Identification of candidate serum biomarkers of childhood-onset growth hormone deficiency using SWATH-MS and feature selection

Characterization of the plasma proteomic profile of Fabry disease: Potential sex- and clinical phenotype-specific biomarkers

Fabry disease (FD) is a X-linked rare lysosomal storage disorder caused by deficient α-galactosidase A (α-GalA) activity. Early diagnosis and the prediction of disease course are complicated by the clinical heterogeneity of FD, as well as by the frequently inconclusive biochemical and genetic test results that do not correlate with clinical course. We sought to identify potential biomarkers of FD to better understand the underlying pathophysiology and clinical phenotypes. We compared the plasma proteomes of 50 FD patients and 50 matched healthy controls using DDA and SWATH-MS. The >30 proteins that were differentially expressed between the 2 groups included proteins implicated in processes such as inflammation, heme and haemoglobin metabolism, oxidative stress, coagulation, complement cascade, glucose and lipid metabolism, and glycocalyx formation. Stratification by sex revealed that certain proteins were differentially expressed in a sex-dependent manner. Apolipoprotein A-IV was upregulated in FD patients with complications, especially those with chronic kidney disease, and apolipoprotein C-III and fetuin-A were identified as possible markers of FD with left ventricular hypertrophy. All these proteins had a greater capacity to identify the presence of complications in FD patients than lyso-GB3, with apolipoprotein A-IV standing out as being more sensitive and effective in differentiating the presence and absence of chronic kidney disease in FD patients than renal markers such as creatinine, glomerular filtration rate and microalbuminuria. Identification of these potential biomarkers can help further our understanding of the pathophysiological processes that underlie the heterogeneous clinical manifestations associated with FD.

Multiplatform tear proteomic profiling reveals novel non-invasive biomarkers for diabetic retinopathy

OBJECTIVES

To investigate a comprehensive proteomic profile of the tear fluid in patients with diabetic retinopathy (DR) and further define non-invasive biomarkers.

METHODS

A cross-sectional, multicentre study that includes 46 patients with DR, 28 patients with diabetes mellitus (DM), and 30 healthy controls (HC). Tear samples were collected with Schirmer strips. As for the discovery set, data-independent acquisition mass spectrometry was used to characterize the tear proteomic profile. Differentially expressed proteins between groups were identified, with gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analysis further developed. Classifying performance of biomarkers for distinguishing DR from DM was compared by the combination of three machine-learning algorithms. The selected biomarker panel was tested in the validation cohort using parallel reaction monitoring mass spectrometry.

RESULTS

Among 3364 proteins quantified, 235 and 88 differentially expressed proteins were identified for DR when compared to HC and DM, respectively, which were fundamentally related to retina homeostasis, inflammation and immunity, oxidative stress, angiogenesis and coagulation, metabolism, and cellular adhesion processes. The biomarker panel consisting of NAD-dependent protein deacetylase sirtuin-2 (SIR2), amine oxidase [flavin-containing] B (AOFB), and U8 snoRNA-decapping enzyme (NUD16) exhibited the best diagnostic performance in discriminating DR from DM, with AUCs of 0.933 and 0.881 in the discovery and validation set, respectively.

CONCLUSIONS

Tear protein dysregulation is comprehensively revealed to be associated with DR onset. The combination of tear SIR2, AOFB, and NUD16 can be a novel potential approach for non-invasive detection or pre-screening of DR.

CLINICAL TRIAL REGISTRATION

Chinese Clinical Trial Registry Identifier: ChiCTR2100054263. https://www.chictr.org.cn/showproj.html?proj=143177 . Date of registration: 2021/12/12.

