Top-ranked Proteins Research Articles

Machine Learning Identifies Key Proteins in Primary Sclerosing Cholangitis Progression and Links High CCL24 to Cirrhosis.

Primary sclerosing cholangitis (PSC) is a rare, progressive disease, characterized by inflammation and fibrosis of the bile ducts, lacking reliable prognostic biomarkers for disease activity. Machine learning applied to broad proteomic profiling of sera allowed for the discovery of markers of disease presence, severity, and cirrhosis and the exploration of the involvement of CCL24, a chemokine with fibro-inflammatory activity. Sera from 30 healthy controls and 45 PSC patients were profiled with proximity extension assay, quantifying the expression of 2870 proteins, and used to train an elastic net model. Proteins that contributed most to the model were tested for correlation to enhanced liver fibrosis (ELF) score and used to perform pathway analysis. Statistical modeling for the presence of cirrhosis was performed with principal component analysis (PCA), and receiver operating characteristics (ROC) curves were used to assess the useability of potential biomarkers. The model successfully predicted the presence of PSC, where the top-ranked proteins were associated with cell adhesion, immune response, and inflammation, and each had an area under receiver operator characteristic (AUROC) curve greater than 0.9 for disease presence and greater than 0.8 for ELF score. Pathway analysis showed enrichment for functions associated with PSC, overlapping with pathways enriched in patients with high levels of CCL24. Patients with cirrhosis showed higher levels of CCL24. This data-driven approach to characterize PSC and its severity highlights potential serum protein biomarkers and the importance of CCL24 in the disease, implying its therapeutic potential in PSC.

The Toll-like Receptor-2/4 Antagonist, Sparstolonin B, and Inflammatory Diseases: A Literature Mining and Network Analysis.

Sparstolonin B (SsnB) is characterized as a new toll-like receptor (TLR)-2/4 antagonist. However, the effects of SsnB on different inflammatory diseases have not been systemically reviewed. We investigated the effects of SsnB on inflammatory diseases with data mining and network analysis of literature, including frequency description, cluster analysis, association rule mining, functional enrichment, and protein-protein interaction (PPI) mining. A total of 27 experimental reports were included. The ARRIVE 2.0 guidelines were used to evaluate the quality of animal studies. Frequency analysis revealed 13 different diseases (cardio-cerebrovascular system diseases account for 23.53%), 12 pharmacological effects (anti-inflammatory effect accounts for 53.85%), and 67 therapeutic targets. The overview of investigation sequence of SsnB studies was depicted by Sankey diagram. Cluster analysis classified the therapeutic targets for SsnB into four main categories: (1) NF-κB; (2) IL-1β, IL-6, and TNF-α; (3) TLR2, TLR4, and MyD88; (4) the other targets. Moreover, the Apriori association discovered two main association pairs: (1) TNF-α, IL-1β, and IL-6 and (2) TLR2, TLR4, and MyD88 (support range 33.33-50%, confidence range 83.33-88.89%). Functional enrichment of the therapeutic targets for SsnB showed that the top enriched items in the biological process were mainly the response to lipopolysaccharide (LPS)/bacterial origin and regulation of cytokine production. Finally, the PPI network and hub gene selection by maximal clique centrality (MCC) algorithm indicated the top ranked proteins were TNF-α, IL-1β, IL-6, AKT1, PPAR-γ, TLR4, CCL2, and TLR2. These results emphasized the importance of TLR2/TLR4-MyD88-NF-κB-IL-1β/IL-6/TNF-α pathways as therapeutic targets of SsnB in inflammatory diseases.

Identification of exosome protein panels as predictive biomarkers for non-small cell lung cancer

BackgroundNon-small cell lung cancer (NSCLC) remains a leading cause of cancer-related deaths worldwide, primarily due to its propensity for metastasis. Patients diagnosed with localized primary cancer have higher survival rates than those with metastasis. Thus, it is imperative to discover biomarkers for the early detection of NSCLC and the timely prediction of tumor metastasis to improve patient outcomes.MethodsHere, we utilized an integrated approach to isolate and characterize plasma exosomes from NSCLC patients as well as healthy individuals. We then conducted proteomics analysis and parallel reaction monitoring to identify and validate the top-ranked proteins of plasma exosomes.ResultsOur study revealed that the proteome in exosomes from NSCLC patients with metastasis was distinctly different from that from healthy individuals. The former had larger diameters and lower concentrations of exosomes than the latter. Furthermore, among the 1220 identified exosomal proteins, we identified two distinct panels of biomarkers. The first panel of biomarkers (FGB, FGG, and VWF) showed potential for early NSCLC diagnosis and demonstrated a direct correlation with the survival duration of NSCLC patients. The second panel of biomarkers (CFHR5, C9, and MBL2) emerged as potential biomarkers for assessing NSCLC metastasis, of which CFHR5 alone was significantly associated with the overall survival of NSCLC patients.ConclusionsThese findings underscore the potential of plasma exosomal biomarkers for early NSCLC diagnosis and metastasis prediction. Notably, CFHR5 stands out as a promising prognostic indicator for NSCLC patients. The clinical utility of exosomal biomarkers offers the potential to enhance the management of NSCLC.

