A predicted physicochemically distinct sub-proteome associated with the intracellular organelle of the anammox bacterium Kuenenia stuttgartiensis

Medema MH, Zhou M, van Hijum SAFT, et al. A predicted physicochemically distinct sub-proteome associated with the intracellular organelle of the anammox bacterium Kuenenia stuttgartiensis. BMC Genomics. 2010;11(1): 299.Background Anaerobic ammonium-oxidizing (anammox) bacteria perform a key step in global nitrogen cycling. These bacteria make use of an organelle to oxidize ammonia anaerobically to nitrogen (N2) and so contribute ~50% of the nitrogen in the atmosphere. It is currently unknown which proteins constitute the organellar proteome and how anammox bacteria are able to specifically target organellar and cell-envelope proteins to their correct final destinations. Experimental approaches are complicated by the absence of pure cultures and genetic accessibility. However, the genome of the anammox bacterium Candidatus "Kuenenia stuttgartiensis" has recently been sequenced. Here, we make use of these genome data to predict the organellar sub-proteome and address the molecular basis of protein sorting in anammox bacteria. Results Two training sets representing organellar (30 proteins) and cell envelope (59 proteins) proteins were constructed based on previous experimental evidence and comparative genomics. Random forest (RF) classifiers trained on these two sets could differentiate between organellar and cell envelope proteins with ~89% accuracy using 400 features consisting of frequencies of two adjacent amino acid combinations. A physicochemically distinct organellar sub-proteome containing 562 proteins was predicted with the best RF classifier. This set included almost all catabolic and respiratory factors encoded in the genome. Apparently, the cytoplasmic membrane performs no catabolic functions. We predict that the Tat-translocation system is located exclusively in the organellar membrane, whereas the Sec-translocation system is located on both the organellar and cytoplasmic membranes. Canonical signal peptides were predicted and validated experimentally, but a specific (N- or C-terminal) signal that could be used for protein targeting to the organelle remained elusive. Conclusions A physicochemically distinct organellar sub-proteome was predicted from the genome of the anammox bacterium K. stuttgartiensis. This result provides strong in silico support for the existing experimental evidence for the existence of an organelle in this bacterium, and is an important step forward in unravelling a geochemically relevant case of cytoplasmic differentiation in bacteria. The predicted dual location of the Sec-translocation system and the apparent absence of a specific N- or C-terminal signal in the organellar proteins suggests that additional chaperones may be necessary that act on an as-yet unknown property of the targeted proteins

M-protein diagnostics in multiple myeloma patients using ultra-sensitive targeted mass spectrometry and an off-the-shelf calibrator

Objectives: Minimal residual disease status in multiple myeloma is an important prognostic biomarker. Recently, personalized blood-based targeted mass spectrometry (MS-MRD) was shown to provide a sensitive and minimally invasive alternative to measure minimal residual disease. However, quantification of MS-MRD requires a unique calibrator for each patient. The use of patient-specific stable isotope labelled (SIL) peptides is relatively costly and time-consuming, thus hindering clinical implementation. Here, we introduce a simplification of MS-MRD by using an off-the-shelf calibrator. SILuMAB-based MS-MRD was performed by spiking a monoclonal stable isotope labeled IgG, Methods: SILuMAB-K1, in the patient serum. The abundance of both M-protein-specific peptides and SILuMAB-specific peptides were monitored by mass spectrometry. The relative ratio between M-protein peptides and SILuMAB peptides allowed for M-protein quantification. We assessed linearity, sensitivity and reproducibility of SILuMAB-based MS-MRD in longitudinally collected sera from the IFM-2009 clinical trial. Results: A linear dynamic range was achieved of over 5 log scales, allowing for M-protein quantification down to 0.001 » g/L. The inter-assay CV of SILuMAB-based MS-MRD was on average 11 » %. Excellent concordance between SIL- and SILuMAB-based MS-MRD was shown (R2&gt;0.985). Additionally, signal intensity of spiked SILuMAB can be used for quality control purpose to assess system performance and incomplete SILuMAB digestion can be used as quality control for sample preparation. Conclusion:Compared to SIL peptides, SILuMAB-based MS-MRD improves the reproducibility, turn-around-times and cost-efficacy of MS-MRD without diminishing its sensitivity and specificity. Furthermore, SILuMAB can be used as a MS-MRD quality control tool to monitor sample preparation efficacy and assay performance.</p

