Membrane Atg8ylation, stress granule formation, and MTOR regulation during lysosomal damage.

The functions of mammalian Atg8 proteins (mATG8s) expand beyond canonical autophagy and include processes collectively referred to as Atg8ylation. Global modulation of protein synthesis under stress conditions is governed by MTOR and liquid-liquid phase separated condensates containing ribonucleoprotein particles known as stress granules (SGs). We report that lysosomal damage induces SGs acting as a hitherto unappreciated inhibitor of protein translation via EIF2A/eIF2α phosphorylation while favoring an ATF4-dependent integrated stress response. SGs are induced by lysosome-damaging agents, SARS-CoV-2 open reading frame 3a protein (ORF3a) expression, Mycobacterium tuberculosis infection, and exposure to proteopathic MAPT/tau. Proteomic studies revealed recruitment to damaged lysosomes of the core SG proteins NUFIP2 and G3BP1 along with the GABARAPs of the mATG8 family. The recruitment of these proteins is independent of SG condensates or canonical autophagy. GABARAPs interact directly with NUFIP2 and G3BP1 whereas Atg8ylation is needed for their recruitment to damaged lysosomes. At the lysosome, NUFIP2 contributes to MTOR inactivation together with LGALS8 (galectin 8) via the Ragulator-RRAGA-RRAGB complex. The separable functions of NUFIP2 and G3BP1 in SG formation vis-a-vis their role in MTOR inactivation are governed by GABARAP and Atg8ylation. Thus, cells employ membrane Atg8ylation to control and coordinate SG and MTOR responses to lysosomal damage.Abbreviations: Atg8: autophagy related 8; ATG: autophagy related; ATF4: activating transcription factor 4; EIF2A/eIF2α: eukaryotic translation initiation factor 2A; GABARAP: GABA type A receptor-associated protein; G3BP1: G3BP stress granule assembly factor 1; LLOMe: L-leucyl-L-leucine methyl ester; LysoIP: lysosome immunopurification; mRNA: messenger ribonucleic acid; MTOR: mechanistic target of rapamycin kinase; NUFIP2: nuclear FMR1 interacting protein 2; ORF3a: open reading frame 3a protein; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; SG: stress granule; TIA1: TIA1 cytotoxic granule associated RNA binding protein.

The 'omics of obesity in B-cell acute lymphoblastic leukemia.

The obesity pandemic currently affects more than 70 million Americans and more than 650 million individuals worldwide. In addition to increasing susceptibility to pathogenic infections (eg, SARS-CoV-2), obesity promotes the development of many cancer subtypes and increases mortality rates in most cases. We and others have demonstrated that, in the context of B-cell acute lymphoblastic leukemia (B-ALL), adipocytes promote multidrug chemoresistance. Furthermore, others have demonstrated that B-ALL cells exposed to the adipocyte secretome alter their metabolic states to circumvent chemotherapy-mediated cytotoxicity. To better understand how adipocytes impact the function of human B-ALL cells, we used a multi-omic RNA-sequencing (single-cell and bulk transcriptomic) and mass spectroscopy (metabolomic and proteomic) approaches to define adipocyte-induced changes in normal and malignant B cells. These analyses revealed that the adipocyte secretome directly modulates programs in human B-ALL cells associated with metabolism, protection from oxidative stress, increased survival, B-cell development, and drivers of chemoresistance. Single-cell RNA sequencing analysis of mice on low- and high-fat diets revealed that obesity suppresses an immunologically active B-cell subpopulation and that the loss of this transcriptomic signature in patients with B-ALL is associated with poor survival outcomes. Analyses of sera and plasma samples from healthy donors and those with B-ALL revealed that obesity is associated with higher circulating levels of immunoglobulin-associated proteins, which support observations in obese mice of altered immunological homeostasis. In all, our multi-omics approach increases our understanding of pathways that may promote chemoresistance in human B-ALL and highlight a novel B-cell-specific signature in patients associated with survival outcomes.

Stress granules and mTOR are regulated by membrane atg8ylation during lysosomal damage.

We report that lysosomal damage is a hitherto unknown inducer of stress granule (SG) formation and that the process termed membrane atg8ylation coordinates SG formation with mTOR inactivation during lysosomal stress. SGs were induced by lysosome-damaging agents including SARS-CoV-2ORF3a, Mycobacterium tuberculosis, and proteopathic tau. During damage, mammalian ATG8s directly interacted with the core SG proteins NUFIP2 and G3BP1. Atg8ylation was needed for their recruitment to damaged lysosomes independently of SG condensates whereupon NUFIP2 contributed to mTOR inactivation via the Ragulator-RagA/B complex. Thus, cells employ membrane atg8ylation to control and coordinate SG and mTOR responses to lysosomal damage.

Comparative performance of two automated machine learning platforms for COVID-19 detection by MALDI-TOF-MS.