Comparative Proteomics and Genome-Wide Druggability Analyses Prioritized Promising Therapeutic Targets against Drug-Resistant Leishmania tropica

Leishmania tropica is a tropical parasite causing cutaneous leishmaniasis (CL) in humans. Leishmaniasis is a serious public health threat, affecting an estimated 350 million people in 98 countries. The global rise in antileishmanial drug resistance has triggered the need to explore novel therapeutic strategies against this parasite. In the present study, we utilized the recently available multidrug resistant L. tropica strain proteome data repository to identify alternative therapeutic drug targets based on comparative subtractive proteomic and druggability analyses. Additionally, small drug-like compounds were scanned against novel targets based on virtual screening and ADME profiling. The analysis unveiled 496 essential cellular proteins of L. tropica that were nonhomologous to the human proteome set. The druggability analyses prioritized nine parasite-specific druggable proteins essential for the parasite's basic cellular survival, growth, and virulence. These prioritized proteins were identified to have appropriate binding pockets to anchor small drug-like compounds. Among these, UDPase and PCNA were prioritized as the top-ranked druggable proteins. The pharmacophore-based virtual screening and ADME profiling predicted MolPort-000-730-162 and MolPort-020-232-354 as the top hit drug-like compounds from the Pharmit resource to inhibit L. tropica UDPase and PCNA, respectively. The alternative drug targets and drug-like molecules predicted in the current study lay the groundwork for developing novel antileishmanial therapies.

Integrated bioinformatics and statistical approaches to explore molecular biomarkers for breast cancer diagnosis, prognosis and therapies.

Integrated bioinformatics and statistical approaches are now playing the vital role in identifying potential molecular biomarkers more accurately in presence of huge number of alternatives for disease diagnosis, prognosis and therapies by reducing time and cost compared to the wet-lab based experimental procedures. Breast cancer (BC) is one of the leading causes of cancer related deaths for women worldwide. Several dry-lab and wet-lab based studies have identified different sets of molecular biomarkers for BC. But they did not compare their results to each other so much either computationally or experimentally. In this study, an attempt was made to propose a set of molecular biomarkers that might be more effective for BC diagnosis, prognosis and therapies, by using the integrated bioinformatics and statistical approaches. At first, we identified 190 differentially expressed genes (DEGs) between BC and control samples by using the statistical LIMMA approach. Then we identified 13 DEGs (AKR1C1, IRF9, OAS1, OAS3, SLCO2A1, NT5E, NQO1, ANGPT1, FN1, ATF6B, HPGD, BCL11A, and TP53INP1) as the key genes (KGs) by protein-protein interaction (PPI) network analysis. Then we investigated the pathogenetic processes of DEGs highlighting KGs by GO terms and KEGG pathway enrichment analysis. Moreover, we disclosed the transcriptional and post-transcriptional regulatory factors of KGs by their interaction network analysis with the transcription factors (TFs) and micro-RNAs. Both supervised and unsupervised learning’s including multivariate survival analysis results confirmed the strong prognostic power of the proposed KGs. Finally, we suggested KGs-guided computationally more effective seven candidate drugs (NVP-BHG712, Nilotinib, GSK2126458, YM201636, TG-02, CX-5461, AP-24534) compared to other published drugs by cross-validation with the state-of-the-art alternatives top-ranked independent receptor proteins. Thus, our findings might be played a vital role in breast cancer diagnosis, prognosis and therapies.