Impact of infection on proteome-wide glycosylation revealed by distinct signatures for bacterial and viral pathogens

Funding Information: This research, part of the PERFORM project, has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 668303 . The samples collected previously were funded by: the European Seventh Framework Programme for Research and Technological Development ( FP7 ) under EUCLIDS Grant Agreement No. 279185 . Funding Information: This research, part of the PERFORM project, has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 668303. The samples collected previously were funded by: the European Seventh Framework Programme for Research and Technological Development (FP7) under EUCLIDS Grant Agreement No. 279185. We would like to thank all the patients and the healthy controls for donating their blood for the EUCLIDS, IRIS, and Westra studies. We would like to thank the Radboud Consortium for Glycoscience for their advice for data interpretation. We also thank the PERFORM consortium (see supplemental material for all participants) for their collaboration and fruitful discussions. Conceptualization, E.W. H.W. and M.J.; Methodology, E.W. M.D. H.W. and M.J.; Investigation, E.W.; Software, A.S. and H.W.; Formal Analysis, E.W. and H.W.; Visualization, E.W. and H.W.; Resources, M.F. L.H. N.K. R.P. M.K. V.W. J.H. and F.M.; Writing – Original Draft, E.W. and M.J.; Writing – Review & Editing, E.W. J.G. A.S. M.F. L.H. N.K. R.P. M.D. M.K. V.W. J.H. F.M. M.L. R.G. A.G. D.L. H.W. and M.J.; Funding Acquisition, M.J. R.G. and M.L.; Supervision, H.W. D.L. and M.J. The authors declare no competing interests. We support inclusive, diverse, and equitable conduct of research. Publisher Copyright: © 2023 The Author(s)Mechanisms of infection and pathogenesis have predominantly been studied based on differential gene or protein expression. Less is known about posttranslational modifications, which are essential for protein functional diversity. We applied an innovative glycoproteomics method to study the systemic proteome-wide glycosylation in response to infection. The protein site-specific glycosylation was characterized in plasma derived from well-defined controls and patients. We found 3862 unique features, of which we identified 463 distinct intact glycopeptides, that could be mapped to more than 30 different proteins. Statistical analyses were used to derive a glycopeptide signature that enabled significant differentiation between patients with a bacterial or viral infection. Furthermore, supported by a machine learning algorithm, we demonstrated the ability to identify the causative pathogens based on the distinctive host blood plasma glycopeptide signatures. These results illustrate that glycoproteomics holds enormous potential as an innovative approach to improve the interpretation of relevant biological changes in response to infection.Peer reviewe

Iterative orthology prediction uncovers new mitochondrial proteins and identifies C12orf62 as the human ortholog of COX14, a protein involved in the assembly of cytochrome c oxidase

BACKGROUND: Orthology is a central tenet of comparative genomics and ortholog identification is instrumental to protein function prediction. Major advances have been made to determine orthology relations among a set of homologous proteins. However, they depend on the comparison of individual sequences and do not take into account divergent orthologs. RESULTS: We have developed an iterative orthology prediction method, Ortho-Profile, that uses reciprocal best hits at the level of sequence profiles to infer orthology. It increases ortholog detection by 20% compared to sequence-to-sequence comparisons. Ortho-Profile predicts 598 human orthologs of mitochondrial proteins from Saccharomyces cerevisiae and Schizosaccharomyces pombe with 94% accuracy. Of these, 181 were not known to localize to mitochondria in mammals. Among the predictions of the Ortho-Profile method are 11 human cytochrome c oxidase (COX) assembly proteins that are implicated in mitochondrial function and disease. Their co-expression patterns, experimentally verified subcellular localization, and co-purification with human COX-associated proteins support these predictions. For the human gene C12orf62, the ortholog of S. cerevisiae COX14, we specifically confirm its role in negative regulation of the translation of cytochrome c oxidase. CONCLUSIONS: Divergent homologs can often only be detected by comparing sequence profiles and profile-based hidden Markov models. The Ortho-Profile method takes advantage of these techniques in the quest for orthologs