Evaluation of a proteomic signature coupled with the kidney failure risk equation in predicting end stage kidney disease in a chronic kidney disease cohort

BACKGROUND

The early identification of patients at high-risk for end-stage renal disease (ESRD) is essential for providing optimal care and implementing targeted prevention strategies. While the Kidney Failure Risk Equation (KFRE) offers a more accurate prediction of ESRD risk compared to static eGFR-based thresholds, it does not provide insights into the patient-specific biological mechanisms that drive ESRD. This study focused on evaluating the effectiveness of KFRE in a UK-based advanced chronic kidney disease (CKD) cohort and investigating whether the integration of a proteomic signature could enhance 5-year ESRD prediction.

METHODS

Using the Salford Kidney Study biobank, a UK-based prospective cohort of over 3000 non-dialysis CKD patients, 433 patients met our inclusion criteria: a minimum of four eGFR measurements over a two-year period and a linear eGFR trajectory. Plasma samples were obtained and analysed for novel proteomic signals using SWATH-Mass-Spectrometry. The 4-variable UK-calibrated KFRE was calculated for each patient based on their baseline clinical characteristics. Boruta machine learning algorithm was used for the selection of proteins most contributing to differentiation between patient groups. Logistic regression was employed for estimation of ESRD prediction by (1) proteomic features; (2) KFRE; and (3) proteomic features alongside KFRE.

RESULTS

SWATH maps with 943 quantified proteins were generated and investigated in tandem with available clinical data to identify potential progression biomarkers. We identified a set of proteins (SPTA1, MYL6 and C6) that, when used alongside the 4-variable UK-KFRE, improved the prediction of 5-year risk of ESRD (AUC = 0.75 vs AUC = 0.70). Functional enrichment analysis revealed Rho GTPases and regulation of the actin cytoskeleton pathways to be statistically significant, inferring their role in kidney function and the pathogenesis of renal disease.

CONCLUSIONS

Proteins SPTA1, MYL6 and C6, when used alongside the 4-variable UK-KFRE achieve an improved performance when predicting a 5-year risk of ESRD. Specific pathways implicated in the pathogenesis of podocyte dysfunction were also identified, which could serve as potential therapeutic targets. The findings of our study carry implications for comprehending the involvement of the Rho family GTPases in the pathophysiology of kidney disease, advancing our understanding of the proteomic factors influencing susceptibility to renal damage.

Metabolic Characteristics and Discriminative Diagnosis of Growth Hormone Deficiency and Idiopathic Short Stature in Preadolescents and Adolescents

Growth hormone deficiency (GHD) and idiopathic short stature (ISS) are the most common types of short stature (SS), but little is known about their pathogenesis, and even less is known about the study of adolescent SS. In this study, nuclear magnetic resonance (NMR)-based metabolomic analysis combined with least absolute shrinkage and selection operator (LASSO) were performed to identify the biomarkers of different types of SS (including 94 preadolescent GHD (PAG), 61 preadolescent ISS (PAI), 43 adolescent GHD (ADG), and 19 adolescent ISS (ADI)), and the receiver operating characteristic curve (ROC) was further used to evaluate the predictive power of potential biomarkers. The results showed that fourteen, eleven, nine, and fifteen metabolites were identified as the potential biomarkers of PAG, PAI, ADG, and ADI compared with their corresponding controls, respectively. The disturbed metabolic pathways in preadolescent SS were mainly carbohydrate metabolism and lipid metabolism, while disorders of amino acid metabolism played an important role in adolescent SS. The combination of aspartate, ethanolamine, phosphocholine, and trimethylamine was screened out to identify PAI from PAG, and alanine, histidine, isobutyrate, methanol, and phosphocholine gave a high classification accuracy for ADI and ADC. The differences in metabolic characteristics between GHD and ISS in preadolescents and adolescents will contribute to the development of individualized clinical treatments in short stature.

Proteomic signature associated with chronic kidney disease (CKD) progression identified by data-independent acquisition mass spectrometry

BACKGROUND

Halting progression of chronic kidney disease (CKD) to established end stage kidney disease is a major goal of global health research. The mechanism of CKD progression involves pro-inflammatory, pro-fibrotic, and vascular pathways, but pathophysiological differentiation is currently lacking.

