A reduced proteomic signature in critically ill Covid-19 patients determined with plasma antibody micro-array and machine learning.

BACKGROUND: COVID-19 is a complex, multi-system disease with varying severity and symptoms. Identifying changes in critically ill COVID-19 patients' proteomes enables a better understanding of markers associated with susceptibility, symptoms, and treatment. We performed plasma antibody microarray and machine learning analyses to identify novel proteins of COVID-19. METHODS: A case-control study comparing the concentration of 2000 plasma proteins in age- and sex-matched COVID-19 inpatients, non-COVID-19 sepsis controls, and healthy control subjects. Machine learning was used to identify a unique proteome signature in COVID-19 patients. Protein expression was correlated with clinically relevant variables and analyzed for temporal changes over hospitalization days 1, 3, 7, and 10. Expert-curated protein expression information was analyzed with Natural language processing (NLP) to determine organ- and cell-specific expression. RESULTS: Machine learning identified a 28-protein model that accurately differentiated COVID-19 patients from ICU non-COVID-19 patients (accuracy = 0.89, AUC = 1.00, F1 = 0.89) and healthy controls (accuracy = 0.89, AUC = 1.00, F1 = 0.88). An optimal nine-protein model (PF4V1, NUCB1, CrkL, SerpinD1, Fen1, GATA-4, ProSAAS, PARK7, and NET1) maintained high classification ability. Specific proteins correlated with hemoglobin, coagulation factors, hypertension, and high-flow nasal cannula intervention (P < 0.01). Time-course analysis of the 28 leading proteins demonstrated no significant temporal changes within the COVID-19 cohort. NLP analysis identified multi-system expression of the key proteins, with the digestive and nervous systems being the leading systems. CONCLUSIONS: The plasma proteome of critically ill COVID-19 patients was distinguishable from that of non-COVID-19 sepsis controls and healthy control subjects. The leading 28 proteins and their subset of 9 proteins yielded accurate classification models and are expressed in multiple organ systems. The identified COVID-19 proteomic signature helps elucidate COVID-19 pathophysiology and may guide future COVID-19 treatment development.

Kinome and phosphoproteome reprogramming underlies the aberrant immune responses in critically ill COVID-19 patients.

SARS-CoV-2 infection triggers extensive host immune reactions, leading to severe diseases in certain individuals. However, the molecular basis underlying the excessive yet non-productive immune responses in severe COVID-19 remains incompletely understood. In this study, we conducted a comprehensive analysis of the peripheral blood mononuclear cell (PBMC) proteome and phosphoproteome in sepsis patients positive or negative for SARS-CoV-2 infection, as well as healthy subjects, using quantitative mass spectrometry. Our findings demonstrate dynamic changes in the COVID-19 PBMC proteome and phosphoproteome during disease progression, with distinctive protein or phosphoprotein signatures capable of distinguishing longitudinal disease states. Furthermore, SARS-CoV-2 infection induces a global reprogramming of the kinome and phosphoproteome, resulting in defective adaptive immune response mediated by the B and T lymphocytes, compromised innate immune responses involving the SIGLEC and SLAM family of immunoreceptors, and excessive cytokine-JAK-STAT signaling. In addition to uncovering host proteome and phosphoproteome aberrations caused by SARS-CoV-2, our work recapitulates several reported therapeutic targets for COVID-19 and identified numerous new candidates, including the kinases PKG1, CK2, ROCK1/2, GRK2, SYK, JAK2/3, TYK2, DNA-PK, PKCδ, and the cytokine IL-12.

A novel multiplex biomarker panel for profiling human acute and chronic kidney disease.

