Novel methods for proteomics analysis of formalin-fixed, paraffin-embedded tissues (FFPE), and their application for biomarker discovery

A wealth of information on proteins involved in many aspects of disease is encased within formalin-fixed paraffin-embedded (FFPE) tissue repositories stored in hospitals worldwide. However, FFPE protein extracts, as described to date, often exhibit a low pattern complexity, and a poor suitability for downstream gel-based proteomic techniques. Thus, an optimised method for extraction of full-length proteins from FFPE tissues was developed. The results obtained analysing FFPE muscle, liver, and thyroid extracts, by GeLC-MS/MS, western immunoblotting, protein arrays, and ELISA, are presented and discussed. Moreover, 2D-PAGE-MS and 2D-DIGE-MS analyses of proteins extracted from fixed skeletal muscle and liver tissues are reported. Finally, the application of 2D-DIGE-MS and GeLC-MS/MS for differential proteomic investigation of FFPE diseased samples was pursued. First, a proteomic comparison between sheep pathological liver samples was carried out, and several stress biomarkers were detected. Subsequently, a thorough biomarker discovery study was conducted, analysing human lung neuroendocrine cancer tissues. GeLC-MS/MS analysis of 3 typical carcinoid and 3 small cell lung carcinoma cases led to the identification of over 400 unique proteins per disease class. According to statistical analysis, a panel of over 30 differentially expressed putative biomarkers is presented. In addition, also 2D-DIGE-MS data clearly support biomarkers identification

Critical comparison of sample preparation strategies for shotgun proteomic analysis of formalin-fixed, paraffin-embedded samples

Introduction and objectives. The growing field of formalin-fixed paraffin-embedded (FFPE) tissue proteomics holds promise for improving translational research. Worldwide archival tissue banks hold a significant number and variety of tissue samples, as well as a wealth of retrospective information regarding diagnosis, prognosis, and response to therapy. This makes them an important resource for protein biomarker discovery and validation. Direct tissue trypsinization (DT) and protein extraction followed by in solution digestion (ISD) or filter-aided sample preparation (FASP) are the most common workflows for shotgun LC-MS/MS analysis of FFPE samples, but a critical comparison of the different methods is currently lacking. Methods DT was preceded by homogenization in ammonium bicarbonate, while ISD and FASP comprised protein extraction in SDS based-buffer, followed by SDS depletion with Detergent Removal Spin Columns and Microcon Ultracel YM-30 filtration devices, respectively. The three workflows were applied to consecutive tissue sections cut from an FFPE liver tissue block, and peptide mixtures were finally analyzed according to a label-free quantitative MS approach. Data were evaluated in terms of method reproducibility and protein/peptide distribution according to localization, MW, pI and hydrophobicity. Results and Discussion. DT showed lower reproducibility, good preservation of high-MW proteins, a general bias towards hydrophilic and acidic proteins, much lower keratin contamination, as well as higher abundance of non tryptic peptides. Conversely, FASP and ISD proteomes were depleted in high-MW proteins and enriched in hydrophobic and membrane proteins; FASP provided higher identification yields, while ISD exhibited higher reproducibility. Conclusion. These results highlight that diverse sample preparation strategies provide significantly different proteomic information, and present typical biases that should be taken into account when dealing with FFPE samples. When a sufficient amount of tissue is available, the complementary use of different methods is suggested to increase proteome coverage and depth.2014-10-06Madrid, Spain13th Human Proteome Organization World Congres

The impact of sequence database choice on metaproteomic results in gut microbiota studies

Background: Elucidating the role of gut microbiota in physiological and pathological processes has recently emerged as a key research aim in life sciences. In this respect, metaproteomics, the study of the whole protein complement of a microbial community, can provide a unique contribution by revealing which functions are actually being expressed by specific microbial taxa. However, its wide application to gut microbiota research has been hindered by challenges in data analysis, especially related to the choice of the proper sequence databases for protein identification. Results: Here, we present a systematic investigation of variables concerning database construction and annotation and evaluate their impact on human and mouse gut metaproteomic results. We found that both publicly available and experimental metagenomic databases lead to the identification of unique peptide assortments, suggesting parallel database searches as a mean to gain more complete information. In particular, the contribution of experimental metagenomic databases was revealed to be mandatory when dealing with mouse samples. Moreover, the use of a "merged" database, containing all metagenomic sequences from the population under study, was found to be generally preferable over the use of sample-matched databases. We also observed that taxonomic and functional results are strongly database-dependent, in particular when analyzing the mouse gut microbiota. As a striking example, the Firmicutes/Bacteroidetes ratio varied up to tenfold depending on the database used. Finally, assembling reads into longer contigs provided significant advantages in terms of functional annotation yields. Conclusions: This study contributes to identify host- and database-specific biases which need to be taken into account in a metaproteomic experiment, providing meaningful insights on how to design gut microbiota studies and to perform metaproteomic data analysis. In particular, the use of multiple databases and annotation tools has to be encouraged, even though this requires appropriate bioinformatic resources