METHODS

Plasma samples of 414 non-dialysis CKD patients, 170 fast progressors (with ∂ eGFR-3 ml/min/1.73 m2/year or worse) and 244 stable patients (∂ eGFR of - 0.5 to + 1 ml/min/1.73 m2/year) with a broad range of kidney disease aetiologies, were obtained and interrogated for proteomic signals with SWATH-MS. We applied a machine learning approach to feature selection of proteins quantifiable in at least 20% of the samples, using the Boruta algorithm. Biological pathways enriched by these proteins were identified using ClueGo pathway analyses.

RESULTS

The resulting digitised proteomic maps inclusive of 626 proteins were investigated in tandem with available clinical data to identify biomarkers of progression. The machine learning model using Boruta Feature Selection identified 25 biomarkers as being important to progression type classification (Area Under the Curve = 0.81, Accuracy = 0.72). Our functional enrichment analysis revealed associations with the complement cascade pathway, which is relevant to CKD as the kidney is particularly vulnerable to complement overactivation. This provides further evidence to target complement inhibition as a potential approach to modulating the progression of diabetic nephropathy. Proteins involved in the ubiquitin-proteasome pathway, a crucial protein degradation system, were also found to be significantly enriched.

CONCLUSIONS

The in-depth proteomic characterisation of this large-scale CKD cohort is a step toward generating mechanism-based hypotheses that might lend themselves to future drug targeting. Candidate biomarkers will be validated in samples from selected patients in other large non-dialysis CKD cohorts using a targeted mass spectrometric analysis.

Data-independent acquisition mass spectrometry in severe rheumatic heart disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD cases

BACKGROUND

Rheumatic heart disease (RHD) remains a major source of morbidity and mortality in developing countries. A deeper insight into the pathogenetic mechanisms underlying RHD could provide opportunities for drug repurposing, guide recommendations for secondary penicillin prophylaxis, and/or inform development of near-patient diagnostics.

METHODS

We performed quantitative proteomics using Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectrometry (SWATH-MS) to screen protein expression in 215 African patients with severe RHD, and 230 controls. We applied a machine learning (ML) approach to feature selection among the 366 proteins quantifiable in at least 40% of samples, using the Boruta wrapper algorithm. The case-control differences and contribution to Area Under the Receiver Operating Curve (AUC) for each of the 56 proteins identified by the Boruta algorithm were calculated by Logistic Regression adjusted for age, sex and BMI. Biological pathways and functions enriched for proteins were identified using ClueGo pathway analyses.

RESULTS

Adiponectin, complement component C7 and fibulin-1, a component of heart valve matrix, were significantly higher in cases when compared with controls. Ficolin-3, a protein with calcium-independent lectin activity that activates the complement pathway, was lower in cases than controls. The top six biomarkers from the Boruta analyses conferred an AUC of 0.90 indicating excellent discriminatory capacity between RHD cases and controls.

CONCLUSIONS

These results support the presence of an ongoing inflammatory response in RHD, at a time when severe valve disease has developed, and distant from previous episodes of acute rheumatic fever. This biomarker signature could have potential utility in recognizing different degrees of ongoing inflammation in RHD patients, which may, in turn, be related to prognostic severity.

Quantitative Proteomic Study Unmasks Fibrinogen Pathway in Polycystic Liver Disease

(1) Background: Polycystic liver disease (PLD) is a heterogeneous group of congenital disorders characterized by bile duct dilatation and cyst development derived from cholangiocytes. Nevertheless, the cystogenesis mechanism is currently unknown and the PLD treatment is limited to liver transplantation. Novel and efficient therapeutic approaches are th6us needed. In this context, the present work has a principal aim to find novel molecular pathways, as well as new therapeutic targets, involved in the hepatic cystogenesis process. (2) Methods: Quantitative proteomics based on SWATH-MS technology were performed comparing hepatic proteomes of Wild Type and mutant/polycystic livers in a polycystic kidney disease (PKD) murine model (Pkd1cond/cond;Tam-Cre-/+). (3) Results: We identified several proteins altered in abundance, with two-fold cut-off up-regulation or down-regulation and an adjusted p-value significantly related to hepatic cystogenesis. Then, we performed enrichment and a protein-protein analysis identifying a cluster focused on hepatic fibrinogens. Finally, we validated a selection of targets by RT-qPCR, Western blotting and immunohistochemistry, finding a high correlation with quantitative proteomics data and validating the fibrinogen complex. (4) Conclusions: This work identified a novel molecular pathway in cystic liver disease, highlighting the fibrinogen complex as a possible new therapeutic target for PLD.

