Machine learning and artificial intelligence for the diagnosis of infectious diseases in immunocompromised patients.

PURPOSE OF REVIEW: Immunocompromised patients are at high risk for infection. During the coronavirus disease (COVID-19) pandemic, immunocompromised patients exhibited increased odds of intensive care unit admission and death. Early pathogen identification is essential to mitigating infection related risk in immunocompromised patients. Artificial intelligence (AI) and machine learning (ML) have tremendous appeal to address unmet diagnostic needs. These AI/ML tools often rely on the wealth of data found in healthcare to enhance our ability to identify clinically significant patterns of disease. To this end, our review provides an overview of the current AI/ML landscape as it applies to infectious disease testing with emphasis on immunocompromised patients. RECENT FINDINGS: Examples include AI/ML for predicting sepsis in high risk burn patients. Likewise, ML is utilized to analyze complex host-response proteomic data to predict respiratory infections including COVID-19. These same approaches have also been applied for pathogen identification of bacteria, viruses, and hard to detect fungal microbes. Future uses of AI/ML may include integration of predictive analytics in point-of-care (POC) testing and data fusion applications. SUMMARY: Immunocompromised patients are at high risk for infections. AI/ML is transforming infectious disease testing and has great potential to address challenges encountered in the immune compromised population.

Multi-omic and comparative analyses revealed monocyte-derived alpha-defensin-1 correlated with COVID-19 severity and inhibited SARS-CoV-2 infection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiological pathogen of coronavirus disease 2019 (COVID-19), a highly contagious disease, spreading quickly and threatening global public health. The symptoms of COVID-19 vary from mild reactions to severe respiratory distress or even fatal outcomes probably due to the different status of host immunity against the virus. Here in the study, we unveiled plasma proteomic signatures and transcriptional patterns of peripheral blood mononuclear cells (PBMCs) using blood samples of 10 COVID-19 patients with different severity. Through systemic analysis, α-defensin-1 (DEFA1) was identified to be elevated in both plasma and PBMCs, and correlated with disease severity and stages. In vitro study demonstrated that DEFA1 was secreted from immunocytes and suppressed SARS-CoV-2 infection of both original and mutated strains with dose dependency. By using sequencing data, we discovered that DEFA1 was activated in monocytes through NF-κB signaling pathway after infection, and secreted into circulation to perturb SARS-CoV-2 infection by interfering protein kinase C expression. It worked mainly during virus replication instead of entry in host cells. Together, the anti-SARS-CoV-2 mechanism of DEFA1 has unveiled a corner of how innate immunity is against SARS-CoV-2 and explored its clinical potential in disease prognosis and therapeutic intervention.

Maintained imbalance of triglycerides, apolipoproteins, energy metabolites and cytokines in long-term COVID-19 syndrome patients.

Background: Deep metabolomic, proteomic and immunologic phenotyping of patients suffering from an infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have matched a wide diversity of clinical symptoms with potential biomarkers for coronavirus disease 2019 (COVID-19). Several studies have described the role of small as well as complex molecules such as metabolites, cytokines, chemokines and lipoproteins during infection and in recovered patients. In fact, after an acute SARS-CoV-2 viral infection almost 10-20% of patients experience persistent symptoms post 12 weeks of recovery defined as long-term COVID-19 syndrome (LTCS) or long post-acute COVID-19 syndrome (PACS). Emerging evidence revealed that a dysregulated immune system and persisting inflammation could be one of the key drivers of LTCS. However, how these biomolecules altogether govern pathophysiology is largely underexplored. Thus, a clear understanding of how these parameters within an integrated fashion could predict the disease course would help to stratify LTCS patients from acute COVID-19 or recovered patients. This could even allow to elucidation of a potential mechanistic role of these biomolecules during the disease course. Methods: This study comprised subjects with acute COVID-19 (n=7; longitudinal), LTCS (n=33), Recov (n=12), and no history of positive testing (n=73). 1H-NMR-based metabolomics with IVDr standard operating procedures verified and phenotyped all blood samples by quantifying 38 metabolites and 112 lipoprotein properties. Univariate and multivariate statistics identified NMR-based and cytokine changes. Results: Here, we report on an integrated analysis of serum/plasma by NMR spectroscopy and flow cytometry-based cytokines/chemokines quantification in LTCS patients. We identified that in LTCS patients lactate and pyruvate were significantly different from either healthy controls (HC) or acute COVID-19 patients. Subsequently, correlation analysis in LTCS group only among cytokines and amino acids revealed that histidine and glutamine were uniquely attributed mainly with pro-inflammatory cytokines. Of note, triglycerides and several lipoproteins (apolipoproteins Apo-A1 and A2) in LTCS patients demonstrate COVID-19-like alterations compared with HC. Interestingly, LTCS and acute COVID-19 samples were distinguished mostly by their phenylalanine, 3-hydroxybutyrate (3-HB) and glucose concentrations, illustrating an imbalanced energy metabolism. Most of the cytokines and chemokines were present at low levels in LTCS patients compared with HC except for IL-18 chemokine, which tended to be higher in LTCS patients. Conclusion: The identification of these persisting plasma metabolites, lipoprotein and inflammation alterations will help to better stratify LTCS patients from other diseases and could help to predict ongoing severity of LTCS patients.

