Computational Methods in Mass Spectrometry-Based Proteomics

Proteome profiling of cutaneous leishmaniasis lesions due to dermotropic Leishmania donovani in Sri Lanka

BACKGROUND

Characterization of the host response in cutaneous leishmaniasis (CL) through proteome profiling has gained limited insights into leishmaniasis research compared to that of the parasite. The primary objective of this study was to comprehensively analyze the proteomic profile of the skin lesions tissues in patients with CL, by mass spectrometry, and subsequent validation of these findings through immunohistochemical methods.

METHODS

Eight lesion specimens from leishmaniasis-confirmed patients and eight control skin biopsies were processed for proteomic profiling by mass spectrometry. Formalin-fixed paraffin-embedded lesion specimens from thirty patients and six control skin specimens were used for Immunohistochemistry (IHC) staining. Statistical analyses were carried out using SPSS software. The chi-square test was used to assess the association between the degree of staining for each marker and the clinical and pathological features.

RESULTS

Sixty-seven proteins exhibited significant differential expression between tissues of CL lesions and healthy controls (p < 0.01), representing numerous enriched biological processes within the lesion tissue, as evident by both the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases. Among these, the integrated endoplasmic reticulum stress response (IERSR) emerges as a pathway characterized by the up-regulated proteins in CL tissues compared to healthy skin. Expression of endoplasmic reticulum (ER) stress sensors, inositol-requiring enzyme-1 (IRE1), protein kinase RNA-like ER kinase (PERK) and activating transcription factor 6 (ATF6) in lesion tissue was validated by immunohistochemistry.

CONCLUSIONS

In conclusion, proteomic profiling of skin lesions carried out as a discovery phase study revealed a multitude of probable immunological and pathological mechanisms operating in patients with CL in Sri Lanka, which needs to be further elaborated using more in-depth and targeted investigations. Further research exploring the intricate interplay between ER stress and CL pathophysiology may offer promising avenues for the development of novel diagnostic tools and therapeutic strategies in combating this disease.

Proteome profiling of cutaneous leishmaniasis lesions due to dermotropic Leishmania donovani in Sri Lanka

Characterization of the host response in cutaneous leishmaniasis (CL) through proteome profiling has gained limited insights in leishmaniasis research, in comparison to that of the parasite. The primary objective of this study was to comprehensively analyze the proteomic profile of the skin lesions tissues in patients with CL, by mass spectrometry, and subsequent validation of these findings through immunohistochemical methods. Sixty-seven proteins exhibited significant differential expression between tissues of CL lesions and healthy controls (p<0.01), representing numerous enriched biological processes within the lesion tissue, as evident by both the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases. Among these, the integrated endoplasmic reticulum stress response (IERSR) emerges as a pathway characterized by the up-regulated proteins in CL tissues compared to healthy skin. Expression of endoplasmic reticulum (ER) stress sensors, inositol-requiring enzyme-1 (IRE1), protein kinase RNA-like ER kinase (PERK), and activating transcription factor 6 (ATF6) in lesion tissue was validated by immunohistochemistry. In conclusion, proteomic profiling of skin lesions carried out as a discovery phase study revealed a multitude of probable immunological and pathological mechanisms operating in patients with CL in Sri Lanka, which needs to be further elaborated using more in-depth and targeted investigations.

Challenges and opportunities for single cell computational proteomics

Single-cell proteomics is growing rapidly and has made several technological advancements. As most research has been focused on improving instrumentation and sample preparation methods, very little attention has been given to algorithms responsible for identifying and quantifying proteins. Given the inherent difference between bulk data and single-cell data, it's necessary to realize that current algorithms being employed on single-cell data were designed for bulk data, and have underlying assumptions that may not hold true for single-cell data. In order to develop and optimize algorithms for single-cell data, we need to characterize the differences between single-cell data and bulk data, and assess how current algorithms perform on single-cell data. Here, we present a review of algorithms responsible for identifying and quantifying peptides and proteins. We will give a review of how each type of algorithm works, assumptions it relies on, how it performs on single-cell data, and possible optimizations and solutions that could be used to address the differences in single-cell data.

AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics

Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility and fragment intensities of a peptide just from the amino acid sequence with good accuracy. However, DL is a very rapidly developing field with new neural network architectures frequently appearing, which are challenging to incorporate for proteomics researchers. Here we introduce AlphaPeptDeep, a modular Python framework built on the PyTorch DL library that learns and predicts the properties of peptides ( https://github.com/MannLabs/alphapeptdeep ). It features a model shop that enables non-specialists to create models in just a few lines of code. AlphaPeptDeep represents post-translational modifications in a generic manner, even if only the chemical composition is known. Extensive use of transfer learning obviates the need for large data sets to refine models for particular experimental conditions. The AlphaPeptDeep models for predicting retention time, collisional cross sections and fragment intensities are at least on par with existing tools. Additional sequence-based properties can also be predicted by AlphaPeptDeep, as demonstrated with a HLA peptide prediction model to improve HLA peptide identification for data-independent acquisition ( https://github.com/MannLabs/PeptDeep-HLA ).

Thiol-based Oxidative Posttranslational Modifications (OxiPTMs) of Plant Proteins

The thiol group of cysteine (Cys) residues, often present in the active center of the protein, is of particular importance to protein function, which is significantly determined by the redox state of a protein's environment. Our knowledge of different thiol-based oxidative posttranslational modifications (oxiPTMs), which compete for specific protein thiol groups, has increased over the last 10 years. The principal oxiPTMs include S-sulfenylation, S-glutathionylation, S-nitrosation, persulfidation, S-cyanylation and S-acylation. The role of each oxiPTM depends on the redox cellular state, which in turn depends on cellular homeostasis under either optimal or stressful conditions. Under such conditions, the metabolism of molecules such as glutathione, NADPH (reduced nicotinamide adenine dinucleotide phosphate), nitric oxide, hydrogen sulfide and hydrogen peroxide can be altered, exacerbated and, consequently, outside the cell's control. This review provides a broad overview of these oxiPTMs under physiological and unfavorable conditions, which can regulate the function of target proteins.

TMT-based proteomic and bioinformatic analyses of human granulosa cells from obese and normal-weight female subjects

BACKGROUND

Increasing evidence supports a relationship between obesity and either infertility or subfertility in women. Most previous omics studies were focused on determining if the serum and follicular fluid expression profiles of subjects afflicted with both obesity-related infertility and polycystic ovary syndrome (PCOS) are different than those in normal healthy controls. As granulosa cells (GCs) are essential for oocyte development and fertility, we determined here if the protein expression profiles in the GCs from obese subjects are different than those in their normal-weight counterpart.

METHODS

GC samples were collected from obese female subjects (n = 14) and normal-weight female subjects (n = 12) who were infertile and underwent in vitro fertilization (IVF) treatment due to tubal pathology. A quantitative approach including tandem mass tag labeling and liquid chromatography tandem mass spectrometry (TMT) was employed to identify differentially expressed proteins. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were then conducted to interrogate the functions and pathways of identified proteins. Clinical, hormonal, and biochemical parameters were also analyzed in both groups.

RESULTS

A total of 228 differentially expressed proteins were noted, including 138 that were upregulated whereas 90 others were downregulated. Significant pathways and GO terms associated with protein expression changes were also identified, especially within the mitochondrial electron transport chain. The levels of free fatty acids in both the serum and follicular fluid of obese subjects were significantly higher than those in matched normal-weight subjects.

CONCLUSIONS

In GCs obtained from obese subjects, their mitochondria were damaged and the endoplasmic reticulum stress response was accompanied by dysregulated hormonal synthesis whereas none of these changes occurred in normal-weight subjects. These alterations may be related to the high FFA and TG levels detected in human follicular fluid.

GeenaR: A Web Tool for Reproducible MALDI-TOF Analysis

Mass spectrometry is a widely applied technology with a strong impact in the proteomics field. MALDI-TOF is a combined technology in mass spectrometry with many applications in characterizing biological samples from different sources, such as the identification of cancer biomarkers, the detection of food frauds, the identification of doping substances in athletes’ fluids, and so on. The massive quantity of data, in the form of mass spectra, are often biased and altered by different sources of noise. Therefore, extracting the most relevant features that characterize the samples is often challenging and requires combining several computational methods. Here, we present GeenaR, a novel web tool that provides a complete workflow for pre-processing, analyzing, visualizing, and comparing MALDI-TOF mass spectra. GeenaR is user-friendly, provides many different functionalities for the analysis of the mass spectra, and supports reproducible research since it produces a human-readable report that contains function parameters, results, and the code used for processing the mass spectra. First, we illustrate the features available in GeenaR. Then, we describe its internal structure. Finally, we prove its capabilities in analyzing oncological datasets by presenting two case studies related to ovarian cancer and colorectal cancer. GeenaR is available at http://proteomics.hsanmartino.it/geenar/.