α-Linolenic and γ-linolenic acids exercise differential antitumor effects on HT-29 human colorectal cancer cells

α-Linolenic acid (ALA, 18:3n-3) and γ-gamma linolenic acid (GLA, 18:3n-6) are polyunsaturated fatty acids (PUFA) that improve the human health. The present study focused on testing the in vitro antitumor actions of pure ALA and GLA on the HT-29 human colorectal cancer cell line. Cell viability was checked by MTT ((3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) test, cell membrane damage by the lactate dehydrogenase assay, apoptosis was tested by both caspase-3 activity trial and transmission electron microscopy images, and protein composition was analyzed by quantitative proteomics analysis. MTT test revealed IC₅₀ values of 230 and 255 μM for ALA and GLA, respectively, at 72 h. After 24 h of incubation, both ALA and GLA induced apoptosis on HT-29 colorectal cancer cells according to the caspase-3 assay and microscopy images. SWATH/MS analysis evidenced that ALA significantly affected the mitochondrial protein import pathway and the citric acid cycle pathway, while GLA did not significantly affect any particular pathway. In summary, both ALA and GLA showed concentration-dependent inhibitory effects on HT-29 cells viability and induced cell death by apoptosis. ALA significantly affected cellular pathways, while GLA does not have specific actions on either pathway. Both n-3 and n-6 C18 PUFA are bioactive food components useful in the colorectal cancer prevention.

Δ9 -Tetrahydrocannabinolic acid alleviates collagen-induced arthritis: Role of PPARγ and CB1 receptors

BACKGROUND AND PURPOSE

Δ⁹ -Tetrahydrocannabinolic acid (Δ⁹ -THCA-A), the precursor of Δ⁹ -THC, is a non-psychotropic phytocannabinoid that shows PPARγ agonist activity. Here, we investigated the ability of Δ⁹ -THCA-A to modulate the classic cannabinoid CB₁ and CB₂ receptors and evaluated its anti-arthritis activity in vitro and in vivo.

EXPERIMENTAL APPROACH

Cannabinoid receptors binding and intrinsic activity, as well as their downstream signalling, were analysed in vitro and in silico. The anti-arthritis properties of Δ⁹ -THCA-A were studied in human chondrocytes and in the murine model of collagen-induced arthritis (CIA). Plasma disease biomarkers were identified by LC-MS/MS based on proteomic and elisa assays.

KEY RESULTS

Functional and docking analyses showed that Δ⁹ -THCA-A can act as an orthosteric CB₁ receptor agonist and also as a positive allosteric modulator in the presence of CP-55,940. Also, Δ⁹ -THCA-A seemed to be an inverse agonist for CB₂ receptors. In vivo, Δ⁹ -THCA-A reduced arthritis in CIA mice, preventing the infiltration of inflammatory cells, synovium hyperplasia, and cartilage damage. Furthermore, Δ⁹ -THCA-A inhibited expression of inflammatory and catabolic genes on knee joints. The anti-arthritic effect of Δ⁹ -THCA-A was blocked by either SR141716 or T0070907. Analysis of plasma biomarkers, and determination of cytokines and anti-collagen antibodies confirmed that Δ⁹ -THCA-A mediated its activity mainly through PPARγ and CB₁ receptor pathways.

