High-throughput Proteomics-Guided Biomarker Discovery of Hepatocellular Carcinoma.

Liver cancer stands as the fifth leading cause of cancer-related deaths globally. Hepatocellular carcinoma (HCC) comprises approximately 85%-90% of all primary liver malignancies. However, only 20-30% of HCC patients qualify for curative therapy, primarily due to the absence of reliable tools for early detection and prognosis of HCC. This underscores the critical need for molecular biomarkers for HCC management. Since proteins reflect disease status directly, proteomics has been utilized in biomarker developments for HCC. In particular, proteomics coupled with liquid chromatography-mass spectrometer (LC-MS) methods facilitate the process of discovering biomarker candidates for diagnosis, prognosis, and therapeutic strategies. In this work, we investigated LC-MS-based proteomics methods through recent reference reviews, with a particular focus on sample preparation and LC-MS methods appropriate for the discovery of HCC biomarkers and their clinical applications. We classified proteomics studies of HCC according to sample types, and we examined the coverage of protein biomarker candidates based on LC-MS methods in relation to study scales and goals. Comprehensively, we proposed protein biomarker candidates categorized by sample types and biomarker types for appropriate clinical use. In this review, we summarized recent LC-MS-based proteomics studies on HCC and proposed potential protein biomarkers. Our findings are expected to expand the understanding of HCC pathogenesis and enhance the efficiency of HCC diagnosis and prognosis, thereby contributing to improved patient outcomes.

BAPCP: A comprehensive and user-friendly web tool for identifying biomarkers from protein microarray technologies.

BACKGROUND AND OBJECTIVE: Proteome microarrays are one of the popular high-throughput screening methods for large-scale investigation of protein interactions in cells. These interactions can be measured on protein chips when coupled with fluorescence-labeled probes, helping indicate potential biomarkers or discover drugs. Several computational tools were developed to help analyze the protein chip results. However, existing tools fail to provide a user-friendly interface for biologists and present only one or two data analysis methods suitable for limited experimental designs, restricting the use cases. METHODS: In order to facilitate the biomarker examination using protein chips, we implemented a user-friendly and comprehensive web tool called BAPCP (Biomarker Analysis tool for Protein Chip Platforms) in this research to deal with diverse chip data distributions. RESULTS: BAPCP is well integrated with standard chip result files and includes 7 data normalization methods and 7 custom-designed quality control/differential analysis filters for biomarker extraction among experiment groups. Moreover, it can handle cost-efficient chip designs that repeat several blocks/samples within one single slide. Using experiments of the human coronavirus (HCoV) protein microarray and the E. coli proteome chip that helps study the immune response of Kawasaki disease as examples, we demonstrated that BAPCP can accelerate the time-consuming week-long manual biomarker identification process to merely 3 min. CONCLUSIONS: The developed BAPCP tool provides substantial analysis support for protein interaction studies and conforms to the necessity of expanding computer usage and exchanging information in bioscience and medicine. The web service of BAPCP is available at https://cosbi.ee.ncku.edu.tw/BAPCP/.

What's the situation with ocular inflammation? A cross-seasonal investigation of proteomic changes in ocular allergy sufferers' tears in Victoria, Australia.

Background: Ocular allergy (OA) is a localized subset of allergy characterized by ocular surface itchiness, redness and inflammation. Inflammation and eye-rubbing, due to allergy-associated itch, are common in OA sufferers and may trigger changes to the ocular surface biochemistry. The primary aim of this study is to assess the differences in the human tear proteome between OA sufferers and Healthy Controls (HCs) across peak allergy season and off-peak season in Victoria, Australia. Methods: 19 participants (14 OA sufferers, 5 HCs) aged 18-45 were recruited for this study. Participants were grouped based on allergy symptom assessment questionnaire scoring. Proteins were extracted from human tear samples and were run on an Orbitrap Mass Spectrometer. Peaks were matched to a DIA library. Data was analyzed using the software MaxQuant, Perseus and IBM SPSS. Results: 1267 proteins were identified in tear samples of OA sufferers and HCs. 23 proteins were differentially expressed between peak allergy season OA suffers vs HCs, and 21 were differentially expressed in off-peak season. Decreased proteins in OA sufferers related to cell structure regulation, inflammatory regulation and antimicrobial regulation. In both seasons, OA sufferers were shown to have increased expression of proteins relating to inflammation, immune responses and cellular development. Conclusion: Tear protein identification showed dysregulation of proteins involved in inflammation, immunity and cellular structures. Proteins relating to cellular structure may suggest a possible link between OA-associated itch and the subsequent ocular surface damage via eye-rubbing, while inflammatory and immune protein changes highlight potential diagnostic and therapeutic biomarkers of OA.

