Peptides In Proteomics Research Articles

Optimization of protein identifications through the use of different chromatographic approaches and bioinformatic pipelines.

Selection of proteomic workflows for a given project can be a daunting task. This research provides a guide outlining the impact on protein identification of different steps such as chromatographic separation, data acquisition strategies, and bioinformatic pipelines. The data presented here will help experts and nonexpert proteomic users to increase proteome coverage and peptide identification. HeLa protein digests were analyzed through different C18 chromatographic columns (15 and 50 cm in length), using top 12 data-dependent acquisition (DDA), top 20 DDA, and data-independent acquisition (DIA) with a nanospray source in positive mode in a Thermo Q Exactive instrument. The raw data were analyzed using different search engines, rescoring approaches, and multi-engine searches. The results were analyzed in the context of peptide and protein identifications, precursor properties, and computation requirements to understand the differences between methods. Our results showed that higher column lengths and top N DDA approaches were able to significantly increase protein identifications. The use of multiple search engines yielded limited gains, whereas the use of rescoring methods clearly outperformed other strategies. Finally, DIA approaches, although successful at generating new identifications, had a limited performance influenced by the previous collection of DDA data, which could prohibitively increase instrument time. Nonetheless, the use of library-free methods showed promising results. Our results highlight the impact of different experimental approaches on proteome coverage. Changes in chromatographic columns, data acquisition, or bioinformatic analysis can significantly increase the number of protein identifications (>400%). Thus, this research provides a reference upon which to build a successful proteomic workflow with different considerations at every step.

Internalization of therapeutic antibodies into dendritic cells as a risk factor for immunogenicity.

Immunogenicity, the unwanted immune response triggered by therapeutic antibodies, poses significant challenges in biotherapeutic development. This response can lead to the production of anti-drug antibodies, potentially compromising the efficacy and safety of treatments. The internalization of therapeutic antibodies into dendritic cells (DCs) is a critical factor influencing immunogenicity. Using monoclonal antibodies, with differences in non-specific cellular uptake, as tools to explore the impact on the overall risk of immunogenicity, this study explores how internalization influences peptide presentation and subsequently T cell activation. To investigate the impact of antibody internalization on immunogenicity, untargeted toolantibodies with engineered positive or negative charge patches were utilized. Immature monocyte-derived DCs (moDCs), known for their physiologically relevant high endocytic activity, were employed for internalization assays, while mature moDCs were used for MHC-II associated peptide proteomics (MAPPs) assays. In addition to the lysosomal accumulation and peptide presentation, subsequent CD4+ T cell activation has been assessed. Consequently, a known CD4+ T cell epitope from ovalbumin was inserted into the tool antibodies to evaluate T cell activation on a single, shared epitope. Antibodies with positive charge patches exhibited higher rates of lysosomal accumulation and epitope presentation compared to those with negative charge patches or neutral surface charge. Furthermore, a direct correlation between internalization rate and presentation on MHC-II molecules could be established. To explore the link between internalization, peptide presentation and CD4+ T cell activation, tool antibodies containing the same OVA epitope were used. Previous observations were not altered by the insertion of the OVA epitope and ultimately, an enhanced CD4+ T cell response correlated with increased internalization in DCs and peptide presentation. These findings demonstrate that the biophysical properties of therapeutic antibodies, particularly surface charge, play a crucial role in their internalization into DCs. Antibodies internalized faster and processed by DCs, are also more prone to be presented on their surface leading to a higher risk of triggering an immune response. These insights underscore the importance of considering antibody surface charge and other properties that enhance cellular accumulation during the preclinical development of biotherapeutics to mitigate immunogenicity risks.

Proteomic and antimicrobial peptide analyses of Buffalo colostrum and mature Milk whey: A comparative study

Buffalo colostrum is the initial mammary secretion after parturition, consisting of nutritional and bioactive components. In this study, we conducted a proteomic analysis of buffalo colostrum whey to identify bioactive proteins and peptides. A total of 107 differentially expressed proteins (DEPs) were identified in buffalo colostrum whey compared to those in mature milk. Gene Ontology analysis revealed that DEPs were primarily associated with immune response and tissue development. KEGG pathway enrichment suggested that colostrum actively enhances nascent immunity involved in interleukin and interferon signaling pathways. Furthermore, candidate antimicrobial peptides (AMPs) of whey protein hydrolysates from buffalo colostrum were characterized, which exhibits broad-spectrum activity against gram-positive and gram-negative pathogens. Overall, this study improves our understanding of protein variations in buffalo lactation, and contributes to the development of AMPs from buffalo colostrum.

