The N-Glycosylation of immunoglobulin G as a novel biomarker of Parkinson’s disease

For neurodegenerative diseases, interventions during the early stages of the disease, before significant neurodegeneration has occurred, are associated with an increased probability of slowing or halting the disease process. In order to intervene early, it is essential that an accurate diagnosis is obtained and that disease progression can be monitored. This is particularly relevant for Parkinson’s disease (PD; International Classification of Diseases version 10) because significant neurodegeneration has already occurred by the time the clinical motor symptoms are present. Therefore, the development of translatable, high-throughput biomarkers for large scale population screening is a crucial area of research. Of promise are the emerging “omics” technologies, which enable the detection of preclinical biomolecule fluctuations associated with the development of different diseases. One such field is glycomics which is the study of the set of sugar structures, hereon in known as glycans, in a given protein, cell or tissue. Notably, the functional diversity of proteins is increased by several magnitudes with the addition of glycans, a process known as glycosylation. The glycosylation of certain proteins, including immunoglobulin G (IgG), has been found to remain fairly stable over short periods of time, with modifications thought to result from changes in the cellular environment or disease presence. Indeed, IgG has the ability to exert both anti-inflammatory and pro-inflammatory effects throughout the body and these properties are controlled by the N-glycosylation of the fragment crystallisable (Fc) portion. To our knowledge, this was the first time that the potential of using IgG glycomic biomarkers to identify people with PD, as well as identify people with PD who are at risk of cognitive decline, was investigated. It was demonstrated that the peripheral IgG glycome in the PD cases was indicative of an increased capacity to biologically age. While advancing age has previously been associated with modifications to the glycosylation of IgG, making them more pro-inflammatory, advancing age was only associated with significant increases in modifications to the peripheral IgG glycome that infer more pro-inflammatory IgG in the PD cases but not the controls. In PD, the severity of the underlying pathology increases as the individual ages and, therefore, is a confounder of the effect of advancing age on pro-inflammation. Consequently, the peripheral IgG in people with PD have a propensity to become more pro-inflammatory at a faster rate as they age, and this may be linked to the severity of pathology during the course of the disease. PD has a heterogeneous presentation of clinical symptoms, and many factors contribute to the development of the disease. While this is true, it was demonstrated that the peripheral IgG glycome does not have utility in identifying risk of cognitive decline, which would result from progression of PD pathology in the central nervous system (CNS). These results are indicative of the peripheral IgG interacting with PD pathology in the enteric nervous system (ENS) as well as when it propagates from the ENS to the CNS along the vagal nerve. Inflammation may facilitate the neuron-to-neuron propagation of PD inclusions along this pathway and thus be a contributor to PD development during the prodromal phase. Hence, the peripheral IgG glycome may be useful as a novel biomarker of PD presence in the prodromal phase of the disease

Erratum to Traditional Chinese medicine and new concepts of predictive, preventive and personalised medicine in diagnosis and treatment of sub-optimal health [The EPMA Journal 5, (2014) 12]

