Diagnostic and progression biomarkers in cerebrospinal fluid of Alzheimer's disease patients.

In this commentary, we address a paper published by Johnson et al. by assessing the robustness of their method to discover diagnostic biomarkers in Alzheimer's disease (AD). In addition, we examine how these newly discovered and previously discovered biomarkers, can play a role in assisting patients with AD and those at risk for developing AD, with an emphasis on the translational hurdles that accompany such discoveries.

The relationship between serum astroglial and neuronal markers and AQP4 and MOG autoantibodies.

BACKGROUND: Certain demyelinating disorders, such as neuromyelitis optica spectrum disorder (NMOSD) and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) exhibit serum autoantibodies against aquaporin-4 (αAQP4) and myelin oligodendrocyte glycoprotein (αMOG). The variability of the autoantibody presentation warrants further research into subtyping each case. METHODS: To elucidate the relationship between astroglial and neuronal protein concentrations in the peripheral circulation with occurrence of these autoantibodies, 86 serum samples were analyzed using immunoassays. The protein concentration of glial fibrillary acidic protein (GFAP), neurofilament light chain (NFL) and tau protein was measured in 3 groups of subcategories of suspected NMOSD: αAQP4 positive (n = 20), αMOG positive (n = 32) and αMOG/αAQP4 seronegative (n = 34). Kruskal-Wallis analysis, univariate predictor analysis, and multivariate logistic regression with ROC curves were performed. RESULTS: GFAP and NFL concentrations were significantly elevated in the αAQP4 positive group (p = 0.003; p = 0.042, respectively), and tau was elevated in the αMOG/αAQP4 seronegative group (p < 0.001). A logistic regression model to classify serostatus was able to separate αAQP4 seropositivity using GFAP + tau, and αMOG seropositivity using tau. The areas under the ROC curves (AUCs) were 0.77 and 0.72, respectively. Finally, a combined seropositivity versus negative status logistic regression model was generated, with AUC = 0.80. CONCLUSION: The 3 markers can univariately and multivariately classify with moderate accuracy the samples with seropositivity and seronegativity for αAQP4 and αMOG.

Glial fibrillary acidic protein, neurofilament light, matrix metalloprotease 3 and fatty acid binding protein 4 as non-invasive brain tumor biomarkers.

BACKGROUND: Gliomas are aggressive malignant tumors, with poor prognosis. There is an unmet need for the discovery of new, non-invasive biomarkers for differential diagnosis, prognosis, and management of brain tumors. Our objective is to validate four plasma biomarkers - glial fibrillary acidic protein (GFAP), neurofilament light (NEFL), matrix metalloprotease 3 (MMP3) and fatty acid binding protein 4 (FABP4) - and compare them with established brain tumor molecular markers and survival. METHODS: Our cohort consisted of patients with benign and malignant brain tumors (GBM = 77, Astrocytomas = 26, Oligodendrogliomas = 23, Secondary tumors = 35, Meningiomas = 70, Schwannomas = 15, Pituitary adenomas = 15, Normal individuals = 30). For measurements, we used ultrasensitive electrochemiluminescence multiplexed immunoassays. RESULTS: High plasma GFAP concentration was associated with GBM, low GFAP and high FABP4 were associated with meningiomas, and low GFAP and low FABP4 were associated with astrocytomas and oligodendrogliomas. NEFL was associated with progression of disease. Several prognostic genetic alterations were significantly associated with all plasma biomarker levels. We found no independent associations between plasma GFAP, NEFL, FABP4 and MMP3, and overall survival. The candidate biomarkers could not reliably discriminate GBM from primary or secondary CNS lymphomas. CONCLUSIONS: GFAP, NEFL, FABP4 and MMP3 are useful for differential diagnosis and prognosis, and are associated with molecular changes in gliomas.