S913 A Network Medicine Approach to Discover Common Novel Drug Target Mechanisms Across Autoimmune Diseases

Introduction: The map of human disease biology called the human interactome (HI) is a map of experimentally determined protein-protein interactions. Understanding the topology and dynamics of patient molecular data through analyses of the HI can reveal proteins and biological processes underlying disease development and progression. Such studies have enabled discovery and reprioritization of drug targets, insights into drug-drug interactions and identification of biomarkers for patient stratification. Application of this technology to autoimmune diseases (ADs) is particularly useful. An estimated 25-35% of patients with an ADs may develop one or more additional ADs. This points to a molecular background of immune system disfunction that could be targeted therapeutically to ameliorate a range of ADs. Methods: A network biology platform was used to generate disease modules, which defined the HI network neighborhood and proteins relevant to the underlying biology of ten diseases. Each protein in the HI was then ranked based on its network-based similarity and connectivity to the disease module to generate a list of proteins with high relevance to disease and were considered candidate protein targets for therapeutic development. Top-ranked proteins in each disease were enriched for protein targets of approved therapies. Results: The pairwise separation of 10 disease modules on the HI is illustrated in Figure 1. Among the autoimmune diseases studied, ulcerative colitis and Crohn’s disease displayed the lowest network separation. Rheumatoid arthritis was closest to multiple sclerosis and psoriatic arthritis. Disease pairs with small separation on the network are expected to have higher biological similarity in terms of common symptoms, comorbidities, and treatment opportunities. This prediction is supported by our discovery of numerous candidate drug targets that were common across all the autoimmune diseases studied. In contrast, proteins that could be targeted to influence neurological conditions such as Parkinson’s disease, Alzheimer’s disease and amyotrophic lateral sclerosis displayed a minimal network relationship to those of the autoimmune diseases. Conclusion: The network relationship between disease-related proteins of seven autoimmune diseases suggested the existence of one or more molecular mechanisms underlying human autoimmunity that traverse traditional medical disease diagnoses. Future work may delineate molecular pathways and patterns that manifest in different autoimmune disease phenotypes.Figure 1.: Disease-disease matrix heatmap showing pairwise separation on the human interactome. Red indicates small separation, reflecting similar biology and treatment opportunity. Blue indicates large separation, reflecting different biology and treatment opportunity. The heatmap suggests similar candidate targets for autoimmune diseases. This was confirmed by our target discovery and cross-comparison. CD = Crohn’s disease, UC = ulcerative colitis, RA = rheumatoid arthritis, MS = multiple sclerosis, PsA = psoriatic arthritis, SLE = systemic lupus erythematosus, PsO = psoriasis, PD = Parkinson’s disease, AD = Alzheimer’s disease, ALS = amyotrophic lateral sclerosis.

A multi-omics study delineates new molecular features and therapeutic targets for esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a major histological subtype of esophageal cancer with inferior prognosis. Here, we conducted comprehensive transcriptomic, proteomic, phosphoproteomic, and metabolomic characterization of human, treatment‐naive ESCC and paired normal adjacent tissues (cohort 1, n = 24) in an effort to identify new molecular vulnerabilities for ESCC and potential therapeutic targets. Integrative analysis revealed a small group of genes that were related to the active posttranscriptional and posttranslational regulation of ESCC. By using proteomic, phosphoproteomic, and metabolomic data, networks of ESCC‐related signaling and metabolic pathways that were closely linked to cancer etiology were unraveled. Notably, integrative analysis of proteomic and phosphoproteomic data pinpointed that certain pathways involved in RNA transcription, processing, and metabolism were stimulated in ESCC. Importantly, proteins with close linkage to ESCC prognosis were identified. By enrolling an ESCC patient cohort 2 (n = 41), three top‐ranked prognostic proteins X‐prolyl aminopeptidase 3 (XPNPEP3), bromodomain PHD finger transcription factor (BPTF), and fibrillarin (FBL) were verified to have increased expression in ESCC. Among these prognostic proteins, only FBL, a well‐known nucleolar methyltransferase, was essential for ESCC cell growth in vitro and in vivo. Furthermore, a validation study using an ESCC patient cohort 3 (n = 100) demonstrated that high FBL expression predicted unfavorable patient survival. Finally, common cancer/testis antigens and established cancer drivers and kinases, all of which could direct therapeutic decisions, were characterized. Collectively, our multi‐omics analyses delineated new molecular features associated with ESCC pathobiology involving epigenetic, posttranscriptional, posttranslational, and metabolic characteristics, and unveiled new molecular vulnerabilities with therapeutic potential for ESCC.

