FractionOptimizer: a method for optimal peptide fractionation in bottom-up proteomics

Bioinformatics in Russia: history and present-day landscape

Bioinformatics has become an interdisciplinary subject due to its universal role in molecular biology research. The current status of Russia's bioinformatics research in Russia is not known. Here, we review the history of bioinformatics in Russia, present the current landscape, and highlight future directions and challenges. Bioinformatics research in Russia is driven by four major industries: information technology, pharmaceuticals, biotechnology, and agriculture. Over the past three decades, despite a delayed start, the field has gained momentum, especially in protein and nucleic acid research. Dedicated and shared centers for genomics, proteomics, and bioinformatics are active in different regions of Russia. Present-day bioinformatics in Russia is characterized by research issues related to genetics, metagenomics, OMICs, medical informatics, computational biology, environmental informatics, and structural bioinformatics. Notable developments are in the fields of software (tools, algorithms, and pipelines), use of high computation power (e.g. by the Siberian Supercomputer Center), and large-scale sequencing projects (the sequencing of 100 000 human genomes). Government funding is increasing, policies are being changed, and a National Genomic Information Database is being established. An increased focus on eukaryotic genome sequencing, the development of a common place for developers and researchers to share tools and data, and the use of biological modeling, machine learning, and biostatistics are key areas for future focus. Universities and research institutes have started to implement bioinformatics modules. A critical mass of bioinformaticians is essential to catch up with the global pace in the discipline.

Mass Spectrometry Acquisition and Fractionation Recommendations for TMT11 and TMT16 Labeled Samples

Proteome coverage and accurate protein quantification are both important for evaluating biological systems; however, compromises between quantification, coverage, and mass spectrometry (MS) resources are often necessary. Consequently, experimental parameters that impact coverage and quantification must be adjusted, depending on experimental goals. Among these parameters is offline prefractionation, which is utilized in MS-based proteomics to decrease sample complexity resulting in higher overall proteome coverage upon MS analysis. Prefractionation leads to increases in required MS analysis time, although this is often mitigated by isobaric labeling using tandem-mass tags (TMT), which allow samples to be multiplexed. Here we evaluate common prefractionation schemes, TMT variants, and MS acquisition methods and their impact on protein quantification and coverage. Furthermore, we provide recommendations for experimental design depending on the experimental goals.

Scorpion Venom as a Source of Antimicrobial Peptides: Overview of Biomolecule Separation, Analysis and Characterization Methods

Scorpion venoms have long captivated scientific researchers, primarily due to the potency and specificity of the mechanism of action of their derived components. Among other molecules, these venoms contain highly active compounds, including antimicrobial peptides (AMPs) and ion channel-specific components that selectively target biological receptors with remarkable affinity. Some of these receptors have emerged as prime therapeutic targets for addressing various human pathologies, including cancer and infectious diseases, and have served as models for designing novel drugs. Consequently, extensive biochemical and proteomic investigations have focused on characterizing scorpion venoms. This review provides a comprehensive overview of the key methodologies used in the extraction, purification, analysis, and characterization of AMPs and other bioactive molecules present in scorpion venoms. Noteworthy techniques such as gel electrophoresis, reverse-phase high-performance liquid chromatography, size exclusion chromatography, and "omics" approaches are explored, along with various combinations of methods that enable bioassay-guided venom fractionation. Furthermore, this review presents four adapted proteomic workflows that lead to the comprehensive dissection of the scorpion venom proteome, with an emphasis on AMPs. These workflows differ based on whether the venom is pre-fractionated using separation techniques or is proteolytically digested directly before further proteomic analyses. Since the composition and functionality of scorpion venoms are species-specific, the selection and sequence of the techniques for venom analyses, including these workflows, should be tailored to the specific parameters of the study.

