Impact of Stress on the Immunogenic Potential of Adalimumab.

Monoclonal antibodies against tumor necrosis factor-alpha (TNFα) are widely used for treatment of inflammatory diseases. However, despite the inhibitory effect this class of drugs has on the immune system, anti-drug antibodies are often formed with continuous use. Particles formed during stress conditions, which can be used to simulate storage and handling conditions of commercial antibodies, have previously been associated with the formation of anti-drug antibodies. This study investigates the relationship between particles, oligomerization, folding and chemical degradation on the in vitro cytokine response toward the TNFα inhibitor adalimumab. Adalimumab aggregates generated using stir and heat stress were fractionated into distinct sub-populations, and their structure and immunogenic potential were evaluated. A chemically degraded sample of adalimumab was included to compare particle composition with the milder accelerated heat and stir stressed conditions. Particles from stressed adalimumab samples induced elevated cytokine levels and CD4+ T cell proliferation in vitro compared to non-stressed samples. Samples enriched with both submicron and subvisible particles of adalimumab induced the strongest cytokine release and the strongest CD4+ T cell proliferation despite maintaining some TNFα inhibitory functionality. Samples that were stressed and subsequently purified of subvisible and submicron particles did not elicit a significantly higher cytokine response or show increased CD4+ T cell proliferation compared to a non-stressed sample. Oxidation-induced chemical modifications in adalimumab, mainly in Met, His, Trp, and Tyr, were not found to be sufficient in absence of particle formation to induce increased CD4+ T cell proliferation or cytokine release despite less decreased TNFα inhibitory activity of adalimumab. These observations provide further evidence that particles do indeed potentiate the immunogenic potential of adalimumab.

A Comprehensive Evaluation of Nanoparticle Tracking Analysis (NanoSight) for Characterization of Proteinaceous Submicron Particles.

Nanoparticle tracking analysis (NTA) has attracted great interest for application in the field of submicron particle characterization for biopharmaceuticals. It has the virtue of direct sample visualization and particle-by-particle tracking, but the complexity of method development has limited its routine applicability. We systematically evaluated data collection and processing parameters as well as sample handling methods using shake-stressed protein samples. The camera shutter and gain were identified as the key factors influencing NTA results. We also demonstrated that sample filtration was necessary for NTA analysis if there were high numbers of micron particles, whereas the choice of filter membrane was critical for data quality. Sample dilution into corresponding formulation buffer did not affect particle size distributions in our study. Finally, NTA analysis exhibited excellent repeatability in intraday comparison of multiple measurements on the same sample and interday comparison on different batches of samples. Shaking-induced protein aggregation could also be sensitively monitored by NTA. In conclusion, NTA analysis can be used as a robust stability-indicating method for the characterization of proteinaceous submicron particles and thereby complement other analytical methods, provided that consistent sample handling and parametric settings are established for the specific case study.

Metabolic profiling in Maturity-onset diabetes of the young (MODY) and young onset type 2 diabetes fails to detect robust urinary biomarkers.

It is important to identify patients with Maturity-onset diabetes of the young (MODY) as a molecular diagnosis determines both treatment and prognosis. Genetic testing is currently expensive and many patients are therefore not assessed and are misclassified as having either type 1 or type 2 diabetes. Biomarkers could facilitate the prioritisation of patients for genetic testing. We hypothesised that patients with different underlying genetic aetiologies for their diabetes could have distinct metabolic profiles which may uncover novel biomarkers. The aim of this study was to perform metabolic profiling in urine from patients with MODY due to mutations in the genes encoding glucokinase (GCK) or hepatocyte nuclear factor 1 alpha (HNF1A), type 2 diabetes (T2D) and normoglycaemic control subjects. Urinary metabolic profiling by Nuclear Magnetic Resonance (NMR) and ultra performance liquid chromatography hyphenated to Q-TOF mass spectrometry (UPLC-MS) was performed in a Discovery set of subjects with HNF1A-MODY (n = 14), GCK-MODY (n = 17), T2D (n = 14) and normoglycaemic controls (n = 34). Data were used to build a valid partial least squares discriminate analysis (PLS-DA) model where HNF1A-MODY subjects could be separated from the other diabetes subtypes. No single metabolite contributed significantly to the separation of the patient groups. However, betaine, valine, glycine and glucose were elevated in the urine of HNF1A-MODY subjects compared to the other subgroups. Direct measurements of urinary amino acids and betaine in an extended dataset did not support differences between patients groups. Elevated urinary glucose in HNF1A-MODY is consistent with the previously reported low renal threshold for glucose in this genetic subtype. In conclusion, we report the first metabolic profiling study in monogenic diabetes and show that, despite the distinct biochemical pathways affected, there are unlikely to be robust urinary biomarkers which distinguish monogenic subtypes from T2D. Our results have implications for studies investigating metabolic profiles in complex traits including T2D.

A genome-wide metabolic QTL analysis in Europeans implicates two loci shaped by recent positive selection.

