Sodium-glucose co-transporter-2 inhibitors and susceptibility to COVID-19: A population-based retrospective cohort study.

Sodium-glucose co-transporter-2 (SGLT2) inhibitors are widely prescribed in people with type 2 diabetes. We aimed to investigate whether SGLT2 inhibitor prescription is associated with COVID-19, when compared with an active comparator. We performed a propensity-score-matched cohort study with active comparators and a negative control outcome in a large UK-based primary care dataset. Participants prescribed SGLT2 inhibitors (n = 9948) and a comparator group prescribed dipeptidyl peptidase-4 (DPP-4) inhibitors (n = 14 917) were followed up from January 30 to July 27, 2020. The primary outcome was confirmed or clinically suspected COVID-19. The incidence rate of COVID-19 was 19.7/1000 person-years among users of SGLT2 inhibitors and 24.7/1000 person-years among propensity-score-matched users of DPP-4 inhibitors. The adjusted hazard ratio was 0.92 (95% confidence interval 0.66 to 1.29), and there was no evidence of residual confounding in the negative control analysis. We did not observe an increased risk of COVID-19 in primary care amongst those prescribed SGLT2 inhibitors compared to DPP-4 inhibitors, suggesting that clinicians may safely use these agents in the everyday care of people with type 2 diabetes during the COVID-19 pandemic.

Renin-angiotensin system inhibitors and susceptibility to COVID-19 in patients with hypertension: a propensity score-matched cohort study in primary care.

INTRODUCTION: Renin-angiotensin system (RAS) inhibitors have been postulated to influence susceptibility to Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This study investigated whether there is an association between their prescription and the incidence of COVID-19 and all-cause mortality. METHODS: We conducted a propensity-score matched cohort study comparing the incidence of COVID-19 among patients with hypertension prescribed angiotensin-converting enzyme I (ACE) inhibitors or angiotensin II type-1 receptor blockers (ARBs) to those treated with calcium channel blockers (CCBs) in a large UK-based primary care database (The Health Improvement Network). We estimated crude incidence rates for confirmed/suspected COVID-19 in each drug exposure group. We used Cox proportional hazards models to produce adjusted hazard ratios for COVID-19. We assessed all-cause mortality as a secondary outcome. RESULTS: The incidence rate of COVID-19 among users of ACE inhibitors and CCBs was 9.3 per 1000 person-years (83 of 18,895 users [0.44%]) and 9.5 per 1000 person-years (85 of 18,895 [0.45%]), respectively. The adjusted hazard ratio was 0.92 (95% CI 0.68 to 1.26). The incidence rate among users of ARBs was 15.8 per 1000 person-years (79 out of 10,623 users [0.74%]). The adjusted hazard ratio was 1.38 (95% CI 0.98 to 1.95). There were no significant associations between use of RAS inhibitors and all-cause mortality. CONCLUSION: Use of ACE inhibitors was not associated with the risk of COVID-19 whereas use of ARBs was associated with a statistically non-significant increase compared to the use of CCBs. However, no significant associations were observed between prescription of either ACE inhibitors or ARBs and all-cause mortality.

Machine learning of COVID-19 clinical data identifies population structures with therapeutic potential.

Clinical outcomes for patients with COVID-19 are heterogeneous and there is interest in defining subgroups for prognostic modeling and development of treatment algorithms. We obtained 28 demographic and laboratory variables in patients admitted to hospital with COVID-19. These comprised a training cohort (n = 6099) and two validation cohorts during the first and second waves of the pandemic (n = 996; n = 1011). Uniform manifold approximation and projection (UMAP) dimension reduction and Gaussian mixture model (GMM) analysis was used to define patient clusters. 29 clusters were defined in the training cohort and associated with markedly different mortality rates, which were predictive within confirmation datasets. Deconvolution of clinical features within clusters identified unexpected relationships between variables. Integration of large datasets using UMAP-assisted clustering can therefore identify patient subgroups with prognostic information and uncovers unexpected interactions between clinical variables. This application of machine learning represents a powerful approach for delineating disease pathogenesis and potential therapeutic interventions.

Development and external validation of prognostic models for COVID-19 to support risk stratification in secondary care.