Before Translating Extracellular Vesicles into Personalized Diagnostics and Therapeutics: What We Could Do.

Extracellular vesicle (EV) research is rapidly advancing from fundamental science to translational applications in EV-based personalized therapeutics and diagnostics. Yet, fundamental questions persist regarding EV biology and mechanisms, particularly concerning the heterogeneous interactions between EVs and cells. While we have made strides in understanding virus delivery and intracellular vesicle transport, our comprehension of EV trafficking remains limited. EVs are believed to mediate intercellular communication through cargo transfer, but uncertainties persist regarding the occurrence and quantification of EV-cargo delivery within acceptor cells. This ambiguity is crucial to address, given the significant translational impact of EVs on therapeutics and diagnostics. This perspective article does not seek to provide exhaustive recommendations and guidance on EV-related studies, as these are well-articulated in position papers and statements by the International Society for Extracellular Vesicles (ISEV), including the 'Minimum Information for Studies of Extracellular Vesicles' (MISEV) 2014, MISEV2018, and the recent MISEV2023. Instead, recognizing the multilayered heterogeneity of EVs as both a challenge and an opportunity, this perspective emphasizes novel approaches to facilitate our understanding of diverse EV biology, address uncertainties, and leverage this knowledge to advance EV-based personalized diagnostics and therapeutics. Specifically, this perspective synthesizes current insights, identifies opportunities, and highlights exciting technological advancements in ultrasensitive single EV or "digital" profiling developed within the author's multidisciplinary group. These newly developed technologies address technical gaps in dissecting the molecular contents of EV subsets, contributing to the evolution of EVs as next-generation liquid biopsies for diagnostics and providing better quality control for EV-based therapeutics.

Identification of Protein Biomarkers for Differentiating Listeria monocytogenes Genetic Lineage III.

Listeria monocytogenes is the causative agent of listeriosis, a severe foodborne illness characterized by septicemia, meningitis, encephalitis, abortions, and occasional death in infants and immunocompromised individuals. L. monocytogenes is composed of four genetic lineages (I, II, III, and IV) and fourteen serotypes. The aim of the current study was to identify proteins that can serve as biomarkers for detection of genetic lineage III strains based on simple antibody-based methods. Liquid chromatography (LC) with electrospray ionization tandem mass spectrometry (ESI MS/MS) followed by bioinformatics and computational analysis were performed on three L. monocytogenes strains (NRRL B-33007, NRRL B-33014, and NRRL B-33077), which were used as reference strains for lineages I, II, and III, respectively. Results from ESI MS/MS revealed 42 unique proteins present in NRRL B-33077 and absent in NRRL B-33007 and NRRL B-33014 strains. BLAST analysis of the 42 proteins against a broader panel of >80 sequenced strains from lineages I and II revealed four proteins [TM2 domain-containing protein (NRRL B-33077_2770), DUF3916 domain-containing protein (NRRL B-33077_1897), DNA adenine methylase (NRRL B-33077_1926), and protein RhsA (NRRL B-33077_1129)] that have no homology with any sequenced strains in lineages I and II. The four genes that encode these proteins were expressed in Escherichia coli strain DE3 and purified. Polyclonal antibodies were prepared against purified recombinant proteins. ELISA using the polyclonal antibodies against 12 L. monocytogenes lineage I, II, and III isolates indicated that TM2 protein and DNA adenine methylase (Dam) detected all lineage III strains with no reaction to lineage I and II strains. In conclusion, two proteins including TM2 protein and Dam are potentially useful biomarkers for detection and differentiation of L. monocytogenes lineage III strains in clinical, environmental, and food processing facilities. Furthermore, these results validate the approach of using a combination of proteomics and bioinformatics to identify useful protein biomarkers.