Abstract 4115: Unleashing the power of MHC associated peptide proteomics: Immunogenicity assessment of oncology drugs

Abstract Bispecifics targeting multiple antigens or epitopes have been showing great promise for treatment of cancer. However, managing unwanted immunogenicity has become a challenge in the development cycle of these promising therapeutics as there is a trend towards higher unwanted immune responses compared with classical monoclonal antibodies. A thorough assessment of their immunogenic potential leads to safe biotherapeutics with high treatment efficacy. MAPPS assays (MHC associated peptide proteomics) enable the precise identification of all the regions of a protein that may evoke an immune response. This information is key for correct risk assessment and for modulating biotherapeutics. High-sensitive MAPPS workflow leads to higher numbers of identified peptides (self + non-self peptides) and gives the deepness of analysis required for confident immunogenicity derisking and modulation. We show the importance of peptides presented by HLA-DP and HLA-DQ (next to the standard HLA-DR presented peptides). Next, we also show the first ever comparison of presented peptides in true immune cells: dendritic cells (as compared to monocyte derived dendritic cells) and B cells. Citation Format: Sofie Pattyn. Unleashing the power of MHC associated peptide proteomics: Immunogenicity assessment of oncology drugs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4115.

Deep Learning-Assisted Analysis of Immunopeptidomics Data.

Liquid chromatography-coupled mass spectrometry (LC-MS/MS) is the primary method to obtain direct evidence for the presentation of disease- or patient-specific human leukocyte antigen (HLA). However, compared to the analysis of tryptic peptides in proteomics, the analysis of HLA peptides still poses computational and statistical challenges. Recently, fragment ion intensity-based matching scores assessing the similarity between predicted and observed spectra were shown to substantially increase the number of confidently identified peptides, particularly in use cases where non-tryptic peptides are analyzed. In this chapter, we describe in detail three procedures on how to benefit from state-of-the-art deep learning models to analyze and validate single spectra, single measurements, and multiple measurements in mass spectrometry-based immunopeptidomics. For this, we explain how to use the Universal Spectrum Explorer (USE), online Oktoberfest, and offline Oktoberfest. For intensity-based scoring, Oktoberfest uses fragment ion intensity and retention time predictions from the deep learning framework Prosit, a deep neural network trained on a very large number of synthetic peptides and tandem mass spectra generated within the ProteomeTools project. The examples shown highlight how deep learning-assisted analysis can increase the number of identified HLA peptides, facilitate the discovery of confidently identified neo-epitopes, or provide assistance in the assessment of the presence of cryptic peptides, such as spliced peptides.

Origins, Technological Advancement, and Applications of Peptidomics.

Peptidomics is the comprehensive characterization of peptides from biological sources instead of heading for a few single peptides in former peptide research. Mass spectrometry allows to detect a multitude of peptides in complex mixtures and thus enables new strategies leading to peptidomics. The term was established in the year 2001, and up to now, this new field has grown to over 3000 publications. Analytical techniques originally developed for fast and comprehensive analysis of peptides in proteomics were specifically adjusted for peptidomics. Although it is thus closely linked to proteomics, there are fundamental differences with conventional bottom-up proteomics. Fundamental technological advancements of peptidomics since have occurred in mass spectrometry and data processing, including quantification, and more slightly in separation technology. Different strategies and diverse sources of peptidomes are mentioned by numerous applications, such as discovery of neuropeptides and other bioactive peptides, including the use of biochemical assays. Furthermore, food and plant peptidomics are introduced similarly. Additionally, applications with a clinical focus are included, comprising biomarker discovery as well as immunopeptidomics. This overview extensively reviews recent methods, strategies, and applications including links to all other chapters of this book.