We have performed a metabolite quantitative trait locus (mQTL) study of the (1)H nuclear magnetic resonance spectroscopy ((1)H NMR) metabolome in humans, building on recent targeted knowledge of genetic drivers of metabolic regulation. Urine and plasma samples were collected from two cohorts of individuals of European descent, with one cohort comprised of female twins donating samples longitudinally. Sample metabolite concentrations were quantified by (1)H NMR and tested for association with genome-wide single-nucleotide polymorphisms (SNPs). Four metabolites' concentrations exhibited significant, replicable association with SNP variation (8.6×10(-11)

Human metabolic profiles are stably controlled by genetic and environmental variation.

¹H Nuclear Magnetic Resonance spectroscopy (¹H NMR) is increasingly used to measure metabolite concentrations in sets of biological samples for top-down systems biology and molecular epidemiology. For such purposes, knowledge of the sources of human variation in metabolite concentrations is valuable, but currently sparse. We conducted and analysed a study to create such a resource. In our unique design, identical and non-identical twin pairs donated plasma and urine samples longitudinally. We acquired ¹H NMR spectra on the samples, and statistically decomposed variation in metabolite concentration into familial (genetic and common-environmental), individual-environmental, and longitudinally unstable components. We estimate that stable variation, comprising familial and individual-environmental factors, accounts on average for 60% (plasma) and 47% (urine) of biological variation in ¹H NMR-detectable metabolite concentrations. Clinically predictive metabolic variation is likely nested within this stable component, so our results have implications for the effective design of biomarker-discovery studies. We provide a power-calculation method which reveals that sample sizes of a few thousand should offer sufficient statistical precision to detect ¹H NMR-based biomarkers quantifying predisposition to disease.

Direct quantitative trait locus mapping of mammalian metabolic phenotypes in diabetic and normoglycemic rat models.

Characterizing the relationships between genomic and phenotypic variation is essential to understanding disease etiology. Information-dense data sets derived from pathophysiological, proteomic and transcriptomic profiling have been applied to map quantitative trait loci (QTLs). Metabolic traits, already used in QTL studies in plants, are essential phenotypes in mammalian genetics to define disease biomarkers. Using a complex mammalian system, here we show chromosomal mapping of untargeted plasma metabolic fingerprints derived from NMR spectroscopic analysis in a cross between diabetic and control rats. We propose candidate metabolites for the most significant QTLs. Metabolite profiling in congenic strains provided evidence of QTL replication. Linkage to a gut microbial metabolite (benzoate) can be explained by deletion of a uridine diphosphate glucuronosyltransferase. Mapping metabotypic QTLs provides a practical approach to understanding genome-phenotype relationships in mammals and may uncover deeper biological complexity, as extended genome (microbiome) perturbations that affect disease processes through transgenomic effects may influence QTL detection.

Metabonomics in diabetes research.

Metabonomics has been defined as "quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification" and can provide information on disease processes, drug toxicity, and gene function. In this approach many samples of biological origin (biofluids such as urine or plasma) are analyzed using techniques that produce simultaneous detection. A variety of analytical metabolic profiling tools are used routinely, are also currently under development, and include proton nuclear magnetic resonance spectroscopy and mass spectrometry with a prior online separation step such as high-performance liquid chromatography, ultra-performance liquid chromatography, or gas chromatography. Data generated by these analytical techniques are often combined with multivariate data analysis, i.e., pattern recognition, for respectively generating and interpreting the metabolic profiles of the investigated samples. Metabonomics has gained great prominence in diabetes research within the last few years and has already been applied to understand the metabolism in a range of animal models and, more recently, attempts have been done to process complex metabolic data sets from clinical studies. A future hope for the metabonomic approach is the identification of biomarkers that are able to highlight individuals likely to suffer from diabetes and enable early diagnosis of the disease or the identification of those at risk. This review summarizes the technologies currently being used in metabonomics, as well as the studies reported related to diabetes prior to a description of the general objective of the research plan of the metabonomics part of the European Union project, Molecular Phenotyping to Accelerate Genomic Epidemiology.

Mechanisms of hydrazine toxicity in rat liver investigated by proteomics and multivariate data analysis.

A proteomics approach combined with multivariate data analysis was used to examine the hepatotoxic effect of hydrazine in 30 male Sprague Dawley rats, assigned to four treatment groups and two control groups. Liver samples from the individual animals were resolved by two-dimensional differential gel electrophoresis (2-D DIGE) and protein patterns from the 2-D gels were analyzed by principal component analysis (PCA) and partial least squares regression (PLSR). The PCA plot was able to describe the variation in the protein expression related to dose and time, by separation or clustering of different animal groups. PLSR followed by variable selection (Jack-knifing) was used to select proteins that varied significantly in relation to the dose related response of the hydrazine treatment. The 10 up-regulated and 10 down-regulated proteins with highest rank in the PLSR model were identified by mass spectrometry. Hydrazine treatment induced altered expression of proteins related to lipid metabolism, Ca(2+) homeostasis, thyroid hormone pathways and stress response. Several of the identified proteins have not previously been implicated in hydrazine toxicity and may thus be regarded as new potential biomarkers of induced liver toxicity.