Nano-biosensors for Diagnosing Infectious and Lifestyle-Related Disease of Human: An Update

At present time, a variety of infectious and lifestyle diseases are becoming lifethreatening day by day. Development in technology and immergence of nanoscience helped to provide a better health care system. Based on the working mechanism nano-biosensors are of majorly two types: electrochemical nanobiosensor and optical nano-biosensor. Nanomaterials used in the nano-biosensor increased their efficacy, sensitivity, and selectivity of the device. Different diseases have different biomarkers to get detected such as, absorption of cholesterol oxidase detect cholesterol, glaucoma in a diabetes patient is detected by cytokine Interleukin 12 in tear, C-reactive protein is detected for liver inflammation, the SARS virus is detected by N-protein and miRNA is a potential biomarker of cancers, especially colorectal cancer. Hitherto, identification of a biomarker for a specific disease is the major work. The accuracy of nanobiosensor in diagnosing diseases put them in demand in the biomedical field. But the major drawback comes with the cost-effectiveness and use of nanomaterial in health sectors focussing on any toxicological impact of the nano-biosensor on health in long run. In this chapter, we present an overview of the working mechanism of different nano-biosensors in diagnosing different infectious and lifestyle diseases. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Could platelet mass index (PMI) be a new prognostic biomarker for COVID-19?

Aim: Although most patients with COVID-19 experience respiratory tract infections, severe reactions to the virus may cause coagulation abnormalities that mimic other systemic coagulopathies associated with severe infections, such as disseminated intravascular coagulation and thrombotic microangiopathy. Fluctuations in platelet markers, which are an indicator of the acute phase response for COVID-19, are of clinical importance. The aim of this study is to evaluate the relationship between disease severity and Platelet Mass Index (MPI) parameters in COVID-19 patients. Material(s) and Method(s): This retrospective observational study was conducted with patients who were diagnosed with COVID-19 in a tertiary hospital. The study was continued with the remaining 280 patients. All laboratory data were scanned retrospectively from patient files and hospital information system. Result(s): A very high positive correlation was found between PMI and PLT. The PMI value in women was significantly higher than in men. It was observed that PMI did not differ significantly in terms of mortality, intubation, CPAP and comorbidity. PMI vs. Pneumonia Ct Severity Score, biochemistry parameters (AST, CRP), hemogram parameters (WBC, HGB, HCT, MCV, LYM, MPV EO) and coagulation factors (aPTT and FIB) at various levels of positive/negative, weak and strong, and significant relationship was found. There was no significant relationship between hormone and D-dimer when compared with PMI. Discussion(s): Although platelet count alone does not provide information about the prognosis of the disease, PMI may guide the clinician as an indicator of lung damage in seriously ill patients.Copyright © 2022, Derman Medical Publishing. All rights reserved.

Electrochemical immunosensor for diagnosis of COVID-19

An immunosensor is a biosensor that detects antigen interactions using a particular antibody bound on the transducer's surface. These biosensors have high selectivity and sensitivity due to their interaction specificity. Owing to this characteristic, this type of sensor is attractive for several applications, especially in the medical area and bioanalysis. Among the types of immunosensors, electrochemical immunosensors have gained prominence due to their simplicity and portability, potentially enabling in situ detection as promising characteristic for analysis in emergency care. In this chapter, the potential of electrochemical immunosensors is presented, especially in applications related to clinical examinations and mainly in the diagnosis of SARS-CoV-2. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. All rights reserved.