Acute and chronic kidney disease continues to confer significant morbidity and mortality in the clinical setting. Despite high prevalence of these conditions, few validated biomarkers exist to predict kidney dysfunction. In this study, we utilized a novel kidney multiplex panel to measure 21 proteins in plasma and urine to characterize the spectrum of biomarker profiles in kidney disease. Blood and urine samples were obtained from age-/sex-matched healthy control subjects (HC), critically-ill COVID-19 patients with acute kidney injury (AKI), and patients with chronic or end-stage kidney disease (CKD/ESKD). Biomarkers were measured with a kidney multiplex panel, and results analyzed with conventional statistics and machine learning. Correlations were examined between biomarkers and patient clinical and laboratory variables. Median AKI subject age was 65.5 (IQR 58.5-73.0) and median CKD/ESKD age was 65.0 (IQR 50.0-71.5). Of the CKD/ESKD patients, 76.1% were on hemodialysis, 14.3% of patients had kidney transplant, and 9.5% had CKD without kidney replacement therapy. In plasma, 19 proteins were significantly different in titer between the HC versus AKI versus CKD/ESKD groups, while NAG and RBP4 were unchanged. TIMP-1 (PPV 1.0, NPV 1.0), best distinguished AKI from HC, and TFF3 (PPV 0.99, NPV 0.89) best distinguished CKD/ESKD from HC. In urine, 18 proteins were significantly different between groups except Calbindin, Osteopontin and TIMP-1. Osteoactivin (PPV 0.95, NPV 0.95) best distinguished AKI from HC, and ß2-microglobulin (PPV 0.96, NPV 0.78) best distinguished CKD/ESKD from HC. A variety of correlations were noted between patient variables and either plasma or urine biomarkers. Using a novel kidney multiplex biomarker panel, together with conventional statistics and machine learning, we identified unique biomarker profiles in the plasma and urine of patients with AKI and CKD/ESKD. We demonstrated correlations between biomarker profiles and patient clinical variables. Our exploratory study provides biomarker data for future hypothesis driven research on kidney disease.

Plasma proteome of Long-COVID patients indicates HIF-mediated vasculo-proliferative disease with impact on brain and heart function.

AIMS: Long-COVID occurs after SARS-CoV-2 infection and results in diverse, prolonged symptoms. The present study aimed to unveil potential mechanisms, and to inform prognosis and treatment. METHODS: Plasma proteome from Long-COVID outpatients was analyzed in comparison to matched acutely ill COVID-19 (mild and severe) inpatients and healthy control subjects. The expression of 3072 protein biomarkers was determined with proximity extension assays and then deconvoluted with multiple bioinformatics tools into both cell types and signaling mechanisms, as well as organ specificity. RESULTS: Compared to age- and sex-matched acutely ill COVID-19 inpatients and healthy control subjects, Long-COVID outpatients showed natural killer cell redistribution with a dominant resting phenotype, as opposed to active, and neutrophils that formed extracellular traps. This potential resetting of cell phenotypes was reflected in prospective vascular events mediated by both angiopoietin-1 (ANGPT1) and vascular-endothelial growth factor-A (VEGFA). Several markers (ANGPT1, VEGFA, CCR7, CD56, citrullinated histone 3, elastase) were validated by serological methods in additional patient cohorts. Signaling of transforming growth factor-ß1 with probable connections to elevated EP/p300 suggested vascular inflammation and tumor necrosis factor-α driven pathways. In addition, a vascular proliferative state associated with hypoxia inducible factor 1 pathway suggested progression from acute COVID-19 to Long-COVID. The vasculo-proliferative process predicted in Long-COVID might contribute to changes in the organ-specific proteome reflective of neurologic and cardiometabolic dysfunction. CONCLUSIONS: Taken together, our findings point to a vasculo-proliferative process in Long-COVID that is likely initiated either prior hypoxia (localized or systemic) and/or stimulatory factors (i.e., cytokines, chemokines, growth factors, angiotensin, etc). Analyses of the plasma proteome, used as a surrogate for cellular signaling, unveiled potential organ-specific prognostic biomarkers and therapeutic targets.

Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning.