The premise of disease-related phenotypes is the definition of the counterpart normality in medical sciences. Contrary to clinical practices that can be carefully planned according to clinical needs, heterogeneity and uncontrollability is the essence of humans in carrying out health studies. Full characterization of consistent phenotypes that define the general population is the basis to individual difference normalization in personalized medicine. Self-claimed normal status may not represent health because asymptomatic subjects may carry chronic diseases at their early stage, such as cancer, diabetes mellitus and atherosclerosis. Currently, treatments for non-communicable chronic diseases (NCD) are implemented after disease onset, which is a very much delayed approach from the perspective of predictive, preventive and personalized medicine (PPPM). A NCD pandemic will develop and be accompanied by increased global economic burden for healthcare systems throughout both developed and developing countries. This paper examples the characterization of the suboptimal health status (SHS) which represents a new PPPM challenge in a population with ambiguous health complaints such as general weakness, unexplained medical syndrome (UMS), chronic fatigue syndrome (CFS), myalgic encephalomyelitis (ME), post-viral fatigue syndrome (PVFS) and chronic fatigue immune dysfunction syndrome (CFIDS). Methods: We applied clinical informatic approaches and developed a questionnaire-suboptimal health status questionnaire-25 (SHSQ-25) for measuring SHS. The validity and reliability of this approach were evaluated in a small pilot study and then in a cross-sectional study of 3,405 participants in China. Results: We found a correlation between SHS and systolic blood pressure, diastolic blood pressure, plasma glucose, total cholesterol and high-density lipoprotein (HDL) cholesterol among men, and a correlation between SHS and systolic blood pressure, diastolic blood pressure, total cholesterol, triglycerides and HDL cholesterol among women. Conclusions: The SHSQ-25 is a self-rated questionnaire of perceived health complaints, which can be used as a new instrument for PPPM. An ongoing longitudinal SHS cohort survey (China Sub-optimal Health Cohort Study, COACS) consisting of 50,000 participants will provide a powerful health trial to use SHSQ-25 for its application to PPPM through patient stratification and therapy monitoring using innovative technologies of predictive diagnostics and prognosis: an effort of paradigm shift from reactive to predictive medicine

Traditional Chinese Medicine And New Concepts Of Predictive, Preventive And Personalized Medicine In Diagnosis And Treatment Of Suboptimal Health

The premise of disease-related phenotypes is the definition of the counterpart normality in medical sciences. Contrary to clinical practices that can be carefully planned according to clinical needs, heterogeneity and uncontrollability is the essence of humans in carrying out health studies. Full characterization of consistent phenotypes that define the general population is the basis to individual difference normalization in personalized medicine. Self-claimed normal status may not represent health because asymptomatic subjects may carry chronic diseases at their early stage, such as cancer, diabetes mellitus and atherosclerosis. Currently, treatments for non-communicable chronic diseases (NCD) are implemented after disease onset, which is a very much delayed approach from the perspective of predictive, preventive and personalized medicine (PPPM). A NCD pandemic will develop and be accompanied by increased global economic burden for healthcare systems throughout both developed and developing countries. This paper examples the characterization of the suboptimal health status (SHS) which represents a new PPPM challenge in a population with ambiguous health complaints such as general weakness, unexplained medical syndrome (UMS), chronic fatigue syndrome (CFS), myalgic encephalomyelitis (ME), post-viral fatigue syndrome (PVFS) and chronic fatigue immune dysfunction syndrome (CFIDS). Methods: We applied clinical informatic approaches and developed a questionnaire-suboptimal health status questionnaire-25 (SHSQ-25) for measuring SHS. The validity and reliability of this approach were evaluated in a small pilot study and then in a cross-sectional study of 3,405 participants in China. Results: We found a correlation between SHS and systolic blood pressure, diastolic blood pressure, plasma glucose, total cholesterol and high-density lipoprotein (HDL) cholesterol among men, and a correlation between SHS and systolic blood pressure, diastolic blood pressure, total cholesterol, triglycerides and HDL cholesterol among women. Conclusions: The SHSQ-25 is a self-rated questionnaire of perceived health complaints, which can be used as a new instrument for PPPM. An ongoing longitudinal SHS cohort survey (China Sub-optimal Health Cohort Study, COACS) consisting of 50,000 participants will provide a powerful health trial to use SHSQ-25 for its application to PPPM through patient stratification and therapy monitoring using innovative technologies of predictive diagnostics and prognosis: an effort of paradigm shift from reactive to predictive medicine