Network topology analysis of essential genes interactome of Helicobacter pylori to explore novel therapeutic targets

The Helicobacter pylori chronic colonization produces a wide range of gastric diseases in the gastric mucosa by abetting inflammation. Amidst coevolution and reorganization of its metabolism with humans, it has become difficult still imperative to understand and prevent its growth. This study focus to explore functional insights into identification of hub proteins/genes by aggregating the behavior of genes connected in a protein-protein interaction (PPI) network. We have constructed a PPI network of 123 essential genes along with 1213 interactions in H. pylori 26695. The degree and other centrality measures analysis assist in identifying the important hub nodes, which are top-ranked proteins. A total of nine proteins (recA, guaA, dnaK, rpsB, rplQ, rpmA, rpmC, rpmF, and rpsE) were obtained with high degree (k), betweenness centrality (BC) value. Gene ontology analysis reveals 8, 5 and 3 GO terms correspond to biological processes, cellular components and molecular function respectively. Gene complexes of hypothetical proteins (HPs) were related to aminoacyl-tRNA biosynthesis, biosynthesis of secondary metabolites, bacterial secretion system and protein export. The MCODE analysis revealed that protein from module M1, M3 and M6 include the proteins which have highest degree and BC values. It is noteworthy to mention that the bifunctional GMP synthase/glutamine amidotransferase protein (guaA), molecular chaperon (dnaK), recombinase A (recA) constitute as hub proteins. As a result, these genes are considered as network hub nodes that might be used as therapeutic targets. Our analysis affords a detailed understanding of the molecular process and pathways regulated by the essential genes in H. pylori 26695.

Reverse vaccinology approach for the identifications of potential vaccine candidates against Salmonella

Salmonella is a leading cause of foodborne pathogen which causes intestinal and systemic diseases across the world. Vaccination is the most effective protection against Salmonella, but the identification and design of an effective broad-spectrum vaccine is still a great challenge, because of the multi-serotypes of Salmonella. Reverse vaccinology is a new tool to discovery and design vaccine antigens combining human immunology, structural biology and computational biology with microbial genomics. In this study, reverse vaccinology, an in-silico approach was established to screen appropriate immunogen targets by calculating the immunogenicity score of 583 non-redundant outer membrane and secreted proteins of Salmonella. Herein among 100 proteins identified with top-ranked scores, 15 representative antigens were selected randomly. Applying the sequence conservation test, four proteins (FliK, BcsZ, FhuA and FepA) remained as potential vaccine candidates for in vivo evaluation of immunogenicity and immunoprotection. All four candidates were capable to trigger the immune response and stimulate the production of antiserum in mice. Furthermore, top-ranked proteins including FliK and BcsZ provided wide antigenic coverage among the multi-serotype of Salmonella. The S. Typhimurium LT2 challenge model used in mice immunized with FliK and BcsZ showed a high relative percentage survival (RPS) of 52.74 % and 64.71 % respectively. In conclusion, this study constructed an in-silico pipeline able to successfully pre-screen the vaccine targets characterized by high immunogenicity and protective immunity. We show that reverse vaccinology allowed screening of appropriate broad-spectrum vaccines for Salmonella.

Proteomic signatures predict preeclampsia in individual cohorts but not across cohorts – implications for clinical biomarker studies

Background Early identification of pregnant women at risk for preeclampsia (PE) is important, as it will enable targeted interventions ahead of clinical manifestations. The quantitative analyses of plasma proteins feature prominently among molecular approaches used for risk prediction. However, derivation of protein signatures of sufficient predictive power has been challenging. The recent availability of platforms simultaneously assessing over 1000 plasma proteins offers broad examinations of the plasma proteome, which may enable the extraction of proteomic signatures with improved prognostic performance in prenatal care. Objective The primary aim of this study was to examine the generalizability of proteomic signatures predictive of PE in two cohorts of pregnant women whose plasma proteome was interrogated with the same highly multiplexed platform. Establishing generalizability, or lack thereof, is critical to devise strategies facilitating the development of clinically useful predictive tests. A second aim was to examine the generalizability of protein signatures predictive of gestational age (GA) in uncomplicated pregnancies in the same cohorts to contrast physiological and pathological pregnancy outcomes. Study design Serial blood samples were collected during the first, second, and third trimesters in 18 women who developed PE and 18 women with uncomplicated pregnancies (Stanford cohort). The second cohort (Detroit), used for comparative analysis, consisted of 76 women with PE and 90 women with uncomplicated pregnancies. Multivariate analyses were applied to infer predictive and cohort-specific proteomic models, which were then tested in the alternate cohort. Gene ontology (GO) analysis was performed to identify biological processes that were over-represented among top-ranked proteins associated with PE. Results The model derived in the Stanford cohort was highly significant (p = 3.9E–15) and predictive (AUC = 0.96), but failed validation in the Detroit cohort (p = 9.7E–01, AUC = 0.50). Similarly, the model derived in the Detroit cohort was highly significant (p = 1.0E–21, AUC = 0.73), but failed validation in the Stanford cohort (p = 7.3E–02, AUC = 0.60). By contrast, proteomic models predicting GA were readily validated across the Stanford (p = 1.1E–454, R = 0.92) and Detroit cohorts (p = 1.1.E–92, R = 0.92) indicating that the proteomic assay performed well enough to infer a generalizable model across studied cohorts, which makes it less likely that technical aspects of the assay, including batch effects, accounted for observed differences. Conclusions Results point to a broader issue relevant for proteomic and other omic discovery studies in patient cohorts suffering from a clinical syndrome, such as PE, driven by heterogeneous pathophysiologies. While novel technologies including highly multiplex proteomic arrays and adapted computational algorithms allow for novel discoveries for a particular study cohort, they may not readily generalize across cohorts. A likely reason is that the prevalence of pathophysiologic processes leading up to the “same” clinical syndrome can be distributed differently in different and smaller-sized cohorts. Signatures derived in individual cohorts may simply capture different facets of the spectrum of pathophysiologic processes driving a syndrome. Our findings have important implications for the design of omic studies of a syndrome like PE. They highlight the need for performing such studies in diverse and well-phenotyped patient populations that are large enough to characterize subsets of patients with shared pathophysiologies to then derive subset-specific signatures of sufficient predictive power.