No Substrate Left behind-Mining of Shotgun Proteomics Datasets Rescues Evidence of Proteolysis by SARS-CoV-2 3CLpro Main Protease.

Proteolytic processing is the most ubiquitous post-translational modification and regulator of protein function. To identify protease substrates, and hence the function of proteases, terminomics workflows have been developed to enrich and detect proteolytically generated protein termini from mass spectrometry data. The mining of shotgun proteomics datasets for such 'neo'-termini, to increase the understanding of proteolytic processing, is an underutilized opportunity. However, to date, this approach has been hindered by the lack of software with sufficient speed to make searching for the relatively low numbers of protease-generated semi-tryptic peptides present in non-enriched samples viable. We reanalyzed published shotgun proteomics datasets for evidence of proteolytic processing in COVID-19 using the recently upgraded MSFragger/FragPipe software, which searches data with a speed that is an order of magnitude greater than many equivalent tools. The number of protein termini identified was higher than expected and constituted around half the number of termini detected by two different N-terminomics methods. We identified neo-N- and C-termini generated during SARS-CoV-2 infection that were indicative of proteolysis and were mediated by both viral and host proteases-a number of which had been recently validated by in vitro assays. Thus, re-analyzing existing shotgun proteomics data is a valuable adjunct for terminomics research that can be readily tapped (for example, in the next pandemic where data would be scarce) to increase the understanding of protease function and virus-host interactions, or other diverse biological processes.

Proteomic profiling of single extracellular vesicles reveals colocalization of SARS-CoV-2 with a CD81/integrin-rich EV subpopulation in sputum from COVID-19 severe patients.

Background: The global outbreak of COVID-19, and the limited availability of clinical treatments, forced researchers around the world to search for the pathogenesis and potential treatments. Understanding the pathogenesis of SARS-CoV-2 is crucial to respond better to the current coronavirus disease 2019 (COVID-19) pandemic. Methods: We collected sputum samples from 20 COVID-19 patients and healthy controls. Transmission electron microscopy was used to observe the morphology of SARS-CoV-2. Extracellular vesicles (EVs) were isolated from sputum and the supernatant of VeroE6 cells, and were characterized by transmission electron microscopy, nanoparticle tracking analysis and Western-Blotting. Furthermore, a proximity barcoding assay was used to investigate immune-related proteins in single EV, and the relationship between EVs and SARS-CoV-2. Result: Transmission electron microscopy images of SARS-COV-2 virus reveal EV-like vesicles around the virion, and western blot analysis of EVs extracted from the supernatant of SARS-COV-2-infected VeroE6 cells showed that they expressed SARS-COV-2 protein. These EVs have the infectivity of SARS-COV-2, and the addition can cause the infection and damage of normal VeroE6 cells. In addition, EVs derived from the sputum of patients infected with SARS-COV-2 expressed high levels of IL6 and TGF-ß, which correlated strongly with expression of the SARS-CoV-2 N protein. Among 40 EV subpopulations identified, 18 differed significantly between patients and controls. The EV subpopulation regulated by CD81 was the most likely to correlate with changes in the pulmonary microenvironment after SARS-CoV-2 infection. Single extracellular vesicles in the sputum of COVID-19 patients harbor infection-mediated alterations in host and virus-derived proteins. Conclusions: These results demonstrate that EVs derived from the sputum of patients participate in virus infection and immune responses. This study provides evidence of an association between EVs and SARS-CoV-2, providing insight into the possible pathogenesis of SARS-CoV-2 infection and the possibility of developing nanoparticle-based antiviral drugs.