BACKGROUND: Survivors of acute COVID-19 often suffer prolonged, diffuse symptoms post-infection, referred to as "Long-COVID". A lack of Long-COVID biomarkers and pathophysiological mechanisms limits effective diagnosis, treatment and disease surveillance. We performed targeted proteomics and machine learning analyses to identify novel blood biomarkers of Long-COVID. METHODS: A case-control study comparing the expression of 2925 unique blood proteins in Long-COVID outpatients versus COVID-19 inpatients and healthy control subjects. Targeted proteomics was accomplished with proximity extension assays, and machine learning was used to identify the most important proteins for identifying Long-COVID patients. Organ system and cell type expression patterns were identified with Natural Language Processing (NLP) of the UniProt Knowledgebase. RESULTS: Machine learning analysis identified 119 relevant proteins for differentiating Long-COVID outpatients (Bonferonni corrected P < 0.01). Protein combinations were narrowed down to two optimal models, with nine and five proteins each, and with both having excellent sensitivity and specificity for Long-COVID status (AUC = 1.00, F1 = 1.00). NLP expression analysis highlighted the diffuse organ system involvement in Long-COVID, as well as the involved cell types, including leukocytes and platelets, as key components associated with Long-COVID. CONCLUSIONS: Proteomic analysis of plasma from Long-COVID patients identified 119 highly relevant proteins and two optimal models with nine and five proteins, respectively. The identified proteins reflected widespread organ and cell type expression. Optimal protein models, as well as individual proteins, hold the potential for accurate diagnosis of Long-COVID and targeted therapeutics.

Cross-immunity against SARS-COV-2 variants of concern in naturally infected critically ill COVID-19 patients.

Critically ill patients infected with SARS-CoV-2 display adaptive immunity, but it is unknown if they develop cross-reactivity to variants of concern (VOCs). We profiled cross-immunity against SARS-CoV-2 VOCs in naturally infected, non-vaccinated, critically ill COVID-19 patients. Wave-1 patients (wild-type infection) were similar in demographics to Wave-3 patients (wild-type/alpha infection), but Wave-3 patients had higher illness severity. Wave-1 patients developed increasing neutralizing antibodies to all variants, as did patients during Wave-3. Wave-3 patients, when compared to Wave-1, developed more robust antibody responses, particularly for wild-type, alpha, beta and delta variants. Within Wave-3, neutralizing antibodies were significantly less to beta and gamma VOCs, as compared to wild-type, alpha and delta. Patients previously diagnosed with cancer or chronic obstructive pulmonary disease had significantly fewer neutralizing antibodies. Naturally infected ICU patients developed adaptive responses to all VOCs, with greater responses in those patients more likely to be infected with the alpha variant, versus wild-type.

Novel plasma protein biomarkers from critically ill sepsis patients.

BACKGROUND: Despite the high morbidity and mortality associated with sepsis, the relationship between the plasma proteome and clinical outcome is poorly understood. In this study, we used targeted plasma proteomics to identify novel biomarkers of sepsis in critically ill patients. METHODS: Blood was obtained from 15 critically ill patients with suspected/confirmed sepsis (Sepsis-3.0 criteria) on intensive care unit (ICU) Day-1 and Day-3, as well as age- and sex-matched 15 healthy control subjects. A total of 1161 plasma proteins were measured with proximal extension assays. Promising sepsis biomarkers were narrowed with machine learning and then correlated with relevant clinical and laboratory variables. RESULTS: The median age for critically ill sepsis patients was 56 (IQR 51-61) years. The median MODS and SOFA values were 7 (IQR 5.0-8.0) and 7 (IQR 5.0-9.0) on ICU Day-1, and 4 (IQR 3.5-7.0) and 6 (IQR 3.5-7.0) on ICU Day-3, respectively. Targeted proteomics, together with feature selection, identified the leading proteins that distinguished sepsis patients from healthy control subjects with ≥ 90% classification accuracy; 25 proteins on ICU Day-1 and 26 proteins on ICU Day-3 (6 proteins overlapped both ICU days; PRTN3, UPAR, GDF8, NTRK3, WFDC2 and CXCL13). Only 7 of the leading proteins changed significantly between ICU Day-1 and Day-3 (IL10, CCL23, TGFα1, ST2, VSIG4, CNTN5, and ITGAV; P < 0.01). Significant correlations were observed between a variety of patient clinical/laboratory variables and the expression of 15 proteins on ICU Day-1 and 14 proteins on ICU Day-3 (P < 0.05). CONCLUSIONS: Targeted proteomics with feature selection identified proteins altered in critically ill sepsis patients relative to healthy control subjects. Correlations between protein expression and clinical/laboratory variables were identified, each providing pathophysiological insight. Our exploratory data provide a rationale for further hypothesis-driven sepsis research.

Elevated vascular transformation blood biomarkers in Long-COVID indicate angiogenesis as a key pathophysiological mechanism.