Validating the ratio of insulin like growth factor binding protein 4 to sex hormone binding globulin as a prognostic predictor of preterm birth in Viet Nam: a case-cohort study.

OBJECTIVE: To validate a serum biomarker developed in the USA for preterm birth (PTB) risk stratification in Viet Nam. METHODS: Women with singleton pregnancies (n = 5000) were recruited between 19+0-23+6 weeks' gestation at Tu Du Hospital, Ho Chi Minh City. Maternal serum was collected from 19+0-22+6 weeks' gestation and participants followed to neonatal discharge. Relative insulin-like growth factor binding protein 4 (IGFBP4) and sex hormone binding globulin (SHBG) abundances were measured by mass spectrometry and their ratio compared between PTB cases and term controls. Discrimination (area under the receiver operating characteristic curve, AUC) and calibration for PTB <37 and <34 weeks' gestation were tested, with model tuning using clinical factors. Measured outcomes included all PTBs (any birth ≤37 weeks' gestation) and spontaneous PTBs (birth ≤37 weeks' gestation with clinical signs of initiation of parturition). RESULTS: Complete data were available for 4984 (99.7%) individuals. The cohort PTB rate was 6.7% (n = 335). We observed an inverse association between the IGFBP4/SHBG ratio and gestational age at birth (p = 0.017; AUC 0.60 [95% CI, 0.53-0.68]). Including previous PTB (for multiparous women) or prior miscarriage (for primiparous women) improved performance (AUC 0.65 and 0.70, respectively, for PTB <37 and <34 weeks' gestation). Optimal performance (AUC 0.74) was seen within 19-20 weeks' gestation, for BMI >21 kg/m2 and age 20-35 years. CONCLUSION: We have validated a novel serum biomarker for PTB risk stratification in a very different setting to the original study. Further research is required to determine appropriate ratio thresholds based on the prevalence of risk factors and the availability of resources and preventative therapies.

Diatomite-Based, Flexible SERS Immunosensor Platform for Rapid, Specific, and Sensitive Detection of Circulating Cancer-Specific Protein Biomarkers in Serum Using Raman Probes.

Cancer is one of the most actively researched diseases having a high mortality rate when not detected at an early stage. Thus, rapid, simultaneous, and sensitive quantification of cancer biomarkers plays an important role in early diagnosis, with patient impact to disability adjusted life years. Herein, a diatomite-based SERS flexible platform for the rapid and sensitive detection of circulating cancer-specific protein biomarkers in serum is presented. In this approach, diatomite/AgNPs strips with maximum SERS activity prepared using the layer-by-layer (LbL) technique were modified with specific antibodies, and specific antigens (HER2, CA15-3, PSA, and MUC4) were captured and detected. By using Raman probes specific to the captured antigens in serum, a SERS limit of detection (LOD) of 0.1 ng/mL was measured (calculated LOD < 0.1 ng/mL). This value is lower than the cutoff amount of cancer antigens in the person's blood. The specificity for the antigens of each antibody was calculated to be higher than 95%. As a result, an immunosensor for rapid detection of cancer biomarkers in serum with good specificity, high sensitivity, good reproducibility, and low cost has been demonstrated. Overall, we show that the prepared diatomite-based SERS substrate with a high surface-to-volume ratio is a useable platform for immunoassay tests.

A proteomic classifier panel for early screening of colorectal cancer: a case control study.