OBJECTIVES: Existing UK prognostic models for patients admitted to the hospital with COVID-19 are limited by reliance on comorbidities, which are under-recorded in secondary care, and lack of imaging data among the candidate predictors. Our aims were to develop and externally validate novel prognostic models for adverse outcomes (death and intensive therapy unit (ITU) admission) in UK secondary care and externally validate the existing 4C score. DESIGN: Candidate predictors included demographic variables, symptoms, physiological measures, imaging and laboratory tests. Final models used logistic regression with stepwise selection. SETTING: Model development was performed in data from University Hospitals Birmingham (UHB). External validation was performed in the CovidCollab dataset. PARTICIPANTS: Patients with COVID-19 admitted to UHB January-August 2020 were included. MAIN OUTCOME MEASURES: Death and ITU admission within 28 days of admission. RESULTS: 1040 patients with COVID-19 were included in the derivation cohort; 288 (28%) died and 183 (18%) were admitted to ITU within 28 days of admission. Area under the receiver operating characteristic curve (AUROC) for mortality was 0.791 (95% CI 0.761 to 0.822) in UHB and 0.767 (95% CI 0.754 to 0.780) in CovidCollab; AUROC for ITU admission was 0.906 (95% CI 0.883 to 0.929) in UHB and 0.811 (95% CI 0.795 to 0.828) in CovidCollab. Models showed good calibration. Addition of comorbidities to candidate predictors did not improve model performance. AUROC for the International Severe Acute Respiratory and Emerging Infection Consortium 4C score in the UHB dataset was 0.753 (95% CI 0.720 to 0.785). CONCLUSIONS: The novel prognostic models showed good discrimination and calibration in derivation and external validation datasets, and performed at least as well as the existing 4C score using only routinely collected patient information. The models can be integrated into electronic medical records systems to calculate each individual patient's probability of death or ITU admission at the time of hospital admission. Implementation of the models and clinical utility should be evaluated.

Therapies for Long COVID in non-hospitalised individuals: from symptoms, patient-reported outcomes and immunology to targeted therapies (The TLC Study).

INTRODUCTION: Individuals with COVID-19 frequently experience symptoms and impaired quality of life beyond 4-12 weeks, commonly referred to as Long COVID. Whether Long COVID is one or several distinct syndromes is unknown. Establishing the evidence base for appropriate therapies is needed. We aim to evaluate the symptom burden and underlying pathophysiology of Long COVID syndromes in non-hospitalised individuals and evaluate potential therapies. METHODS AND ANALYSIS: A cohort of 4000 non-hospitalised individuals with a past COVID-19 diagnosis and 1000 matched controls will be selected from anonymised primary care records from the Clinical Practice Research Datalink, and invited by their general practitioners to participate on a digital platform (Atom5). Individuals will report symptoms, quality of life, work capability and patient-reported outcome measures. Data will be collected monthly for 1 year.Statistical clustering methods will be used to identify distinct Long COVID-19 symptom clusters. Individuals from the four most prevalent clusters and two control groups will be invited to participate in the BioWear substudy which will further phenotype Long COVID symptom clusters by measurement of immunological parameters and actigraphy.We will review existing evidence on interventions for postviral syndromes and Long COVID to map and prioritise interventions for each newly characterised Long COVID syndrome. Recommendations will be made using the cumulative evidence in an expert consensus workshop. A virtual supportive intervention will be coproduced with patients and health service providers for future evaluation.Individuals with lived experience of Long COVID will be involved throughout this programme through a patient and public involvement group. ETHICS AND DISSEMINATION: Ethical approval was obtained from the Solihull Research Ethics Committee, West Midlands (21/WM/0203). Research findings will be presented at international conferences, in peer-reviewed journals, to Long COVID patient support groups and to policymakers. TRIAL REGISTRATION NUMBER: 1567490.

ATLAS2030 Pediatric Gait Exoskeleton: Changes on Range of Motion, Strength and Spasticity in Children With Cerebral Palsy. A Case Series Study.

Background: Cerebral Palsy (CP), the most common motor disability in childhood, affects individual's motor skills, movement and posture. This results in limited activity and a low social participation. The ATLAS2030 exoskeleton is a pediatric device that enables gait rehabilitation for children with neurological or neuromuscular pathologies with gait pathology. Purpose: To study changes in relation to range of motion (ROM), strength and spasticity in children with CP after using the ATLAS2030 gait exoskeleton. Methods and Participants: Three children (mean age 8.0 ± 2.0), two girls and one boy, two of them with GMFCS IV and one with GMFCS III, received robot-assisted gait training (RAGT) with ATLAS2030 for one month. Results: The average time of exoskeleton use was 54.7 ± 10.4 min in all sessions, and all participants were able to perform all exercises. The strength of all muscle groups was increased after the 10 sessions for the participants assessed and the limited ROM in the sagittal plane (hip and knee extension and ankle dorsiflexion) decreased after the use of the exoskeleton compared to the initial state. Spasticity was reduced at the end of the sessions after the use of the exoskeleton compared to their initial state. Conclusion: The ROM, spasticity and strength were improved after RAGT with ATLAS2030 exoskeleton in these children with CP. However, further studies with larger samples should be carried out to confirm our findings.