BACKGROUND: Long-COVID is characterized by prolonged, diffuse symptoms months after acute COVID-19. Accurate diagnosis and targeted therapies for Long-COVID are lacking. We investigated vascular transformation biomarkers in Long-COVID patients. METHODS: A case-control study utilizing Long-COVID patients, one to six months (median 98.5 days) post-infection, with multiplex immunoassay measurement of sixteen blood biomarkers of vascular transformation, including ANG-1, P-SEL, MMP-1, VE-Cad, Syn-1, Endoglin, PECAM-1, VEGF-A, ICAM-1, VLA-4, E-SEL, thrombomodulin, VEGF-R2, VEGF-R3, VCAM-1 and VEGF-D. RESULTS: Fourteen vasculature transformation blood biomarkers were significantly elevated in Long-COVID outpatients, versus acutely ill COVID-19 inpatients and healthy controls subjects (P < 0.05). A unique two biomarker profile consisting of ANG-1/P-SEL was developed with machine learning, providing a classification accuracy for Long-COVID status of 96%. Individually, ANG-1 and P-SEL had excellent sensitivity and specificity for Long-COVID status (AUC = 1.00, P < 0.0001; validated in a secondary cohort). Specific to Long-COVID, ANG-1 levels were associated with female sex and a lack of disease interventions at follow-up (P < 0.05). CONCLUSIONS: Long-COVID patients suffer prolonged, diffuse symptoms and poorer health. Vascular transformation blood biomarkers were significantly elevated in Long-COVID, with angiogenesis markers (ANG-1/P-SEL) providing classification accuracy of 96%. Vascular transformation blood biomarkers hold potential for diagnostics, and modulators of angiogenesis may have therapeutic efficacy.

Patients with severe COVID-19 do not have elevated autoantibodies against common diagnostic autoantigens.

OBJECTIVES: Infection by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the causative pathogen of coronavirus disease 2019 (COVID-19) presents occasionally with an aberrant autoinflammatory response, including the presence of elevated circulating autoantibodies in some individuals. Whether the development of autoantibodies against self-antigens affects COVID-19 outcomes remains unclear. To better understand the prognostic role of autoantibodies in COVID-19, we quantified autoantibodies against 23 markers that are used for diagnosis of autoimmune disease. To this end, we used serum samples from patients with severe [intensive care unit (ICU)] and moderate (ward) COVID-19, across two to six consecutive time points, and compared autoantibody levels to uninfected healthy and ICU controls. METHODS: Acute and post-acute serum (from 1 to 26 ICU days) was collected from 18 ICU COVID-19-positive patients at three to six time points; 18 ICU COVID-19-negative patients (sampled on ICU day 1 and 3); 21 ward COVID-19-positive patients (sampled on hospital day 1 and 3); and from 59 healthy uninfected controls deriving from two cohorts. Levels of IgG autoantibodies against 23 autoantigens, commonly used for autoimmune disease diagnosis, were measured in serum samples using MSD® U-PLEX electrochemiluminescence technology (MSD division Meso Scale Discovery®), and results were compared between groups. RESULTS: There were no significant elevations of autoantibodies for any of the markers tested in patients with severe COVID-19. CONCLUSIONS: Sample collections at longer time points should be considered in future studies, for assessing the possible development of autoantibody responses following infection with SARS-CoV-2.

Proteinase 3 contributes to endothelial dysfunction in an experimental model of sepsis.

In sepsis-induced inflammation, polymorphonuclear neutrophils (PMNs) contribute to vascular dysfunction. The serine proteases proteinase 3 (PR3) and human leukocyte elastase (HLE) are abundant in PMNs and are released upon degranulation. While HLE's role in inflammation-induced endothelial dysfunction is well studied, PR3's role is largely uninvestigated. We hypothesized that PR3, similarly to HLE, contributes to vascular barrier dysfunction in sepsis. Plasma PR3 and HLE concentrations and their leukocyte mRNA levels were measured by ELISA and qPCR, respectively, in sepsis patients and controls. Exogenous PR3 or HLE was applied to human umbilical vein endothelial cells (HUVECs) and HUVEC dysfunction was assessed by FITC-dextran permeability and electrical resistance. Both PR3 and HLE protein and mRNA levels were significantly increased in sepsis patients (P < 0.0001 and P < 0.05, respectively). Additionally, each enzyme independently increased HUVEC monolayer FITC-dextran permeability (P < 0.01), and decreased electrical resistance in a time- and dose-dependent manner (P < 0.001), an effect that could be ameliorated by novel treatment with carbon monoxide-releasing molecule 3 (CORM-3). The serine protease PR3, in addition to HLE, lead to vascular dysfunction and increased endothelial permeability, a hallmark pathological consequence of sepsis-induced inflammation. CORMs may offer a new strategy to reduce serine protease-induced vascular dysfunction.

