Stem-cell based, machine learning approach for optimizing natural killer cell-based personalized immunotherapy for high-grade ovarian cancer.

Advanced high-grade serous ovarian cancer continues to be a therapeutic challenge for those affected using the current therapeutic interventions. There is an increasing interest in personalized cancer immunotherapy using activated natural killer (NK) cells. NK cells account for approximately 15% of circulating white blood cells. They are also an important element of the tumor microenvironment (TME) and the body's immune response to cancers. In the present study, DeepNEU-C2Rx, a machine learning platform, was first used to create validated artificially induced pluripotent stem cell simulations. These simulations were then used to generate wild-type artificially induced NK cells (aiNK-WT) and TME simulations. Once validated, the aiNK-WT simulations were exposed to artificially induced high-grade serous ovarian cancer represented by aiOVCAR3. Cytolytic activity of aiNK was evaluated in presence and absence of aiOVCAR3 and data were compared with the literature for validation. The TME simulations suggested 26 factors that could be evaluated based on their ability to enhance aiNK-WT cytolytic activity in the presence of aiOVCAR3. The addition of programmed cell death-1 inhibitor leads to significant reinvigoration of aiNK cytolytic activity. The combination of programmed cell death-1 and glycogen synthase kinase 3 inhibitors showed further improvement. Further addition of ascitic fluid factor inhibitors leads to optimal aiNK activation. Our data showed that NK cell simulations could be used not only to pinpoint novel immunotherapeutic targets to reinvigorate the activity of NK cells against cancers, but also to predict the outcome of targeting tumors with specific genetic expression and mutation profiles.

Advances in understanding N-glycosylation structure, function, and regulation in health and disease.

N-linked glycosylation is a post-translational modification crucial for membrane protein folding, stability and other cellular functions. Alteration of membrane protein N-glycans is implicated in wide range of pathological conditions including cancer metastasis, chronic inflammatory diseases, and viral pathogenesis. Even though the roles of N-glycans have been studied extensively, our knowledge of their mechanisms remains unclear due to the lack of detailed structural analysis of the N-glycome. Mapping the N-glycome landscape will open new avenues to explore disease mechanisms and identify novel therapeutic targets. This review discusses the diverse structure of N-linked glycans, the function and regulation of N-glycosylation in health and disease, and ends with a focus on recent approaches to target N-glycans in rheumatoid arthritis and cancer metastasis.

Rapid and accurate agglutination-based testing for SARS-CoV-2 antibodies.

We have developed a rapid, accurate, and cost-effective serologic test for SARS-CoV-2 virus, which caused the COVID-19 pandemic, on the basis of antibody-dependent agglutination of antigen-coated latex particles. When validated using plasma samples that are positive or negative for SARS-CoV-2, the agglutination assay detected antibodies against the receptor-binding domain of the spike (S-RBD) or the nucleocapsid protein of SARS-CoV-2 with 100% specificity and ∼98% sensitivity. Furthermore, we found that the strength of the S-RBD antibody response measured by the agglutination assay correlated with the efficiency of the plasma in blocking RBD binding to the angiotensin-converting enzyme 2 in a surrogate neutralization assay, suggesting that the agglutination assay might be used to identify individuals with virus-neutralizing antibodies. Intriguingly, we found that >92% of patients had detectable antibodies on the day of a positive viral RNA test, suggesting that the agglutination antibody test might complement RNA testing for the diagnosis of SARS-CoV-2 infection.

Epitope-specific antibody responses differentiate COVID-19 outcomes and variants of concern.

BACKGROUNDThe 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.METHODSUsing 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.RESULTSWe 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.CONCLUSIONEpitope-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.FUNDINGOntario 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.

Lung organoid simulations for modelling and predicting the effect of mutations on SARS-CoV-2 infectivity.

The global pandemic caused by the SARS-CoV-2 virus continues to spread. Infection with SARS- CoV-2 causes COVID-19, a disease of variable severity. Mutation has already altered the SARS-CoV-2 genome from its original reported sequence and continued mutation is highly probable. These mutations can: (i) have no significant impact (they are silent), (ii) result in a complete loss or reduction of infectivity, or (iii) induce increase in infectivity. Physical generation, for research purposes, of viral mutations that could enhance infectivity are controversial and highly regulated. The primary purpose of this project was to evaluate the ability of the DeepNEU machine learning stem-cell simulation platform to enable rapid and efficient assessment of the potential impact of viral loss-of-function (LOF) and gain-of-function (GOF) mutations on SARS-CoV-2 infectivity. Our data suggest that SARS-CoV-2 infection can be simulated in human alveolar type lung cells. Simulation of infection in these lung cells can be used to model and assess the impact of LOF and GOF mutations in the SARS-CoV2 genome. We have also created a four- factor infectivity measure: the DeepNEU Case Fatality Rate (dnCFR). dnCFR can be used to assess infectivity based on the presence or absence of the key viral proteins (NSP3, Spike-RDB, N protein, and M protein). dnCFR was used in this study, not to only assess the impact of different mutations on SARS-CoV2 infectivity, but also to categorize the effects of mutations as loss of infectivity or gain of infectivity events.