89976 ASSESSING PROTEIN BIOMARKERS ROLE IN CVD RISK PREDICTION IN PERSONS LIVING WITH HIV (PWH)

ABSTRACT IMPACT: Our findings could potentially identify CVD at-risk persons living with HIV who might benefit from aggressive risk-reduction. OBJECTIVES/GOALS: PWH have higher rates of CVD than the general population yet CVD risk prediction models rely on traditional risk factors and fail to capture the heterogeneity of CVD risk in PWH. Here we identify protein biomarkers that are able to discriminate between CVD cases and controls in PWH, and we assess their added benefit beyond traditional risk factors. METHODS/STUDY POPULATION: We analyzed 459 baseline protein expression levels from five OLINK panels in a matched CVD (MI, coronary revascularization, stroke, CVD death) case-control study with 390 PWH from INSIGHT trials (131 cases, 259 controls). We formed 200 datasets via bootstrap. For each bootstrap set, a two-component partial least squares discriminant model (PLSDA) was fit. The importance of each variable in the discrimination of cases and controls in the PLSDA projection was assessed by the variable importance in projection (VIP) score. Proteins with average VIP scores > 1 were used in penalized logistic regression models with elastic net penalty, and proteins were ranked based on the number of times the protein had a nonzero coefficient. Proteins in the top 25th percentile were considered to have high discrimination. RESULTS/ANTICIPATED RESULTS: Participants had mean age 47 years, 13% were females, 4.9% had CVD at baseline and 69% were on ART at baseline. Eight proteins including the hepatocyte growth factor and interleukin-6 were identified as able to distinguish between CVD cases and controls within PWH. A protein score (PS) of the top-ranked proteins was developed using the bootstrap (for weights) and the entire data. The PS was found to be predictive of CVD independent of established CVD and HIV factors (Odds ratio: 2.17 CI: 1.58-2.99). A model with the PS and traditional risk factors had a 5.9% improvement in AUC over the baseline model (AUC=0.731 vs 0.69), which is an increase in model predictive power of 18%. Individuals with a PS above the median score were 3.1 (CI: 1.83- 5.41) times more likely to develop CVD than those with a protein score below the median score. DISCUSSION/SIGNIFICANCE OF FINDINGS: A protein score developed improved discrimination of PWH with CVD and those without, and helped identify PWH with high risk for developing CVD. If validated, this score and/or the individual proteins could be used in addition with established factors to identify CVD at-risk individuals who might benefit from aggressive risk-reduction.

Neural network based prediction of less side effect causing cancer drug targets in the network of MAPK pathways

Computational side-effect prediction tools assist in rational drug design to decrease the late-stage failure of the drugs. Irrational selection of cancer drug targets in the deregulated MAPK pathways causes side effects. Network centralities and biological features - Degree, Radiality, Eccentricity, Closeness, Bridging, Stress, Pagerank centralities, essentiality, pathway-specific proteins, disease-causing proteins, protein domains are exploited quantitatively. We train an artificial neural network (ANN) with 15 selected features for the binary classification of side effects causing and less side-effect causing drug targets among the non-targeted proteins. Top ranked proteins among the Degree, Eccentricity, betweenness centralities, possessing GO-based molecular function, involved in more than one Biocarta pathways, domain content are prone to cause a number of side effects than other centralities and functional features. We predicted the following 15 less side effect causing cancer drug targets - Shc, Rap 1a, Mos, Tpl-2, PAC1, 4EBP1, GAB1, LAD, MEF2, ZAK, GADD45, TAB2, TAB1, ELK1 and SRF.