Critical comparison of sample preparation strategies for shotgun proteomic analysis of formalin-fixed, paraffin-embedded samples: insights from liver tissue

Background: The growing field of formalin-fixed paraffin-embedded (FFPE) tissue proteomics holds promise for improving translational research. Direct tissue trypsinization (DT) and protein extraction followed by in solution digestion (ISD) or filter-aided sample preparation (FASP) are the most common workflows for shotgun analysis of FFPE samples, but a critical comparison of the different methods is currently lacking. Experimental design: DT, FASP and ISD workflows were compared by subjecting to the same label-free quantitative approach three independent technical replicates of each method applied to FFPE liver tissue. Data were evaluated in terms of method reproducibility and protein/peptide distribution according to localization, MW, pI and hydrophobicity. Results: DT showed lower reproducibility, good preservation of high-MW proteins, a general bias towards hydrophilic and acidic proteins, much lower keratin contamination, as well as higher abundance of non-tryptic peptides. Conversely, FASP and ISD proteomes were depleted in high-MW proteins and enriched in hydrophobic and membrane proteins; FASP provided higher identification yields, while ISD exhibited higher reproducibility. Conclusions: These results highlight that diverse sample preparation strategies provide significantly different proteomic information, and present typical biases that should be taken into account when dealing with FFPE samples. When a sufficient amount of tissue is available, the complementary use of different methods is suggested to increase proteome coverage and depth.Pubblicat

Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data

Background. A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multi-region sequencing experiments or the sequencing of individual cancer cells. However, rarely the same method can support both data types. Results. We introduce TRaIT, a computational framework to infer mutational graphs that model the accumulation of multiple types of somatic alterations driving tumour evolution. Compared to other tools, TRaIT supports multi-region and single-cell sequencing data within the same statistical framework, and delivers expressive models that capture many complex evolutionary phenomena. TRaIT improves accuracy, robustness to data-specific errors and computational complexity compared to competing methods. Conclusions. We show that the application of TRaIT to single-cell and multi-region cancer datasets can produce accurate and reliable models of single-tumour evolution, quantify the extent of intra-tumour heterogeneity and generate new testable experimental hypotheses

high throughput genomic and proteomic technologies in the fight against infectious diseases

New technologies have shown significant promise in the fight against infectious diseases, with the discovery of novel molecular targets for in vitro diagnostics and the improved design of vaccines. In developing countries, especially in areas of neglected diseases and resources-poor settings, a number of technological innovations are further needed, such as the integration of old and new biomarkers in suitable analysis platforms, the simplification of existing analysis systems, and the improvement of sample preservation and management. However, in these areas, identification of new biomarkers for infectious diseases is still a core issue in the diagnostic quest. Similarly, new technologies will allow scientists to design vaccines with improved immunogenicity, efficacy and safety in the local area, according to the circulating pathogenic strains and the genetic background of the population to be immunized. In this work we review the current omics-based technologies and their potential for accelerating the development of next generation vaccines and the identification of biomarkers suitable for point-of-care (POC) diagnostic applications

Metaproteogenomics Reveals Taxonomic and Functional Changes between Cecal and Fecal Microbiota in Mouse

Previous studies on mouse models report that cecal and fecal microbial communities may differ in the taxonomic structure, but little is known about their respective functional activities. Here, we employed a metaproteogenomic approach, including 16S rRNA gene sequencing, shotgun metagenomics and shotgun metaproteomics, to analyze the microbiota of paired mouse cecal contents (CCs) and feces, with the aim of identifying changes in taxon-specific functions. As a result, Gram-positive anaerobes were observed as considerably higher in CCs, while several key enzymes, involved in oxalate degradation, glutamate/glutamine metabolism, and redox homeostasis, and most actively expressed by Bacteroidetes, were clearly more represented in feces. On the whole, taxon and function abundance appeared to vary consistently with environmental changes expected to occur throughout the transit from the cecum to outside the intestine, especially when considering metaproteomic data. The results of this study indicate that functional and metabolic differences exist between CC and stool samples, paving the way to further metaproteogenomic investigations aimed at elucidating the functional dynamics of the intestinal microbiota

Evaluating the impact of different sequence databases on metaproteome analysis: insights from a lab-assembled microbial mixture