CONCLUSION AND IMPLICATIONS

Δ⁹ -THCA-A modulates CB₁ receptors through the orthosteric and allosteric binding sites. In addition, Δ⁹ -THCA-A exerts anti-arthritis activity through CB₁ receptors and PPARγ pathways, highlighting its potential for the treatment of chronic inflammatory diseases such as rheumatoid arthritis.

[Current aspects of diagnosis and treatment of adult GH-deficiency]

Adult growth hormone (GH) deficiency (AGHD) is a condition characterized by alterations in body composition, lipid and carbohydrate metabolism, bone mineral density and poor quality of life; however, clinical presentations of AGHD are mostly non-specific. Untreated AGHD is associated with increased cardiovascular morbidity and mortality. Stimulation tests are used for the diagnosis: insulin tolerance test, glucagon stimulation test, growth-hormone releasing hormone and arginine stimulation test. Moreover, in 2017 FDA approved the use of macimorelin (oral GH secretagogue) for the diagnosis of AGHD. In childhood GH-deficiency, apolipoprotein A-IV, CFHR4 (complement factor H-related protein 4) and PBP (platelet basic protein) were identified as potential biomarkers of the disease, however, this was not investigated in AGHD. GH treatment starts from the minimal dose, which allows minimizing the adverse effects. According to published meta-analyses, AGHD treatment generally does not lead to increased risk of malignancy and recurrence of sellar neoplasms in adult patients. Published data on GH receptor polymorphism associations with treatment efficacy remains controversial. Development of long-acting GH formulations is a currect perspective for the increase of treatment compliance.

Proteomic investigation on bio-corona of Au, Ag and Fe nanoparticles for the discovery of triple negative breast cancer serum protein biomarkers

Nowadays, there are no targeted therapeutic modalities for triple negative breast cancer (TNBC). This disease is associated with poor prognosis and worst clinical outcome because of the aggressive nature of the tumor, delayed diagnosis, and non-specific symptoms in the early stages. Therefore, identification of novel specific TNBC serum biomarkers for screening and therapeutic purposes remains an urgent clinical requirement. New user-friendly and cheap methods for biomarker identification are needed, and nanotechnology offers new opportunities. When dispersed in blood, nanoparticles (NPs) are covered by a protein shell termed "protein corona" (PC). While alterations in protein patterns are challeging to detect by conventional blood analyses, PC acts as a "nano-concentrator" of serum proteins with affinity for NPs' surface. So, the characterization of PC could allow the detection of otherwise undetectable changes in protein concentration at an early stage of the disease or after chemotherapy or surgery. To explore this research idea, serum samples from 8 triple negative breast cancer (TNBC) patients and 8 patients without malignancy were allowed to interact with gold nanoparticles (AuNPs: 10.02 ± 0.91 nm), silver nanoparticles (AgNPs: 9.73 ± 1.70 nm) and magnetic nanoparticles (MNPs: (9.30 ± 0.67 nm). Here, in order to identify biomarker candidates in serum of TNBC patients, these nanomaterials were combined with electrophoretic separation (SDS-PAGE) to performed qualitative and quantitative comparisons of the serum proteomes of TNBC patients (n = 8) and healthy controls (n = 8) by liquid chromatography tandem-mass spectrometry (LC-MS/MS) analysis. The results were validated through a sequential window acquisition of all theoretical mass spectra (SWATH) analysis, performed in total serum samples (patients and controls) using this approach as a multiple reaction monitoring (MRM) analysis. SIGNIFICANCE: It is well known that several proteins presented in human serum are important biomarkers for the diagnosis or prognosis of different diseases, as triple negative breast cancer (TNBC). Determining how nanomaterials as gold nanoparticles (AuNPs: 10.02 ± 0.91 nm), silver nanoparticles (AgNPs: 9.73 ± 1.70 nm) and magnetic nanoparticles (MNPs: (9.30 ± 0.67 nm) interact with human serum will assist not only in understanding their effects on the biological system (biocompability and toxicity), but also to obtain information for developing novel nanomaterials with high specificity and selectivity towards proteins with an important biological function (prognostic and diagnostic protein biomarkers).