Diagnostic Role of Platelet to Lymphocyte Ratio and Platelet Parameters in COVID-19 Disease

Backgrounds: This study aimed to analyze the applicability of platelet parameters in assessing the severity of COVID-19 disease. Material(s) and Method(s): Patients with RT-PCR confirmed COVID-19 in the Pathology department of a tertiary care hospital in south India from June to December 2020 were included in this study. Clinical details and laboratory parameters of these patients were obtained. The difference between the studied variables in two groups was assessed using independent t-test. The optimum cut-off value of platelet to lymphocyte ratio (PLR) to differentiate between the tested groups was estimated using ROC (receiver operator curve) analysis. Finding(s): This study was conducted on 218 COVID-19 patients, of whom 17.9% showed thrombocytopenia at the time of admission. Among the hematological parameters, PLR, absolute lymphocyte count (ALC), platelet distribution width (PDW), D-dimer, and erythrocyte sedimentation rate (ESR) were significantly different between the ICU (intensive care unit) and non-ICU groups. Increased PLR values were associated with the disease severity. Conclusion(s): PLR could be used as an additional biomarker in assessing the severity of COVID-19 disease, and a cut-off value of 210.27 is optimal to differentiate severe COVID-19 disease from its mild and moderate forms with 79% specificity.Copyright © 2023, TMU Press.

MicroRNAs: The Master Regulators of the Breast Cancer Tumor Microenvironment

Breast cancer is the most commonly diagnosed cancer globally and is among the leading causes of cancer deaths worldwide. Breast cancer mortality rates are increasing due to delays in diagnosis, prognosis, and treatment caused by the coronavirus disease 2019 (COVID-19) pandemic. Identification and validation of blood-based breast cancer biomarkers for early detection is a top priority worldwide. MicroRNAs (miRNAs) show the potential to serve as breast cancer biomarkers. miRNAs are small, endogenously produced RNAs that regulate growth and development. However, oncogenic miRNAs also play a major role in tumor growth and can alter the tumor microenvironment (TME) in favor of cancer metastasis. The TME represents a complex network of diverse cancerous and noncancerous cell types, secretory proteins, growth factors, and miRNAs. Complex interactions within the TME can promote cancer progression and metastasis via multiple mechanisms, including oxidative stress, hypoxia, angiogenesis, lymphangiogenesis, and cancer stem cell regulation. Here, we decipher the mechanisms of miRNA regulating the TME, intending to use that knowledge to identify miRNAs as therapeutic targets in breast cancer and use miRNAs as blood-based biomarkers. © Springer Nature Singapore Pte Ltd. 2022.

Association of Alpha 1 Antitrypsin Deficiency with COVID-19 Mortality: Basis for Clinical Trials

Coronavirus disease 2019 (COVID-19) has emerged as a pandemic leading to unprecedented disruption of global health and economy. Countries with a large population of European/Hispanic ancestry have been found to have the highest COVID-19 related case fatality rates. This prompted us with an interesting question that whether host immune programming and host genetic modifiers might be responsible for the higher mortality rate in these ethnicities. Transmembrane protease serine 2 (TMPRSS2) is critical in priming the viral spike protein and the host ACE2 receptor before the virus enters into the host cell. Recent results from the COVID-19 Host Genetics Initiative identified ELF5 rs766826 as a protective factor to severe COVID-19 which decreases the expression of TMPRSS2. Moreover, multiple studies have experimentally demonstrated that alpha 1 antitrypsin (A1AT) (encoded by SERPINA1 gene) is an inhibitor of TMPRSS2 and provided support to the already approved therapy as a candidate for COVID-19. Interestingly A1AT deficiency is common among Europeans and Latinos. We have also analyzed the gnomAD dataset to show that Europeans and Latinos have a substantially higher carrier frequency of AlAT deficiency (~12%) compared to other large ethnicities. A1AT has the dual role of an antiviral and anti-inflammatory molecule for treating COVID-19. To date, eight clinical trials have been started to find out the effectiveness of A1AT in COVID-19. Low A1AT level in severe COVID-19 has also been found to be a poor prognostic marker. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Biomarker-Concordant Steroid Administration in Severe Coronavirus Disease-2019.