Rational Engineering of Minimally Immunogenic Nucleases for Gene Therapy

Genome editing using CRISPR-Cas systems is a promising avenue for the treatment of genetic diseases. On the horizon for FDA approval next year are several of the first treatment tools involving CRISPR based gene editing for sickle cell anemia and beta thalassemia (Frangoul et al. 2021). However, cellular and humoral immunogenicity of genome editing tools, which originate from bacteria, complicates their clinical use(Charlesworth et al. 2019; Chew 2018; Mehta and Merkel 2020). Here we report reduced immunogenicity (Red)(i)-variants of two clinically-relevant nucleases, SaCas9 and AsCas12a. Through MHC-associated peptide proteomics (MAPPs) analysis, we identified putative immunogenic epitopes on each nuclease(Sekiguchi et al. 2018). Then, we used computational modeling to rationally design these proteins to evade the immune response(King et al. 2014). SaCas9 and AsCas12a Redi variants were substantially less recognized by adaptive immune components, including reduced binding affinity to MHC molecules and attenuated generation of cytotoxic T cell responses, while maintaining wild-type levels of activity and specificity. In vivo editing of PCSK9 with SaCas9.Redi variant was comparable in efficiency to wild-type SaCas9, but significantly reduced undesired immune responses. This demonstrates the utility of this approach in engineering proteins to evade immune detection and expand in vivo clinical use for a wide assortment of hematological disorders.

Development of a semi-automated MHC-associated peptide proteomics (MAPPs) method using streptavidin bead-based immunoaffinity capture and nano LC-MS/MS to support immunogenicity risk assessment in drug development.

Major histocompatibility complex (MHC)-Associated Peptide Proteomics (MAPPs) is an ex vivo method used to assess the immunogenicity risk of biotherapeutics. MAPPs can identify potential T-cell epitopes within the biotherapeutic molecule. Using adalimumab treated human monocyte derived dendritic cells (DCs) and a pan anti-HLA-DR antibody (Ab), we systematically automated and optimized biotin/streptavidin (SA)-capture antibody coupling, lysate incubation with capture antibody, as well as the washing and elution steps of a MAPPs method using functionalized magnetic beads and a KingFisher Magnetic Particle processor. Automation of these steps, combined with capturing using biotinylated-Ab/SA magnetic beads rather than covalently bound antibody, improved reproducibility as measured by minimal inter-and intra-day variability, as well as minimal analyst-to-analyst variability. The semi-automated MAPPs workflow improved sensitivity, allowing for a lower number of cells per analysis. The method was assessed using five different biotherapeutics with varying immunogenicity rates ranging from 0.1 to 48% ADA incidence in the clinic. Biotherapeutics with ≥10%immunogenicity incidence consistently presented more peptides (1.8-28 fold) and clusters (10-21 fold) compared to those with <10% immunogenicity incidence. Our semi-automated MAPPs method provided two main advantages over a manual workflow- the robustness and reproducibility affords confidence in the epitopes identified from as few as 5 to 10 donors and the method workflow can be readily adapted to incorporate different capture Abs in addition to anti-HLA-DR. The incorporation of semi-automated MAPPs with biotinylated-Ab/SA bead-based capture in immunogenicity screening strategies allows the generation of more consistent and reliable data, helping to improve immunogenicity prediction capabilities in drug development. MHC associated peptide proteomics (MAPPs), Immunogenicity risk assessment, in vitro/ex vivo, biotherapeutics, Major Histocompatibility Complex Class II (MHC II), LC-MS, Immunoaffinity Capture, streptavidin magnetic beads.

The MHC Associated Peptide Proteomics assay is a useful tool for the non-clinical assessment of immunogenicity.

The propensity of therapeutic proteins to elicit an immune response, poses a significant challenge in clinical development and safety of the patients. Assessment of immunogenicity is crucial to predict potential adverse events and design safer biologics. In this study, we employed MHC Associated Peptide Proteomics (MAPPS) to comprehensively evaluate the immunogenic potential of re-engineered variants of immunogenic FVIIa analog (Vatreptacog Alfa). Our finding revealed the correlation between the protein sequence affinity for MHCII and the number of peptides identified in a MAPPS assay and this further correlates with the reduced T-cell responses. Moreover, MAPPS enable the identification of "relevant" T cell epitopes and may contribute to the development of biologics with lower immunogenic potential.

Assessing MAPPs assay as a tool to predict the immunogenicity potential of protein therapeutics.

MHC-II-associated peptide proteomics (MAPPs) is a mass spectrometry-based (MS) method to identify naturally presented MHC-II-associated peptides that could elicit CD4+T cell activation. MAPPs assay is considered one of the assays that better characterize the safety of biotherapeutics by driving the selection of the best candidates concerning their immunogenicity risk. However, there is little knowledge about the impact of bead material on the recovery of MHC-II MS-eluted ligands in MAPPs assays. Here, we firstly describe a robust MAPPs protocol by implementing streptavidin magnetic beads for the isolation of these peptides instead of commonly used NHS-activated beads. Moreover, we assessed the impact of the cell medium used for cell cultures on the morphology and recovery of the in vitro-generated APCs, and its potential implications in the amount of MHC-II isolated peptides. We also described an example of a MAPPs assay application to investigate drug-induced immunogenicity of two bispecific antibodies and compared them with monospecific trastuzumab IgG1 control. This work highlighted the importance of MAPPs in the preclinical in vitro strategy to mitigate the immunogenicity risk of biotherapeutics.