Artificially Induced Pluripotent Stem Cell-Derived Whole-Brain Organoid for Modelling the Pathophysiology of Metachromatic Leukodystrophy and Drug Repurposing.

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.

NEUBOrg: Artificially Induced Pluripotent Stem Cell-Derived Brain Organoid to Model and Study Genetics of Alzheimer's Disease Progression.

Alzheimer's disease (AD) is the most common type of neurodegenerative diseases. There are over 44 million people living with the disease worldwide. While there are currently no effective treatments for AD, induced pluripotent stem cell-derived brain organoids have the potential to provide a better understanding of Alzheimer's pathogenesis. Nevertheless, developing brain organoid models is expensive, time consuming and often does not reflect disease progression. Using accurate and inexpensive computer simulations of human brain organoids can overcome the current limitations. Induced whole brain organoids (aiWBO) will greatly expand our ability to model AD and can guide wet lab research. In this study, we have successfully developed and validated artificially induced a whole brain organoid platform (NEUBOrg) using our previously validated machine learning platform, DeepNEU (v6.1). Using NEUBorg platform, we have generated aiWBO simulations of AD and provided a novel approach to test genetic risk factors associated with AD progression and pathogenesis.

Viral pandemic preparedness: A pluripotent stem cell-based machine-learning platform for simulating SARS-CoV-2 infection to enable drug discovery and repurposing.

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.

DeepNEU: Artificially Induced Stem Cell (aiPSC) and Differentiated Skeletal Muscle Cell (aiSkMC) Simulations of Infantile Onset POMPE Disease (IOPD) for Potential Biomarker Identification and Drug Discovery.

Infantile onset Pompe disease (IOPD) is a rare and lethal genetic disorder caused by the deletion of the acid alpha-glucosidase (GAA) gene. This gene encodes an essential lysosomal enzyme that converts glycogen to glucose. While enzyme replacement therapy helps some, our understanding of disease pathophysiology is limited. In this project we develop computer simulated stem cells (aiPSC) and differentiated skeletal muscle cells (aiSkMC) to empower IOPD research and drug discovery. Our Artificial Intelligence (AI) platform, DeepNEU v3.6 was used to generate aiPSC and aiSkMC simulations with and without GAA expression. These simulations were validated using peer reviewed results from the recent literature. Once the aiSkMC simulations (IOPD and WT) were validated they were used to evaluate calcium homeostasis and mitochondrial function in IOPD. Lastly, we used aiSkMC IOPD simulations to identify known and novel biomarkers and potential therapeutic targets. The aiSkMC simulations of IOPD correctly predicted genotypic and phenotypic features that were reported in recent literature. The probability that these features were accurately predicted by chance alone using the binomial test is 0.0025. The aiSkMC IOPD simulation correctly identified L-type calcium channels (VDCC) as a biomarker and confirmed the positive effects of calcium channel blockade (CCB) on calcium homeostasis and mitochondrial function. These published data were extended by the aiSkMC simulations to identify calpain(s) as a novel potential biomarker and therapeutic target for IOPD. This is the first time that computer simulations of iPSC and differentiated skeletal muscle cells have been used to study IOPD. The simulations are robust and accurate based on available published literature. We also demonstrated that the IOPD simulations can be used for potential biomarker identification leading to targeted drug discovery. We will continue to explore the potential for calpain inhibitors with and without CCB as effective therapy for IOPD.

Single-Layer ß Sheets as Versatile Protein-Binding Modules.

In this issue of Structure, Li et al. (2019) describe the structure of the MORN4-Myo3a complex, revealing that the MORN repeats in MORN4 form a single-layer antiparallel ß sheet and employs an extensive surface area in binding Myo3a. Their findings suggest that single-layer ß sheets are versatile protein-binding modules.

Molecular mechanisms of cutis laxa- and distal renal tubular acidosis-causing mutations in V-ATPase a subunits, ATP6V0A2 and ATP6V0A4.