Identification of brain-enriched proteins in CSF as biomarkers of relapsing remitting multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) is a clinically and biologically heterogenous disease with currently unpredictable progression and relapse. After the development and success of neurofilament as a cerebrospinal fluid (CSF) biomarker, there is reinvigorated interest in identifying other markers of or contributors to disease. The objective of this study is to probe the predictive potential of a panel of brain-enriched proteins on MS disease progression and subtype. METHODS: This study includes 40 individuals with MS and 14 headache controls. The MS cohort consists of 20 relapsing remitting (RR) and 20 primary progressive (PP) patients. The CSF of all individuals was analyzed for 63 brain enriched proteins using a method of liquid-chromatography tandem mass spectrometry. Wilcoxon rank sum test, Kruskal-Wallis one-way ANOVA, logistic regression, and Pearson correlation were used to refine the list of candidates by comparing relative protein concentrations as well as relation to known imaging and molecular biomarkers. RESULTS: We report 30 proteins with some relevance to disease, clinical subtype, or severity. Strikingly, we observed widespread protein depletion in the disease CSF as compared to control. We identified numerous markers of relapsing disease, including KLK6 (kallikrein 6, OR = 0.367, p < 0.05), which may be driven by active disease as defined by MRI enhancing lesions. Other oligodendrocyte-enriched proteins also appeared at reduced levels in relapsing disease, namely CNDP1 (carnosine dipeptidase 1), LINGO1 (leucine rich repeat and Immunoglobin-like domain-containing protein 1), MAG (myelin associated glycoprotein), and MOG (myelin oligodendrocyte glycoprotein). Finally, we identified three proteins-CNDP1, APLP1 (amyloid beta precursor like protein 1), and OLFM1 (olfactomedin 1)-that were statistically different in relapsing vs. progressive disease raising the potential for use as an early biomarker to discriminate clinical subtype. CONCLUSIONS: We illustrate the utility of targeted mass spectrometry in generating potential targets for future biomarker studies and highlight reductions in brain-enriched proteins as markers of the relapsing remitting disease stage.

Transforming diagnostics: The implementation of digital pathology in clinical laboratories.

Digital pathology (DP) has emerged as a cutting-edge technology that promises to revolutionise diagnostics in clinical laboratories. This perspective article explores the implementation planning and considerations of DP in a single multicentre institution in Canada, the University Health Network, discussing benefits, challenges, potential implications and considerations for future adopters. We examine the transition from traditional microscopy to digital slide scanning and its impact on pathology practice, patient care and medical research. Furthermore, we address the regulatory, infrastructure and change management considerations for successful integration into clinical laboratories. By highlighting the advantages and addressing concerns, we aim to shed light on the transformative potential of DP and its role in shaping the future of diagnostics.

Computational pathology: an evolving concept.

The initial enthusiasm about computational pathology (CP) and artificial intelligence (AI) was that they will replace pathologists entirely on the way to fully automated diagnostics. It is becoming clear that currently this is not the immediate model to pursue. On top of the legal and regulatory complexities surrounding its implementation, the majority of tested machine learning (ML)-based predictive algorithms do not display the exquisite performance needed to render them unequivocal, standalone decision makers for matters with direct implications to human health. We are thus moving into a different model of "computer-assisted diagnostics", where AI is there to provide support, rather than replacing, the pathologist. Herein we focus on the practical aspects of CP, from a pathologist perspective. There is a wide range of potential applications where CP can enhance precision of pathology diagnosis, tailor prognostic and predictive information, as well as save time. There are, however, a number of potential limitations for CP that currently hinder their wider adoption in the clinical setting. We address the key necessary steps towards clinical implementation of computational pathology, discuss the significant obstacles that hinders its adoption in the clinical context and summarize some proposed solutions. We conclude that the advancement of CP in the clinic is a promising resource-intensive endeavour that requires broad and inclusive collaborations between academia, industry, and regulatory bodies.

Multiplex Proteomics in the Identification of Potential Biomarkers of Very Severe Sinusoidal Obstruction Syndrome/Veno-Occlusive Disease in Allogeneic Hematopoietic Cell Transplant Patients Treated with Defibrotide.