BACKGROUND: Although corticosteroids have become the standard of care for patients with coronavirus disease-2019 (COVID-19) on supplemental oxygen, there is growing evidence of differential treatment response. This study aimed to evaluate if there was an association between biomarker-concordant corticosteroid treatment and COVID-19 outcomes. METHODS: This registry-based cohort study included adult COVID-19 hospitalized patients between January 2020 and December 2021 from 109 institutions. Patients with available C-reactive protein (CRP) levels within 48 h of admission were evaluated. Those on steroids before admission, stayed in the hospital for <48 h, or were not on oxygen support were excluded. Corticosteroid treatment was biomarker-concordant if given with high baseline CRP ≥150 mg/L or withheld with low CRP (<150 mg/L) and vice-versa was considered discordant (low CRP with steroids, high CRP without steroids). Hospital mortality was the primary outcome. Sensitivity analyses were conducted using varying CRP level thresholds. The model interaction was tested to determine steroid effectiveness with increasing CRP levels. RESULTS: Corticosteroid treatment was biomarker-concordant in 1778 (49%) patients and discordant in 1835 (51%). The concordant group consisted of higher-risk patients than the discordant group. After adjusting for covariates, the odds of in-hospital mortality were significantly lower in the concordant group than the discordant (odds ratio [95% confidence interval (C.I.)] = 0.71 [0.51, 0.98]). Similarly, adjusted mortality difference was significant at the CRP thresholds of 100 and 200 mg/L (odds ratio [95% C.I.] = 0.70 [0.52, 0.95] and 0.57 [0.38, 0.85], respectively), and concordant steroid use was associated with lower need for invasive ventilation for 200 mg/L threshold (odds ratio [95% C.I.] = 0.52 [0.30, 0.91]). In contrast, no outcome benefit was observed at CRP threshold of 50. When the model interaction was tested, steroids were more effective at reducing mortality as CRP levels increased. CONCLUSION: Biomarker-concordant corticosteroid treatment was associated with lower odds of in-hospital mortality in severe COVID-19.

Review on two-dimensional material-based field-effect transistor biosensors: accomplishments, mechanisms, and perspectives.

Field-effect transistor (FET) is regarded as the most promising candidate for the next-generation biosensor, benefiting from the advantages of label-free, easy operation, low cost, easy integration, and direct detection of biomarkers in liquid environments. With the burgeoning advances in nanotechnology and biotechnology, researchers are trying to improve the sensitivity of FET biosensors and broaden their application scenarios from multiple strategies. In order to enable researchers to understand and apply FET biosensors deeply, focusing on the multidisciplinary technical details, the iteration and evolution of FET biosensors are reviewed from exploring the sensing mechanism in detecting biomolecules (research direction 1), the response signal type (research direction 2), the sensing performance optimization (research direction 3), and the integration strategy (research direction 4). Aiming at each research direction, forward perspectives and dialectical evaluations are summarized to enlighten rewarding investigations.

Expression profile of HERVs and inflammatory mediators detected in nasal mucosa as a predictive biomarker of COVID-19 severity.

Introduction: Our research group and others demonstrated the implication of the human endogenous retroviruses (HERVs) in SARS-CoV-2 infection and their association with disease progression, suggesting HERVs as contributing factors in COVID-19 immunopathology. To identify early predictive biomarkers of the COVID-19 severity, we analyzed the expression of HERVs and inflammatory mediators in SARS-CoV-2-positive and -negative nasopharyngeal/oropharyngeal swabs with respect to biochemical parameters and clinical outcome. Methods: Residuals of swab samples (20 SARS-CoV-2-negative and 43 SARS-CoV-2-positive) were collected during the first wave of the pandemic and expression levels of HERVs and inflammatory mediators were analyzed by qRT-Real time PCR. Results: The results obtained show that infection with SARS-CoV-2 resulted in a general increase in the expression of HERVs and mediators of the immune response. In particular, SARS-CoV-2 infection is associated with increased expression of HERV-K and HERV-W, IL-1ß, IL-6, IL-17, TNF-α, MCP-1, INF-γ, TLR-3, and TLR-7, while lower levels of IL-10, IFN-α, IFN-ß, and TLR-4 were found in individuals who underwent hospitalization. Moreover, higher expression of HERV-W, IL-1ß, IL-6, IFN-α, and IFN-ß reflected the respiratory outcome of patients during hospitalization. Interestingly, a machine learning model was able to classify hospitalized vs not hospitalized patients with good accuracy based on the expression levels of HERV-K, HERV-W, IL-6, TNF-a, TLR-3, TLR-7, and the N gene of SARS-CoV-2. These latest biomarkers also correlated with parameters of coagulation and inflammation. Discussion: Overall, the present results suggest HERVs as contributing elements in COVID-19 and early genomic biomarkers to predict COVID-19 severity and disease outcome.