Fasting Proinsulin Independently Predicts Incident Type 2 Diabetes in the General Population

Fasting proinsulin levels may serve as a marker of beta-cell dysfunction and predict type 2 diabetes (T2D) development. Kidneys have been found to be a major site for the degradation of proinsulin. We aimed to evaluate the predictive value of proinsulin for the risk of incident T2D added to a base model of clinical predictors and examined potential effect modification by variables related to kidney function. Proinsulin was measured in plasma with U-PLEX platform using ELISA immunoassay. We included 5001 participants without T2D at baseline and during a median follow up of 7.2 years; 271 participants developed T2D. Higher levels of proinsulin were associated with increased risk of T2D independent of glucose, insulin, C-peptide, and other clinical factors (hazard ratio (HR): 1.28; per 1 SD increase 95% confidence interval (CI): 1.08-1.52). Harrell's C-index for the Framingham offspring risk score was improved with the addition of proinsulin (p = 0.019). Furthermore, we found effect modification by hypertension (p = 0.019), eGFR (p = 0.020) and urinary albumin excretion (p = 0.034), consistent with an association only present in participants with hypertension or kidney dysfunction. Higher fasting proinsulin level is an independent predictor of incident T2D in the general population, particularly in participants with hypertension or kidney dysfunction

Plasma C-Peptide and Risk of Developing Type 2 Diabetes in the General Population

C-peptide measurement may represent a better index of pancreatic β-cell function compared to insulin. While insulin is mainly cleared by liver, C-peptide is mainly metabolized by kidneys. The aim of our study was to evaluate the association between baseline plasma C-peptide level and the development of type 2 diabetes independent of glucose and insulin levels and to examine potential effect-modification by variables related to kidney function. We included 5176 subjects of the Prevention of Renal and Vascular End-Stage Disease study without type 2 diabetes at baseline. C-peptide was measured in plasma with an electrochemiluminescent immunoassay. Cox proportional hazards regression was used to evaluate the association between C-peptide level and type 2 diabetes development. Median C-peptide was 722 (566-935) pmol/L. During a median follow-up of 7.2 (6.0-7.7) years, 289 individuals developed type 2 diabetes. In multivariable-adjusted Cox regression models, we observed a significant positive association of C-peptide with the risk of type 2 diabetes independent of glucose and insulin levels (hazard ratio (HR): 2.35; 95% confidence interval (CI): 1.49-3.70). Moreover, we found significant effect modification by hypertension and albuminuria (p < 0.001 and p = 0.001 for interaction, respectively), with a stronger association in normotensive and normo-albuminuric subjects and absence of an association in subjects with hypertension or albuminuria. In this population-based cohort, elevated C-peptide levels are associated with an increased risk of type 2 diabetes independent of glucose, insulin levels, and clinical risk factors. Elevated C-peptide level was not independently associated with an increased risk of type 2 diabetes in individuals with hypertension or albuminuria

N-linked glycosylation of the M-protein variable region:glycoproteogenomics reveals a new layer of personalized complexity in multiple myeloma