Neural network based prediction of less side effect causing cancer drug targets in the network of MAPK pathways

Computational side-effect prediction tools assist in rational drug design to decrease the late-stage failure of the drugs. Irrational selection of cancer drug targets in the deregulated MAPK pathways causes side effects. Network centralities and biological features - Degree, Radiality, Eccentricity, Closeness, Bridging, Stress, Pagerank centralities, essentiality, pathway-specific proteins, disease-causing proteins, protein domains are exploited quantitatively. We train an artificial neural network (ANN) with 15 selected features for the binary classification of side effects causing and less side-effect causing drug targets among the non-targeted proteins. Top ranked proteins among the Degree, Eccentricity, betweenness centralities, possessing GO-based molecular function, involved in more than one Biocarta pathways, domain content are prone to cause a number of side effects than other centralities and functional features. We predicted the following 15 less side effect causing cancer drug targets - Shc, Rap 1a, Mos, Tpl-2, PAC1, 4EBP1, GAB1, LAD, MEF2, ZAK, GADD45, TAB2, TAB1, ELK1 and SRF.

Quantitative proteomics identifies a plasma multi-protein model for detection of hepatocellular carcinoma

More efficient biomarkers are needed to facilitate the early detection of hepatocellular carcinoma (HCC). We aimed to identify candidate biomarkers for HCC detection by proteomic analysis. First, we performed a global proteomic analysis of 10 paired HCC and non-tumor tissues. Then, we validated the top-ranked proteins by targeted proteomic analyses in another tissue cohort. At last, we used enzyme-linked immunosorbent assays to validate the candidate biomarkers in multiple serum cohorts including HCC cases (HCCs), cirrhosis cases (LCs), and normal controls (NCs). We identified and validated 33 up-regulated proteins in HCC tissues. Among them, eight secretory or membrane proteins were further evaluated in serum, revealing that aldo–keto reductase family 1 member B10 (AKR1B10) and cathepsin A (CTSA) can distinguish HCCs from LCs and NCs. The area under the curves (AUCs) were 0.891 and 0.894 for AKR1B10 and CTSA, respectively, greater than that of alpha-fetoprotein (AFP; 0.831). Notably, combining the three proteins reached an AUC of 0.969, which outperformed AFP alone (P < 0.05). Furthermore, the serum AKR1B10 levels dramatically decreased after surgery. AKR1B10 and CTSA are potential serum biomarkers for HCC detection. The combination of AKR1B10, CTSA, and AFP may improve the HCC diagnostic efficacy.

Revisiting the secretion mechanism(s) of macrophage migration inhibitory factor-welcome to the "UPS club".

Revisiting the secretion mechanism(s) of macrophage migration inhibitory factor-welcome to the "UPS club".

A Plasma Protein Biomarker Strategy for Detection of Small Intestinal Neuroendocrine Tumors

Background: Small intestinal neuroendocrine tumors (SI-NETs) are difficult to diagnose in the early stage of disease. Current blood biomarkers such as chromogranin A (CgA) and 5-hydroxyindolacetic acid have low sensitivity (SEN) and specificity (SPE). This is a first preplanned interim analysis (Nordic non-interventional, prospective, exploratory, EXPLAIN study [NCT02630654]). Its objective is to investigate if a plasma protein multi-biomarker strategy can improve diagnostic accuracy (ACC) in SI-NETs. Methods: At the time of diagnosis, before any disease-specific treatment was initiated, blood was collected from patients with advanced SI-NETs and 92 putative cancer-related plasma proteins from 135 patients were analyzed and compared with the results of age- and sex-matched controls (n = 143), using multiplex proximity extension assay and machine learning techniques. Results: Using a random forest model including 12 top ranked plasma proteins in patients with SI-NETs, the multi-biomarker strategy showed SEN and SPE of 89 and 91%, respectively, with negative predictive value (NPV) and positive predictive value (PPV) of 90 and 91%, respectively, to identify patients with regional or metastatic disease with an area under the receiver operator characteristic curve (AUROC) of 99%. In 30 patients with normal CgA concentrations, the model provided a diagnostic SPE of 98%, SEN of 56%, and NPV 90%, PPV of 90%, and AUROC 97%, regardless of proton pump inhibitor intake. Conclusion: This interim analysis demonstrates that a multi-biomarker/machine learning strategy improves diagnostic ACC of patients with SI-NET at the time of diagnosis, especially in patients with normal CgA levels. The results indicate that this multi-biomarker strategy can be useful for early detection of SI-NETs at presentation and conceivably detect recurrence after radical primary resection.

Exploring Diseases/Traits and Blood Proteins Causally Related to Expression of ACE2, the Putative Receptor of SARS-CoV-2: A Mendelian Randomization Analysis Highlights Tentative Relevance of Diabetes-Related Traits.