In-Depth Metaproteomics Analysis of Oral Microbiome for Lung Cancer

The human oral microbiome correlates with numerous diseases, including lung cancer. Identifying the functional changes by metaproteomics helps understand the disease-related dysbiosis, yet characterizing low-abundant bacteria is challenging. Here, we developed a free-flow isoelectric focusing electrophoresis-mass spectrometry- (FFIEF-MS-) based metaproteomics strategy to reduce host interferences and enrich low-abundant bacteria for in-depth interpretation of the oral microbiome. With our method, the number of interfering peptides decreased by 52.87%, whereas the bacterial peptides and species increased by 94.97% and 44.90%, respectively, compared to the conventional metaproteomics approach. We identified 3647 bacterial proteins, which is the most comprehensive oral metaproteomics study to date. Lung cancer-associated bacteria were validated among an independent cohort. The imbalanced Fusobacterium nucleatum and Prevotella histicola and their dysregulated functions in inhibiting immune response and maintaining cell redox homeostasis were revealed. The FFIEF-MS may serve as a valuable strategy to study the mechanisms between human diseases and microbiomes with broader applications.

Development of a high-pH reversed-phase well plate for peptide fractionation and deep proteome analysis of cells and exosomes

The complexity of the proteome often limits the number of identified proteins in the nanoflow LC-MS (nanoLC-MS) analysis of samples. Therefore, peptide fractionation is essential for reducing the sample complexity and improving the proteome coverage. In this study, to achieve high-pH reversed-phase (RP)-well plate fractionation for high-throughput proteomics analysis, C18 particles were coated on a 96-well plate, and the sample-loading processes were optimized for high-pH fractionation. The sample capacity of the high-pH RP-well plate was estimated to be ~6 μg of protein. There were 1.85- and 1.71-fold increases in the number of protein groups and peptides identified, respectively, with high-pH RP-well plate fractionation, compared to those without fractionation. In addition, with alkaline C18 well plate fractionation, exosome markers could be detected using ~1 μg of a protein digest of exosomes by microflow LC-MS (microLC-MS). These results illustrate that high-pH RP-well plate fractionation has superior sensitivity and effectiveness in preparing trace amounts of proteins for deep proteome analysis.

A Prostate Cancer Proteomics Database for SWATH-MS Based Protein Quantification

Simple Summary

Prostate cancer is the third most frequent cancer in men worldwide, with a notable increase in prevalence over the past two decades. The PSA is the only well-established protein biomarker for prostate cancer diagnosis, staging, and surveillance. It frequently leads to inaccurate diagnosis and overtreatment since it is an organ-specific biomarker rather than a tumour-specific biomarker. As a result, one of the primary goals of prostate cancer proteome research is to identify novel biomarkers that can be used with or instead of PSA, particularly in non-invasive blood samples. Thousands of peptides or assays were detected in blood samples from patients with low- to high-grade prostate cancer and healthy individuals, allowing data processing of sequential window acquisition of all theoretical mass spectra (SWATH-MS). By assisting in the detection of prostate cancer biomarkers in blood samples, this useful resource will improve our understanding of the role of proteomics in prostate cancer diagnosis and risk assessment.

Abstract

Prostate cancer is the most frequent form of cancer in men, accounting for more than one-third of all cases. Current screening techniques, such as PSA testing used in conjunction with routine procedures, lead to unnecessary biopsies and the discovery of low-risk tumours, resulting in overdiagnosis. SWATH-MS is a well-established data-independent (DI) method requiring prior knowledge of targeted peptides to obtain valuable information from SWATH maps. In response to the growing need to identify and characterise protein biomarkers for prostate cancer, this study explored a spectrum source for targeted proteome analysis of blood samples. We created a comprehensive prostate cancer serum spectral library by combining data-dependent acquisition (DDA) MS raw files from 504 patients with low, intermediate, or high-grade prostate cancer and healthy controls, as well as 304 prostate cancer-related protein in silico assays. The spectral library contains 114,684 transitions, which equates to 18,479 peptides translated into 1227 proteins. The robustness and accuracy of the spectral library were assessed to boost confidence in the identification and quantification of prostate cancer-related proteins across an independent cohort, resulting in the identification of 404 proteins. This unique database can facilitate researchers to investigate prostate cancer protein biomarkers in blood samples. In the real-world use of the spectrum library for biomarker detection, using a signature of 17 proteins, a clear distinction between the validation cohort’s pre- and post-treatment groups was observed. Data are available via ProteomeXchange with identifier PXD028651.