The a subunit is the largest of 15 different subunits that make up the vacuolar H+-ATPase (V-ATPase) complex, where it functions in proton translocation. In mammals, this subunit has four paralogous isoforms, a1-a4, which may encode signals for targeting assembled V-ATPases to specific intracellular locations. Despite the functional importance of the a subunit, its structure remains controversial. By studying molecular mechanisms of human disease-causing missense mutations within a subunit isoforms, we may identify domains critical for V-ATPase targeting, activity and/or regulation. cDNA-encoded FLAG-tagged human wildtype ATP6V0A2 (a2) and ATP6V0A4 (a4) subunits and their mutants, a2P405L (causing cutis laxa), and a4R449H and a4G820R (causing renal tubular acidosis, dRTA), were transiently expressed in HEK 293 cells. N-Glycosylation was assessed using endoglycosidases, revealing that a2P405L, a4R449H, and a4G820R were fully N-glycosylated. Cycloheximide (CHX) chase assays revealed that a2P405L and a4R449H were unstable relative to wildtype. a4R449H was degraded predominantly in the proteasomal pathway, whereas a2P405L was degraded in both proteasomal and lysosomal pathways. Immunofluorescence studies disclosed retention in the endoplasmic reticulum and defective cell-surface expression of a4R449H and defective Golgi trafficking of a2P405L Co-immunoprecipitation studies revealed an increase in association of a4R449H with the V0 assembly factor VMA21, and a reduced association with the V1 sector subunit, ATP6V1B1 (B1). For a4G820R, where stability, degradation, and trafficking were relatively unaffected, 3D molecular modeling suggested that the mutation causes dRTA by blocking the proton pathway. This study provides critical information that may assist rational drug design to manage dRTA and cutis laxa.

N-linked glycosylation of a subunit isoforms is critical for vertebrate vacuolar H+ -ATPase (V-ATPase) biosynthesis.

The a subunit of the V0 membrane-integrated sector of human V-ATPase has four isoforms, a1-a4, with diverse and crucial functions in health and disease. They are encoded by four conserved paralogous genes, and their vertebrate orthologs have positionally conserved N-glycosylation sequons within the second extracellular loop, EL2, of the a subunit membrane domain. Previously, we have shown directly that the predicted sequon for the a4 isoform is indeed N-glycosylated. Here we extend our investigation to the other isoforms by transiently transfecting HEK 293 cells to express cDNA constructs of epitope-tagged human a1-a3 subunits, with or without mutations that convert Asn to Gln at putative N-glycosylation sites. Expression and N-glycosylation were characterized by immunoblotting and mobility shifts after enzymatic deglycosylation, and intracellular localization was determined using immunofluorescence microscopy. All unglycosylated mutants, where predicted N-glycosylation sites had been eliminated by sequon mutagenesis, showed increased relative mobility on immunoblots, identical to what was seen for wild-type a subunits after enzymatic deglycosylation. Cycloheximide-chase experiments showed that unglycosylated subunits were turned over at a higher rate than N-glycosylated forms by degradation in the proteasomal pathway. Immunofluorescence colocalization analysis showed that unglycosylated a subunits were retained in the ER, and co-immunoprecipitation studies showed that they were unable to associate with the V-ATPase assembly chaperone, VMA21. Taken together with our previous a4 subunit studies, these observations show that N-glycosylation is crucial in all four human V-ATPase a subunit isoforms for protein stability and ultimately for functional incorporation into V-ATPase complexes.

N-Linked Glycosylation Is Required for Vacuolar H+ -ATPase (V-ATPase) a4 Subunit Stability, Assembly, and Cell Surface Expression.

The a subunit is the largest of 14 different subunits that make up the V-ATPase complex. In mammalian species this membrane protein has four paralogous isoforms, a1-a4. Clinically, a subunit isoforms are implicated in diverse diseases; however, little is known about their structure and function. The subunit has conserved, predicted N-glycosylation sites, and the a3 isoform has been directly shown to be N-glycosylated. Here we ask if human a4 (ATP6V0A4) is N-glycosylated at the predicted site, Asn489. We transfected HEK 293 cells, using the pCDNA3.1 expression-vector system, to express cDNA constructs of epitope-tagged human a4 subunit, with or without mutations to eliminate the putative glycosylation site. Glycosylation was characterized also by treatment with endoglycosidases; expression and localization were assessed by immunoblotting and immunofluorescence. Endoglycosidase-treated wild type (WT) a4 showed increased relative mobility on immunoblots, compared with untreated WT a4. This relative mobility was identical to that of unglycosylated mutant a4N489D , demonstrating that the a4 subunit is glycosylated. Cycloheximide pulse-chase experiments showed that the unglycosylated subunit degraded at a higher rate than the N-glycosylated form. Unglycosylated a4 was degraded mostly in the proteasomal pathway, but also, in part, through the lysosomal pathway. Immunofluorescence colocalization data showed that unglycosylated a4 was mostly retained in the ER, and that plasma membrane trafficking was defective. Co-immunoprecipitation studies suggested that a4N489D does not assemble with the V-ATPase V1 domain. Taken together, these data show that N-glycosylation plays a crucial role in a4 stability, and in V-ATPase assembly and trafficking to the plasma membrane. J. Cell. Biochem. 117: 2757-2768, 2016. © 2016 Wiley Periodicals, Inc.