A mechanism that transduces lysosomal damage signals to stress granule formation for cell survival (preprint)

Lysosomal damage poses a significant threat to cell survival. Our previous work has reported that lysosomal damage induces stress granule (SG) formation. However, the importance of SG formation in determining cell fate and the precise mechanisms through which lysosomal damage triggers SG formation remains unclear. Here, we show that SG formation is initiated via a novel calcium-dependent pathway and plays a protective role in promoting cell survival in response to lysosomal damage. Mechanistically, we demonstrate that during lysosomal damage, ALIX, a calcium-activated protein, transduces lysosomal damage signals by sensing calcium leakage to induce SG formation by controlling the phosphorylation of eIF2α. ALIX modulates eIF2α phosphorylation by regulating the association between PKR and its activator PACT, with galectin-3 exerting a negative effect on this process. We also found this regulatory event of SG formation occur on damaged lysosomes. Collectively, these investigations reveal novel insights into the precise regulation of SG formation triggered by lysosomal damage, and shed light on the interaction between damaged lysosomes and SGs. Importantly, SG formation is significant for promoting cell survival in the physiological context of lysosomal damage inflicted by SARS-CoV-2 ORF3a, adenovirus infection, Malaria hemozoin, proteopathic tau as well as environmental hazard silica.

COMPARATIVE PERFORMANCE OF TWO AUTOMATED MACHINE LEARNING PLATFORMS FOR COVID-19 DETECTION BY MALDI-TOF-MS (preprint)

The 2019 novel coronavirus infectious disease (COVID-19) pandemic has resulted in an unsustainable need for diagnostic tests. Currently, molecular tests are the accepted standard for the detection of SARS-CoV-2. Mass spectrometry (MS) enhanced by machine learning (ML) has recently been postulated to serve as a rapid, high-throughput, and low-cost alternative to molecular methods. Automated ML is a novel approach that could move mass spectrometry techniques beyond the confines of traditional laboratory settings. However, it remains unknown how different automated ML platforms perform for COVID-19 MS analysis. To this end, the goal of our study is to compare algorithms produced by two commercial automated ML platforms (Platforms A and B). Our study consisted of MS data derived from 361 subjects with molecular confirmation of COVID-19 status including SARS-CoV-2 variants. The top optimized ML model with respect to positive percent agreement (PPA) within Platforms A and B exhibited an accuracy of 94.9%, PPA of 100%, negative percent agreement (NPA) of 93%, and an accuracy of 91.8%, PPA of 100%, and NPA of 89%, respectively. These results illustrate the MS methods robustness against SARS-CoV-2 variants and highlight similarities and differences in automated ML platforms in producing optimal predictive algorithms for a given dataset.

Novel Application of Automated Machine Learning With Maldi-Tof-Ms For Rapid High-Throughput Screening of Covid-19: A Proof Of Concept (preprint)

The 2019 novel coronavirus infectious disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created an unsustainable need for molecular diagnostic testing. Molecular approaches such as reverse transcription (RT) polymerase chain reaction (PCR) offers highly sensitive and specific means to detect SARS-CoV-2 RNA, however, despite it being the accepted “gold standard”, molecular platforms often require a tradeoff between speed versus throughput. Matrix assisted laser desorption ionization (MALDI) – time of flight (TOF) – mass spectrometry (MS) has been proposed as a potential solution for COVID-19 testing and finding a balance between analytical performance, speed, and throughput, without relying on impacted supply chains. Combined with machine learning (ML), this MALDI-TOF-MS approach could overcome logistical barriers encountered by current testing paradigms. We evaluated the analytical performance of an ML-enhanced MALDI-TOF-MS method for screening COVID-19. Residual nasal swab samples from adult volunteers were used for testing and compared against RT-PCR. Two optimized ML models were identified, exhibiting accuracy of 98.3%, positive percent agreement (PPA) of 100%, negative percent agreement (NPA) of 96%, and accuracy of 96.6%, PPA of 98.5%, and NPA of 94% respectively. Machine learning enhanced MALDI-TOF-MS for COVID-19 testing exhibited performance comparable to existing commercial SARS-CoV-2 tests.

Immunologically distinct responses occur in the CNS of COVID-19 patients (preprint)

One third of COVID-19 patients develop significant neurological symptoms, yet SARS-CoV-2 is rarely detected in central nervous system (CNS) tissue, suggesting a potential role for parainfectious processes, including neuroimmune responses. We therefore examined immune parameters in cerebrospinal fluid (CSF) and blood samples from a cohort of patients with COVID-19 and significant neurological complications. We found divergent immunological responses in the CNS compartment, including increased levels of IL-12 and IL-12-associated innate and adaptive immune cell activation. Moreover, we found increased proportions of B cells in the CSF relative to the periphery and evidence of clonal expansion of CSF B cells, suggesting a divergent intrathecal humoral response to SARS-CoV-2. Indeed, all COVID-19 cases examined had anti-SARS-CoV-2 IgG antibodies in the CSF whose target epitopes diverged from serum antibodies. We directly examined whether CSF resident antibodies target self-antigens and found a significant burden of CNS autoimmunity, with the CSF from most patients recognizing neural self-antigens. Finally, we produced a panel of monoclonal antibodies from patients CSF and show that these target both anti-viral and anti-neural antigens--including one mAb specific for the spike protein that also recognizes neural tissue. This exploratory immune survey reveals evidence of a compartmentalized and self-reactive immune response in the CNS meriting a more systematic evaluation of neurologically impaired COVID-19 patients. One Sentence SummaryA subset of COVID-19 patients with neurologic impairment show cerebrospinal fluid-specific immune alterations that point to both neuroinvasion and anti-neural autoimmunity as potential causes of impairment.