INTRODUCTION: Despite well-established clinical criteria for diagnosis of sinusoidal obstruction syndrome/veno-occlusive disease (SOS/VOD) following allogeneic hematopoietic cell transplantation (HCT), there is a lack of established diagnostic protein biomarkers. METHODS: Prospective samples were collected from patients with very severe SOS/VOD at diagnosis and days +3, +7, +14, and +30 post-initiation of defibrotide. Samples from age-matched controls with no VOD were collected at days +14, +30, +60, +90, and +180 following allogeneic HCT. Serum samples were analyzed for 2,925 protein levels by antibody-based proximity extension assay (PEA). Mean differences in the log-transformed abundance values were compared using t tests in a volcano plot. RESULTS: Five patients with very severe SOS/VOD and 5 control patients were compared. Ten proteins were identified that showed a statistically significant and log-transformed 3-fold increase in concentration. They were CALCA, CCL20, GPR37, IGFBP4, IL1RL1, SLC39A14, SPINK4, FABP3, MYL3, and CHCHD10. Four different proteins, namely, CD83, leukocyte associated immunoglobulin-like receptor 2 (LAIR2), CD7, and HEM6 showed a significant decrease with defibrotide treatment. SOS/VOD resolved in 80% (n = 4) of patients, while 1 patient deceased due to SOS/VOD. CONCLUSION: PEA technology identified 10 proteins that were significantly elevated in patients with very severe SOS/VOD. Prospective studies in a larger cohort using this technology may be able to conclusively identify diagnostic protein biomarkers for SOS/VOD.

Beyond the amyloid hypothesis: how current research implicates autoimmunity in Alzheimer's disease pathogenesis.

The amyloid hypothesis has so far been at the forefront of explaining the pathogenesis of Alzheimer's Disease (AD), a progressive neurodegenerative disorder that leads to cognitive decline and eventual death. Recent evidence, however, points to additional factors that contribute to the pathogenesis of this disease. These include the neurovascular hypothesis, the mitochondrial cascade hypothesis, the inflammatory hypothesis, the prion hypothesis, the mutational accumulation hypothesis, and the autoimmunity hypothesis. The purpose of this review was to briefly discuss the factors that are associated with autoimmunity in humans, including sex, the gut and lung microbiomes, age, genetics, and environmental factors. Subsequently, it was to examine the rise of autoimmune phenomena in AD, which can be instigated by a blood-brain barrier breakdown, pathogen infections, and dysfunction of the glymphatic system. Lastly, it was to discuss the various ways by which immune system dysregulation leads to AD, immunomodulating therapies, and future directions in the field of autoimmunity and neurodegeneration. A comprehensive account of the recent research done in the field was extracted from PubMed on 31 January 2022, with the keywords "Alzheimer's disease" and "autoantibodies" for the first search input, and "Alzheimer's disease" with "IgG" for the second. From the first search, 19 papers were selected, because they contained recent research on the autoantibodies found in the biofluids of patients with AD. From the second search, four papers were selected. The analysis of the literature has led to support the autoimmune hypothesis in AD. Autoantibodies were found in biofluids (serum/plasma, cerebrospinal fluid) of patients with AD with multiple methods, including ELISA, Mass Spectrometry, and microarray analysis. Through continuous research, the understanding of the synergistic effects of the various components that lead to AD will pave the way for better therapeutic methods and a deeper understanding of the disease.

The therapeutic relevance of the Kallikrein-Kinin axis in SARS-cov-2-induced vascular pathology.

While coronavirus disease 2019 (COVID-19) begins as a respiratory infection, it progresses as a systemic disease involving multiorgan microthromboses that underly the pathology. SARS-CoV-2 enters host cells via attachment to the angiotensin-converting enzyme 2 (ACE2) receptor. ACE2 is widely expressed in a multitude of tissues, including the lung (alveolar cells), heart, intestine, kidney, testis, gallbladder, vasculature (endothelial cells), and immune cells. Interference in ACE2 signaling could drive the aforementioned systemic pathologies, such as endothelial dysfunction, microthromboses, and systemic inflammation, that are typically seen in patients with severe COVID-19. ACE2 is a component of the renin-angiotensin system (RAS) and is intimately associated with the plasma kallikrein-kinin system (KKS). As many papers are published on the role of ACE and ACE2 in COVID-19, we will review the role of bradykinin, and more broadly the KSS, in SARS-CoV-2-induced vascular dysfunction. Furthermore, we will discuss the possible therapeutic interventions that are approved and in development for the following targets: coagulation factor XII (FXII), tissue kallikrein (KLK1), plasma kallikrein (KLKB1), bradykinin (BK), plasminogen activator inhibitor (PAI-1), bradykinin B1 receptor (BKB1R), bradykinin B2 receptor (BKB2R), ACE, furin, and the NLRP3 inflammasome. Understanding these targets may prove of value in the treatment of COVID-19 as well as in other virus-induced coagulopathies in the future.