Proteomic Analysis in Morquio A Cells Treated with Immobilized Enzymatic Replacement Therapy on Nanostructured Lipid Systems

Morquio A syndrome, or mucopolysaccharidosis type IVA (MPS IVA), is a lysosomal storage disease due to mutations in the N-acetylgalactosamine-6-sulfatase (GALNS) gene. Systemic skeletal dysplasia and the related clinical features of MPS IVA are due to disruption of cartilage and its extracellular matrix, leading to an imbalance of growth. Enzyme replacement therapy (ERT) with recombinant human GALNS, alpha elosulfase, provides a systemic treatment. However, this therapy has a limited impact on skeletal dysplasia because the infused enzyme cannot penetrate cartilage and bone. Therefore, an alternative therapeutic approach to reach the cartilage is an unmet challenge. We have developed a new drug delivery system based on a nanostructure lipid carrier with the capacity to immobilize enzymes used for ERT and to target the lysosomes. This study aimed to assess the effect of the encapsulated enzyme in this new delivery system, using in vitro proteomic technology. We found a greater internalization of the enzyme carried by nanoparticles inside the cells and an improvement of cellular protein routes previously impaired by the disease, compared with conventional ERT. This is the first qualitative and quantitative proteomic assay that demonstrates the advantages of a new delivery system to improve the MPS IVA ERT.

A systematic review on machine learning in sellar region diseases: quality and reporting items

INTRODUCTION

Machine learning methods in sellar region diseases present a particular challenge because of the complexity and the necessity for reproducibility. This systematic review aims to compile the current literature on sellar region diseases that utilized machine learning methods and to propose a quality assessment tool and reporting checklist for future studies.

METHODS

PubMed and Web of Science were searched to identify relevant studies. The quality assessment included five categories: unmet needs, reproducibility, robustness, generalizability and clinical significance.

RESULTS

Seventeen studies were included with the diagnosis of general pituitary neoplasms, acromegaly, Cushing's disease, craniopharyngioma and growth hormone deficiency. 87.5% of the studies arbitrarily chose one or two machine learning models. One study chose ensemble models, and one study compared several models. 43.8% of studies did not provide the platform for model training, and roughly half did not offer parameters or hyperparameters. 62.5% of the studies provided a valid method to avoid over-fitting, but only five reported variations in the validation statistics. Only one study validated the algorithm in a different external database. Four studies reported how to interpret the predictors, and most studies (68.8%) suggested possible clinical applications of the developed algorithm. The workflow of a machine-learning study and the recommended reporting items were also provided based on the results.

CONCLUSIONS

Machine learning methods were used to predict diagnosis and posttreatment outcomes in sellar region diseases. Though most studies had substantial unmet need and proposed possible clinical application, replicability, robustness and generalizability were major limits in current studies.

Proteomics Study Reveals That Docosahexaenoic and Arachidonic Acids Exert Different In Vitro Anticancer Activities in Colorectal Cancer Cells

Two polyunsaturated fatty acids, docosahexaenoic acid (DHA) and arachidonic acid (ARA), as well as derivatives, such as eicosanoids, regulate different activities, affecting transcription factors and, therefore, DNA transcription, being a critical step for the functioning of fatty-acid-derived signaling. This work has attempted to determine the in vitro anticancer activities of these molecules linked to the gene transcription regulation of HT-29 colorectal cancer cells. We applied the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide test along with lactate dehydrogenase and caspase-3 assays; proteome changes were assessed by "sequential windowed acquisition of all theoretical mass spectra" quantitative proteomics, followed by pathway analysis, to determine the affected molecular mechanisms. In all assays, DHA inhibited cell proliferation of HT-29 cells to a higher extent than ARA and acted primarily by downregulating proteasome particles, while ARA presented a dramatic effect on all six DNA replication helicase particles. The results indicated that both DHA and ARA are potential chemopreventive agent candidates.