BACKGROUND: Diagnosis of colorectal cancer (CRC) during early stages can greatly improve patient outcome. Although technical advances in the field of genomics and proteomics have identified a number of candidate biomarkers for non-invasive screening and diagnosis, developing more sensitive and specific methods with improved cost-effectiveness and patient compliance has tremendous potential to help combat the disease. METHODS: We enrolled three cohorts of 479 subjects, including 226 CRC cases, 197 healthy controls, and 56 advanced precancerous lesions (APC). In the discovery cohort, we used quantitative mass spectrometry to measure the expression profile of plasma proteins and applied machine-learning to select candidate proteins. We then developed a targeted mass spectrometry assay to measure plasma concentrations of seven proteins and a logistic regression classifier to distinguish CRC from healthy subjects. The classifier was further validated using two independent cohorts. RESULTS: The seven-protein panel consisted of leucine rich alpha-2-glycoprotein 1 (LRG1), complement C9 (C9), insulin-like growth factor binding protein 2 (IGFBP2), carnosine dipeptidase 1 (CNDP1), inter-alpha-trypsin inhibitor heavy chain 3 (ITIH3), serpin family A member 1 (SERPINA1), and alpha-1-acid glycoprotein 1 (ORM1). The panel classified CRC and healthy subjects with high accuracy, since the area under curve (AUC) of the training and testing cohort reached 0.954 and 0.958. The AUC of the two independent validation cohorts was 0.905 and 0.909. In one validation cohort, the panel had an overall sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 89.9%, 81.8%, 89.2%, and 82.9%, respectively. In another blinded validation cohort, the panel classified CRC from healthy subjects with a sensitivity of 81.5%, specificity of 97.9%, and overall accuracy of 92.0%. Finally, the panel was able to detect APC with a sensitivity of 49%. CONCLUSIONS: This seven-protein classifier is a clear improvement compared to previously published blood-based protein biomarkers for detecting early-stage CRC, and is of translational potential to develop into a clinically useful assay.

Associations between genetically predicted plasma protein levels and Alzheimer's disease risk: a study using genetic prediction models.

BACKGROUND: Specific peripheral proteins have been implicated to play an important role in the development of Alzheimer's disease (AD). However, the roles of additional novel protein biomarkers in AD etiology remains elusive. The availability of large-scale AD GWAS and plasma proteomic data provide the resources needed for the identification of causally relevant circulating proteins that may serve as risk factors for AD and potential therapeutic targets. METHODS: We established and validated genetic prediction models for protein levels in plasma as instruments to investigate the associations between genetically predicted protein levels and AD risk. We studied 71,880 (proxy) cases and 383,378 (proxy) controls of European descent. RESULTS: We identified 69 proteins with genetically predicted concentrations showing associations with AD risk. The drugs almitrine and ciclopirox targeting ATP1A1 were suggested to have a potential for being repositioned for AD treatment. CONCLUSIONS: Our study provides additional insights into the underlying mechanisms of AD and potential therapeutic strategies.

Identification of FOXP1 as a favorable prognostic biomarker and tumor suppressor in intrahepatic cholangiocarcinoma.

BACKGROUND: Forkhead-box protein P1 (FOXP1) has been proposed to have both oncogenic and tumor-suppressive properties, depending on tumor heterogeneity. However, the role of FOXP1 in intrahepatic cholangiocarcinoma (ICC) has not been previously reported. METHODS: Immunohistochemistry was performed to detect FOXP1 expression in ICC and normal liver tissues. The relationship between FOXP1 levels and the clinicopathological characteristics of patients with ICC was evaluated. Finally, in vitro and in vivo experiments were conducted to examine the regulatory role of FOXP1 in ICC cells. RESULTS: FOXP1 was significantly downregulated in the ICC compared to their peritumoral tissues (p < 0.01). The positive rates of FOXP1 were significantly lower in patients with poor differentiation, lymph node metastasis, invasion into surrounding organs, and advanced stages (p < 0.05). Notably, patients with FOXP1 positivity had better outcomes (overall survival) than those with FOXP1 negativity (p < 0.05), as revealed by Kaplan-Meier survival analysis. Moreover, Cox multivariate analysis showed that negative FOXP1 expression, advanced TNM stages, invasion, and lymph node metastasis were independent prognostic risk factors in patients with ICC. Lastly, overexpression of FOXP1 inhibited the proliferation, migration, and invasion of ICC cells and promoted apoptosis, whereas knockdown of FOXP1 had the opposite role. CONCLUSION: Our findings suggest that FOXP1 may serve as a novel outcome predictor for ICC as well as a tumor suppressor that may contribute to cancer treatment.