COVID-19 has become a major public health problem. There is good evidence that ACE2 is a receptor for SARS-CoV-2, and high expression of ACE2 may increase susceptibility to infection. We aimed to explore risk factors affecting susceptibility to infection and prioritize drug repositioning candidates, based on Mendelian randomization (MR) studies on ACE2 lung expression. We conducted a phenome-wide MR study to prioritize diseases/traits and blood proteins causally linked to ACE2 lung expression in GTEx. We also explored drug candidates whose targets overlapped with the top-ranked proteins in MR, as these drugs may alter ACE2 expression and may be clinically relevant. The most consistent finding was tentative evidence of an association between diabetes-related traits and increased ACE2 expression. Based on one of the largest genome-wide association studies on type 2 diabetes mellitus (T2DM) to date (N = 898,130), T2DM was causally linked to raised ACE2 expression (P = 2.91E-03; MR-IVW). Significant associations (at nominal level; P < 0.05) with ACE2 expression were observed across multiple diabetes data sets and analytic methods for T1DM, T2DM, and related traits including early start of insulin. Other diseases/traits having nominal significant associations with increased expression included inflammatory bowel disease, (estrogen receptor-positive) breast cancer, lung cancer, asthma, smoking, and elevated alanine aminotransferase. We also identified drugs that may target the top-ranked proteins in MR, such as fostamatinib and zinc. Our analysis suggested that diabetes and related traits may increase ACE2 expression, which may influence susceptibility to infection (or more severe infection). However, none of these findings withstood rigorous multiple testing corrections (at false discovery rate <0.05). Proteome-wide MR analyses might help uncover mechanisms underlying ACE2 expression and guide drug repositioning. Further studies are required to verify our findings.

Dynamic Proteome Profiling of Protein Fractional and Molar Synthesis Rates in Human Muscle in vivo

This study aimed to develop a method for measuring the abundance and synthesis rate of human muscle proteins in molar rather than fractional (%/d) terms. Losses in proteostasis are implicated in ageing and chronic diseases but research is hindered by a lack of methods for studying protein dynamics in humans. Protein‐specific fractional synthesis rate (FSR) can be measured by stable isotope labelling and peptide mass spectrometry (MS). However, FSR is a measurement of an unknown whole, i.e. protein abundance. If protein abundance changes or is different between conditions, the interpretation of FSR data may be confounded.Three physically active males (21 ± 1 years; height 178 ± 1 cm; weight 75 ± 5 kg) gave their informed consent to the ethically approved procedures. Newly synthesised proteins were labelled by oral consumption of deuterium oxide (50 ml of 99.8 atom %) taken four times per day during days 1–5, and two times per day during days 6–12. Samples of blood and vastus lateralis were collected after an overnight fast on days 0, 4, 8 and 12. Precursor enrichment and incorporation of deuterium oxide into muscle proteins were determined by liquid chromatography tandem MS. Protein abundances were normalised to 30 fmol yeast alcohol dehydrogenase added during sample preparation. FSR (%/d) was calculated from time‐dependent changes in peptide mass isotopomer abundances. FSR and abundance data (fmol/ μg protein) were combined to calculate molar synthesis rates (MSR; fmol/ μg protein/ d).Abundance data were collected for 1,368 proteins that had at least 1 unique peptide. FSR data were calculated from 3,944 peptides representing 940 proteins (average 3 peptides per protein, range 1 – 53 peptides per protein). In total, 769 proteins had abundance and FSR data in two or more participants. The median (M), lower‐(Q1) and upper‐quartile (Q3) values for protein FSR (%/d) were M = 4.39, Q1 = 0.96, Q3 = 7.84. Alpha skeletal muscle actin had the lowest FSR (0.28 ± 0.1 %/d) whereas the highest recorded FSR was 54.43 ± 8.9 %/d (nuclear protein localization protein 4 homolog). tRNA ligases were common amongst top‐ranked proteins by FSR but were ranked amongst the lowest in abundance e.g. Leucine‐‐tRNA ligase was the lowest abundance (5 amol/ μg protein) protein detected. The most abundant protein was myoglobin (1.52 pmol/ μg protein). Average MSR (fmol/ μg protein/ d) was M = 0.05 (Q1 = 0.017, Q3 = 0.13). Metabolic enzymes were amongst the top‐ranked proteins by MSR (range 1–10 fmol/ μg protein/ d) alongside cullin‐associated NEDD8‐dissociated protein 2 (0.9 ± 0.2 fmol/ μg protein/ d) and antioxidant enzymes, including mitochondrial superoxide dismutase [Mn], peroxiredoxin 3, peroxiredoxin 2 and glutathione S‐transferase‐P (each ~0.5 fmol/ μg protein/ d).MSR proteomic data offers new insight to the allocation of cellular resources. Future applications investigating muscle adaptation or differences between health and disease may uncover unexpected insight regarding which proteins are most synthesised and highlight new avenues of research in human muscle physiology.