Nonsteroidal Antiinflammatory Drugs and Susceptibility to COVID-19.

OBJECTIVE: To identify whether active use of nonsteroidal antiinflammatory drugs (NSAIDs) increases susceptibility to developing suspected or confirmed coronavirus disease 2019 (COVID-19) compared to the use of other common analgesics. METHODS: We performed a propensity score-matched cohort study with active comparators, using a large UK primary care data set. The cohort consisted of adult patients age ≥18 years with osteoarthritis (OA) who were followed up from January 30 to July 31, 2020. Patients prescribed an NSAID (excluding topical preparations) were compared to those prescribed either co-codamol (paracetamol and codeine) or co-dydramol (paracetamol and dihydrocodeine). A total of 13,202 patients prescribed NSAIDs were identified, compared to 12,457 patients prescribed the comparator drugs. The primary outcome measure was the documentation of suspected or confirmed COVID-19, and the secondary outcome measure was all-cause mortality. RESULTS: During follow-up, the incidence rates of suspected/confirmed COVID-19 were 15.4 and 19.9 per 1,000 person-years in the NSAID-exposed group and comparator group, respectively. Adjusted hazard ratios for suspected or confirmed COVID-19 among the unmatched and propensity score-matched OA cohorts, using data from clinical consultations in primary care settings, were 0.82 (95% confidence interval [95% CI] 0.62-1.10) and 0.79 (95% CI 0.57-1.11), respectively, and adjusted hazard ratios for the risk of all-cause mortality were 0.97 (95% CI 0.75-1.27) and 0.85 (95% CI 0.61-1.20), respectively. There was no effect modification by age or sex. CONCLUSION: No increase in the risk of suspected or confirmed COVID-19 or mortality was observed among patients with OA in a primary care setting who were prescribed NSAIDs as compared to those who received comparator drugs. These results are reassuring and suggest that in the absence of acute illness, NSAIDs can be safely prescribed during the ongoing pandemic.

Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition.

MOTIVATION: LC-MS allows for the identification and quantification of proteins from biological samples. As with any high-throughput technology, systematic biases are often observed in LC-MS data, making normalization an important preprocessing step. Normalization models need to be flexible enough to capture biases of arbitrary complexity, while avoiding overfitting that would invalidate downstream statistical inference. Careful normalization of MS peak intensities would enable greater accuracy and precision in quantitative comparisons of protein abundance levels. RESULTS: We propose an algorithm, called EigenMS, that uses singular value decomposition to capture and remove biases from LC-MS peak intensity measurements. EigenMS is an adaptation of the surrogate variable analysis (SVA) algorithm of Leek and Storey, with the adaptations including (i) the handling of the widespread missing measurements that are typical in LC-MS, and (ii) a novel approach to preventing overfitting that facilitates the incorporation of EigenMS into an existing proteomics analysis pipeline. EigenMS is demonstrated using both large-scale calibration measurements and simulations to perform well relative to existing alternatives. AVAILABILITY: The software has been made available in the open source proteomics platform DAnTE (Polpitiya et al., 2008)) (http://omics.pnl.gov/software/), as well as in standalone software available at SourceForge (http://sourceforge.net).

A statistical framework for protein quantitation in bottom-up MS-based proteomics.

MOTIVATION: Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level. RESULTS: We present a statistical model that carefully accounts for informative missingness in peak intensities and allows unbiased, model-based, protein-level estimation and inference. The model is applicable to both label-based and label-free quantitation experiments. We also provide automated, model-based, algorithms for filtering of proteins and peptides as well as imputation of missing values. Two LC/MS datasets are used to illustrate the methods. In simulation studies, our methods are shown to achieve substantially more discoveries than standard alternatives. AVAILABILITY: The software has been made available in the open-source proteomics platform DAnTE (http://omics.pnl.gov/software/).

Prophylaxis for postoperative atrial fibrillation: A quality initiative study exploring adherence to NICE guidance in a UK tertiary cardiothoracic intensive care unit.