Expanding the MAPPs Assay to Accommodate MHC-II Pan Receptors for Improved Predictability of Potential T Cell Epitopes.

A critical step in the immunogenicity cascade is attributed to human leukocyte antigen (HLA) II presentation triggering T cell immune responses. The liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based major histocompatibility complex (MHC) II-associated peptide proteomics (MAPPs) assay is implemented during preclinical risk assessments to identify biotherapeutic-derived T cell epitopes. Although studies indicate that HLA-DP and HLA-DQ alleles are linked to immunogenicity, most MAPPs studies are restricted to using HLA-DR as the dominant HLA II genotype due to the lack of well-characterized immunoprecipitating antibodies. Here, we address this issue by testing various commercially available clones of MHC-II pan (CR3/43, WR18, and Tü39), HLA-DP (B7/21), and HLA-DQ (SPV-L3 and 1a3) antibodies in the MAPPs assay, and characterizing identified peptides according to binding specificity. Our results reveal that HLA II receptor-precipitating reagents with similar reported specificities differ based on clonality and that MHC-II pan antibodies do not entirely exhibit pan-specific tendencies. Since no individual antibody clone is able to recover the complete HLA II peptide repertoire, we recommend a mixed strategy of clones L243, WR18, and SPV-L3 in a single immunoprecipitation step for more robust compound-specific peptide detection. Ultimately, our optimized MAPPs strategy improves the predictability and additional identification of T cell epitopes in immunogenicity risk assessments.

Identification of motif-based interactions between SARS-CoV-2 protein domains and human peptide ligands pinpoint antiviral targets

The virus life cycle depends on host-virus protein-protein interactions, which often involve a disordered protein region binding to a folded protein domain. Here, we used proteomic peptide phage display (ProP-PD) to identify peptides from the intrinsically disordered regions of the human proteome that bind to folded protein domains encoded by the SARS-CoV-2 genome. Eleven folded domains of SARS-CoV-2 proteins were found to bind 281 peptides from human proteins, and affinities of 31 interactions involving eight SARS-CoV-2 protein domains were determined (KD ∼ 7-300 μM). Key specificity residues of the peptides were established for six of the interactions. Two of the peptides, binding Nsp9 and Nsp16, respectively, inhibited viral replication. Our findings demonstrate how high-throughput peptide binding screens simultaneously identify potential host-virus interactions and peptides with antiviral properties. Furthermore, the high number of low-affinity interactions suggest that overexpression of viral proteins during infection may perturb multiple cellular pathways.

Motif-dependent binding on the intervening domain regulates O-GlcNAc transferase.

The modification of intracellular proteins with O-linked β-N-acetylglucosamine (O-GlcNAc) moieties is a highly dynamic process that spatiotemporally regulates nearly every important cellular program. Despite its significance, little is known about the substrate recognition and regulation modes of O-GlcNAc transferase (OGT), the primary enzyme responsible for O-GlcNAc addition. In this study, we identified the intervening domain (Int-D), a poorly understood protein fold found only in metazoan OGTs, as a specific regulator of OGT protein-protein interactions and substrate modification. Using proteomic peptide phage display (ProP-PD) coupled with structural, biochemical and cellular characterizations, we discovered a strongly enriched peptide motif, employed by the Int-D to facilitate specific O-GlcNAcylation. We further show that disruption of Int-D binding dysregulates important cellular programs, including response to nutrient deprivation and glucose metabolism. These findings illustrate a mode of OGT substrate recognition and offer key insights into the biological roles of this unique domain.

The HLA class I immunopeptidomes of AAV capsid proteins.