A Magnetically Driven Tandem Chip Enables Rapid Isolation and Multiplexed Profiling of Extracellular Vesicles.

The protein phenotypes of extracellular vesicles (EVs) have emerged as promising biomarkers for cancer diagnosis and treatment monitoring. However, the technical challenges in rapid isolation and multiplexed molecular detection of EVs have limited their clinical practice. Herein, we developed a magnetically driven tandem chip to achieve streamlined rapid isolation and multiplexed profiling of surface protein biomarkers of EVs. Driven by magnetic force, the magnetic nanomixers not only act as tiny stir bars to promote mass transfer and enhance reaction efficiency of EVs, but also transport on communicating vessels of the tandem chip continuously and expedite the assay workflow. We designed cyclic surface enhancement of Raman scattering (SERS) tags to bind with target EVs and then release them by exonuclease I, eliminating steric hindrance and amplifying the SERS signal of multiple protein biomarkers on EVs. Due to the excellent assay performance, six breast cancer biomarkers were detected simultaneously on EVs using only 10 µL plasma within 1.5 h. The unweighted SUM signature offers great accuracy in discriminating breast cancer patients from healthy donors. Overall, the dynamic magnetic driving tandem chip offers a new avenue to advance the clinical application of EV-based liquid biopsy.

A computational biology approach to identify potential protein biomarkers and drug targets for sporadic amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by the loss of upper and lower motor neurons. The sporadic ALS (sALS) is a multigenic disorder and the complex mechanisms underlying its onset are still not fully delineated. Despite the recent scientific advancements, certain aspects of ALS pathogenic targets need to be yet clarified. The aim of the presented study is to identify potential genetic biomarkers and drug targets for sALS, by analysing gene expression profiles, presented in the publicly available GSE68605 dataset, of motor neurons cells obtained from sALS patients. We used different computational approaches including differential expression analysis, protein network mapping, candidate protein biomarker (CPB) identification, elucidation of the role of functional modules, and molecular docking analysis. The resultant top ten up- and downregulated genes were further used to construct protein-protein interaction network (PPIN). The PPIN analysis resulted in identifying four CPBs (namely RIOK2, AKT1, CTNNB1, and TNF) that commonly overlapped with one another in network parameters (degree, bottleneck and maximum neighbourhood component). The RIOK2 protein emerged as a potential mediator of top five functional modules that are associated with RNA binding, lipoprotein particle receptor binding in pre-ribosome, and interferon, cytokine-mediated signaling pathway. Furthermore, molecular docking analysis revealed that cyclosporine exhibited the highest binding affinity (-8.6 kJ/mol) with RIOK2, and surpassed the FDA-approved ALS drugs, such as riluzole and edaravone. This suggested that cyclosporine may serve as a promising candidate for targeting RIOK2 downregulation observed in sALS patients. In order to validate our computational results, it is suggested that in vitro and in vivo studies may be conducted in future to provide a more detailed understanding of ALS diagnosis, prognosis, and therapeutic intervention.

Proteomic-based electrochemical non-invasive biosensor for early breast cancer diagnosis.