A diagnostic model for COVID-19 based on proteomics analysis.

BACKGROUND AND OBJECTIVE: Early diagnosis of Coronavirus Disease 2019 (COVID-19) can help save patients' lives before the disease turns severe. This can be achieved through an effective and correct treatment protocol. In this paper, a prediction model is proposed to detect infected cases and determine the severity level of the disease. METHODS: The proposed model is based on utilizing proteins and metabolites as features for each patient, which are then analyzed using feature selection methods such as Principal Component Analysis (PCA), Information Gain (IG), and analysis of Variance (ANOVA) to select the most significant features. The model employs three classifiers, namely K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF), to predict and classify the severity level of the COVID-19 infection. The proposed model is evaluated using four performance measures: accuracy, sensitivity, specificity, and precision. RESULTS: The experiment results show that the proposed model accuracy can reach 80% using RF classifier with PCA. The PCA selects 22 proteins and 10 metabolites. While ANOVA selects 9 proteins and 5 metabolites. The accuracy reaches 92% after applying RF classifier with the ANOVA. Finally, the accuracy reaches 93% using the RF classifier with only ten features. The selected features are 7 proteins and 3 metabolites. Moreover, it shows that the selected features have a relation to the immune system and respiratory systems. CONCLUSION: The proposed model uses three classifiers and shows promising results by selecting the important features and maximizing the prediction accuracy.

Integrative proteomic profiling of lung tissues and blood in acute respiratory distress syndrome.

Introduction: Acute respiratory distress syndrome and acute lung injury (ARDS/ALI) still lack a recognized diagnostic test and pharmacologic treatments that target the underlying pathology. Methods: To explore the sensitive non-invasive biomarkers associated with pathological changes in the lung of direct ARDS/ALI, we performed an integrative proteomic analysis of lung and blood samples from lipopolysaccharide (LPS)-induced ARDS mice and COVID-19-related ARDS patients. The common differentially expressed proteins (DEPs) were identified based on combined proteomic analysis of serum and lung samples in direct ARDS mice model. The clinical value of the common DEPs was validated in lung and plasma proteomics in cases of COVID-19-related ARDS. Results: We identified 368 DEPs in serum and 504 in lung samples from LPS-induced ARDS mice. Gene ontology (GO) classification and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that these DEPs in lung tissues were primarily enriched in pathways, including IL-17 and B cell receptor signaling pathways, and the response to stimuli. In contrast, DEPs in the serum were mostly involved in metabolic pathways and cellular processes. Through network analysis of protein-protein interactions (PPI), we identified diverse clusters of DEPs in the lung and serum samples. We further identified 50 commonly upregulated and 10 commonly downregulated DEPs in the lung and serum samples. Internal validation with a parallel-reacted monitor (PRM) and external validation in the Gene Expression Omnibus (GEO) datasets further showed these confirmed DEPs. We then validated these proteins in the proteomics of patients with ARDS and identified six proteins (HP, LTA4H, S100A9, SAA1, SAA2, and SERPINA3) with good clinical diagnostic and prognostic value. Discussion: These proteins can be viewed as sensitive and non-invasive biomarkers associated with lung pathological changes in the blood and could potentially serve as targets for the early detection and treatment of direct ARDS especially in hyperinflammatory subphenotype.

Proteomics and cytokine analyses distinguish myalgic encephalomyelitis/chronic fatigue syndrome cases from controls.

BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, heterogenous disease characterized by unexplained persistent fatigue and other features including cognitive impairment, myalgias, post-exertional malaise, and immune system dysfunction. Cytokines are present in plasma and encapsulated in extracellular vesicles (EVs), but there have been only a few reports of EV characteristics and cargo in ME/CFS. Several small studies have previously described plasma proteins or protein pathways that are associated with ME/CFS. METHODS: We prepared extracellular vesicles (EVs) from frozen plasma samples from a cohort of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) cases and controls with prior published plasma cytokine and plasma proteomics data. The cytokine content of the plasma-derived extracellular vesicles was determined by a multiplex assay and differences between patients and controls were assessed. We then performed multi-omic statistical analyses that considered not only this new data, but extensive clinical data describing the health of the subjects. RESULTS: ME/CFS cases exhibited greater size and concentration of EVs in plasma. Assays of cytokine content in EVs revealed IL2 was significantly higher in cases. We observed numerous correlations among EV cytokines, among plasma cytokines, and among plasma proteins from mass spectrometry proteomics. Significant correlations between clinical data and protein levels suggest roles of particular proteins and pathways in the disease. For example, higher levels of the pro-inflammatory cytokines Granulocyte-Monocyte Colony-Stimulating Factor (CSF2) and Tumor Necrosis Factor (TNFα) were correlated with greater physical and fatigue symptoms in ME/CFS cases. Higher serine protease SERPINA5, which is involved in hemostasis, was correlated with higher SF-36 general health scores in ME/CFS. Machine learning classifiers were able to identify a list of 20 proteins that could discriminate between cases and controls, with XGBoost providing the best classification with 86.1% accuracy and a cross-validated AUROC value of 0.947. Random Forest distinguished cases from controls with 79.1% accuracy and an AUROC value of 0.891 using only 7 proteins. CONCLUSIONS: These findings add to the substantial number of objective differences in biomolecules that have been identified in individuals with ME/CFS. The observed correlations of proteins important in immune responses and hemostasis with clinical data further implicates a disturbance of these functions in ME/CFS.