Cellular immune responses against AAV vector capsid represent an obstacle for successful gene therapy. Previous studies have used overlapping peptides spanning the entire capsid sequence to identify T cell epitopes recognized by AAV-specific CD8+ T cells. However, the repertoire of peptides naturally displayed by HLA class I molecules for CD8 T cell recognition is unknown. Using mRNA transfected monocyte-derived dendritic cells (MDDCs) and MHC-associated peptide proteomics (MAPPs), we identified the HLA class I immunopeptidomes of AAV2, AAV6 and AAV9 capsids. MDDCs were isolated from a panel of healthy donors that have diverse alleles across the US population. mRNA-transfected MDDCs were lysed, the peptide:HLA complexes immunoprecipitated, and peptides eluted and analyzed by mass spectrometry. We identified 65 AAV capsid-derived peptides loaded on HLA class I molecules of mRNA transfected monocyte derived dendritic cells. The HLA class I peptides are distributed along the entire capsid and more than 60% are contained within HLA class II clusters. Most of the peptides are organized as single species, however we identified twelve clusters containing at least 2 peptides of different lengths. Only 9% of the identified peptides have been previously identified as T cell epitopes, demonstrating that the immunogenicity potential for the vast majority of the AAV HLA class I immunopeptidome remains uncharacterized. In contrast, 12 immunogenic epitopes identified before were not found to be naturally processed in our study. Remarkably, 11 naturally presented AAV peptides were highly conserved among the three serotypes analyzed suggesting the possibility of cross-reactive AAV-specific CD8 T cells. This work is the first comprehensive study identifying the naturally displayed HLA class I peptides derived from the capsid of AAVs. The results from this study can be used to generate strategies to assess immunogenicity risk and cross-reactivity among serotypes during gene therapies.

Pyroglutamination-Induced Changes in the Physicochemical Features of a CXCR4 Chemokine Peptide: Kinetic and Structural Analysis.

We investigate the physicochemical effects of pyroglutamination on the QHALTSV-NH2 peptide, a segment of cytosolic helix 8 of the human C-X-C chemokine G-protein-coupled receptor type 4 (CXCR4). This modification, resulting from the spontaneous conversion of glutamine to pyroglutamic acid, has significant impacts on the physicochemical features of peptides. Using a static approach, we compared the transformation in different conditions and experimentally found that the rate of product formation increases with temperature, underscoring the need for caution during laboratory experiments to prevent glutamine cyclization. Circular dichroism experiments revealed that the QHALTSV-NH2 segment plays a minor role in the structuration of H8 CXCR4; however, its pyroglutaminated analogue interacts differently with its chemical environment, showing increased susceptibility to solvent variations compared to the native form. The pyroglutaminated analogue exhibits altered behavior when interacting with lipid models, suggesting a significant impact on its interaction with cell membranes. A unique combination of atomic force microscopy and infrared nanospectroscopy revealed that pyroglutamination affects supramolecular self-assembly, leading to highly packed molecular arrangements and a crystalline structure. Moreover, the presence of pyroglumatic acid has been found to favor the formation of amyloidogenic aggregates. Our findings emphasize the importance of considering pyroglutamination in peptide synthesis and proteomics and its potential significance in amyloidosis.

Investigating the human and non-obese diabetic mouse MHC class II immunopeptidome using protein language modelling.

Identifying peptides associated with the major histocompability complex class II (MHCII) is a central task in the evaluation of the immunoregulatory function of therapeutics and drug prototypes. MHCII-peptide presentation prediction has multiple biopharmaceutical applications, including the safety assessment of biologics and engineered derivatives in silico, or the fast progression of antigen-specific immunomodulatory drug discovery programs in immune disease and cancer. This has resulted in the collection of large-scale data sets on adaptive immune receptor antigenic responses and MHC-associated peptide proteomics. In parallel, recent deep learning algorithmic advances in protein language modelling (PLM) have shown potential in leveraging large collections of sequence data and improve MHC presentation prediction. Here, we train a compact transformer model (AEGIS) on human and mouse MHCII immunopeptidome data, including a preclinical murine model, and evaluate its performance on the peptide presentation prediction task. We show that the transformer performs on par with existing deep learning algorithms and that combining datasets from multiple organisms increases model performance. We trained variants of the model with and without MHCII information. In both alternatives, the inclusion of peptides presented by the I-Ag7 MHC class II molecule expressed by the non-obese diabetic (NOD) mice enabled for the first time the accuratein silico prediction of presented peptides in a preclinical type 1 diabetes model organism, which has promising therapeutic applications. The source code is available at https://github.com/Novartis/AEGIS. Supplementary data are available at Bioinformatics online.

Development of a Liquid Chromatography and High-Resolution and -Accuracy Mass Spectrometry Method to Evaluate New Biotherapeutic Entity Processing in Human Liver Lysosomes.