Myocarditis and autoimmunity.

INTRODUCTION: Autoimmune myocarditis may develop due to heterogeneous causes. Myocarditis is often caused by viral infections, but it can also be caused by systemic autoimmune diseases. Immune checkpoint inhibitors and virus vaccines induce immune activation, and they can cause the development of myocarditis, as well as several immune-related adverse events. The development of myocarditis is dependent on the genetic factors of the host, and the major histocompatibility complex (MHC) may be an important determinant of the type and severity of the disease. However, non-MHC immunoregulatory genes may also play a role in determining susceptibility. AREA COVERED: This review summarizes the current knowledge of the etiology, pathogenesis, diagnosis, and treatment of autoimmune myocarditis with a particular focus on viral infection, autoimmunity, and biomarkers of myocarditis. EXPERT OPINION: An endomyocardial biopsy may not be the gold standard for the diagnosis of myocarditis. Cardiac magnetic resonance imaging is useful in diagnosing autoimmune myocarditis. Recently identified biomarkers of inflammation and myocyte injury are promising for the diagnosis of myocarditis when measured simultaneously. Future treatments should focus on the appropriate diagnosis of the etiologic agent, as well as on the specific stage of the evolution of immune and inflammatory processes.

Analyzing the role of ACE2, AR, MX1 and TMPRSS2 genetic markers for COVID-19 severity.

BACKGROUND: The use of molecular biomarkers for COVID-19 remains unconclusive. The application of a molecular biomarker in combination with clinical ones that could help classifying aggressive patients in first steps of the disease could help clinician and sanitary system a better management of the disease. Here we characterize the role of ACE2, AR, MX1, ERG, ETV5 and TMPRSS2 for trying a better classification of COVID-19 through knowledge of the disease mechanisms. METHODS: A total of 329 blood samples were genotyped in ACE2, MX1 and TMPRSS2. RNA analyses were also performed from 258 available samples using quantitative polymerase chain reaction for genes: ERG, ETV5, AR, MX1, ACE2, and TMPRSS2. Moreover, in silico analysis variant effect predictor, ClinVar, IPA, DAVID, GTEx, STRING and miRDB database was also performed. Clinical and demographic data were recruited from all participants following WHO classification criteria. RESULTS: We confirm the use of ferritin (p < 0.001), D-dimer (p < 0.010), CRP (p < 0.001) and LDH (p < 0.001) as markers for distinguishing mild and severe cohorts. Expression studies showed that MX1 and AR are significantly higher expressed in mild vs severe patients (p < 0.05). ACE2 and TMPRSS2 are involved in the same molecular process of membrane fusion (p = 4.4 × 10-3), acting as proteases (p = 0.047). CONCLUSIONS: In addition to the key role of TMPSRSS2, we reported for the first time that higher expression levels of AR are related with a decreased risk of severe COVID-19 disease in females. Moreover, functional analysis demonstrates that ACE2, MX1 and TMPRSS2 are relevant markers in this disease.

Free Light Chains κ and λ as New Biomarkers of Selected Diseases.

