Sally Esmail

Tandem mass tag (TMT)-based proteomics facilitate multiplexing in mass spectrometry (MS)-based quantification and identification of proteins and their post-translational modifications. The use of TMT isobaric tags can enable multiplexing of up to 18 samples using commercially available kits. A single TMT experiment can quantify proteome, serine, threonine phosphorylation, and tyrosine phosphorylation. Of note, tyrosine phosphorylation is of low abundance, and identification/quantification can be improved using two complementary strategies. First, by employing SH2 superbinder which increases the number of identified sites. The SH2 Superbinder is more cost-effective than the commonly used phosphotyrosine antibodies. Second, by employing phosphotyrosine booster strategy, a pervanadate-treated channel to boost the signal of low-abundant phosphotyrosine. Noteworthy, pervanadate boost increases the likelihood of low abundant peptide to be selected for MS2, and facilitating the detection of > 6000 proteins, 10,000 unique pS/T and 1000 unique pY sites.

BACKGROUND: The role of humoral immunity in COVID-19 is not fully understood, owing, in large part, to the complexity of antibodies produced in response to the SARS-CoV-2 infection. There is a pressing need for serology tests to assess patient-specific antibody response and predict clinical outcome. METHODS: Using SARS-CoV-2 proteome and peptide microarrays, we screened 146 COVID-19 patients’ plasma samples to identify antigens and epitopes. This enabled us to develop a master epitope array and an epitope-specific agglutination assay to gauge antibody responses systematically and with high resolution. RESULTS: We identified linear epitopes from the spike (S) and nucleocapsid (N) proteins and showed that the epitopes enabled higher resolution antibody profiling than the S or N protein antigen. Specifically, we found that antibody responses to the S-811–825, S-881–895, and N-156–170 epitopes negatively or positively correlated with clinical severity or patient survival. Moreover, we found that the P681H and S235F mutations associated with the coronavirus variant of concern B.1.1.7 altered the specificity of the corresponding epitopes. CONCLUSION: Epitope-resolved antibody testing not only affords a high-resolution alternative to conventional immunoassays to delineate the complex humoral immunity to SARS-CoV-2 and differentiate between neutralizing and non-neutralizing antibodies, but it also may potentially be used to predict clinical outcome. The epitope peptides can be readily modified to detect antibodies against variants of concern in both the peptide array and latex agglutination formats. FUNDING: Ontario Research Fund (ORF) COVID-19 Rapid Research Fund, Toronto COVID-19 Action Fund, Western University, Lawson Health Research Institute, London Health Sciences Foundation, and Academic Medical Organization of Southwestern Ontario (AMOSO) Innovation Fund.

Optimizing patient care based on precision oncology will inevitably become the standard of care. If we accept the principle that every persons’ cancer is different then the most effective therapies will have to be designed for the individual patient and for their tumors genetic profile. Access to tumor mutational profiling is now widely available but continues to be limited by cost and actionable information. For example, novel combinations of approved drugs are rarely considered. These considerations lead us to hypothesize that artificially induced Lung Adenocarcinoma (LUAD) derived lung organoids could provide a novel, alternate approach for LUAD disease modeling and large-scale targeted drug screening. In this project, we used data from a commercially available tumor mutation profile to generate and then validate the artificially induced LUAD-derived lung organoid simulations (aiLUNG-LUAD) to model LUAD and identify several drug combinations that effectively reverse the tumors’ genotypic and phenotypic features when compared with placebo. These results complement previous LUAD-derived lung organoids research and provide a novel and widely applicable cancer drug-screening approach for precision/individualized oncology.

Metachromatic leukodystrophy (MLD) is a rare neurodegenerative disease that results from a deficiency of the lysosomal enzyme arylsulfatase A (ARSA). Worldwide, there are between one in 40,000 and one in 160,000 people living with the disease. While there are currently no effective treatments for MLD, induced pluripotent stem cell-derived brain organoids have the potential to provide a better understanding of MLD pathogenesis. However, developing brain organoid models is expensive, time consuming and may not accurately reflect disease progression. Using accurate and inexpensive computer simulations of human brain organoids could overcome the current limitations. Artificially induced whole-brain organoids (aiWBO) have the potential to greatly expand our ability to model MLD and guide future wet lab research. In this study, we have upgraded and validated our artificially induced whole-brain organoid platform (NEUBOrg) using our previously validated machine learning platform, DeepNEU (v6.2). Using this upgraded NEUBorg, we have generated aiWBO simulations of MLD and provided a novel approach to evaluate factors associated with MLD pathogenesis, disease progression and new potential therapeutic options.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has become a global crisis. To gain systems-level insights into its pathogenesis, we compared the blood proteome and phosphoproteome of ICU patients with or without SARS-CoV-2 infection, and healthy control subjects by quantitative mass spectrometry. We find that COVID-19 is marked with hyperactive T cell and B cell signaling, compromised innate immune response, and dysregulated inflammation, coagulation, metabolism, RNA splicing, transcription and translation pathways. SARS-CoV-2 infection causes global reprogramming of the kinome and kinase-substrate network, resulting in defective antiviral defense via the CK2-OPN-IL-12/IFN-I axis, lymphocyte cell death via aberrant JAK/STAT signaling, and inactivation of innate immune cells via inhibitory SIRPA, SIGLEC and SLAM family receptor signaling. Our work identifies CK2, SYK, JAK3, TYK2 and IL-12 as potential targets for immunomodulatory treatment of severe COVID-19 and provides a valuable approach and resource for deciphering the mechanism of pathogen-host interactions.

Infection with the SARS-CoV-2 virus has rapidly become a global pandemic for which we were not prepared. Several clinical trials using previously approved drugs and drug combinations are urgently under way to improve the current situation. A vaccine option has only recently become available, but worldwide distribution is still a challenge. It is imperative that, for future viral pandemic preparedness, we have a rapid screening technology for drug discovery and repurposing. The primary purpose of this research project was to evaluate the DeepNEU stem-cell based platform by creating and validating computer simulations of artificial lung cells infected with SARS-CoV-2 to enable the rapid identification of antiviral therapeutic targets and drug repurposing. The data generated from this project indicate that (a) human alveolar type lung cells can be simulated by DeepNEU (v5.0), (b) these simulated cells can then be infected with simulated SARS-CoV-2 virus, (c) the unsupervised learning system performed well in all simulations based on available published wet lab data, and (d) the platform identified potentially effective anti-SARS-CoV2 combinations of known drugs for urgent clinical study. The data also suggest that DeepNEU can identify potential therapeutic targets for expedited vaccine development. We conclude that based on published data plus current DeepNEU results, continued development of the DeepNEU platform will improve our preparedness for and response to future viral outbreaks. This can be achieved through rapid identification of potential therapeutic options for clinical testing as soon as the viral genome has been confirmed.