Discovery of novel glioma serum biomarkers by proximity extension assay.

BACKGROUND: Gliomas are among the most malignant tumors, with a very poor prognosis. Early diagnosis is highly desirable since it can help implement more effective treatments for smaller tumors, which have not yet extensively metastasized. Improving early diagnosis may facilitate access of patients to clinical trials and prepare them for the future availability of new disease-modifying treatments. METHODS: We analyzed retrospective samples collected at diagnosis (before therapy initiation), with PEA (Olink Proteomics), quantifying about 3000 proteins. We utilized 30 plasmas from gliomas (20 glioblastomas, 5 anaplastic astrocytomas, 5 anaplastic oligodendrogliomas) and 20 meningiomas (as controls). We then analyzed the data to identify proteins which either alone, or in combination, could discriminate gliomas from meningiomas, or correlate with clinical and molecular alterations. RESULTS: We identified 8 plasma proteins which were increased in gliomas vs. meningiomas (GFAP, NEFL, EDDM3B, PROK1, MMP3, CTRL, GP2, SPINT3) and 4 proteins which were decreased in gliomas vs. meningiomas (FABP4, ALDH3A1, IL-12B and OXT). Partition algorithms and logistic regression algorithms with two biomarkers (GFAP and FABP4) achieved sensitivity of 83% and 93% at 100% and 90% specificity, respectively. The strongest single marker was GFAP with an area under the ROC curve (AUC) of 0.86. The AUC for the GFAP-FABP4 combination was 0.98. CONCLUSION: PEA is a powerful new proteomic technology for biomarker discovery. GFAP and a handful of other plasma biomarkers may be useful for early glioma detection and probably, prognosis. STATEMENT: Detecting gliomas as early as possible is highly desirable since it can significantly improve the chances of effective treatments. Reliable glioma biomarkers can timely inform glioma patients about the efficacy of their prescribed treatment. Our results reveal some novel putative glioma markers that may prove valuable, when used alone or in combination, towards improved clinical care of gliomas. In order to better appreciate the potential usefulness of these markers, their performance needs to be further validated in a larger cohort of samples.

Cerebrospinal fluid camk2a levels at baseline predict long-term progression in multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) remains a highly unpredictable disease. Many hope that fluid biomarkers may contribute to better stratification of disease, aiding the personalisation of treatment decisions, ultimately improving patient outcomes. OBJECTIVE: The objective of this study was to evaluate the predictive value of CSF brain-specific proteins from early in the disease course of MS on long term clinical outcomes. METHODS: In this study, 34 MS patients had their CSF collected and stored within 5 years of disease onset and were then followed clinically for at least 15 years. CSF concentrations of 64 brain-specific proteins were analyzed in the 34 patient CSF, as well as 19 age and sex-matched controls, using a targeted liquid-chromatography tandem mass spectrometry approach. RESULTS: We identified six CSF brain-specific proteins that significantly differentiated MS from controls (p < 0.05) and nine proteins that could predict disease course over the next decade. CAMK2A emerged as a biomarker candidate that could discriminate between MS and controls and could predict long-term disease progression. CONCLUSION: Targeted approaches to identify and quantify biomarkers associated with MS in the CSF may inform on long term MS outcomes. CAMK2A may be one of several candidates, warranting further exploration.

Multiplex proteomics using proximity extension assay for the identification of protein biomarkers predictive of acute graft-vs.-host disease in allogeneic hematopoietic cell transplantation.