Diagnostic and prognostic markers are necessary to help in patient diagnosis and the prediction of future clinical events or disease progression. As promising biomarkers of selected diseases, the free light chains (FLCs) κ and λ were considered. Measurements of FLCs are currently used in routine diagnostics of, for example, multiple myeloma, and the usefulness of FLCs as biomarkers of monoclonal gammopathies is well understood. Therefore, this review focuses on the studies concerning FLCs as new potential biomarkers of other disorders in which an inflammatory background has been observed. We performed a bibliometric review of studies indexed in MEDLINE to assess the clinical significance of FLCs. Altered levels of FLCs were observed both in diseases strongly connected with inflammation such as viral infections, tick-borne diseases or rheumatic disorders, and disorders that are moderately associated with immune system reactions, e.g., multiple sclerosis, diabetes, cardiovascular disorders and cancers. Increased concentrations of FLCs appear to be a useful prognostic marker in patients with multiple sclerosis or tick-borne encephalitis. Intensive synthesis of FLCs may also reflect the production of specific antibodies against pathogens such as SARS-CoV-2. Moreover, abnormal FLC concentrations might predict the development of diabetic kidney disease in patients with type 2 diabetes. Markedly elevated levels are also associated with increased risk of hospitalization and death in patients with cardiovascular disorders. Additionally, FLCs have been found to be increased in rheumatic diseases and have been related to disease activity. Furthermore, it has been suggested that inhibition of FLCs would reduce the progression of tumorigenesis in breast cancer or colitis-associated colon carcinogenesis. In conclusion, abnormal levels of κ and λ FLCs, as well as the ratio of κ:λ, are usually the result of disturbances in the synthesis of immunoglobulins as an effect of overactive inflammatory reactions. Therefore, it seems that κ and λ FLCs may be significant diagnostic and prognostic biomarkers of selected diseases. Moreover, the inhibition of FLCs appears to be a promising therapeutical target for the treatment of various disorders where inflammation plays an important role in the development or progression of the disease.

Neutrophil/Lymphocyte Ratio (NLR) and Lymphocyte/Monocyte Ratio (LMR) - Risk of Death Inflammatory Biomarkers in Patients with COVID-19.

Aim: The aim of our retrospective study was search for new prognostic parameters, which can help quickly and cheaply identify patients with risk for severe course of SARS-CoV-2 infection. Materials and Methods: The following peripheral blood combination biomarkers were calculated: NLR (neutrophil/lymphocytes ratio), LMR (lymphocyte/monocyte ratio), PLR (platelet/lymphocyte ratio), dNLR (neutrophils/(white blood cells - neutrophils)), NLPR (neutrophil/(lymphocyte × platelet ratio)) in 374 patients who were admitted to the Temporary Hospital no 2 of Clinical Hospital in Bialystok (Poland) with COVID-19. The patients were divided into four groups depending on the severity of the course of COVID-19 using MEWS classification. Results: The NLR and dNLR were significantly increased with the severity of COVID-19, according to MEWS score. The AUC for the assessed parameters was higher in predicting death in patients with COVID-19: NLR (0.656, p=0.0018, cut-off=6.22), dNLR (0.615, p=0.02, cut-off=3.52) and LMR (0.609, p=0.03, cut-off=2.06). Multivariate COX regression analysis showed that NLR median above 5.56 (OR: 1.050, P=0.002), LMR median below 2.23 (OR: 1.021, P=0.011), and age >75 years old (OR: 1.072, P=0.000) had a significant association with high risk of death during COVID-19. Conclusion: Our results indicate that NLR, dNLR, and LMR calculated on admission to the hospital can quickly and easy identify patients with risk of a more severe course of COVID-19. Increase NLR and decrease LMR have a significant predictive value in COVID-19 patient's mortality and might be a potential biomarker for predicting death in COVID-19 patients.

Computational identification of differentially-expressed genes as suggested novel COVID-19 biomarkers: A bioinformatics analysis of expression profiles.