Unraveling proteome changes and potential regulatory proteins of bovine follicular Granulosa cells by mass spectrometry and multi-omics analysis

Background

In previous study, we performed next-gene sequencing to investigate the differentially expressed transcripts of bovine follicular granulosa cells (GCs) at dominant follicle (DF) and subordinate follicle (SF) stages during first follicular wave. Present study is designed to further identify the key regulatory proteins and signaling pathways associated with follicular development using label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) and multi-omics data analysis approach.

Methods

DF and SF from three cattle were collected by daily ultrasonography. The GCs were isolated from each follicle, total proteins were digested by trypsin, and then proteomic analyzed via LC-MS/MS, respectively. Proteins identified were retrieved from Uniprot-COW fasta database, and differentially expressed proteins were used to functional enrichment and KEGG pathway analysis. Proteome data and transcriptome data obtained from previous studies were integrated.

Results

Total 3409 proteins were identified from 30,321 peptides (FDR ≤0.01) obtained from LC-MS/MS analysis and 259 of them were found to be differentially expressed at different stage of follicular development (fold Change > 2, P < 0.05). KEGG pathway analysis of proteome data revealed important signaling pathways associated with follicular development, multi-omics data analysis results showed 13 proteins were identified as being differentially expressed in DF versus SF.

Conclusions

This study represents the first investigation of transcriptome and proteome of bovine follicles and offers essential information for future investigation of DF and SF in cattle. It also will enrich the theory of animal follicular development.

Electronic supplementary material

The online version of this article (10.1186/s12953-019-0152-1) contains supplementary material, which is available to authorized users.

Proteomic study of skeletal muscle in obesity and type 2 diabetes: progress and potential

INTRODUCTION

Skeletal muscle is the major site of insulin-stimulated glucose uptake and imparts the beneficial effects of exercise, and hence is an important site of insulin resistance in obesity and type 2 diabetes (T2D). Despite extensive molecular biology-oriented research the molecular mechanisms underlying insulin resistance in skeletal muscle remain to be established. Areas covered: The proteomic capabilities have greatly improved over the last decades. This review summarizes the technical challenges in skeletal muscle proteomics studies as well as the results of quantitative proteomic studies of skeletal muscle in relation to obesity, T2D, and exercise. Expert commentary: Current available proteomic studies contribute to the view that insulin resistance in obesity and T2D is associated with increased glycolysis and reduced mitochondrial oxidative metabolism in skeletal muscle, and that the latter can be improved by exercise. Future proteomics studies should be designed to markedly intensify the identification of abnormalities in metabolic and signaling pathways in skeletal muscle of insulin-resistant individuals to increase the understanding of the pathogenesis of T2D, but more importantly to identify multiple novel targets of treatment of which at least some can be safely targeted by novel drugs to treat and prevent T2D and reduce risk of cardiovascular disease.

Protein-protein cross-linking and human health: the challenge of elucidating with mass spectrometry

INTRODUCTION

In several biomedical research fields, the cross-linking of peptides and proteins has an important impact on health and wellbeing. It is therefore of crucial importance to study this class of post-translational modifications in detail. The huge potential of mass spectrometric technologies in the mapping of these protein-protein cross-links is however overshadowed by the challenges that the field has to overcome. Areas covered: In this review, we summarize the different pitfalls and challenges that the protein-protein cross-linking field is confronted with when using mass spectrometry approaches. We additionally focus on native disulfide bridges as an example and provide some examples of cross-links that are important in the biomedical field. Expert commentary: The current flow of methodological improvements, mainly from the chemical cross-linking field, has delivered a significant contribution to deciphering native and insult-induced cross-links. Although an automated data analysis of proteome-wide peptide cross-linking is currently only possible in chemical cross-linking experiments, the field is well on the way towards a more automated analysis of native and insult-induced cross-links in raw mass spectrometry data that will boost its potential in biomedical applications.