OBJECTIVES: Allogeneic hematopoietic cell transplantation (HCT) is associated with acute graft-vs.-host disease (aGVHD). The presented study applied a novel multiplex antibody-based proximity extension assay (PEA) proteomic platform that can detect thousands of serum proteins simultaneously for the identification of potential biomarkers of aGVHD. METHODS: Serum samples from 28 patients who underwent allogeneic HCT for acute myeloid leukemia (AML) were analyzed; 17 were diagnosed with grade II-IV aGVHD while 11 patients were not. Samples collected on day -6, day 0, +14, +30, +60 and +90 post-HCT were analyzed for the relative concentrations of 552 proteins. The concentration of each protein from baseline to the closest time point before onset of aGVHD, or to the latest time point in control patients, was documented. RESULTS: Individualized analysis identified 26 proteins demonstrating ≥3-fold increase at aGVHD onset compared to baseline, eliminating proteins with a similar increase in controls. Another approach used paired t-testing and logistic regression that identified a four-marker panel, including SLAMF7, IL-1ra, BTN3A2 and DAB2, where individual log-likelihood ratios ranged from 3.99 to 8.15 (logistic regression, p=0.004-0.046). When combined, the four-marker panel demonstrated an area under the curve (AUC) of 0.90 (95% CI: 0.78-1.00; p=0.0006) with high negative predictive value of 81.8% and positive predictive value of 86.7%. All four markers play a physiological role in immune regulation. Among these, three were also present in the individualized analysis (SLAMF7, IL-1ra and BTN3A2). CONCLUSIONS: We conclude that serum proteins identified using multiplex proteomics, particularly SLAMF7, IL-1ra, BTN3A2 and DAB2, may potentially predict aGVHD.

Quantitation of cardiac troponin I in cancer patients treated with immune checkpoint inhibitors: a case-control study.

OBJECTIVES: Immune checkpoint inhibitors (ICIs) cause a variety of toxicities, including immune-related adverse events (irAEs), but there are no biomarkers to predict their development. Guidelines recommend measuring circulating cardiac troponin I (cTnI) during ICI therapy to detect related cardiotoxicities. Moreover, elevated cTnI has also been associated with worse outcomes in non-cardiac patients, including cancer. Thus here, we investigated whether cTnI levels were higher in patients with irAEs. METHODS: The study consisted of three groups; 21 cancer patients undergoing ICI immunotherapies who presented with irAEs, four patients without irAEs, and 20 healthy controls. Patient samples were assessed at baseline (n=25), during ICI treatment (n=25, median=6 weeks of treatment) and at toxicity (n=6, median=13 weeks of treatment). In addition to blood high sensitivity cardiac troponin I (hs-cTnI), anti-thyroglobulin (TG) and anti-thyroid peroxidase (TPO) antibodies were also quantitated to detect thyroid dysfunction, constituting the second leading toxicity (23.8%) after pneumonitis (28.6%). RESULTS: Four patients with irAEs (n=4/21; 19%) and one without irAEs (n=1/4; 25%) showed higher hs-cTnI levels at any time-point; the remaining had physiological levels. None of these patients developed cardiotoxicity. Concurrent elevated levels of anti-thyroid antibodies and hs-cTnI were detected in one patient with thyroid dysfunction (n=1/5, 20%). However, these antibodies were also elevated in three patients (n=3/16, 19%) with non-thyroid irAEs and in up to 40% of healthy controls. CONCLUSIONS: hs-cTnI was not elevated in patients with irAEs, but larger studies are needed to confirm these observations.

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.

Ultrasensitive assay for saliva-based SARS-CoV-2 antigen detection.

OBJECTIVES: Widespread SARS-CoV-2 testing is invaluable for identifying asymptomatic/pre-symptomatic individuals. There remains a technological gap for highly reliable, easy, and quick SARS-CoV-2 diagnostic tests suitable for frequent mass testing. Compared to nasopharyngeal (NP) swab-based tests, saliva-based methods are attractive due to easier and safer sampling. Current saliva-based SARS-CoV-2 rapid antigen tests (RATs) are hindered by limited analytical sensitivity. Here, we report one of the first ultrasensitive, saliva-based SARS-CoV-2 antigen assays with an analytical sensitivity of <0.32 pg/mL, corresponding to four viral RNA copies/µL, which is comparable to that of PCR-based tests. METHODS: Using the novel electrochemiluminescence (ECL)-based immunoassay, we measured the SARS-CoV-2 nucleocapsid (N) antigen concentration in 105 salivas, obtained from non-COVID-19 and COVID-19 patients. We then verified the results with a second, independent cohort of 689 patients (3.8% SARS-CoV-2 positivity rate). We also compared our method with a widely used point-of-care rapid test. RESULTS: In the first cohort, at 100% specificity, the sensitivity was 92%. Our assay correctly identified samples with viral loads up to 35 CT cycles by saliva-based PCR. Paired NP swab-based PCR results were obtained for 86 cases. Our assay showed high concordance with saliva-based and NP swab-based PCR in samples with negative (<0.32 pg/mL) and strongly positive (>2 pg/mL) N antigen concentrations. In the second cohort, at 100% specificity, sensitivity was also 92%. Our assay is about 700-fold more sensitive than the Abbott Panbio Rapid Test. CONCLUSIONS: We demonstrated the ultrasensitivity and specificity assay and its concordance with PCR. This novel assay is especially valuable when compliance to frequent swabbing may be problematic.