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 method's robustness against SARS-CoV-2 variants and highlight similarities and differences in automated ML platforms in producing optimal predictive algorithms for a given dataset.

Identification of Endogenous Peptides in Nasal Swab Transport Media used in MALDI-TOF-MS Based COVID-19 Screening.

Mass spectrometry (MS) based diagnostic detection of 2019 novel coronavirus infectious disease (COVID-19) has been postulated to be a useful alternative to classical PCR based diagnostics. These MS based approaches have the potential to be both rapid and sensitive and can be done on-site without requiring a dedicated laboratory or depending on constrained supply chains (i.e., reagents and consumables). Matrix-assisted laser desorption ionization (MALDI)-time-of-flight (TOF) MS has a long and established history of microorganism detection and systemic disease assessment. Previously, we have shown that automated machine learning (ML) enhanced MALDI-TOF-MS screening of nasal swabs can be both sensitive and specific for COVID-19 detection. The underlying molecules responsible for this detection are generally unknown nor are they required for this automated ML platform to detect COVID-19. However, the identification of these molecules is important for understanding both the mechanism of detection and potentially the biology of the underlying infection. Here, we used nanoscale liquid chromatography tandem MS to identify endogenous peptides found in nasal swab saline transport media to identify peptides in the same the mass over charge (m/z) values observed by the MALDI-TOF-MS method. With our peptidomics workflow, we demonstrate that we can identify endogenous peptides and endogenous protease cut sites. Further, we show that SARS-CoV-2 viral peptides were not readily detected and are highly unlikely to be responsible for the accuracy of MALDI based SARS-CoV-2 diagnostics. Further analysis with more samples will be needed to validate our findings, but the methodology proves to be promising.

Mammalian hybrid pre-autophagosomal structure HyPAS generates autophagosomes.

The biogenesis of mammalian autophagosomes remains to be fully defined. Here, we used cellular and in vitro membrane fusion analyses to show that autophagosomes are formed from a hitherto unappreciated hybrid membrane compartment. The autophagic precursors emerge through fusion of FIP200 vesicles, derived from the cis-Golgi, with endosomally derived ATG16L1 membranes to generate a hybrid pre-autophagosomal structure, HyPAS. A previously unrecognized apparatus defined here controls HyPAS biogenesis and mammalian autophagosomal precursor membranes. HyPAS can be modulated by pharmacological agents whereas its formation is inhibited upon severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection or by expression of SARS-CoV-2 nsp6. These findings reveal the origin of mammalian autophagosomal membranes, which emerge via convergence of secretory and endosomal pathways, and show that this process is targeted by microbial factors such as coronaviral membrane-modulating proteins.

ATG9A protects the plasma membrane from programmed and incidental permeabilization.

The integral membrane protein ATG9A plays a key role in autophagy. It displays a broad intracellular distribution and is present in numerous compartments, including the plasma membrane (PM). The reasons for the distribution of ATG9A to the PM and its role at the PM are not understood. Here, we show that ATG9A organizes, in concert with IQGAP1, components of the ESCRT system and uncover cooperation between ATG9A, IQGAP1 and ESCRTs in protection from PM damage. ESCRTs and ATG9A phenocopied each other in protection against PM injury. ATG9A knockouts sensitized the PM to permeabilization by a broad spectrum of microbial and endogenous agents, including gasdermin, MLKL and the MLKL-like action of coronavirus ORF3a. Thus, ATG9A engages IQGAP1 and the ESCRT system to maintain PM integrity.

Divergent and self-reactive immune responses in the CNS of COVID-19 patients with neurological symptoms.

Individuals with coronavirus disease 2019 (COVID-19) frequently develop neurological symptoms, but the biological underpinnings of these phenomena are unknown. Through single-cell RNA sequencing (scRNA-seq) and cytokine analyses of cerebrospinal fluid (CSF) and blood from individuals with COVID-19 with neurological symptoms, we find compartmentalized, CNS-specific T cell activation and B cell responses. All affected individuals had CSF anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies whose target epitopes diverged from serum antibodies. In an animal model, we find that intrathecal SARS-CoV-2 antibodies are present only during brain infection and not elicited by pulmonary infection. We produced CSF-derived monoclonal antibodies from an individual with COVID-19 and found that these monoclonal antibodies (mAbs) target antiviral and antineural antigens, including one mAb that reacted to spike protein and neural tissue. CSF immunoglobulin G (IgG) from 5 of 7 patients showed antineural reactivity. This immune survey reveals evidence of a compartmentalized immune response in the CNS of individuals with COVID-19 and suggests a role of autoimmunity in neurologic sequelae of COVID-19.

Novel application of automated machine learning with MALDI-TOF-MS for rapid high-throughput screening of COVID-19: a proof of concept.

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.