COVID-19 was declared a pandemic in March 2020, and since then, it has not stopped spreading like wildfire in almost every corner of the world, despite the many efforts made to stem its spread. SARS-CoV-2 has one of the biggest genomes among RNA viruses and presents unique characteristics that differentiate it from other coronaviruses, making it even more challenging to find a cure or vaccine that is efficient enough. This work aims, using RNA sequencing (RNA-Seq) data, to evaluate whether the expression of specific human genes in the host can vary in different grades of disease severity and to determine the molecular origins of the differences in response to SARS-CoV-2 infection in different patients. In addition to quantifying gene expression, data coming from RNA-Seq allow for the discovery of new transcripts, the identification of alternative splicing events, the detection of allele-specific expression, and the detection of post-transcriptional alterations. For this reason, we performed differential expression analysis on different expression profiles of COVID-19 patients, using RNA-Seq data coming from NCBI public repository, and we obtained the lists of all differentially expressed genes (DEGs) emerging from 7 experimental conditions. We performed a Gene Set Enrichment Analysis (GSEA) on these genes to find possible correlations between DEGs and known disease phenotypes. We mainly focused on DEGs coming out from the analysis of the contrasts involving severe conditions to infer any possible relation between a worsening of the clinical picture and an over-representation of specific genes. Based on the obtained results, this study indicates a small group of genes that result up-regulated in the severe form of the disease. EXOSC5, MESD, REXO2, and TRMT2A genes are not differentially expressed or not present in the other conditions, being for that reason, good biomarkers candidates for the severe form of COVID-19 disease. The use of specific over-expressed genes, whether up-regulated or down-regulated, which have an individual role in each different condition of COVID-19 as a biomarker, can assist in early diagnosis.

The relationship between salivary Fibroblast Growth Factor-2 and cortisol reactivity and psychological outcomes prior to and during the COVID-19 pandemic.

Background: Fibroblast growth factor-2 (FGF2) is a biomarker that is associated with depression, anxiety and stress in rodents. In humans, we have previously demonstrated that salivary FGF2 increased following stress in a similar pattern to cortisol, and FGF2 (but not cortisol) reactivity predicted repetitive negative thinking, a transdiagnostic risk factor for mental illness. The current study assessed the relationship between FGF2, cortisol, and mental health before and during the COVID-19 pandemic. Methods: We employed a longitudinal correlational design using a convenience sample. We assessed whether FGF2 and cortisol reactivity following the Trier Social Stress Task (TSST) were associated with DASS-21 depression, anxiety and stress, measured at the time of the TSST in 2019-20 (n = 87; time 1), and then again in May 2020 during the first wave of COVID-19 in Sydney (n = 34 of the original sample; time 2). Results: FGF2 reactivity (but not absolute FGF2 levels) at time 1 predicted depression, anxiety, and stress across timepoints. Cortisol reactivity at time 1 was associated with stress over timepoints, and absolute cortisol levels were associated with depression across timepoints. Limitations: The sample was comprised of mostly healthy participants from a student population, and there was high attrition between timepoints. The outcomes need to be replicated in larger, more diverse, samples. Conclusions: FGF2 and cortisol may be uniquely predictive of mental health outcomes in healthy samples, potentially allowing for early identification of at-risk individuals.

Association between echocardiographic features and inflammatory biomarkers with clinical outcomes in COVID-19 patients in Saudi Arabia.

Background: Respiratory infections are one of the most common comorbidities identified in hospitalized patients. The coronavirus disease 2019 (COVID-19) pandemic greatly impacted healthcare systems, including acute cardiac services. Aim: This study aimed to describe the echocardiographic findings of patients with COVID-19 infections and their correlations with inflammatory biomarkers, disease severity, and clinical outcomes. Methods: This observational study was conducted between June 2021 and July 2022. The analysis included all patients diagnosed with COVID-19 who had transthoracic echocardiographic (TTE) scans within 72 h of admission. Results: The enrolled patients had a mean age of 55.6 ± 14.7 years, and 66.1% were male. Of the 490 enrolled patients, 203 (41.4%) were admitted to the intensive care unit (ICU). Pre-ICU TTE findings showed significantly higher incidence right ventricular dysfunction (28 [13.8%] vs. 23 [8.0%]; P = 0.04) and left ventricular (LV) regional wall motion abnormalities (55 [27.1%] vs. 29 [10.1%]; p < 0.001) in ICU patients compared to non-ICU patients. In-hospital mortality was 11 (2.2%), all deaths of ICU patients. The most sensitive predictors of ICU admission (p < 0.05): cardiac troponin I level (area under the curve [AUC] = 0.733), followed by hs-CRP (AUC = 0.620), creatine kinase-MB (AUC = 0.617), D-dimer (AUC = 0.599), and lactate dehydrogenase (AUC = 0.567). Binary logistic regression showed that reduced LV ejection fraction (LVEF), elevated pulmonary artery systolic pressure, and dilated right ventricle were echocardiographic predictors of poor outcomes (p < 0.05). Conclusion: Echocardiography is a valuable tool in assessing admitted patients with COVID-19. Lower LVEF, pulmonary hypertension, higher D-dimer, C-reactive protein, and B-type natriuretic peptide levels were predictors of poor outcomes.