PACS-1 and adaptor protein-1 mediate ACTH trafficking to the regulated secretory pathway.

The regulated secretory pathway is a specialized form of protein secretion found in endocrine and neuroendocrine cell types. Pro-opiomelanocortin (POMC) is a pro-hormone that utilizes this pathway to be trafficked to dense core secretory granules (DCSGs). Within this organelle, POMC is processed to multiple bioactive hormones that play key roles in cellular physiology. However, the complete set of cellular membrane trafficking proteins that mediate the correct sorting of POMC to DCSGs remain unknown. Here, we report the roles of the phosphofurin acidic cluster sorting protein - 1 (PACS-1) and the clathrin adaptor protein 1 (AP-1) in the targeting of POMC to DCSGs. Upon knockdown of PACS-1 and AP-1, POMC is readily secreted into the extracellular milieu and fails to be targeted to DCSGs.

Identification of Novel Subcellular Localization and Trafficking of HIV-1 Nef Variants from Reference Strains G (F1.93.HH8793) and H (BE.93.VI997).

The human immunodeficiency virus type 1 (HIV-1) accessory protein Nef, plays an essential role in disease progression and pathogenesis via hijacking the host cellular membrane-trafficking machinery. Interestingly, HIV-1 group-M subtypes display differences in the rate of disease progression. However, few reports investigated how the cellular behaviors and activities of Nef isolates from reference strains may differ between HIV-1 group-M subtypes. Here, we characterize how differing cellular distributions of Nef proteins across group-M subtypes may impact protein function using immunofluorescence microscopy and flow cytometric analysis. We demonstrate that Nef variants isolated from HIV-1 group-M subtypes display differences in expression, with low expressing Nef proteins from reference strains of subtypes G (F1.93.HH8793) and H (BE.93.VI997) also displaying decreased functionality. Additionally, we demonstrate variations in the subcellular distribution and localization of these Nef proteins. Nef from subtype G (F1.93.HH8793) and H (BE.93.VI997) reference strains also failed to colocalize with the trans-Golgi network, and were not differentially localized to cellular markers of multivesicular bodies or lysosomes. Strikingly, our results demonstrate that HIV-1 Nef proteins from reference strains G (F1.93.HH8793) and H (BE.93.VI997) highly colocalize with labeled mitochondrial compartments.

The interaction between HIV-1 Nef and adaptor protein-2 reduces Nef-mediated CD4+ T cell apoptosis.

Acquired Immune Deficiency Syndrome is characterized by a decline in CD4+ T cells. Here, we elucidated the mechanism underlying apoptosis in Human Immunodeficiency Virus-1 (HIV-1) infection by examining host apoptotic pathways hijacked by the HIV-1 Nef protein in the CD4+ T-cell line Sup-T1. Using a panel of Nef mutants unable to bind specific host proteins we uncovered that Nef generates pro- and anti-apoptotic signals. Apoptosis increased upon mutating the motifs involved in the interaction of Nef:AP-1 (NefM20A or NefEEEE62-65AAAA) or Nef:AP-2 (NefLL164/165AA), implying these interactions limit Nef-mediated apoptosis. In contrast, disrupting the Nef:PAK2 interaction motifs (NefH89A or NefF191A) reduced apoptosis. To validate further, apoptosis was measured after short-hairpin RNA knock-down of AP-1, AP-2 and PAK2. AP-2α depletion enhanced apoptosis, demonstrating that disrupting the Nef:AP-2α interaction limits Nef-mediated apoptosis. Collectively, we describe a mechanism by which HIV-1 regulates cell survival and demonstrate the consequence of interfering with Nef:host protein interactions.

