not optimally utilized under some growth conditions and may constrain the proteome space available to ribosomes.

The underlying molecular mechanisms of autoimmune diseases are poorly understood. To unravel the autoimmune processes across diseases, comprehensive and unbiased analyses of proteins targets recognized by the adaptive immune system are needed. Here we present an approach starting from high-density peptide arrays to characterize autoantibody repertoires and to identify new autoantigens. A set of ten plasma and serum samples from subjects with multiple sclerosis, narcolepsy, and without any disease diagnosis were profiled on a peptide array representing the whole proteome, hosting 2.2 million 12-mer peptides with a six amino acid lateral shift. On the basis of the IgG reactivities found on these whole-proteome peptide micro arrays, a set of 23 samples was then studied on a targeted array with 174 000 12-mer peptides of single amino acid lateral shift. Finally, verification of IgG reactivities was conducted with a larger sample set (n = 448) using the bead-based peptide microarrays. The presented workflow employed three different peptide microarray formats to discover and resolve the epitopes of human autoantibodies and revealed two potentially new autoantigens: MAP3K7 in multiple sclerosis and NRXN1 in narcolepsy. The presented strategy provides insights into antibody repertoire reactivity at a peptide level and may accelerate the discovery and validation of autoantigens in human diseases.

An important issue for molecular biology is to establish whether transcript levels of a given gene can be used as proxies for the corresponding protein levels. Here, we have developed a targeted proteomics approach for a set of human non-secreted proteins based on parallel reaction monitoring to measure, at steady-state conditions, absolute protein copy numbers across human tissues and cell lines and compared these levels with the corresponding mRNA levels using transcriptomics. The study shows that the transcript and protein levels do not correlate well unless a gene-specific RNA-to-protein (RTP) conversion factor independent of the tissue type is introduced, thus significantly enhancing the predictability of protein copy numbers from RNA levels. The results show that the RTP ratio varies significantly with a few hundred copies per mRNA molecule for some genes to several hundred thousands of protein copies per mRNA molecule for others. In conclusion, our data suggest that transcriptome analysis can be used as a tool to predict the protein copy numbers per cell, thus forming an attractive link between the field of genomics and proteomics.

This month: AI that learns patterns and facts, new protein-RNA and protein-protein relationships, engineering signaling and metabolism, and more variants of Cas9.

The brain is a vital organ and because it is well shielded from the outside environment, possibilities for noninvasive analysis are often limited. Instead, fluids taken from the spinal cord or circulatory system are preferred sources for the discovery of candidate markers within neurological diseases. In the context of multiple sclerosis (MS), we applied an affinity proteomic strategy and screened 22 plasma samples with 4595 antibodies (3450 genes) on bead arrays, then defined 375 antibodies (334 genes) for targeted analysis in a set of 172 samples and finally used 101 antibodies (43 genes) on 443 plasma as well as 573 cerebrospinal spinal fluid (CSF) samples. This revealed alteration of protein profiles in relation to MS subtypes for IRF8, IL7, METTL14, SLC30A7, and GAP43. Respective antibodies were subsequently used for immunofluorescence on human post-mortem brain tissue with MS pathology for expression and association analysis. There, antibodies for IRF8, IL7, and METTL14 stained neurons in proximity of lesions, which highlighted these candidate protein targets for further studies within MS and brain tissue. The affinity proteomic translation of profiles discovered by profiling human body fluids and tissue provides a powerful strategy to suggest additional candidates to studies of neurological disorders.