Sequential depletion coupled to C18 sequential extraction as a rapid tool for human serum multiple profiling

Dithiothreitol-based protein equalisation in the context of multiple myeloma: Enhancing proteomic analysis and therapeutic insights

In this study, we employed the dithiothreitol-based protein equalisation technique and analytical proteomics to better understand myeloma diseases by comparing the proteomes of pellets and supernatants formed upon application of DTT on serum samples. The number of unique proteins found in pellets was 252 for healthy individuals and 223 for multiple myeloma patients. The comparison of these proteomes showed 97 dysregulated proteins. The unique proteins found for supernatants were 264 for healthy individuals and 235 for multiple myeloma patients. The comparison of these proteomes showed 87 dysregulated proteins. The analytical proteomic comparison of both groups of dysregulated proteins is translated into parallel dysregulated pathways, including chaperone-mediated autophagy and protein folding, addressing potential therapeutic interventions. Future research endeavours in personalised medicine should prioritize refining analytical proteomic methodologies using serum dithiothreitol-based protein equalisation to explore innovative therapeutic strategies. We highlight the advanced insights gained from this analytical strategy in studying multiple myeloma, emphasising its complex nature and the critical role of personalised, targeted analytical techniques in enhancing therapeutic efficacy in personalised medicine.

Prognostic model to predict the incidence of radiographic knee osteoarthritis

OBJECTIVE

Early diagnosis of knee osteoarthritis (KOA) in asymptomatic stages is essential for the timely management of patients using preventative strategies. We develop and validate a prognostic model useful for predicting the incidence of radiographic KOA (rKOA) in non-radiographic osteoarthritic subjects and stratify individuals at high risk of developing the disease.

METHODS

Subjects without radiographic signs of KOA according to the Kellgren and Lawrence (KL) classification scale (KL=0 in both knees) were enrolled in the OA initiative (OAI) cohort and the Prospective Cohort of A Coruña (PROCOAC). Prognostic models were developed to predict rKOA incidence during a 96-month follow-up period among OAI participants based on clinical variables and serum levels of the candidate protein biomarkers APOA1, APOA4, ZA2G and A2AP. The predictive capability of the biomarkers was assessed based on area under the curve (AUC), and internal validation was performed to correct for overfitting. A nomogram was plotted based on the regression parameters. Model performance was externally validated in the PROCOAC.

RESULTS

282 participants from the OAI were included in the development dataset. The model built with demographic, anthropometric and clinical data (age, sex, body mass index and WOMAC pain score) showed an AUC=0.702 for predicting rKOA incidence during the follow-up. The inclusion of ZA2G, A2AP and APOA1 data significantly improved the model's sensitivity and predictive performance (AUC=0.831). The simplest model, including only clinical covariates and ZA2G and A2AP serum levels, achieved an AUC=0.826. Both models were internally cross-validated. Predictive performance was externally validated in an independent dataset of 100 individuals from the PROCOAC (AUC=0.713).

CONCLUSION

A novel prognostic model based on common clinical variables and protein biomarkers was developed and externally validated to predict rKOA incidence over a 96-month period in individuals without any radiographic signs of disease. The resulting nomogram is a useful tool for stratifying high-risk populations and could potentially lead to personalised medicine strategies for treating OA.

Ultrasonic-assisted extraction and digestion of proteins from solid biopsies followed by peptide sequential extraction hyphenated to MALDI-based profiling holds the promise of distinguishing renal oncocytoma from chromophobe renal cell carcinoma

A novel analytical approach is proposed to discriminate between solid biopsies of chromophobe renal cell carcinoma (chRCC) and renal oncocytoma (RO). The method comprises the following steps: (i) ultrasonic extraction of proteins from solid biopsies, (ii) protein depletion with acetonitrile, (iii) ultrasonic assisted in-solution digestion using magnetic nanoparticle with immobilized trypsin, (iv) C18 tip-based preconcentration of peptides, (v) sequential extraction of the peptides with ACN, (vi) MALDI-snapshot of the extracts and (vii) investigation of the extract containing the most discriminating features using high resolution mass spectrometry. With this approach we have been able to differentially cluster renal oncocytoma and chromophobe renal cell carcinoma and identified 18 proteins specific to chromophobe and seven unique to renal oncocytoma. Chromophobes express proteins associated with ATP function (ATP5I & 5E; VATE1 & G2; ADT2), glycolysis (PGK1) and neuromedin whilst oncocytomas express ATP5H, ATPA, DEPD7 and TRIPB thyroid receptor interacting protein.