Identification of potential biomarkers to predict organ morbidity in COVID-19: A repository based proteomics perspective.

SARS-CoV-2 causes substantial extrapulmonary manifestations in addition to pulmonary disease. Some of the major organs affected are cardiovascular, hematological and thrombotic, renal, neurological, and digestive systems. These types of muti-organ dysfunctions make it difficult and challenging for clinicians to manage and treat COVID-19 patients. The article focuses to identify potential protein biomarkers that can flag various organ systems affected in COVID-19. Publicly reposited high throughput proteomic data from human serum (HS), HEK293T/17 (HEK) and Vero E6 (VE) kidney cell culture were downloaded from ProteomeXchange consortium. The raw data was analyzed in Proteome Discoverer 2.4 to delineate the complete list of proteins in the three studies. These proteins were analyzed in Ingenuity Pathway Analysis (IPA) to associate them to various organ diseases. The shortlisted proteins were analyzed in MetaboAnalyst 5.0 to shortlist potential biomarker proteins. These were then assessed for disease-gene association in DisGeNET and validated by Protein-protein interactome (PPI) and functional enrichment studies (GO_BP, KEGG and Reactome pathways) in STRING. Protein profiling resulted in shortlisting 20 proteins in 7 organ systems. Of these 15 proteins showed at least 1.25-fold changes with a sensitivity and specificity of 70%. Association analysis further shortlisted 10 proteins with a potential association with 4 organ diseases. Validation studies established possible interacting networks and pathways affected, confirmingh the ability of 6 of these proteins to flag 4 different organ systems affected in COVID-19 disease. This study helps to establish a platform to seek protein signatures in different clinical phenotypes of COVID-19. The potential biomarker candidates that can flag organ systems involved are: (a) Vitamin K-dependent protein S and Antithrombin-III for hematological disorders; (b) Voltage-dependent anion-selective channel protein 1 for neurological disorders; (c) Filamin-A for cardiovascular disorder and, (d) Peptidyl-prolyl cis-trans isomerase A and Peptidyl-prolyl cis-trans isomerase FKBP1A for digestive disorders.

Identification of Key Genes Related to Immune Cells in Patients with COVID-19 Via Integrated Bioinformatics-Based Analysis.