Urinary proteomics combined with home blood pressure telemonitoring for health care reform trial-First progress report.

High blood pressure (BP) and type-2 diabetes (T2DM) are forerunners of chronic kidney disease and left ventricular dysfunction. Home BP telemonitoring (HTM) and urinary peptidomic profiling (UPP) are technologies enabling risk stratification and personalized prevention. UPRIGHT-HTM (NCT04299529) is an investigator-initiated, multicenter, open-label, randomized trial with blinded endpoint evaluation designed to assess the efficacy of HTM plus UPP (experimental group) over HTM alone (control group) in guiding treatment in asymptomatic patients, aged 55-75 years, with ≥5 cardiovascular risk factors. From screening onwards, HTM data can be freely accessed by all patients and their caregivers; UPP results are communicated early during follow-up to patients and caregivers in the intervention group, but at trial closure in the control group. From May 2021 until January 2023, 235 patients were screened, of whom 53 were still progressing through the run-in period and 144 were randomized. Both groups had similar characteristics, including average age (62.0 years) and the proportions of African Blacks (81.9%), White Europeans (16.7%), women 56.2%, home (31.2%), and office (50.0%) hypertension, T2DM (36.4%), micro-albuminuria (29.4%), and ECG (9.7%) and echocardiographic (11.5%) left ventricular hypertrophy. Home and office BP were 128.8/79.2 mm Hg and 137.1/82.7 mm Hg, respectively, resulting in a prevalence of white-coat, masked and sustained hypertension of 40.3%, 11.1%, and 25.7%. HTM persisted after randomization (48 681 readings up to 15 January 2023). In conclusion, results predominantly from low-resource sub-Saharan centers proved the feasibility of this multi-ethnic trial. The COVID-19 pandemic caused delays and differential recruitment rates across centers.

Immunology of SARS-CoV-2 infection and vaccination.

Comprehensive elucidation of humoral immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and vaccination is critical for understanding coronavirus disease 2019 (COVID-19) pathogenesis in general and developing antibody-based diagnostic and therapeutic strategies specifically. Following the emergence of SARS-CoV-2, significant scientific research has been conducted worldwide using omics, sequencing and immunologic approaches. These studies have been critical to the successful development of vaccines. Here, the current understanding of SARS-CoV-2 immunogenic epitopes, humoral immunity to SARS-CoV-2 structural proteins and non-structural proteins, SARS-CoV-2-specific antibodies, and T-cell responses in convalescents and vaccinated individuals are reviewed. Additionally, we explore the integrated analysis of proteomic and metabolomic data to examine mechanisms of organ injury and identify potential biomarkers. Insight into the immunologic diagnosis of COVID-19 and improvements of laboratory methods are highlighted.

Neutrophil elastase decreases SARS-CoV-2 spike protein binding to human bronchial epithelia by clipping ACE-2 ectodomain from the epithelial surface.