Simplifying the human serum proteome for discriminating patients with bipolar disorder of other psychiatry conditions

PURPOSE

An exploratory analysis using proteomic strategies in blood serum of patients with bipolar disorder (BD), and with other psychiatric conditions such as Schizophrenia (SCZ), can provide a better understanding of this disorder, as well as their discrimination based on their proteomic profile.

METHODS

The proteomic profile of blood serum samples obtained from patients with BD using lithium or other drugs (N=14), healthy controls, including non-family (HCNF; N=3) and family (HCF; N=9), patients with schizophrenia (SCZ; N=23), and patients using lithium for other psychiatric conditions (OD; N=4) were compared. Four methods for simplifying the serum samples proteome were evaluated for both removing the most abundant proteins and for enriching those of lower-abundance: protein depletion with acetonitrile (ACN), dithiothreitol (DTT), sequential depletion using DTT and ACN, and protein equalization using commercial ProteoMiner® kit (PM). For proteomic evaluation, 2-D DIGE and nanoLC-MS/MS analysis were employed.

RESULTS

PM method was the best strategy for removing proteins of high abundance. Through 2-D DIGE gel image comparison, 37 protein spots were found differentially abundant (p<0.05, Student's t-test), which exhibited ≥2.0-fold change of the average value of normalized spot intensities in the serum of SCZ, BD and OD patients compared to subject controls (HCF and HCNF). From these spots detected, 13 different proteins were identified: ApoA1, ApoE, ApoC3, ApoA4, Samp, SerpinA1, TTR, IgK, Alb, VTN, TR, C4A and C4B.

CONCLUSIONS

Proteomic analysis allowed the discrimination of patients with BD from patients with other mental disorders, such as SCZ. The findings in this exploratory study may also contribute for better understanding the pathophysiology of these disorders and finding potential serum biomarkers for these conditions.

A cost-effective method to get insight into the peritoneal dialysate effluent proteome

Protein depletion with acetonitrile and protein equalization with dithiothreitol have been assessed with success as proteomics tools for getting insight into the peritoneal dialysate effluent proteome. The methods proposed are cost-effective, fast and easy of handling, and they match the criteria of analytical minimalism: low sample volume and low reagent consumption. Using two-dimensional gel electrophoresis and peptide mass fingerprinting, a total of 72 unique proteins were identified. Acetonitrile depletes de PDE proteome from high-abundance proteins, such as albumin, and enriches the sample in apolipo-like proteins. Dithiothreitol equalizes the PDE proteome by diminishing the levels of albumin and enriching the extract in immunoglobulin-like proteins. The annotation per gene ontology term reveals the same biological paths being affected for patients undergoing peritoneal dialysis, namely that the largest number of proteins lost through peritoneal dialysate are extracellular proteins involved in regulation processes through binding.

SIGNIFICANCE

Renal failure is a growing problem worldwide, and particularly in Europe where the population is getting older. Up-to-date there is a focus of interest in peritoneal dialysis (PD), as it provides a better quality of life and autonomy of the patients than other renal replacement therapies such as haemodialysis. However, PD can only be used during a short period of years, as the peritoneum lost its permeability through time. Therefore to make a breakthrough in PD and consequently contribute to better healthcare system it is urgent to find a group of biomarkers of peritoneum degradation. Here we report on two cost-effective methods for protein depletion in peritoneal dialysate effluent (PDE). The use of ACN and DTT over PDE to deplete high abundant proteins or to equalize the concentration of proteins, respectively, performs well and with similar protein profiles than when the same chemicals are used in human plasma samples. ACN depletes de PDE proteome from large proteins, such as albumin, and enriches the sample in apolipoproteins. DTT equalizes the PDE proteome by diminishing the levels of large proteins such as albumin and enriching the extract in immunoglobulins. Although the number and type of proteins identified are different, the annotation per gene ontology term reveals the same biological paths being affected for patients undergoing peritoneal dialysate. Thus, the largest number of proteins lost through peritoneal dialysate belongs to the group of extracellular proteins involved in regulation processes through binding. As for the searching of biomarkers, DTT seems to be the most promising of the two methods because acts as an equalizer and it allows interrogating more proteins in the same sample.