Global variability of the human IgG glycome

Štambuk, Jerko; Nakić, Natali; Vučković, Frano; Pučić‐Baković, Maja; Razdorov, Genadij; Trbojević‐Akmačić, Irena; Novokmet, Mislav; Keser, Toma; Vilaj, Marija; Pavić, Tamara; Gudelj, Ivan; Šimurina, Mirna; Song, Manshu; Wang, Hao; Salihović, Marijana Peričić; Campbell, Harry; Rudan, Igor; Kolčić, Ivana; Eller, Leigh Anne; Robb, Merlin L.; Halfvarson, Jonas; Kurtoğlu, Metin; Annese, Vito; Škarić‐Jurić, Tatjana; Molokhia, Mariam; Polašek, Ozren; Hayward, Caroline; Kibuuka, Hannah; Thaqi, Kujtim; Primorac, Dragan; Gieger, Christian; Nitayaphan, Sorachai; Spector, Tim D.; Wang, Youxin; Tillin, Therese; Chaturvedi, Nish; Wilson, James F.; Schanfield, Moses S.; Филипенко, М. Л.; Wang, Wei; Lauc, Gordan

doi:10.1101/535237

Cited by 5 publications

(4 citation statements)

References 43 publications

(65 reference statements)

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“…On the other hand, the levels of digalactosylated (G2) glycans increased after bariatric surgery and at sequential timepoints, in accordance with a reduced inflammatory potential of the circulating IgG. The increased levels of IgG galactosylation were previously associated with younger biological age and are considered, in a way, as a measure of an individual's well-being [9,12]. Our results demonstrate that weight loss, resulting from bariatric surgery, can initiate the reversal from an old-like to a young-like IgG N-glycome, potentially reversing the clock for biological age.…”

Section: Time (Months) Standardized Glycan Measurementmentioning

confidence: 98%

“…Progressive age-related changes of IgG N-glycosylation have been extensively studied [7,9,10] and the GlycanAge model has been proposed to express the difference between chronological and IgG N-glycome ageing [11]. The age of the IgG N-glycome can be estimated through the levels of agalactosylated species, which increase with ageing and are associated with enhanced immune activation [12]. The opposite applies for digalactosylated IgG glycoforms, which are usually related to a younger age.…”

Section: Introductionmentioning

confidence: 99%

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Extensive weight loss reduces glycan age by altering IgG N-glycosylation

et al. 2021

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Background Obesity, a major global health problem, is associated with increased cardiometabolic morbidity and mortality. Protein glycosylation is a frequent posttranslational modification, highly responsive to inflammation and ageing. The prospect of biological age reduction, by changing glycosylation patterns through metabolic intervention, opens many possibilities. We have investigated whether weight loss interventions affect inflammation- and ageing-associated IgG glycosylation changes, in a longitudinal cohort of bariatric surgery patients. To support potential findings, BMI-related glycosylation changes were monitored in a longitudinal twins cohort. Methods IgG N-glycans were chromatographically profiled in 37 obese patients, subjected to low-calorie diet, followed by bariatric surgery, across multiple timepoints. Similarly, plasma-derived IgG N-glycan traits were longitudinally monitored in 1680 participants from the TwinsUK cohort. Results Low-calorie diet induced a marked decrease in the levels of IgG N-glycans with bisecting GlcNAc, whose higher levels are usually associated with ageing and inflammatory conditions. Bariatric surgery resulted in extensive alterations of the IgG N-glycome that accompanied progressive weight loss during 1-year follow-up. We observed a significant increase in digalactosylated and sialylated glycans, and a substantial decrease in agalactosylated and core fucosylated IgG N-glycans (adjusted p value range 7.38 × 10−04–3.94 × 10−02). This IgG N-glycan profile is known to be associated with a younger biological age and reflects an enhanced anti-inflammatory IgG potential. Loss of BMI over a 20 year period in the TwinsUK cohort validated a weight loss-associated agalactosylation decrease (adjusted p value 1.79 × 10−02) and an increase in digalactosylation (adjusted p value 5.85 × 10−06). Conclusions Altogether, these findings highlight that weight loss substantially affects IgG N-glycosylation, resulting in reduced glycan and biological age.

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Section: Time (Months) Standardized Glycan Measurementmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Extensive weight loss reduces glycan age by altering IgG N-glycosylation

et al. 2021

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“…[108] "C-dots," dye-labeled mesoporous silica nanoparticles functionalized with a targeting peptide, are currently under clinical trials for image-guided intraoperative mapping of lymph node metastasis in head and neck, breast and colorectal cancers. [109] Nevertheless, only a few nanoparticle-based imaging agents have been clinically approved. One reason for their limited use is their unique pharmacokinetic profile that does not always fit the requirements of an imaging agent.…”

Section: Deciding When To Use Nanomedicinementioning

confidence: 99%

Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine

et al. 2019

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Artificial intelligence (AI) and nanotechnology are two fields that are instrumental in realizing the goal of precision medicine—tailoring the best treatment for each cancer patient. Recent conversion between these two fields is enabling better patient data acquisition and improved design of nanomaterials for precision cancer medicine. Diagnostic nanomaterials are used to assemble a patient‐specific disease profile, which is then leveraged, through a set of therapeutic nanotechnologies, to improve the treatment outcome. However, high intratumor and interpatient heterogeneities make the rational design of diagnostic and therapeutic platforms, and analysis of their output, extremely difficult. Integration of AI approaches can bridge this gap, using pattern analysis and classification algorithms for improved diagnostic and therapeutic accuracy. Nanomedicine design also benefits from the application of AI, by optimizing material properties according to predicted interactions with the target drug, biological fluids, immune system, vasculature, and cell membranes, all affecting therapeutic efficacy. Here, fundamental concepts in AI are described and the contributions and promise of nanotechnology coupled with AI to the future of precision cancer medicine are reviewed.

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“…Progressive age-related changes of IgG glycosylation have been extensively studied [7,9,10] and the GlycanAge model has been proposed to express the difference between chronological and IgG glycome ageing [11]. The age of the IgG glycome might be estimated through the levels of agalactosylated species, which increase with ageing and are associated with enhanced immune activation [12]. The opposite applies for digalactosylated IgG glycoforms, which are usually related to a younger age.…”

Section: Introductionmentioning

confidence: 99%