Bivalent non-human gal-α1-3-gal glycan epitopes in the Fc region of a monoclonal antibody model can be recognized by anti-Gal-α1-3-Gal IgE antibodies.

Monoclonal antibody (mAb) production using non-human cells can introduce non-human glycan epitopes including terminal galactosyl-α1-3-galactose (α1-3-Gal) moieties. Cetuximab is a commercial mAb associated with causing anaphylaxis in some patients due to the binding of endogenous anti-α1-3-Gal IgE to the Fab (containing bi-α1-3-galactosylated glycans) but not to the Fc region (containing mono-α1-3-galactosylated glycans). Despite being low in abundance in typical commercial mAbs, the inherent sensitivity of cell culture conditions on glycosylation profiles, and the development of novel glycoengineering strategies, novel antibody-based modalities, and biosimilars by various manufacturers with varying procedures, necessitates a better understanding of the structural requirements for anti-α1-3-Gal IgE binding to the Fc region. Herein, we synthesized mAb glycoforms with varying degrees and regioisomers of α1-3-galactosylation and tested their binding to two commercial anti-α1-3-Gal human IgE antibodies derived from a human patient with allergies to red meat (comprising α1-3-Gal epitopes), as well as to the FcγRIIIA receptor. Our results demonstrate that unexpectedly, anti-α1-3-Gal human IgE antibodies can bind to Fc glycans, with bi-α1-3-galactosylation being the most important factor, highlighting that their presence in the Fc region may be considered as a potential critical quality attribute, particularly when using novel platforms in mAb-based biotherapeutics.

Specific location of galactosylation in an afucosylated antiviral monoclonal antibody affects its FcγRIIIA binding affinity.

Monoclonal antibodies (mAbs) comprise an essential type of biologic therapeutics and are used to treat diseases because of their anti-cancer and anti-inflammatory properties, and their ability to protect against respiratory infections. Its production involves post-translational glycosylation, a biosynthetic process that conjugates glycans to proteins, which plays crucial roles in mAb bioactivities including effector functions and pharmacokinetics. These glycans are heterogeneous and have diverse chemical structures whose composition is sensitive to manufacturing conditions, rendering the understanding of how specific glycan structures affect mAb bioactivity challenging. There is a need to delineate the effects of specific glycans on mAb bioactivity to determine whether changes in certain glycosylation profiles (that can occur during manufacturing) will significantly affect product quality. Using enzymatic transglycosylation with chemically-defined N-glycans, we show that galactosylation at a specific location of N-glycans in an afucosylated anti-viral mAb is responsible for FcγRIIIA binding and antibody-dependent cell-mediated cytotoxicity (ADCC) activity. We report a facile method to obtain purified asymmetric mono-galactosylated biantennary complex N-glycans, and their influence on bioactivity upon incorporation into an afucosylated mAb. Using ELISA, surface plasmon resonance and flow cytometry, we show that galactosylation of the α6 antenna, but not the α3 antenna, consistently increases FcγRIIIA binding affinity. We confirm its relevance in an anti-viral model of respiratory syncytial virus (RSV) using an adapted ADCC reporter assay. We further correlate this structure-function relationship to the interaction of the galactose residue of the α6 antenna with the protein backbone using 2D-1H-15N-NMR, which showed that galactosylation of at this location exhibited chemical shift perturbations compared to glycoforms lacking this galactose residue. Our results highlight the importance of identifying and quantifying specific glycan isomers to ensure adequate quality control in batch-to-batch and biosimilar comparisons.

Associations Between Soluble LDLR and Lipoproteins in a White Cohort and the Effect of PCSK9 Loss-of-Function.

Context: Elevated circulating cholesterol-rich low-density lipoprotein (LDL) particles increase coronary artery disease risk. Cell-surface hepatic LDL receptors (LDLRs) clear 70% of these particles from circulation. The ectodomain of LDLR is shed into circulation, preventing it from removing LDL particles. The role that LDLR ectodomain shedding plays as a regulatory mechanism is unknown. Objective: We describe LDLR shedding via the relationships between circulating soluble LDLRs (sLDLRs) and serum lipoproteins, serum proprotein convertase subtilin/kexin type 9 (PCSK9; a negative regulator of LDLR), and clinical parameters in a white Canadian population. Design: Population-based, cross-sectional study. Settings: Clinical Research Center, The Ottawa Hospital, and Faculty of Medicine, University of Ottawa. Participants: Two hundred seventy-three white Canadians. Intervention: None. Main Outcome Measures: sLDLR measured by ELISA; serum lipids and PCSK9, PCSK9 genotypes, and clinical parameters from previous analyses. Results: sLDLRs correlated strongly with triglycerides (TG; r = 0.624, P < 0.0001) and moderately with LDL cholesterol (r = 0.384, P < 0.0001), and high-density lipoprotein cholesterol (r = -0.307, P = 0.0003). Only TG correlations were unaffected by PCSK9 variations. sLDLR levels were significantly elevated in those with TG >50th or LDL cholesterol >75th percentiles. Conclusions: Serum sLDLR levels correlate with several lipoprotein parameters, especially TG, and the presence of PCSK9 loss-of-function variants alters sLDLR levels and correlations, except for TG. Ectodomain LDLR shedding has a role in LDL metabolism, distinct from PCSK9, with interplay between these two pathways that regulate cell-surface LDLRs. Findings suggest alteration of LDLR shedding could emerge as a target to treat dyslipidemia.

Erratum to: No training required: experimental tests support homology-based DNA assembly as a best practice in synthetic biology.

[This corrects the article DOI: 10.1186/s13036-015-0006-z.].