BACKGROUND: Acute onset atrial fibrillation is a common dysrhythmia experienced by patients following cardiac surgery which can often cause morbidity and extended hospital length of stay. The primary aim of the study was to explore adherence to National Institute for Health and Care Excellence (NICE) guidance which suggests the need for prophylaxis for postoperative atrial fibrillation (POAF). Secondary aims were to explore factors contributing to the development POAF and the impact of POAF on patient-centred outcomes. METHODS: An analysis consisting of descriptive statistics and regression models was conducted using 138 patient's records who underwent cardiac surgery between January and March 2017. RESULTS: We identified 83 (62%) patients on prophylactic rate control medications prior to surgery. During the study period, a total of 50 patients (36%) developed POAF, of which 28 were on prophylactic medication prior to surgery. Patients who developed POAF had significantly prolonged hospital length of stay compared to those who did not develop POAF. CONCLUSION: Our study identified a significant proportion of patients not being offered prophylactic rate control prior to cardiac surgery. It is clear that poor patient outcomes are associated with the development of POAF and therefore there is an important need to ensure preventative measures are implemented in guidance relating to the management of these patients. Our results also suggest that tight management of clinical and physiological risk factors prior and during cardiac surgery may improve outcomes in this group of patients and could be considered in future enhanced recovery after cardiac surgery protocols.

Obstructive Sleep Apnea, a Risk Factor for Cardiovascular and Microvascular Disease in Patients With Type 2 Diabetes: Findings From a Population-Based Cohort Study.

OBJECTIVE: To determine the risk of cardiovascular disease (CVD), microvascular complications, and mortality in patients with type 2 diabetes who subsequently develop obstructive sleep apnea (OSA) compared with patients with type 2 diabetes without a diagnosis of OSA. RESEARCH DESIGN AND METHODS: This age-, sex-, BMI-, and diabetes duration-matched cohort study used data from a U.K. primary care database from 1 January 2005 to 17 January 2018. Participants aged ≥16 years with type 2 diabetes were included. Exposed participants were those who developed OSA after their diabetes diagnosis; unexposed participants were those without diagnosed OSA. Outcomes were composite CVD (ischemic heart disease [IHD], stroke/transient ischemic attack [TIA], heart failure [HF]), peripheral vascular disease (PVD), atrial fibrillation (AF), peripheral neuropathy (PN), diabetes-related foot disease (DFD), referable retinopathy, chronic kidney disease (CKD), and all-cause mortality. The same outcomes were explored in patients with preexisting OSA before a diagnosis of type 2 diabetes versus diabetes without diagnosed OSA. RESULTS: A total of 3,667 exposed participants and 10,450 matched control participants were included. Adjusted hazard ratios for the outcomes were as follows: composite CVD 1.54 (95% CI 1.32, 1.79), IHD 1.55 (1.26, 1.90), HF 1.67 (1.35, 2.06), stroke/TIA 1.57 (1.27, 1.94), PVD 1.10 (0.91, 1.32), AF 1.53 (1.28, 1.83), PN 1.32 (1.14, 1.51), DFD 1.42 (1.16, 1.74), referable retinopathy 0.99 (0.82, 1.21), CKD (stage 3-5) 1.18 (1.02, 1.36), albuminuria 1.11 (1.01, 1.22), and all-cause mortality 1.24 (1.10, 1.40). In the prevalent OSA cohort, the results were similar, but some associations were not observed. CONCLUSIONS: Patients with type 2 diabetes who develop OSA are at increased risk of CVD, AF, PN, DFD, CKD, and all-cause mortality compared with patients without diagnosed OSA. Patients with type 2 diabetes who develop OSA are a high-risk population, and strategies to detect OSA and prevent cardiovascular and microvascular complications should be implemented.

Subunit architecture of intact protein complexes from mass spectrometry and homology modeling.

