Anti-citrullinated protein antibodies (ACPAs) are involved in the pathogenesis of rheumatoid arthritis. N-glycosylation pattern of ACPA-IgG and healthy IgG Fc differs. The aim of this study is to determine the relative sialylation and galactosylation level of ACPAs and control IgG to assess their capability of inducing TNFα production, and furthermore, to analyze the correlations between the composition of Fc glycans and inflammatory markers in RA. We isolated IgG from sera of healthy volunteers and RA patients, and purified ACPAs on a citrulline-peptide column. Immunocomplexes (IC) were formed by adding an F(ab)₂ fragment of anti-human IgG. U937 cells were used to monitor the binding of IC to FcγR and to trigger TNFα release determined by ELISA. To analyze glycan profiles, control IgG and ACPA-IgG were digested with trypsin and the glycosylation patterns of glycopeptides were analyzed by determining site-specific N-glycosylation using nano-UHPLC-MS/MS. We found that both sialylation and galactosylation levels of ACPA-IgG negatively correlate with inflammation-related parameters such as CRP, ESR, and RF. Functional assays show that dimerized ACPA-IgG significantly enhances TNFα release in an FcγRI-dependent manner, whereas healthy IgG does not. TNFα production inversely correlates with the relative intensities of the G0 glycoform, which lacks galactose and terminal sialic acid moieties.

The front-line imaging modalities computed tomography (CT) and X-ray play important roles for triaging COVID patients. Thoracic CT has been accepted to have higher sensitivity than a chest X-ray for COVID diagnosis. Considering the limited access to resources (both hardware and trained personnel) and issues related to decontamination, CT may not be ideal for triaging suspected subjects. Artificial intelligence (AI) assisted X-ray based application for triaging and monitoring require experienced radiologists to identify COVID patients in a timely manner with the additional ability to delineate and quantify the disease region is seen as a promising solution for widespread clinical use. Our proposed solution differs from existing solutions presented by industry and academic communities. We demonstrate a functional AI model to triage by classifying and segmenting a single chest X-ray image, while the AI model is trained using both X-ray and CT data. We report on how such a multi-modal training process improves the solution compared to single modality (X-ray only) training. The multi-modal solution increases the AUC (area under the receiver operating characteristic curve) from 0.89 to 0.93 for a binary classification between COVID-19 and non-COVID-19 cases. It also positively impacts the Dice coefficient (0.59 to 0.62) for localizing the COVID-19 pathology. To compare the performance of experienced readers to the AI model, a reader study is also conducted. The AI model showed good consistency with respect to radiologists. The DICE score between two radiologists on the COVID group was 0.53 while the AI had a DICE value of 0.52 and 0.55 when compared to the segmentation done by the two radiologists separately. From a classification perspective, the AUCs of two readers was 0.87 and 0.81 while the AUC of the AI is 0.93 based on the reader study dataset. We also conducted a generalization study by comparing our method to the-state-art methods on independent datasets. The results show better performance from the proposed method. Leveraging multi-modal information for the development benefits the single-modal inferencing.

Lung cancer is one of the most commonly diagnosed cancer types. Studying the molecular changes that occur in lung cancer is important to understand tumor formation and identify new therapeutic targets and early markers of the disease to decrease mortality. Glycosaminoglycan chains play important roles in various signaling events in the tumor microenvironment. Therefore, we have determined the quantity and sulfation characteristics of chondroitin sulfate and heparan sulfate in formalin-fixed paraffin-embedded human lung tissue samples belonging to different lung cancer types as well as tumor adjacent normal areas. Glycosaminoglycan disaccharide analysis was performed using HPLC-MS following on-surface lyase digestion. Significant changes were identified predominantly in the case of chondroitin sulfate; for example, the total amount was higher in tumor tissue compared to the adjacent normal tissue. We also observed differences in the degree of sulfation and relative proportions of individual chondroitin sulfate disaccharides between lung cancer types and adjacent normal tissue. Furthermore, the differences in the 6-O-/4-O-sulfation ratio of chondroitin sulfate were different between the lung cancer types. Our pilot study revealed that further investigation of the role of chondroitin sulfate chains and enzymes involved in their biosynthesis is an important aspect of lung cancer research.

Rheumatoid arthritis (RA) is the most common inflammatory polyarthritis. In addition, 60–80% of patients express anti-citrullinated protein antibodies (ACPAs), which serve as a diagnostic marker for RA. The effector functions of these autoantibodies can be heavily affected by the N-glycosylation of their Fc region. Here we present a comparison of the Fc N-glycosylation of ACPA IgG to that of non-ACPA IgG from the same patients, and of healthy controls, in an IgG isoform-specific manner. We isolated ACPA and normal serum IgG, digested by trypsin, and separated the resulting peptide mixture by a reversed-phase nanoLC coupled to a Bruker Maxis II Q-TOF, and determined the relative abundance of glycoforms. The paired analysis of galactosylation and sialylation of the IgG subclasses of ACPA and non-ACPA IgG has shown a significant, moderate negative correlation with the inflammatory markers, the level of C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR), as well as with rheuma-factor (RF), but not with the disease activity score (DAS) or cyclic citrullinated peptide specific antibodies (anti-CCP). However, we detected a significant negative correlation between glycosylation and DAS in the non-ACPA IgG fractions. Furthermore, the isoform-specific analysis revealed additional insight into the changes of the glycosylation features of IgG in RA: changes in the frequencies of the bisecting GlcNAc unit between sample groups could be explained by only the IgG1 isoform; while invariance in fucosylation is the result of the superposition of two isoforms with opposite changes. These results highlight the importance of analyzing immunoglobulin glycosylation in an isoform-specific manner.

SuFEx chemistry is based on the unique reactivity of the sulfonyl fluoride group with a range of nucleophiles. Accordingly, sulfonyl fluorides label multiple nucleophilic amino acid residues, making these reagents popular in both chemical biology and medicinal chemistry applications. The reactivity of sulfonyl fluorides nominates this warhead chemotype as a candidate for an external, activation-free general labelling tag. Here, we report the synthesis and characterization of a small sulfonyl fluoride library that yielded the 3-carboxybenzenesulfonyl fluoride warhead for tagging tractable targets at nucleophilic residues. Based on these results, we propose that coupling diverse fragments to this warhead would result in a library of sulfonyl fluoride bits (SuFBits), available for screening against protein targets. SuFBits will label the target if it binds to the core fragment, which facilitates the identification of weak fragments by mass spectrometry.

We have performed CID experiments on a triple quadrupole instrument, lowering the collision gas pressure by 50 times compared to its conventional value. The results show that at very low-collision gas pressure, single collisions dominate the spectra. Indirectly, these results suggest that under conventional conditions, 20–50 collisions may be typical in CID experiments. The results show a marked difference between low- and high-pressure CID spectra, the latter being characterized in terms of ‘slow heating’ and predominance of consecutive reactions. The results indicate that under single collision conditions, the collisional energy transfer efficiency is very high: nearly 100% of the center of mass kinetic energy is converted to internal energy.