Patients with cystic fibrosis (CF) have decreased severity of severe acute respiratory syndrome-like coronavirus-2 (SARS-CoV-2) infections, but the underlying cause is unknown. Patients with CF have high levels of neutrophil elastase (NE) in the airway. We examined whether respiratory epithelial angiotensin-converting enzyme 2 (ACE-2), the receptor for the SARS-CoV-2 spike protein, is a proteolytic target of NE. Soluble ACE-2 levels were quantified by ELISA in airway secretions and serum from patients with and without CF, the association between soluble ACE-2 and NE activity levels was evaluated in CF sputum. We determined that NE activity was directly correlated with increased ACE-2 in CF sputum. Additionally, primary human bronchial epithelial (HBE) cells, exposed to NE or control vehicle, were evaluated by Western analysis for the release of cleaved ACE-2 ectodomain fragment into conditioned media, flow cytometry for the loss of cell surface ACE-2, its impact on SARS-CoV-2 spike protein binding. We found that NE treatment released ACE-2 ectodomain fragment from HBE and decreased spike protein binding to HBE. Furthermore, we performed NE treatment of recombinant ACE-2-Fc-tagged protein in vitro to assess whether NE was sufficient to cleave recombinant ACE-2-Fc protein. Proteomic analysis identified specific NE cleavage sites in the ACE-2 ectodomain that would result in loss of the putative N-terminal spike-binding domain. Collectively, data support that NE plays a disruptive role in SARS-CoV-2 infection by catalyzing ACE-2 ectodomain shedding from the airway epithelia. This mechanism may reduce SARS-CoV-2 virus binding to respiratory epithelial cells and decrease the severity of COVID19 infection.

PepGM: a probabilistic graphical model for taxonomic inference of viral proteome samples with associated confidence scores.

MOTIVATION: Inferring taxonomy in mass spectrometry-based shotgun proteomics is a complex task. In multi-species or viral samples of unknown taxonomic origin, the presence of proteins and corresponding taxa must be inferred from a list of identified peptides, which is often complicated by protein homology: many proteins do not only share peptides within a taxon but also between taxa. However, the correct taxonomic inference is crucial when identifying different viral strains with high-sequence homology-considering, e.g., the different epidemiological characteristics of the various strains of severe acute respiratory syndrome-related coronavirus-2. Additionally, many viruses mutate frequently, further complicating the correct identification of viral proteomic samples. RESULTS: We present PepGM, a probabilistic graphical model for the taxonomic assignment of virus proteomic samples with strain-level resolution and associated confidence scores. PepGM combines the results of a standard proteomic database search algorithm with belief propagation to calculate the marginal distributions, and thus confidence scores, for potential taxonomic assignments. We demonstrate the performance of PepGM using several publicly available virus proteomic datasets, showing its strain-level resolution performance. In two out of eight cases, the taxonomic assignments were only correct on the species level, which PepGM clearly indicates by lower confidence scores. AVAILABILITY AND IMPLEMENTATION: PepGM is written in Python and embedded into a Snakemake workflow. It is available at https://github.com/BAMeScience/PepGM.

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.

BIRCH: An Automated Workflow for Evaluation, Correction, and Visualization of Batch Effect in Bottom-Up Mass Spectrometry-Based Proteomics Data.

Recent surges in large-scale mass spectrometry (MS)-based proteomics studies demand a concurrent rise in methods to facilitate reliable and reproducible data analysis. Quantification of proteins in MS analysis can be affected by variations in technical factors such as sample preparation and data acquisition conditions leading to batch effects, which adds to noise in the data set. This may in turn affect the effectiveness of any biological conclusions derived from the data. Here we present Batch-effect Identification, Representation, and Correction of Heterogeneous data (BIRCH), a workflow for analysis and correction of batch effect through an automated, versatile, and easy to use web-based tool with the goal of eliminating technical variation. BIRCH also supports diagnosis of the data to check for the presence of batch effects, feasibility of batch correction, and imputation to deal with missing values in the data set. To illustrate the relevance of the tool, we explore two case studies, including an iPSC-derived cell study and a Covid vaccine study to show different context-specific use cases. Ultimately this tool can be used as an extremely powerful approach for eliminating technical bias while retaining biological bias, toward understanding disease mechanisms and potential therapeutics.

Multi-Omics Profiling for Health.

The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.

Targeted Blood Plasma Proteomics and Hemostasis Assessment of Post COVID-19 Patients with Acute Myocardial Infarction.