Proteomic studies have yielded detailed lists of protein components. Relatively little is known, however, of interactions between proteins or of their spatial arrangement. To bridge this gap, we are developing a mass spectrometry approach based on intact protein complexes. By studying intact complexes, we show that we are able to not only determine the stoichiometry of all subunits present but also deduce interaction maps and topological arrangements of subunits. To construct an interaction network, we use tandem mass spectrometry to define peripheral subunits and partial denaturation in solution to generate series of subcomplexes. These subcomplexes are subsequently assigned using tandem mass spectrometry. To facilitate this assignment process, we have developed an iterative search algorithm (SUMMIT) to both assign protein subcomplexes and generate protein interaction networks. This software package not only allows us to construct the subunit architecture of protein assemblies but also allows us to explore the limitations and potential of our approach. Using series of hypothetical complexes, generated at random from protein assemblies containing between six and fourteen subunits, we highlight the significance of tandem mass spectrometry for defining subunits present. We also demonstrate the importance of pairwise interactions and the optimal numbers of subcomplexes required to assign networks with up to fourteen subunits. To illustrate application of our approach, we describe the overall architecture of two endogenous protein assemblies isolated from yeast at natural expression levels, the 19S proteasome lid and the RNA exosome. In constructing our models, we did not consider previous electron microscopy images but rather deduced the subunit architecture from series of subcomplexes and our network algorithm. The results show that the proteasome lid complex consists of a bicluster with two tetrameric lobes. The exosome lid, by contrast, is a six-membered ring with three additional bridging subunits that confer stability to the ring and with a large subunit located at the base. Significantly, by combining data from MS and homology modeling, we were able to construct an atomic model of the yeast exosome. In summary, the architectural and atomic models of both protein complexes described here have been produced in advance of high-resolution structural data and as such provide an initial model for testing hypotheses and planning future experiments. In the case of the yeast exosome, the atomic model is validated by comparison with the atomic structure from X-ray diffraction of crystals of the reconstituted human exosome, which is homologous to that of the yeast. Overall therefore this mass spectrometry and homology modeling approach has given significant insight into the structure of two previously intractable protein complexes and as such has broad application in structural biology.

Structural organization of the 19S proteasome lid: insights from MS of intact complexes.

The 26S proteasome contains a 19S regulatory particle that selects and unfolds ubiquitinated substrates for degradation in the 20S catalytic particle. To date there are no high-resolution structures of the 19S assembly, nor of the lid or base subcomplexes that constitute the 19S. Mass spectra of the intact lid complex from Saccharomyces cerevisiae show that eight of the nine subunits are present stoichiometrically and that a stable tetrameric subcomplex forms in solution. Application of tandem mass spectrometry to the intact lid complex reveals the subunit architecture, while the coupling of a cross-linking approach identifies further interaction partners. Taking together our results with previous analyses we are able to construct a comprehensive interaction map. In summary, our findings allow us to identify a scaffold for the assembly of the particle and to propose a regulatory mechanism that prevents exposure of the active site until assembly is complete. More generally, the results highlight the potential of mass spectrometry to add crucial insight into the structural organization of an endogenous, wild-type complex.

Mixed-isotope labeling with LC-IMS-MS for characterization of protein-protein interactions by chemical cross-linking.

Chemical cross-linking of proteins followed by proteolysis and mass spectrometric analysis of the resulting cross-linked peptides provides powerful insight into the quaternary structure of protein complexes. Mixed-isotope cross-linking (a method for distinguishing intermolecular cross-links) was coupled with liquid chromatography, ion mobility spectrometry and mass spectrometry (LC-IMS-MS) to provide an additional separation dimension to the traditional cross-linking approach. This method produced multiplet m/z peaks that are aligned in the IMS drift time dimension and serve as signatures of intermolecular cross-linked peptides. We developed an informatics tool to use the amino acid sequence information inherent in the multiplet spacing for accurate identification of the cross-linked peptides. Because of the separation of cross-linked and non-cross-linked peptides in drift time, our LC-IMS-MS approach was able to confidently detect more intermolecular cross-linked peptides than LC-MS alone.

Regulatory response to carbon starvation in Caulobacter crescentus.

Bacteria adapt to shifts from rapid to slow growth, and have developed strategies for long-term survival during prolonged starvation and stress conditions. We report the regulatory response of C. crescentus to carbon starvation, based on combined high-throughput proteome and transcriptome analyses. Our results identify cell cycle changes in gene expression in response to carbon starvation that involve the prominent role of the FixK FNR/CAP family transcription factor and the CtrA cell cycle regulator. Notably, the SigT ECF sigma factor mediates the carbon starvation-induced degradation of CtrA, while activating a core set of general starvation-stress genes that respond to carbon starvation, osmotic stress, and exposure to heavy metals. Comparison of the response of swarmer cells and stalked cells to carbon starvation revealed four groups of genes that exhibit different expression profiles. Also, cell pole morphogenesis and initiation of chromosome replication normally occurring at the swarmer-to-stalked cell transition are uncoupled in carbon-starved cells.

Subunit architecture of multimeric complexes isolated directly from cells.