Objectives: Multiple myeloma (MM) is a plasma cell malignancy characterized by a monoclonal expansion of plasma cells that secrete a characteristic M-protein. This M-protein is crucial for diagnosis and monitoring of MM in the blood of patients. Recent evidence has emerged suggesting that N-glycosylation of the M-protein variable (Fab) region contributes to M-protein pathogenicity, and that it is a risk factor for disease progression of plasma cell disorders. Current methodologies lack the specificity to provide a site-specific glycoprofile of the Fab regions of M-proteins. Here, we introduce a novel glycoproteogenomics method that allows detailed M-protein glycoprofiling by integrating patient specific Fab region sequences (genomics) with glycoprofiling by glycoproteomics. Methods: Glycoproteogenomics was used for the detailed analysis of de novo N-glycosylation sites of M-proteins. First, Genomic analysis of the M-protein variable region was used to identify de novo N-glycosylation sites. Subsequently glycopeptide analysis with LC-MS/MS was used for detailed analysis of the M-protein glycan sites. Results: Genomic analysis uncovered a more than two-fold increase in the Fab Light Chain N-glycosylation of M-proteins of patients with Multiple Myeloma compared to Fab Light Chain N-glycosylation of polyclonal antibodies from healthy individuals. Subsequent glycoproteogenomics analysis of 41 patients enrolled in the IFM 2009 clinical trial revealed that the majority of the Fab N-glycosylation sites were fully occupied with complex type glycans, distinguishable from Fc region glycans due to high levels of sialylation, fucosylation and bisecting structures. Conclusions: Together, glycoproteogenomics is a powerful tool to study de novo Fab N-glycosylation in plasma cell dyscrasias.</p

Mass Spectrometry for Identification, Monitoring, and Minimal Residual Disease Detection of M-Proteins

BACKGROUND: Monoclonal gammopathies (MGs) are plasma cell disorders defined by the clonal expansion of plasma cells, resulting in the characteristic excretion of a monoclonal immunoglobulin (M-protein). M-protein detection and quantification are integral parts of the diagnosi

N-linked glycosylation of the M-protein variable region:glycoproteogenomics reveals a new layer of personalized complexity in multiple myeloma

Objectives: Multiple myeloma (MM) is a plasma cell malignancy characterized by a monoclonal expansion of plasma cells that secrete a characteristic M-protein. This M-protein is crucial for diagnosis and monitoring of MM in the blood of patients. Recent evidence has emerged suggesting that N-glycosylation of the M-protein variable (Fab) region contributes to M-protein pathogenicity, and that it is a risk factor for disease progression of plasma cell disorders. Current methodologies lack the specificity to provide a site-specific glycoprofile of the Fab regions of M-proteins. Here, we introduce a novel glycoproteogenomics method that allows detailed M-protein glycoprofiling by integrating patient specific Fab region sequences (genomics) with glycoprofiling by glycoproteomics. Methods: Glycoproteogenomics was used for the detailed analysis of de novo N-glycosylation sites of M-proteins. First, Genomic analysis of the M-protein variable region was used to identify de novo N-glycosylation sites. Subsequently glycopeptide analysis with LC-MS/MS was used for detailed analysis of the M-protein glycan sites. Results: Genomic analysis uncovered a more than two-fold increase in the Fab Light Chain N-glycosylation of M-proteins of patients with Multiple Myeloma compared to Fab Light Chain N-glycosylation of polyclonal antibodies from healthy individuals. Subsequent glycoproteogenomics analysis of 41 patients enrolled in the IFM 2009 clinical trial revealed that the majority of the Fab N-glycosylation sites were fully occupied with complex type glycans, distinguishable from Fc region glycans due to high levels of sialylation, fucosylation and bisecting structures. Conclusions: Together, glycoproteogenomics is a powerful tool to study de novo Fab N-glycosylation in plasma cell dyscrasias.</p

Impact of infection on proteome-wide glycosylation revealed by distinct signatures for bacterial and viral pathogens

Mechanisms of infection and pathogenesis have predominantly been studied based on differential gene or protein expression. Less is known about posttranslational modifications, which are essential for protein functional diversity. We applied an innovative glycoproteomics method to study the systemic proteome-wide glycosylation in response to infection. The protein site-specific glycosylation was characterized in plasma derived from well-defined controls and patients. We found 3862 unique features, of which we identified 463 distinct intact glycopeptides, that could be mapped to more than 30 different proteins. Statistical analyses were used to derive a glycopeptide signature that enabled significant differentiation between patients with a bacterial or viral infection. Furthermore, supported by a machine learning algorithm, we demonstrated the ability to identify the causative pathogens based on the distinctive host blood plasma glycopeptide signatures. These results illustrate that glycoproteomics holds enormous potential as an innovative approach to improve the interpretation of relevant biological changes in response to infection