Metaproteomics enables the investigation of the protein repertoire expressed by complex microbial communities. However, to unleash its full potential, refinements in bioinformatic approaches for data analysis are still needed. In this context, sequence databases selection represents a major challenge. This work assessed the impact of different databases in metaproteomic investigations by using a mock microbial mixture including nine diverse bacterial and eukaryotic species, which was subjected to shotgun metaproteomic analysis. Then, both the microbial mixture and the single microorganisms were subjected to next generation sequencing to obtain experimental metagenomic- and genomic-derived databases, which were used along with public databases (namely, NCBI, UniProtKB/SwissProt and UniProtKB/TrEMBL, parsed at different taxonomic levels) to analyze the metaproteomic dataset. First, a quantitative comparison in terms of number and overlap of peptide identifications was carried out among all databases. As a result, only 35% of peptides were common to all database classes; moreover, genus/species-specific databases provided up to 17% more identifications compared to databases with generic taxonomy, while the metagenomic database enabled a slight increment in respect to public databases. Then, database behavior in terms of false discovery rate and peptide degeneracy was critically evaluated. Public databases with generic taxonomy exhibited a markedly different trend compared to the counterparts. Finally, the reliability of taxonomic attribution according to the lowest common ancestor approach (using MEGAN and Unipept software) was assessed. The level of misassignments varied among the different databases, and specific thresholds based on the number of taxon-specific peptides were established to minimize false positives. This study confirms that database selection has a significant impact in metaproteomics, and provides critical indications for improving depth and reliability of metaproteomic results. Specifically, the use of iterative searches and of suitable filters for taxonomic assignments is proposed with the aim of increasing coverage and trustworthiness of metaproteomic data.</br

1H-NMR-Based Metabolomics in Autism Spectrum Disorder and Pediatric Acute-Onset Neuropsychiatric Syndrome

We recently described a unique plasma metabolite profile in subjects with pediatric acute-onset neuropsychiatric syndrome (PANS), suggesting pathogenic models involving specific patterns of neurotransmission, neuroinflammation, and oxidative stress. Here, we extend the analysis to a group of patients with autism spectrum disorder (ASD), as a consensus has recently emerged around its immune-mediated pathophysiology with a widespread involvement of brain networks. This observational case-control study enrolled patients referred for PANS and ASD from June 2019 to May 2020, as well as neurotypical age and gender-matched control subjects. Thirty-four PANS outpatients, fifteen ASD outpatients, and twenty-five neurotypical subjects underwent physical and neuropsychiatric evaluations, alongside serum metabolomic analysis with 1H-NMR. In supervised models, the metabolomic profile of ASD was significantly different from controls (p = 0.0001), with skewed concentrations of asparagine, aspartate, betaine, glycine, lactate, glucose, and pyruvate. Metabolomic separation was also observed between PANS and ASD subjects (p = 0.02), with differences in the concentrations of arginine, aspartate, betaine, choline, creatine phosphate, glycine, pyruvate, and tryptophan. We confirmed a unique serum metabolomic profile of PANS compared with both ASD and neurotypical subjects, distinguishing PANS as a pathophysiological entity per se. Tryptophan and glycine appear as neuroinflammatory fingerprints of PANS and ASD, respectively. In particular, a reduction in glycine would primarily affect NMDA-R excitatory tone, overall impairing downstream glutamatergic, dopaminergic, and GABAergic transmissions. Nonetheless, we found metabolomic similarities between PANS and ASD that suggest a putative role of N-methyl-D-aspartate receptor (NMDA-R) dysfunction in both disorders. Metabolomics-based approaches could contribute to the identification of novel ASD and PANS biomarkers

International Coordination of Long-Term Ocean Biology Time Series Derived from Satellite Ocean Color Data

[ABSTRACT] In this paper, we will describe plans to coordinate the initial development of long-term ocean biology time series derived from global ocean color observations acquired by the United States, Japan and Europe, Specifically, we have been commissioned by the International Ocean Color Coordinating Group (IOCCG) to coordinate the development of merged products derived from the OCTS, SeaWiFS, MODIS, MERIS and GLI imagers. Each of these missions will have been launched by the year 2002 and will have produced global ocean color data products. Our goal is to develop and document the procedures to be used by each space agency (NASA, NASDA, and ESA) to merge chlorophyll, primary productivity, and other products from these missions. This coordination is required to initiate the production of long-term ocean biology time series which will be continued operationally beyond 2002. The purpose of the time series is to monitor interannual to decadal-scale variability in oceanic primary productivity and to study the effects of environmental change on upper ocean biogeochemical processes