Impact of biobanks on research outcomes in rare diseases: a systematic review

BACKGROUND: Alleviating the burden of rare diseases requires research into new diagnostic and therapeutic strategies. We undertook a systematic review to identify and compare the impact of stand-alone registries, registries with biobanks, and rare disease biobanks on research outcomes in rare diseases. METHODS: A systematic review and meta-aggregation was conducted using the preferred reporting items for systematic reviews and meta-analyses (the PRISMA statement). English language publications were sourced from PubMed, Medline, Scopus, and Web of Science. Original research papers that reported clinical, epidemiological, basic or translational research findings derived from data contained in stand-alone registries, registries with biobanks, and rare disease biobanks were considered. Articles selected for inclusion were assessed using the critical appraisal instruments by JBI-QARI. Each article was read in its entirety and findings were extracted using the online data extraction software from JBI-QARI. RESULTS: Thirty studies including 28 rare disease resources were included in the review. Of those, 14 registries were not associated to biobank infrastructure, 9 registries were associated with biobank infrastructure, and 6 were rare disease biobank resources. Stand-alone registries had the capacity to uncover the natural history of disease and contributed to evidence-based practice. When annexed to biobank infrastructure, registries could also identify and validate biomarkers, uncover novel genes, elucidate pathogenesis at the Omics level, and develop new therapeutic strategies. Rare disease biobanks in this review had similar capacity for biological investigations, but in addition, had far greater sample numbers and higher quality laboratory techniques for quality assurance processes. DISCUSSION: We examined the research outcomes of three specific populations: stand-alone registries, registries with biobanks, and stand-alone rare disease biobanks and demonstrated that there are key differences among these resources. These differences are a function of the resources\u27 design, aims, and objectives, with each resource having a distinctive and important role in contributing to the body of knowledge for rare disease research. Whilst stand-alone registries had the capacity to uncover the natural history of disease, develop best practice, replace clinical trials, and improve patient outcomes, they were limited in their capacity to conduct basic research. The role of basic research in rare disease research is vital; scientists must first understand the pathways of disease before they can develop appropriate interventions. Rare disease biobanks, on the other hand (particularly larger biobanks), had the key infrastructure required to conduct basic research, making novel Omics discoveries, identify and validate biomarkers, uncover novel genes, and develop new therapeutic strategies. However, these stand-alone rare disease biobanks did not collect comprehensive data or impact on clinical observations like a rare disease registry. Rare disease research is important not only for rare diseases, but also for also common diseases. For example, research of low-density lipoprotein (LDL)-receptors in the rare disease known as familial hypercholesterolemia led to the discovery of statins, a drug therapy that is now used routinely to prevent heart disease. CONCLUSIONS: Rare diseases are still under-researched worldwide. This review made the important observation that registries with biobanks had the function of both stand-alone registries (the capacity to collect comprehensive clinical and epidemiological data) and stand-alone rare disease biobanks (the ability to contribute to Omics research). We found registries with biobanks offer a unique, practical, cost-effective, and impactful solution for rare disease research. Linkage of stand-alone registries to rare disease biobanks will provide the appropriate resources required for the effective translation of basic research into clinical practice. Furthermore, facilitators such as collaboration, engagement, blended recruitment, pro-active marketing, broad consent, and virtual biobank online catalogues will, if utilised, add to the success of these resources. These important observations can serve to direct future rare diseases research efforts, ultimately improve patient outcomes and alleviate the significant burden associated with rare disease for clinicians, hospitals, society, and most importantly, the patients and their families

Unravelling immunoglobulin G Fc N-glycosylation: A dynamic marker potentiating predictive, preventive and personalised medicine

Multiple factors influence immunoglobulin G glycosylation, which in turn affect the glycoproteins’ function on eliciting an anti-inflammatory or pro-inflammatory response. It is prudent to underscore these processes when considering the use of immunoglobulin G N-glycan moieties as an indication of disease presence, progress, or response to therapeutics. It has been demonstrated that the altered expression of genes that encode enzymes involved in the biosynthesis of immunoglobulin G N-glycans, receptors, or complement factors may significantly modify immunoglobulin G effector response, which is important for regulating the immune system. The immunoglobulin G N-glycome is highly heterogenous; however, it is considered an interphenotype of disease (a link between genetic predisposition and environmental exposure) and so has the potential to be used as a dynamic biomarker from the perspective of predictive, preventive, and personalised medicine. Undoubtedly, a deeper understanding of how the multiple factors interact with each other to alter immunoglobulin G glycosylation is crucial. Herein we review the current literature on immunoglobulin G glycoprotein structure, immunoglobulin G Fc glycosylation, associated receptors, and complement factors, the downstream effector functions, and the factors associated with the heterogeneity of immunoglobulin G glycosylation