COVID-19 has spread all over the world which poses a serious threat to social economic development and public health. Despite enormous progress has been made in the prevention and treatment of COVID-19, the specific mechanism and biomarker related to disease severity or prognosis have not been clarified yet. Our study intended to further explore the diagnostic markers of COVID-19 and their relationship with serum immunology by bioinformatics analysis. The datasets about COVID-19 were downloaded from the Gene Expression Omnibus (GEO) dataset. The differentially expressed genes (DEGs) were selected via the limma package. Then, weighted gene co-expression network analysis (WGCNA) was conducted to identify the critical module associated with the clinic status. The intersection DEGs were processed for further enrichment analysis. The final diagnostic genes for COVID-19 were selected and verified through special bioinformatics algorithms. There were significant DEGs between the normal and COVID-19 patients. These genes were mainly enriched in cell cycle, complement and coagulation cascade, extracellular matrix (ECM) receptor interaction, and the P53 signaling pathway. As much as 357 common intersected DEGs were selected in the end. These DEGs were enriched in organelle fission, mitotic cell cycle phase transition, DNA helicase activity, cell cycle, cellular senescence, and P53 signaling pathway. Our study also identified CDC25A, PDCD6, and YWAHE were potential diagnostic markers of COVID-19 with the AUC (area under curve), 0.958 (95% CI 0.920-0.988), 0.941(95% CI 0.892-0.980), and 0.929 (95% CI 0.880-0.971). Moreover, CDC25A, PDCD6, and YWAHE were correlated with plasma cells, macrophages M0, T cells CD4 memory resting, T cells CD8, dendritic cells, and NK cells. Our study discovered that CDC25A, PDCD6, and YWAHE can be used as diagnostic markers for COVID-19. Moreover, these biomarkers were also closely associated with immune cell infiltration, which plays a pivotal role in the diagnosis and progression of COVID-19.

Proteogenomic Features of the Highly Polymorphic Histidine-rich Glycoprotein Arose Late in Evolution.

Histidine-rich glycoprotein (HRG) is a liver-produced protein circulating in human serum at high concentrations of around 125 µg/ml. HRG belongs to the family of type-3 cystatins and has been implicated in a plethora of biological processes, albeit that its precise function is still not well understood. Human HRG is a highly polymorphic protein, with at least five variants with minor allele frequencies of more than 10%, variable in populations from different parts of the world. Considering these five mutations we can theoretically expect 35 = 243 possible genetic HRG variants in the population. Here, we purified HRG from serum of 44 individual donors and investigated by proteomics the occurrence of different allotypes, each being either homozygote or heterozygote for each of the five mutation sites. We observed that some mutational combinations in HRG were highly favored, while others were apparently missing, although they ought to be present based on the independent assembly of these five mutation sites. To further explore this behavior, we extracted data from the 1000 genome project (n ∼ 2500 genomes) and assessed the frequency of different HRG mutants in this larger dataset, observing a prevailing agreement with our proteomics data. From all the proteogenomic data we conclude that the five different mutation sites in HRG are not occurring independently, but several mutations at different sites are fully mutually exclusive, whereas others are highly intwined. Specific mutations do also affect HRG glycosylation. As the levels of HRG have been suggested as a protein biomarker in a variety of biological processes (e.g., aging, COVID-19 severity, severity of bacterial infections), we here conclude that the highly polymorphic nature of the protein needs to be considered in such proteomics evaluations, as these mutations may affect HRG's abundance, structure, posttranslational modifications, and function.

Immunogenic cell death-led discovery of COVID-19 biomarkers and inflammatory infiltrates.

Immunogenic cell death (ICD) serves a critical role in regulating cell death adequate to activate an adaptive immune response, and it is associated with various inflammation-related diseases. However, the specific role of ICD-related genes in COVID-19 remains unclear. We acquired COVID-19-related information from the GEO database and a total of 14 ICD-related differentially expressed genes (DEGs) were identified. These ICD-related DEGs were closely associated with inflammation and immune activity. Afterward, CASP1, CD4, and EIF2AK3 among the 14 DEGs were selected as feature genes based on LASSO, Random Forest, and SVM-RFE algorithms, which had reliable diagnostic abilities. Moreover, functional enrichment analysis indicated that these feature genes may have a potential role in COVID-19 by being involved in the regulation of immune response and metabolism. Further CIBERSORT analysis demonstrated that the variations in the immune microenvironment of COVID-19 patients may be correlated with CASP1, CD4, and EIF2AK3. Additionally, 33 drugs targeting 3 feature genes had been identified, and the ceRNA network demonstrated a complicated regulative association based on these feature genes. Our work identified that CASP1, CD4, and EIF2AK3 were diagnostic genes of COVID-19 and correlated with immune activity. This study presents a reliable diagnostic signature and offers an overview to investigate the mechanism of COVID-19.