Proteomic Profiling of the Human Tissue and Biological Fluid Proteome.

In-depth analysis of the human genome sequence has led to the annotation of approximately 20,000 human protein-coding genes. Although mass spectrometry (MS)-based workflows have made a great headway in achieving near genome-wide coverage, an equivalent complete map of the human proteome remains elusive. Delineating the spatial distribution of all human proteins at the organ, tissue, and cellular level can offer insight into health and disease and represents an excellent reference for the discovery of biomarkers and therapeutic targets. Here, we performed label-free liquid chromatography coupled to tandem MS (LC-MS/MS) to profile the normal human proteome. In total, we analyzed 117 samples from 46 normal tissues and organs at autopsy. Our high-resolution MS approach allowed for the quantification of 10,438 unique proteins. In order to expand our coverage of the human proteome, we combined our previously published biological fluid proteomic data from healthy individuals. We considered data from seven biological fluids, including urine, cerebrospinal fluid, synovial fluid, seminal plasma, sweat, cervical vaginal fluid, and nipple aspirate fluid. Overall, we generated tandem mass spectra corresponding to 13,028 unique human protein-coding genes. Although our analysis did not accomplish complete proteome coverage, it should be an important complementary resource for future biomarker discovery.

COVID-19: from an acute to chronic disease? Potential long-term health consequences.

Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite pulmonary impairments being the most prevalent, extra-pulmonary manifestations of COVID-19 are abundant. Confirmed COVID-19 cases have now surpassed 57.8 million worldwide as of 22 November 2020. With estimated case fatality rates (number of deaths from COVID-19 divided by number of confirmed COVID-19 cases) varying between 1 and 7%, there will be a large population of recovered COVID-19 patients that may acquire a multitude of long-term health consequences. While the multi-organ manifestations of COVID-19 are now well-documented, the potential long-term implications of these manifestations remain to be uncovered. In this review, we turn to previous similar coronaviruses (i.e. SARS-CoV-1 and Middle East respiratory syndrome coronavirus [MERS-CoV]) in combination with known health implications of SARS-CoV-2 infection to predict potential long-term effects of COVID-19, including pulmonary, cardiovascular, hematologic, renal, central nervous system, gastrointestinal, and psychosocial manifestations, in addition to the well-known post-intensive care syndrome. It is necessary to monitor COVID-19 patients after discharge to understand the breadth and severity of long-term effects. This can be accomplished by repurposing or initiating large cohort studies to not only focus on the long-term consequences of SARS-CoV-2 infection, but also on acquired immune function as well as ethno-racial group and household income disparities in COVID-19 cases and hospitalizations. The future for COVID-19 survivors remains uncertain, and if this virus circulates among us for years to come, long-term effects may accumulate exponentially.

The role of kallikreins in inflammatory skin disorders and their potential as therapeutic targets.

The skin is a vital organ of the human body, serving numerous protective and functional roles that are essential for survival. Residing in the epidermis are various epidermal proteases responsible for the establishment and regulation of barrier function. The human tissue kallikrein-related peptidase family conserves homeostasis of the skin barrier through their roles in desquamation, antimicrobial defense, innate immune response, and barrier maintenance. The activity of kallikreins is tightly regulated and dysregulation of kallikrein activity is seen to contribute to the formation of several inflammatory skin disorders. This review highlights the roles of kallikreins in skin homeostasis and pathologies. Due to their part in these skin disorders, inhibitors of the skin kallikreins have become attractive therapeutics. Over the past few years, both natural and synthetic inhibitors of several kallikreins have been identified and are undergoing further development as treatments to restore compromised barrier function. This review summarizes the kallikrein inhibitors under development for this purpose. These inhibitors remain promising therapeutics in cases of severe skin inflammation not well managed by current therapies.