In Silico Target Identification and Molecular Docking Studies of Natural Cytotoxic Compound Borivilianoside H

Background: Borivilianoside H is a naturally occurring anti-cancer compound with known cytotoxicity against human colorectal cancer cell line (HCT-116) and human adenocarcinoma cell line (HT-29). The present study describes the pharmacophore modelling, molecular docking, and molecular dynamics simulation approaches to predict the target proteins of borivilianoside H along with its binding affinity to the selected proteins. Methods: A 3-dimensional structure of borivilianoside H was constructed using Avogadro from its 2-D coordinates retrieved from the Pubchem Compound database. Target proteins associated with cancers were identified based on the 95% normalized fit score of PharmMapper. The crystal structures of the targets were retrieved from Protein Data Bank and molecular docking was performed with Autodock Vina 1.1.2. MD simulations were carried out via Google Cloud Platform. ADMET characteristics for borivilianoside H were determined using admetSAR web server. Results: Among the selected 7 top-ranked target proteins, fibroblast activation protein (FAP) exhibited the highest binding affinity followed by serum albumin (ALB), bone morphogenetic protein 2 (BMP2) and kinesin-like protein 11 (KIF11). However, the best fit was found with KIF11, where both the steroidal and oligosaccharide moieties of borivilianoside H were involved in interacting with the protein cavity. KIF11 was thus found to be the most suitable target for the anti-cancer effect of borivilianoside. ADMET analysis revealed its suitability as an intravenous drug. Conclusions: The targets predicted using this approach will serve as leads for the possible use of borivilianoside H, one of the active ingredients of Chlorophytum borivilianum as an anti-cancer drug.

DOP31 Serum protein markers for early and differential IBD diagnosis validated by machine learning approaches

Abstract Background The inflammatory bowel diseases (IBD), Crohn’s disease (CD) and ulcerative colitis (UC) are chronic inflammatory conditions with a polygenic and multifactorial pathogenesis. Intensified treatment early in the disease course of IBD results in better outcomes. This is, however, challenged by the diagnostic delay faced in IBD, and especially in CD. Therefore, markers supporting early and differential diagnosis are needed. In this study, we aimed to discriminate IBD patients from non-IBD controls, and CD from UC patients, using serum protein profiles combined with an IBD polygenic risk score. Methods Patients naïve for immunosuppressives and biologicals, and without previous IBD-related surgery were prospectively included within 3 months after diagnosis, across three Belgian IBD referral centres (PANTHER study). We collected serum from 127 patients (88 CD, 39 UC) and 66 age- and gender-matched non-IBD controls. Relative serum levels of 576 unique proteins were quantified (OLINK). Proteins were ranked according to (1) adjusted (adj.) p values obtained from differential expression analysis; (2) importance scores from machine-learning feature-selection algorithms (univariate feature selection, logistic regression with L2 penalty and Random Forest). For all individuals, a weighted IBD polygenic risk score (PRS) was calculated (PRSice 2.0) for the 242 known IBD risk loci. Receiver operating characteristics (ROC) and area under the curve (AUC) analysis were performed to measure the performance of top-ranked proteins and the IBD PRS (R package ROCR). Results Following statistical analysis, 243 serum proteins were found to be differentially expressed (adj. p &amp;lt; 0.05) between IBD patients and controls. Three top-ranked markers were also identified as top 10 ranked proteins by all feature-selection algorithms, and resulted in a significant AUC of 93% (95% CI: 89–97%) to distinguish IBD from controls. While adding the IBD PRS did not further contribute (AUC 93% [95% CI: 89–97%]), the top-ranked protein on its own had a strong discriminative power with an AUC of 87% (95% CI: 82–92%). When comparing UC and CD, we found 15 differentially expressed proteins. Two proteins ranked within the top 10 across all feature-selection algorithms. This two-marker panel could discriminate UC from CD with an accuracy of 88% (95% CI: 82–96%). Adding the IBD PRS did not further improve the prediction model (AUC=88% [95% CI: 81–96%]). Conclusion Machine learning approaches validated top differentially expressed serum proteins with diagnostic potential in IBD. We identified a three-marker panel classifying IBD patients and non-IBD controls, and a two-marker panel discriminating UC from CD.