Breast cancer is the second highest cause of cancer-related mortality in women worldwide and in the United States, accounting for around 571,000 deaths per year. Early detection of breast cancer increases treatment results and the possibility of a cure. While existing diagnostic modalities such as mammography, ultrasound, and biopsy exist, some are prohibitively expensive, uncomfortable, time-consuming, and have limited sensitivity, necessitating the development of a cost-effective, rapid, and highly sensitive approach such as an electrochemical biosensor. Our research focuses on detecting breast cancer patients using the ECM1 biomarker, which has higher expression in synthetic urine. Our study has two primary objectives: (i) Diverse ECM1 protein concentrations are measured using electrochemical impedance spectroscopy and ELISA. Establishing a standard curve for the electrochemical biosensor by calibrating ECM-1 protein levels using electrochemical impedance spectroscopy. (ii) Validation of the effectiveness of the electrochemical biosensor. This aim entails testing the unknown concentration of ECM1 in the synthetic urine to ensure the efficiency of the biosensor to detect the biomarker in the early stages. The results show that the synthetic urine solution's ECM-1 detection range ranges from 1 pg/ml to 500 ng/ml. This shows that by detecting changes in ECM-1 protein levels in patient urine, the electrochemical biosensor can consistently diagnose breast cancer in its early stages or during increasing recurrence. Our findings highlight the electrochemical biosensor's efficacy in detecting early-stage breast cancer biomarkers (ECM-1) in patient urine. Further studies will be conducted with patient samples and develop handheld hardware for patient usage.

Identification of blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects.

Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional observational studies report associations of multiple circulating proteins with PCa risk. However, the existing findings may be subject to incoherent biases of conventional epidemiologic studies. To better characterize their associations, herein, we evaluated associations of genetically predicted concentrations of plasma proteins with PCa risk. We developed comprehensive genetic prediction models for protein levels in plasma. After testing 1308 proteins in 79 194 cases and 61 112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL, 24 proteins showed significant associations with PCa risk, including 16 previously reported proteins and eight novel proteins. Of them, 14 proteins showed negative associations and 10 showed positive associations with PCa risk. For 18 of the identified proteins, potential functional somatic changes of encoding genes were detected in PCa patients in The Cancer Genome Atlas (TCGA). Genes encoding these proteins were significantly involved in cancer-related pathways. We further identified drugs targeting the identified proteins, which may serve as candidates for drug repurposing for treating PCa. In conclusion, this study identifies novel protein biomarker candidates for PCa risk, which may provide new perspectives on the etiology of PCa and improve its therapeutic strategies.

A comprehensive review of novel biomarkers in the diagnosis of intrahepatic cholestasis of pregnancy.

Intrahepatic cholestasis of pregnancy (ICP) is a disease specific to pregnancy, featuring maternal itching and elevated serum bile acid levels. It leads to a series of adverse pregnancy outcomes as well as increased fetal mortality. Routine biochemical markers fail to fulfill the tremendous clinical requirements, thereby novel effective biomarkers are urgently desired. In our review, the potential novel biomarkers for ICP diagnosis are classified into four categories and elaborated in detail. The concrete diagnostic performances (sensitivity, specificity and area under the curve) of these biomarkers are demonstrated in tables. Moreover, the relationships between some biomarkers and ICP pathogenesis are briefly expounded. Nevertheless, only a few novel biomarkers are ideal, and their clinical applicability requires more evidence from larger multicenter trials.

Biomarker Discovery for Early Diagnosis of Papillary Thyroid Carcinoma Using High-Throughput Enhanced Quantitative Plasma Proteomics.

The incidence of thyroid cancer (TC) has been increasing over the last 50 years worldwide. A higher rate of overdiagnosis in indolent thyroid lesions has resulted in unnecessary treatment. An accurate detection of TC at an early stage is highly demanded. We aim to develop an enhanced isobaric labeling-based high-throughput plasma quantitative proteomics to identify biomarkers in a discovery cohort. Selected candidates were tested by enzyme-linked immunosorbent assay (ELISA) in the training cohort and validation cohort. In total, 1063 proteins were quantified, and 129 proteins were differentially expressed between patients and healthy subjects. Serum levels of ISG15 and PLXNB2 were significantly elevated in patients with papillary thyroid cancer (PTC) or thyroid adenoma, compared to healthy subjects (p < 0.001) and patients with nodular goiter (p < 0.001). Receiver operating characteristic (ROC) analysis of combined markers (ISG15 and PLXNB2) significantly distinguished PTC from healthy control (HC) subjects. Similar differentiations were also found between thyroid adenoma and HC subjects. Notably, this combined marker could distinguish stage-I PTC from HC subjects (area under the curve (AUC) = 0.872). Our results revealed that ISG15 and PLXNB2 are independent diagnostic biomarkers for PTC and thyroid adenoma, showing a promising value for the early detection of PTC.