The Expected Number of Background Disease Events during Mass Immunization in China

It is critical to distinguish events that are temporarily associated with, but not caused by, vaccination from those caused by vaccination during mass immunization. We performed a literature search in China National Knowledge Infrastructure and Pubmed databases. The number of coincident events was calculated based on its incidence rate and periods after receipt of a dose of hypothesized vaccine. We included background incidences of Guillain-Barre´ syndrome, anaphylaxis, seizure, sudden adult death syndrome, sudden cardiac death, spontaneous abortion, and preterm labour or delivery. In a cohort of 10 million individuals, 7.71 cases of Guillain-Barre´ syndrome would be expected to occur within six weeks of vaccination as coincident background cases. Even for rare events, a large number of events can be expected in a short period because of the large population targeted for immunization. These findings may encourage health authorities to screen the safety of vaccines against unpredictable pathogens

The genetic contribution of CIDEA polymorphisms, haplotypes and loci interaction to obesity in a Han Chinese population

To investigate the association of tag-SNPs and haplotype structures of the CIDEA gene with obesity in a Han Chinese population. Five single nucleotide polymorphisms (SNPs) (rs1154588/V115F, rs4796955/SNP1, rs8092502/SNP2, rs12962340/SNP3 and rs7230480/SNP4) in the CIDEA gene were genotyped in a case-control study. Genotyping was performed using the sequenom matrixassisted laser desorption/ionization time-of-flight mass spectrometry iPLEX platform. There were significant differences between the obese and control groups in genotype distributions of V115F (P\u3e0.001), SNP1 (P = 0.006) and SNP2 (P = 0.005). Carriers of V115F-TT, SNP1-GG and SNP2-CC genotypes had a 2.84-fold (95 % CI 1.73-4.66), 2.19-fold (95 % CI 1.09-4.38) and 4.37-fold (95 % CI 1.21-15.08) increased risk for obesity, respectively. Haplotype analysis showed that GTTC (SNP1/ SNP2/V115F/SNP4) had 1.41-fold (95 % CI 1.02-1.95) increased risk for obesity; whereas, haplotype TTGC had 0.48-fold (95 % CI 0.24-0.96) decreased risk for obesity. Using the multifactor dimensionality reduction method, the best model including SNP1, SNP2, V115F and SNP4 polymorphisms was identified with a maximum testing accuracy to 59.32 % and a perfect cross-validation consistency of 10/10 (P = 0.011). Logistic analysis indicated that there was a significant interaction between SNP1 and V115F associated with obesity. Subjects having both genotypes of SNP1/GG and V115F/TT were more susceptible to obesity in the Han Chinese population (OR 2.66, 95 %: 1.22-5.80). Genotypes of V115F/TT, SNP1/ GG and SNP2/CC and haplotype GTTC of CIDEA gene were identified as risk factors for obesity in the Han Chinese population. The interaction between SNP1 and V115F could play a joint role in the development of obesity

The association between subclass-specific IgG Fc N-glycosylation profiles and hypertension in the Uygur, Kazak, Kirgiz, and Tajik populations

