Professor Jonathan Blackburn
Affiliations
- Full Member, Institute of Infectious Disease and Molecular Medicine
- Research Chair in Proteomics
- Head of Division Chemical and Systems Biology, Department of Integrative Biomedical Sciences, Faculty of Health Sciences
- Head, D-CYPHR Molecular Phenotyping Hub, IDM.
- Elected member of the Academy of Science of South Africa; University of Cape Town Fellow; Distinguished Fellow, Standard BioTools
Key Expertise Key Expertise
Bioinformatics Biomarker Discovery, Bacterial Infections Cancer (Prevalent), Diagnosis, Genomic & Precision Medicine, Microbiome Molecular Medicine, Non-communicable Disease Omics, Protein biochemistry T-and B-cell Immunology, TB
Main Research Focus
Jonathan applies expertise has gained in academia and in a US biotech environment to drive interdisciplinary, collaborative, translational research programs in the precision medicine field, aimed at providing a new level of understanding and prediction concerning the individual nature of disease progression and drug response.
His research strategy is to leverage his Research Group's established technological platforms, technical expertise, and clinical collaborations in order to drive internationally leading clinical proteomics research programs that have a strong translational emphasis. Their overarching goal is to combine basic and clinical research to create a pipeline from protein and antibody biomarker discovery, through to validation, and ultimately to application in the human health sector.
Jonathan's academic expertise ranges from mechanistic enzymology, protein biochemistry, molecular biology to protein microarrays and mass spectrometry-based proteomics. He is currently interested in the translational applications of protein microarray technology, as well as mass spectrometry-based proteomics and metabolomics, in diagnostic marker discovery and validation, in unravelling molecular mechanisms of disease, and in predicting patient response to treatment in cancers, autoimmune- and infectious diseases. He has published over 150 peer reviewed papers, nine book chapters, edited one book and has 33 granted patents.
His current proteomic research programs focus on the application of novel immunoproteomic technologies to study human disease, including lung, pancreatic and prostate cancers; autoimmune diseases such as SLE; tuberculosis and HIV-associated disorders; & an increasing focus on the role of microbiomes in communicable and non-communicable disease. Through my research, I aim to identify and translate diagnostic and prognostic biomarkers for use in clinical settings, as well as to unravel underlying molecular mechanisms of diseases that disproportionately affect African populations.
Most Significant Paper Authored in 2024
Maimela PWMM, Smith M, Nel AJM, Bernam SDP, Jonas EG, Blackburn JM (2024).
Pancreatic ductal adenocarcinoma (PDAC) is a heterogeneous cancer, with minimal response to therapeutic intervention and with 85% of cases diagnosed at an advanced stage due to lack of early symptoms, highlighting the importance of understanding PDAC immunology in greater detail. Here, we applied an immunoproteomic approach to investigate autoantibody responses against cancer-testis and tumour-associated antigens in PDAC using a high-throughput multiplexed protein microarray platform, comparing humoral immune responses in serum and at the site of disease in order to shed new light on immune responses in the tumor microenvironment. We simultaneously quantified serum or tissue IgG and IgA antibody isotypes and subclasses in a cohort of PDAC, disease control and healthy patients, observing inter alia that subclass utilization in tumor tissue samples was predominantly immune suppressive IgG4 and inflammatory IgA2, contrasting with predominant IgG3 and IgA1 subclass utilization in matched sera and implying local autoantibody production at the site of disease in an immune-tolerant environment. By comparison, serum autoantibody subclass profiling for the disease controls identified IgG4, IgG1, and IgA1 as the abundant subclasses. Combinatorial analysis of serum autoantibody responses identified panels of candidate biomarkers. The top IgG panel included ACVR2B, GAGE1, LEMD1, MAGEB1 and (sensitivity, specificity and AUC values of 0.933, 0.767 and 0.906). Conversely, the top IgA panel included AURKA, GAGE1, MAGEA10, PLEKHA5 and XAGE3aV1 (sensitivity, specificity, and AUC values of 1.000, 0.800, and 0.954). Assessment of antigen-specific serum autoantibody glycoforms revealed abundant sialylation on IgA in PDAC, consistent with an immune suppressive IgA response to disease.