HIV-1 Nef sequesters MHC-I intracellularly by targeting early stages of endocytosis and recycling.

A defining characteristic of HIV-1 infection is the ability of the virus to persist within the host. Specifically, MHC-I downregulation by the HIV-1 accessory protein Nef is of critical importance in preventing infected cells from cytotoxic T-cell mediated killing. Nef downregulates MHC-I by modulating the host membrane trafficking machinery, resulting in the endocytosis and eventual sequestration of MHC-I within the cell. In the current report, we utilized the intracellular protein-protein interaction reporter system, bimolecular fluorescence complementation (BiFC), in combination with super-resolution microscopy, to track the Nef/MHC-I interaction and determine its subcellular localization in cells. We demonstrate that this interaction occurs upon Nef binding the MHC-I cytoplasmic tail early during endocytosis in a Rab5-positive endosome. Disruption of early endosome regulation inhibited Nef-dependent MHC-I downregulation, demonstrating that Nef hijacks the early endosome to sequester MHC-I within the cell. Furthermore, super-resolution imaging identified that the Nef:MHC-I BiFC complex transits through both early and late endosomes before ultimately residing at the trans-Golgi network. Together we demonstrate the importance of the early stages of the endocytic network in the removal of MHC-I from the cell surface and its re-localization within the cell, which allows HIV-1 to optimally evade host immune responses.

Where in the Cell Are You? Probing HIV-1 Host Interactions through Advanced Imaging Techniques.

Viruses must continuously evolve to hijack the host cell machinery in order to successfully replicate and orchestrate key interactions that support their persistence. The type-1 human immunodeficiency virus (HIV-1) is a prime example of viral persistence within the host, having plagued the human population for decades. In recent years, advances in cellular imaging and molecular biology have aided the elucidation of key steps mediating the HIV-1 lifecycle and viral pathogenesis. Super-resolution imaging techniques such as stimulated emission depletion (STED) and photoactivation and localization microscopy (PALM) have been instrumental in studying viral assembly and release through both cell-cell transmission and cell-free viral transmission. Moreover, powerful methods such as Forster resonance energy transfer (FRET) and bimolecular fluorescence complementation (BiFC) have shed light on the protein-protein interactions HIV-1 engages within the host to hijack the cellular machinery. Specific advancements in live cell imaging in combination with the use of multicolor viral particles have become indispensable to unravelling the dynamic nature of these virus-host interactions. In the current review, we outline novel imaging methods that have been used to study the HIV-1 lifecycle and highlight advancements in the cell culture models developed to enhance our understanding of the HIV-1 lifecycle.

Viral bimolecular fluorescence complementation: a novel tool to study intracellular vesicular trafficking pathways.

The Human Immunodeficiency Virus type 1 (HIV-1) accessory protein Nef interacts with a multitude of cellular proteins, manipulating the host membrane trafficking machinery to evade immune surveillance. Nef interactions have been analyzed using various in vitro assays, co-immunoprecipitation studies, and more recently mass spectrometry. However, these methods do not evaluate Nef interactions in the context of viral infection nor do they define the sub-cellular location of these interactions. In this report, we describe a novel bimolecular fluorescence complementation (BiFC) lentiviral expression tool, termed viral BiFC, to study Nef interactions with host cellular proteins in the context of viral infection. Using the F2A cleavage site from the foot and mouth disease virus we generated a viral BiFC expression vector capable of concurrent expression of Nef and host cellular proteins; PACS-1, MHC-I and SNX18. Our studies confirmed the interaction between Nef and PACS-1, a host membrane trafficking protein involved in Nef-mediated immune evasion, and demonstrated co-localization of this complex with LAMP-1 positive endolysosomal vesicles. Furthermore, we utilized viral BiFC to localize the Nef/MHC-I interaction to an AP-1 positive endosomal compartment. Finally, viral BiFC was observed between Nef and the membrane trafficking regulator SNX18. This novel demonstration of an association between Nef and SNX18 was localized to AP-1 positive vesicles. In summary, viral BiFC is a unique tool designed to analyze the interaction between Nef and host cellular proteins by mapping the sub-cellular locations of their interactions during viral infection.