Comparative iTRAQ proteomics identified proteins in fresh and frozen thawed yak spermatozoa.

The changes in semen and cryodamage after the cryopreservation process negatively affect sperm function and motility. However, possible proteomic alterations of yak semen during cryopreservation have not yet been achieved. In this study, we compared proteomes of fresh and frozen thawed yak sperm using iTRAQ combined with LC-MS/MS proteome approach. Totally, 2064 proteins were quantitatively identified, including 161 in fresh sperm that showed significant differences compared to frozen thawed sperm. According to the Gene ontology (GO) enrichment analysis, differentially expressed proteins (DEPs) are predominantly associated with spermatogenesis, tricarboxylic acid cycle, ATP synthesis, and differentiation biological process. Furthermore, the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that DEPs were mainly involved in metabolic pathways related to pyruvate metabolism, carbon metabolism, glycolysis/gluconeogenesis, together with the citrate (TCA) cycle. In the analysis of the protein-protein interaction (PPI) network, 15 potential proteins (PDHB, DLAT, PDHA2, PGK1, TP5C1, etc.) that could be related to the sperm quality of the yaks were obtained. Furthermore, 6 DEPs were validated by parallel reaction monitoring (PRM), confirming that the iTRAQ data were reliable. These results indicate that cryopreservation alters the proteome of yak sperm, which is possibly related to cryodamage and fertilization ability.

Quantitative Proteomic Analysis of MCM3 in ThinPrep Samples of Patients with Cervical Preinvasive Cancer.

Triage methods for cervical cancer detection show moderate accuracy and present considerable false-negative and false-positive result rates. A complementary diagnostic parameter could help improve the accuracy of identifying patients who need treatment. A pilot study was performed using a targeted proteomics approach with opportunistic ThinPrep samples obtained from women collected at the hospital's outpatient clinic to determine the concentration levels of minichromosome maintenance-3 (MCM3) and envoplakin (EVPL) proteins. Forty samples with 'negative for intraepithelial lesion or malignancy' (NILM), 21 samples with 'atypical squamous cells of undetermined significance' (ASC-US), and 33 samples with 'low-grade squamous intraepithelial lesion and worse' (≥LSIL) were analyzed, using cytology and the patients' histology reports. Highly accurate concordance was obtained for gold-standard-confirmed samples, demonstrating that the MCM3/EVPL ratio can discriminate between non-dysplastic and dysplastic samples. On that account, we propose that MCM3 and EVPL are promising candidate protein biomarkers for population-based cervical cancer screening.

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.

Protein Biomarkers in Chronic Kidney Disease in Children-What Do We Know So Far?

Chronic kidney disease (CKD) in children is a major concern of medical care and public health as it is related to high morbidity and mortality due to progression to end-stage kidney disease (ESKD). It is essential to identify patients with a risk of developing CKD to implement therapeutic interventions. Unfortunately, conventional markers of CKD, such as serum creatinine, glomerular filtration rate (GFR) and proteinuria, have many limitations in serving as an early and specific diagnostic tool for this condition. Despite the above, they are still the most frequently utilized as we do not have better. Studies from the last decade identified multiple CKD blood and urine protein biomarkers but mostly assessed the adult population. This article outlines some recent achievements and new perspectives in finding a set of protein biomarkers that might improve our ability to prognose CKD progression in children, monitor the response to treatment, or even become a potential therapeutic target.