No training required: experimental tests support homology-based DNA assembly as a best practice in synthetic biology.

The Registry of Standard Biological Parts imposes sequence constraints to enable DNA assembly using restriction enzymes. Alnahhas et al. (Journal of Biological Engineering 2014, 8:28) recently argued that these constraints should be revised because they impose an unnecessary burden on contributors that use homology-based assembly. To add to this debate, we tested four different homology-based methods, and found that students using these methods on their first attempt have a high probability of success. Because of their ease of use and high success rates, we believe that homology-based assembly is a best practice of Synthetic Biology, and recommend that the Registry implement the changes proposed by Alnahhas et al. to better support their use.

Quantitative epistasis analysis and pathway inference from genetic interaction data.

Inferring regulatory and metabolic network models from quantitative genetic interaction data remains a major challenge in systems biology. Here, we present a novel quantitative model for interpreting epistasis within pathways responding to an external signal. The model provides the basis of an experimental method to determine the architecture of such pathways, and establishes a new set of rules to infer the order of genes within them. The method also allows the extraction of quantitative parameters enabling a new level of information to be added to genetic network models. It is applicable to any system where the impact of combinatorial loss-of-function mutations can be quantified with sufficient accuracy. We test the method by conducting a systematic analysis of a thoroughly characterized eukaryotic gene network, the galactose utilization pathway in Saccharomyces cerevisiae. For this purpose, we quantify the effects of single and double gene deletions on two phenotypic traits, fitness and reporter gene expression. We show that applying our method to fitness traits reveals the order of metabolic enzymes and the effects of accumulating metabolic intermediates. Conversely, the analysis of expression traits reveals the order of transcriptional regulatory genes, secondary regulatory signals and their relative strength. Strikingly, when the analyses of the two traits are combined, the method correctly infers ~80% of the known relationships without any false positives.

Chromosomal position effects are linked to sir2-mediated variation in transcriptional burst size.

Gene expression noise varies with genomic position and is a driving force in the evolution of chromosome organization. Nevertheless, position effects remain poorly characterized. Here, we present a systematic analysis of chromosomal position effects by characterizing single-cell gene expression from euchromatic positions spanning the length of a eukaryotic chromosome. We demonstrate that position affects gene expression by modulating the size of transcriptional bursts, rather than their frequency, and that the histone deacetylase Sir2 plays a role in this process across the chromosome.

Phenotypic impact of regulatory noise in cellular stress-response pathways.

Recent studies indicate that intrinsic promoter-mediated gene expression noise can confer a selective advantage under acute environmental stress by providing beneficial phenotypic diversity within cell populations. To investigate how extrinsic gene expression noise impacts the fitness of cell populations under stress, we engineered two nearly isogenic budding yeast strains; one carrying a two-step regulatory cascade that allows for precise control of the noise transmitted from a transcriptional regulator to a downstream stress-inducing gene, and one carrying a network with low constant upstream noise. The fitness and gene expression of these strains were compared under acute and prolonged stress exposure. Using a phenomenological modeling approach, we predicted that increased noise should confer a fitness advantage under high stress conditions, but reciprocally reduce the resistance of the population to low stress. The model also predicted that extrinsic noise might serve as a basis for phenotypic plasticity whereby gene expression distributions are modulated in response to prolonged stress. Experimentally, we confirmed the predicted differential fitness advantage of extrinsic noise under acute stress, as well as the predicted modulation of gene expression under prolonged stress. However, contrary to model predictions, strains with low and high extrinsic noise showed very similar adaptive responses to prolonged stress. This suggests that while phenotypic heterogeneity generated by noise in regulatory signals can confer increased robustness to acute stress, it is not a requirement for the observed long-term phenotypic plasticity.

Identification of response-modulated genetic interactions by sensitivity-based epistatic analysis.

BACKGROUND: High-throughput genomics has enabled the global mapping of genetic interactions based on the phenotypic impact of combinatorial genetic perturbations. An important next step is to understand how these networks are dynamically remodelled in response to environmental stimuli. Here, we report on the development and testing of a method to identify such interactions. The method was developed from first principles by treating the impact on cellular growth of environmental perturbations equivalently to that of gene deletions. This allowed us to establish a novel neutrality function marking the absence of epistasis in terms of sensitivity phenotypes rather than fitness. We tested the method by identifying fitness- and sensitivity-based interactions involved in the response to drug-induced DNA-damage of budding yeast Saccharomyces cerevisiae using two mutant libraries - one containing transcription factor deletions, and the other containing deletions of DNA repair genes. RESULTS: Within the library of transcription factor deletion mutants, we observe significant differences in the sets of genetic interactions identified by the fitness- and sensitivity-based approaches. Notably, among the most likely interactions, only ~50% were identified by both methods. While interactions identified solely by the sensitivity-based approach are modulated in response to drug-induced DNA damage, those identified solely by the fitness-based method remained invariant to the treatment. Comparison of the identified interactions to transcriptional profiles and protein-DNA interaction data indicate that the sensitivity-based method improves the identification of interactions involved in the DNA damage response. Additionally, for the library containing DNA repair mutants, we observe that the sensitivity-based method improves the grouping of functionally related genes, as well as the identification of protein complexes, involved in DNA repair. CONCLUSION: Our results show that the identification of response-modulated genetic interactions can be improved by incorporating the effect of a changing environment directly into the neutrality function marking the absence of epistasis. We expect that this extension of conventional epistatic analysis will facilitate the development of dynamic models of gene networks from quantitative measurements of genetic interactions. While the method was developed for growth phenotype, it should apply equally well for other phenotypes, including the expression of fluorescent reporters.