Hypertension results from the interaction of genetic and acquired factors. IgG occurs in the form of different subclasses, of which the effector functions show significant variation. The detailed differences between the glycosylation profiles of the individual IgG subclasses may be lost in a profiling method for total IgG N-glycosylation. In this study, subclass-specific IgG Fc glycosylation profile was investigated in the four northwestern Chinese minority populations, namely, Uygur (UIG), Kazak (KZK), Kirgiz (KGZ), and Tajik (TJK), composed of 274 hypertensive patients and 356 healthy controls. The results showed that ten directly measured IgG N-glycan traits (i.e., IgG1G0F, IgG2G0F, IgG2G1FN, IgG2G1FS, IgG2G2S, IgG4G0F, IgG4G1FS, IgG4G1S, IgG4G2FS, and IgG4G2N) representing galactosylation and sialylation are significantly associated with hypertension, with IgG4 consistently showing weaker associations of its sialylation, across the four ethnic groups. We observed a modest improvement on the AUC of ROC curve when the IgG Fc N-glycan traits are added into the glycan-based model (difference between AUCs, 0.044, 95% CI: 0.016-0.072, P = 0.002). The AUC of the diagnostic model indicated that the subclass-specific IgG Fc N-glycan profiles provide more information reinforcing current models utilizing age, gender, BMI, and ethnicity, and demonstrate the potential of subclass-specific IgG Fc N-glycosylation profiles to serve as a biomarker for hypertension. Further research is however required to determine the additive value of subclass-specific IgG Fc N-glycosylation on top of biomarkers, which are currently used

Quantifying the heterogeneity of the immunoglobulin G N-Glycome in an ageing Australian population: The Busselton Healthy Ageing Study

The use of immunoglobulin G N-glycomics to study chronic non-communicable disorders and other complex phenotypes emerged following the Human Genome Project. The consortium discovered that most phenotypes were too complex to be explained by genetics alone. Thus, the biological importance of epigenetics was recognised; heritable modifications to gene expression rather than the genome itself. Nglycosylation is a form of epigenetic regulation known as a post-translational modification. It stabilises the immunoglobulin G structure and alters downstream responses elicited by the antibody and is extensively studied as a candidate biomarker in the post-genomic era. The N-glycosylation of immunoglobulin G itself is complex, with glycosyltransferases and glycosylhydrolases influencing the biosynthesis of the branching structures. Moreover, altered N-glycosylation is associated with an array of phenotypes. Our research team considers the N-glycome as an interphenotype of subclinical health status; an amalgamation of genetic predisposition, environmental exposure and health behaviours over the life-course. This underscores the value of the immunoglobulin G N-glycome in the shift towards predictive, preventive and personalised medicine. However, there is still considerable heterogeneity even among individuals with the same disorder, which warrants further investigation to improve precision of the biomarker. This thesis aimed to determine the degree the underlying genome and clinical factors explain the heterogeneity of the immunoglobulin G N-glycome. I used a subset of the cross-sectional population-based Busselton Healthy Ageing Study (n=637, 54.0% female, 46.2 to 68.3 years of age). The participants represent a highly homogenous population (99% identify as Caucasian with Caucasian parents), and all noninstitutionalised ‘Baby-boomers’ (adults born between 1946 and 1964) listed on the electoral roll in the City of Busselton between 2010 and 2016 were eligible to participate. Three studies were designed to address the thesis aim. Firstly, previous IgG-related genetic polymorphisms were successfully validated using association studies of the N-glycan features. Secondly, next-generation sequencing of leucocyte mRNA was modelled with the N-glycome. Differentially expressed genes were identified, as well as the implementation of a multivariate model to integrate the ‘omics datasets. Finally, clinical factors and health behaviours were modelled using various statistics, extending on previous research. Collectively, however, the three studies evidenced potential utility of the immunoglobulin G N-glycome in identifying cardiometabolic disorders and associated risk factors. A polymorphism with genome-wide significance had pleiotropy to type 2 diabetes mellitus. Additionally, the clinical studies correlated cardiometabolic risk factors (central adiposity, blood pressure, C-reactive protein, triglycerides, fasting blood glucose and insulin) as well as the health behaviours excessive alcohol consumption and current smoking status (both associated with increased risk of cardiometabolic disorders) to an increase in pro-inflammatory immunoglobulin G glycoforms, thus potentiating involvement of immunoglobulin G in the pathophysiology of these phenotypes. Overall, this data-driven thesis identified several factors explaining immunoglobulin G N-glycome heterogeneity. These should be considered in subsequent translational research, to improve the precision of this complex biomarker when stratifying populations of interest