Biotherapeutic immunogenicity remains a great challenge for researchers because multiple factors trigger immune responses. Predicting and assessing the potential human immune response against biological drugs could represent an impressive breakthrough toward generating potentially safer and more efficacious therapeutic proteins. This article describes an invitro assay that can contribute to evaluating the potential immunogenicity of biotherapeutics by focusing on lysosomal proteolysis. We selected human liver lysosomes (hLLs) from four different donors as a surrogate invitro model instead of APC lysosomes because they are a ready-to-use lysosomal source. To assess the biological comparability of this surrogate to APC lysosomal extract, we compared the proteome content of hLLs with literature data of lysosomal fractions extracted from murine bone marrow and human blood-derived dendritic cells. Then we tested infliximab (IFX; Remicade) under different proteolytic conditions using liquid chromatography and high-resolution and -accuracy mass spectrometry to better define the degradation kinetics inside the lysosomes. hLLs revealed similar enzymatic content compared with human and murine dendritic cell lysosomes. Degradation assays demonstrated that our liquid chromatography and high-resolution and -accuracy mass spectrometry method could identify both the intact protein and the peptides resulting from proteolysis with high specificity and resolution. The rapid and easy assay described in this article can be extremely useful for evaluating the immunogenic risk associated with therapeutic proteins. In addition, this method can complement information from MHC class II-associated peptide proteomics assays and other invitro and in silico techniques.

Mzion enables deep and precise identification of peptides in data-dependent acquisition proteomics

Sensitive and reliable identification of proteins and peptides pertains the basis of proteomics. We introduce Mzion, a new database search tool for data-dependent acquisition (DDA) proteomics. Our tool utilizes an intensity tally strategy and achieves generally a higher performance in terms of depth and precision across 20 datasets, ranging from large-scale to single-cell proteomics. Compared to several other search engines, Mzion matches on average 20% more peptide spectra at tryptic enzymatic specificity and 80% more at no enzymatic specificity from six large-scale, global datasets. Mzion also identifies more phosphopeptide spectra that can be explained by fewer proteins, demonstrated by six large-scale, local datasets corresponding to the global data. Our findings highlight the potential of Mzion for improving proteomic analysis and advancing our understanding of protein biology.

Shotgun Proteomics Analysis, Functional Networks, and Peptide Biomarkers for Seafood-Originating Biogenic-Amine-Producing Bacteria.

Biogenic amine-producing bacteria are responsible for the production of basic nitrogenous compounds (histamine, cadaverine, tyramine, and putrescine) following the spoilage of food due to microorganisms. In this study, we adopted a shotgun proteomics strategy to characterize 15 foodborne strains of biogenic-amine-producing bacteria. A total of 10,673 peptide spectrum matches belonging to 4081 peptides and corresponding to 1811 proteins were identified. Relevant functional pathways were determined, and strains were differentiated into hierarchical clusters. An expected protein-protein interaction network was created (260 nodes/1973 interactions). Most of the determined proteins were associated with networks/pathways of energy, putrescine metabolism, and host-virus interaction. Additionally, 556 peptides were identified as virulence factors. Moreover, 77 species-specific peptide biomarkers corresponding to 64 different proteins were proposed to identify 10 bacterial species. This represents a major proteomic dataset of biogenic-amine-producing strains. These results may also be suitable for new treatments for food intoxication and for tracking microbial sources in foodstuffs.

Optimizing human α-galactosidase for treatment of Fabry disease

Fabry disease is caused by a deficiency of α-galactosidase A (GLA) leading to the lysosomal accumulation of globotriaosylceramide (Gb3) and other glycosphingolipids. Fabry patients experience significant damage to the heart, kidney, and blood vessels that can be fatal. Here we apply directed evolution to generate more stable GLA variants as potential next generation treatments for Fabry disease. GLAv05 and GLAv09 were identified after screening more than 12,000 GLA variants through 8 rounds of directed evolution. Both GLAv05 and GLAv09 exhibit increased stability at both lysosomal and blood pH, stability to serum, and elevated enzyme activity in treated Fabry fibroblasts (19-fold) and GLA–/– podocytes (10-fold). GLAv05 and GLAv09 show improved pharmacokinetics in mouse and non-human primates. In a Fabry mouse model, the optimized variants showed prolonged half-lives in serum and relevant tissues, and a decrease of accumulated Gb3 in heart and kidney. To explore the possibility of diminishing the immunogenic potential of rhGLA, amino acid residues in sequences predicted to bind MHC II were targeted in late rounds of GLAv09 directed evolution. An MHC II-associated peptide proteomics assay confirmed a reduction in displayed peptides for GLAv09. Collectively, our findings highlight the promise of using directed evolution to generate enzyme variants for more effective treatment of lysosomal storage diseases.