The molecular mechanisms underlying cardiovascular complications after the SARS-CoV-2 infection remain unknown. The goal of our study was to analyze the features of blood coagulation, platelet aggregation, and plasma proteomics in COVID-19 convalescents with AMI. The study included 66 AMI patients and 58 healthy volunteers. The groups were divided according to the anti-N IgG levels (AMI post-COVID (n = 44), AMI control (n = 22), control post-COVID (n = 31), and control (n = 27)). All participants underwent rotational thromboelastometry, thrombodynamics, impedance aggregometry, and blood plasma proteomics analysis. Both AMI groups of patients demonstrated higher values of clot growth rates, thrombus size and density, as well as the elevated levels of components of the complement system, proteins modifying the state of endothelium, acute-phase and procoagulant proteins. In comparison with AMI control, AMI post-COVID patients demonstrated decreased levels of proteins connected to inflammation and hemostasis (lipopolysaccharide-binding protein, C4b-binding protein alpha-chain, plasma protease C1 inhibitor, fibrinogen beta-chain, vitamin K-dependent protein S), and altered correlations between inflammation and fibrinolysis. A new finding is that AMI post-COVID patients opposite the AMI control group, are characterized by a less noticeable growth of acute-phase proteins and hemostatic markers that could be explained by prolonged immune system alteration after COVID-19.

Plasma Proteomics Unveil Novel Immune Signatures and Biomarkers upon SARS-CoV-2 Infection.

Several elements have an impact on COVID-19, including comorbidities, age and sex. To determine the protein profile changes in peripheral blood caused by a SARS-CoV-2 infection, a proximity extension assay was used to quantify 1387 proteins in plasma samples among 28 Finnish patients with COVID-19 with and without comorbidities and their controls. Key immune signatures, including CD4 and CD28, were changed in patients with comorbidities. Importantly, several unreported elevated proteins in patients with COVID-19, such as RBP2 and BST2, which show anti-microbial activity, along with proteins involved in extracellular matrix remodeling, including MATN2 and COL6A3, were identified. RNF41 was downregulated in patients compared to healthy controls. Our study demonstrates that SARS-CoV-2 infection causes distinct plasma protein changes in the presence of comorbidities despite the interpatient heterogeneity, and several novel potential biomarkers associated with a SARS-CoV-2 infection alone and in the presence of comorbidities were identified. Protein changes linked to the generation of SARS-CoV-2-specific antibodies, long-term effects and potential association with post-COVID-19 condition were revealed. Further study to characterize the identified plasma protein changes from larger cohorts with more diverse ethnicities of patients with COVID-19 combined with functional studies will facilitate the identification of novel diagnostic, prognostic biomarkers and potential therapeutic targets for patients with COVID-19.

Proteomic data and structure analysis combined reveal interplay of structural rigidity and flexibility on selectivity of cysteine cathepsins.

Addressing the elusive specificity of cysteine cathepsins, which in contrast to caspases and trypsin-like proteases lack strict specificity determining P1 pocket, calls for innovative approaches. Proteomic analysis of cell lysates with human cathepsins K, V, B, L, S, and F identified 30,000 cleavage sites, which we analyzed by software platform SAPS-ESI (Statistical Approach to Peptidyl Substrate-Enzyme Specific Interactions). SAPS-ESI is used to generate clusters and training sets for support vector machine learning. Cleavage site predictions on the SARS-CoV-2 S protein, confirmed experimentally, expose the most probable first cut under physiological conditions and suggested furin-like behavior of cathepsins. Crystal structure analysis of representative peptides in complex with cathepsin V reveals rigid and flexible sites consistent with analysis of proteomics data by SAPS-ESI that correspond to positions with heterogeneous and homogeneous distribution of residues. Thereby support for design of selective cleavable linkers of drug conjugates and drug discovery studies is provided.

Quantitative protein biomarker panels: a path to improved clinical practice through proteomics.

The utilisation of protein biomarker panels, rather than individual protein biomarkers, offers a more comprehensive representation of human physiology. It thus has the potential to improve diagnosis, prognosis and the differentiation of responders from nonresponders in the context of precision medicine. Although several proteomic techniques exist for measuring biomarker panels, the integration of proteomics into clinical practice has been limited. In this Commentary, we highlight the significance of quantitative protein biomarker panels in clinical medicine and outline the challenges that must be addressed in order to identify the most promising panels and implement them in clinical routines to realise their medical potential. Furthermore, we argue that the absolute quantification of protein panels through targeted mass spectrometric assays remains the most promising technology for translating proteomics into routine clinical applications due to its high flexibility, low sample costs, independence from affinity reagents and low entry barriers for its integration into existing laboratory workflows.

De novo design of protein interactions with learned surface fingerprints.

Physical interactions between proteins are essential for most biological processes governing life1. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications2-9. Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein-protein interactions10. We hypothesized that these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof of principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1 and CTLA-4. Several designs were experimentally optimized, whereas others were generated purely in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling an approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.