Recent developments in purification strategies, together with mass spectrometry (MS)-based proteomics, have identified numerous in vivo protein complexes and suggest the existence of many others. Standard proteomics techniques are, however, unable to describe the overall stoichiometry, subunit interactions and organization of these assemblies, because many are heterogeneous, are present at relatively low cellular abundance and are frequently difficult to isolate. We combine two existing methodologies to tackle these challenges: tandem affinity purification to isolate sufficient quantities of highly pure native complexes, and MS of the intact assemblies and subcomplexes to determine their structural organization. We optimized our protocol with two protein assemblies from Saccharomyces cerevisiae (scavenger decapping and nuclear cap-binding complexes), establishing subunit stoichiometry and identifying substoichiometric binding. We then targeted the yeast exosome, a nuclease with ten different subunits, and found that by generating subcomplexes, a three-dimensional interaction map could be derived, demonstrating the utility of our approach for large, heterogeneous cellular complexes.

Gas phase ion chemistry of charged silver(I) adenine ions via multistage mass spectrometry experiments and DFT calculations.

Electrospray ionization (ESI) of solutions containing adenine and AgNO(3) yields polymeric [Ad(x)+ Ag(y)-zH]((y-z)+) species. Density functional theory (DFT) calculations have been used to examine potential structures for several of the smaller ions while multistage mass spectrometry experiments have been used to probe their unimolecular reactivity (via collision-induced dissociation (CID)) and bimolecular reactivity (via ion-molecule reactions with the neutral reagents acetonitrile, methanol, butylamine and pyridine). DFT calculations of neutral adenine tautomers and their silver ion adducts provide insights into the binding modes of adenine. We find that the most stable [Ad + Ag](+) ion does not correspond to the most stable neutral adenine tautomer, consistent with previous studies that have shown that transition metal ions can stabilize rare tautomeric forms of nucleobases. Both the charge and the stoichiometry of the [Ad(x)+ Ag(y)-zH]((y-z)+) complexes play pivotal roles in directing the types of fragmentation and ion-molecule reactions observed. Thus, [Ad(2)+ Ag(2)](2+) is observed to dissociate to [Ad + Ag](+) and to react with butylamine via proton transfer, while [Ad(2)+ Ag(2)- H](+) fragments via loss of neutral adenine to form the [Ad + Ag(2)- H](+) ion and does not undergo proton transfer to butylamine. DFT calculations on several isomeric [Ad(2)+ Ag(2)](2+) ions suggest that planar centrosymmetric cations, in which two adjacent silver atoms are bridged by two N7H adenine tautomers via N(3),N(9)-bidentate interactions, are the most stable. The [Ad + Ag(2)-H](+) ion adds two neutral reagents in ion-molecule reactions, consistent with the presence of two vacant coordination sites. It undergoes a silver atom loss to form the [Ad + Ag - H](+) radical cation, which in turn fragments quite differently to the even electron [Ad + Ag](+) ion. Several other pairs of radical cation/even electron adenine-silver complexes were also found to undergo different fragmentation reactions.

Characterization of an antagonist interleukin-6 dimer by stable isotope labeling, cross-linking, and mass spectrometry.

The homodimeric form of a recombinant cytokine interleukin-6 (IL-6(D)) is known to antagonize IL-6 signaling. In this study, spatially proximal residues between IL-6 chains in IL-6(D) were identified using a method for specific recognition of intermolecular cross-linked peptides. Our strategy involved mixing 1:1 (15)N-labeled and unlabeled ((14)N) protein to form a mixture of isotopically labeled and unlabeled homodimers, which was chemically cross-linked. This cross-linked IL-6(D) was subjected to proteolysis by trypsin and the generated peptides were analyzed by electrospray ionization time-of-flight mass spectrometry (MS). Molecular ions from cross-linked peptides of intermolecular origin are labeled with [(15)N/(15)N] + [(15)N/(14)N] + [(14)N/(15)N] + [(14)N/(14)N] yielding readily identified triplet/quadruplet MS peaks. All other peptide species are labeled with [(15)N] + [(14)N] yielding doublet peaks. Intermolecular cross-linked peptides were identified by MS, and cross-linked residues were identified. This intermolecular cross-link detection method, which we have designated "mixed isotope cross-linking" MIX may have more general application to protein-protein interaction studies. The pattern of proximal residues found was consistent with IL-6(D) having a domain-swapped fold similar to IL-10 and interferon-gamma. This fold implies that IL-6(D)-mediated antagonism of IL-6 signaling is caused by obstruction of cooperative gp130 binding on IL-6(D), rather than direct blocking of gp-130-binding sites on IL-6(D).