Novel severe traumatic brain injury blood outcome biomarkers identified with proximity extension assay.

OBJECTIVES: Severe traumatic brain injury (sTBI) patients suffer high mortality. Accurate prognostic biomarkers have not been identified. In this exploratory study, we performed targeted proteomics on plasma obtained from sTBI patients to identify potential outcome biomarkers. METHODS: Blood sample was collected from patients admitted to the ICU suffering a sTBI, using standardized clinical and computerized tomography (CT) imaging criteria. Age- and sex-matched healthy control subjects and sTBI patients were enrolled. Targeted proteomics was performed on plasma with proximity extension assays (1,161 proteins). RESULTS: Cohorts were well-balanced for age and sex. The majority of sTBI patients were injured in motor vehicle collisions and the most frequent head CT finding was subarachnoid hemorrhage. Mortality rate for sTBI patients was 40%. Feature selection identified the top performing 15 proteins for identifying sTBI patients from healthy control subjects with a classification accuracy of 100%. The sTBI proteome was dominated by markers of vascular pathology, immunity/inflammation, cell survival and macrophage/microglia activation. Receiver operating characteristic (ROC) curve analyses demonstrated areas-under-the-curves (AUC) for identifying sTBI that ranged from 0.870-1.000 (p≤0.005). When mortality was used as outcome, ROC curve analyses identified the top 3 proteins as Willebrand factor (vWF), Wnt inhibitory factor-1 (WIF-1), and colony stimulating factor-1 (CSF-1). Combining vWF with either WIF-1 or CSF-1 resulted in excellent mortality prediction with AUC of 1.000 for both combinations (p=0.011). CONCLUSIONS: Targeted proteomics with feature classification and selection distinguished sTBI patients from matched healthy control subjects. Two protein combinations were identified that accurately predicted sTBI patient mortality. Our exploratory findings require confirmation in larger sTBI patient populations.

A proteome-wide immuno-mass spectrometric identification of serum autoantibodies.

BACKGROUND: Autoantibodies are produced when tolerance to self-antigens is broken and they can be mediators of tissue injury and systemic inflammation. They are excellent biomarkers because they are minimally invasive to screen and are highly abundant in serum due to limited proteolysis and slow clearance. Conventionally used methods of identifying autoantibodies in patient sera include indirect immunofluorescence, enzyme-linked immunoabsorbent assays (ELISAs) and protein microarrays. Here we present a novel proteome-wide immuno-mass spectrometric method to identify serum autoantibody targets. METHODS: Serum samples from patients with inflammatory bowel disease (IBD) were analyzed by ELISA for the presence of autoantibodies to CUB and zona pellucida-like domain-containing protein 1 (CUZD1). Protein was extracted from the human pancreas as well as 16 other human tissues to make a complex tissue lysate protein mixture. Antibodies in patient sera were immobilized and purified on protein G magnetic beads and subsequently incubated with pancreatic lysate containing CUZD1 or the aforementioned complex tissue lysate. After extensive washing, antibody-bound protein antigens were trypsin-digested and identified using shotgun mass spectrometry. RESULTS: The protocol was optimized for the immunoaffinity purification of autoantibody targets from tissue lysate, using CUZD1 from pancreatic lysate and anti-CUZD1 autoantibodies present in IBD patient serum as a proof-of-concept. Pancreatic secretory granule membrane major glycoprotein 2, whose autoantibodies are a known biomarker of Crohn's disease, was also immunoprecipitated from IBD patient serum, as an additional internal positive control. CONCLUSIONS: This study demonstrates the effectiveness of a proteomic approach to identify serum autoantibody targets, using immunoaffinity purification followed by tandem mass spectrometry. Our methodology is applicable for proteome-wide analysis of autoantibody targets in a wide variety of clinical settings.