Molecular pathophysiology of diabetes mellitus during pregnancy with antenatal complications

Gestational diabetes mellitus is a daunting problem accompanied by severe fetal development complications and type 2 diabetes mellitus in postpartum. Diagnosis of diabetic conditions occurs only in the second trimester, while associated antenatal complications are typically revealed even later. We acquired an assay of peripheral and cord blood samples of patients with different types of diabetes mellitus who delivered either healthy newborns or associated with fetopathy complications. Obtained data were handled with qualitative and quantitative analysis. Pathways of molecular events involved in diabetes mellitus and fetopathy were reconstructed based on the discovered markers and their quantitative alteration. Plenty of pathways were integrated to differentiate the type of diabetes and to recognize the impact of the diabetic condition on fetal development. The impaired triglycerides transport, glucose uptake, and consequent insulin resistance are mostly affected by faulted lipid metabolism (APOM, APOD, APOH, APOC1) and encouraged by oxidative stress (CP, TF, ORM2) and inflammation (CFH, CFB, CLU) as a secondary response accompanied by changes in matrix architecture (AFM, FBLN1, AMBP). Alterations in proteomes of peripheral and cord blood were expectedly unequal. Both up- and downregulated markers were accommodated in the cast of molecular events interconnected with the lipid metabolism, RXR/PPAR-signaling pathway, and extracellular architecture modulation. The obtained results congregate numerous biological processes to molecular events that underline diabetes during gestation and uncover some critical aspects affecting fetal growth and development.

A Critical Review of Bottom-Up Proteomics: The Good, the Bad, and the Future of this Field

Proteomics is the field of study that includes the analysis of proteins, from either a basic science prospective or a clinical one. Proteins can be investigated for their abundance, variety of proteoforms due to post-translational modifications (PTMs), and their stable or transient protein–protein interactions. This can be especially beneficial in the clinical setting when studying proteins involved in different diseases and conditions. Here, we aim to describe a bottom-up proteomics workflow from sample preparation to data analysis, including all of its benefits and pitfalls. We also describe potential improvements in this type of proteomics workflow for the future.

Differential Lysis Approach Enables Selective Extraction of Taxon-Specific Proteins for Gut Metaproteomics

Changes in microbiome composition and function have been linked to human health and diseases. Metaproteomics provides invaluable functional information on the state of a microbiome. However, lower-abundance bacteria in complex microbiomes are difficult to observe by metaproteomics. In this study, stepwise differential lysis protocols were developed for human stool microbiomes to separate different microbial species and to increase the depth of metaproteomic measurements. We achieved differential lysis of Gram-positive (G⁺) and Gram-negative (G^-) bacteria, selective enrichment of specific bacteria, and functional enrichment by our stepwise differential lysis protocols. Therefore, differential lysis can serve as a fractionation method to reduce sample complexity and selectively extract proteins from specific taxa for deep metaproteomic studies.

Pyteomics 4.0: Five Years of Development of a Python Proteomics Framework

Many of the novel ideas that drive today's proteomic technologies are focused essentially on experimental or data-processing workflows. The latter are implemented and published in a number of ways, from custom scripts and programs, to projects built using general-purpose or specialized workflow engines; a large part of routine data processing is performed manually or with custom scripts that remain unpublished. Facilitating the development of reproducible data-processing workflows becomes essential for increasing the efficiency of proteomic research. To assist in overcoming the bioinformatics challenges in the daily practice of proteomic laboratories, 5 years ago we developed and announced Pyteomics, a freely available open-source library providing Python interfaces to proteomic data. We summarize the new functionality of Pyteomics developed during the time since its introduction.