Expert Review of Proteomics最新文献

Background: Botulinum neurotoxins (BoNTs) are a group of neurotoxins produced by Clostridium bacteria. Their effect on neuro-muscular connections through cleaving proteins of the SNARE complex results in blocking acetylcholine signal transduction. The FDA-approved mouse bioassay, which involves exposing live mice to potentially contaminated food, is the most widely used method. However, this assay is costly, time-consuming, and raises ethical concerns. Therefore, there is a need for alternative assays that can enzymatically measure the activity of BoNTs.

Research design and methods: We present an approach that combines the EndoPep-MS assay with protein affinity chips fabricated using ion soft-landing technology. Toxic activity is indirectly assessed by monitoring the N- and C-terminal fragments of the substrate peptide. This new method employs a protein array with affinity molecules targeting either the BoNT/A1 or the substrate peptide. Both variants enable in-situ reaction and detection of substrate peptides via MALDI-ToF MS on the protein chip.

Results: This method demonstrated successful detection of active BoNT/A1 in both buffer and complex matrices, achieving a detection limit of 0.5 ng/mL.

Conclusions: This study reports the in-situ detection of botulotoxin A1 using functionalized MALDI chips. The advantages of the MALDI chip technology include speed, robustness, cost-effectiveness, and possible automatization.

Introduction: The emergence of personalized medicine (PM) has shifted the focus of healthcare from the traditional 'one-size-fits-all' approach to strategies tailored to individual patients, accounting for genetic, environmental, and lifestyle factors. Acoustic ejection mass spectrometry (AEMS) is a novel technology that offers a robust and scalable platform for high-throughput MS readout. AEMS achieves analytical speeds of one sample per second while maintaining high data quality, broad compound coverage, and minimal sample preparation, making it an invaluable tool for PM.

Areas covered: This article explores the potential of AEMS in critical PM applications, including therapeutic drug monitoring (TDM), proteomics, metabolomics, and mass spectrometry imaging. AEMS simplifies conventional workflows by minimizing sample preparation, enhancing automation compatibility, and enabling direct analysis of complex biological matrices.

Expert opinion: Integrating AEMS with orthogonal separation techniques such as differential mobility spectrometry (DMS) further addresses challenges in isomer discrimination, expanding the platform's analytical capabilities. Additionally, the development of high-throughput data processing tools could further enable AEMS to accelerate the development of personalized medicine.

Introduction: Cancer is the second-leading cause of death worldwide and accurate biomarkers for early detection and disease monitoring are needed to improve outcomes. Biological fluids, such as blood and urine, are ideal samples for biomarker measurements as they can be routinely collected with relatively minimally invasive methods. However, proteomics analysis of fluids has been a challenge due to the high dynamic range of its protein content. Advances in data-independent acquisition (DIA) mass spectrometry-based proteomics can address some of the technical challenges in the analysis of biofluids, thus enabling the ability for mass spectrometry to propel large-scale biomarker discovery.

Areas covered: We reviewed principles of DIA and its recent applications in cancer biomarker discovery using biofluids. We summarized DIA proteomics studies using biological fluids in the context of cancer research over the past decade, and provided a comprehensive overview of the benefits and challenges of DIA-MS.

Expert opinion: Various studies showed the potential of DIA-MS in identifying putative cancer biomarkers in a high-throughput manner. However, the lack of proper study design and standardization of methods across platforms still needs to be addressed to fully utilize the benefits of DIA-MS to accelerate the biomarker discovery and verification processes.

Introduction: A holistic view on biological systems is today a reality with the application of multi-omic technologies. These technologies allow the profiling of genome, epigenome, transcriptome, proteome, metabolome as well as newly emerging 'omes.' While the multiple layers of data accumulate, their integration and reconciliation in a single system map is a cumbersome exercise that faces many challenges. Application to human health and disease requires large sample sizes, robust methodologies and high-quality standards.

Areas covered: We review the different methods used to integrate multi-omics, as recent ones including artificial intelligence. With proteomics as an anchor technology, we then present selected applications of its data combination with other omics layers in clinical research, mainly covering literature from the last five years in the Scopus and/or PubMed databases.

Expert opinion: Multi-omics is powerful to comprehensively type molecular layers and link them to phenotype. Yet, technologies and data are very diverse and still strategies and methodologies to properly integrate these modalities are needed.

Introduction: Rare diseases (RDs) are a heterogeneous group of diseases recognized as a relevant global health priority but posing aspects of complexity, such as geographical scattering of affected individuals, improper/late diagnosis, limited awareness, difficult surveillance and monitoring, limited understanding of natural history, and lack of treatment. Usually, RDs have a pediatric onset and are life-long, multisystemic, and associated with a poor prognosis.

Areas covered: In this work, we review how high-throughput omics technologies such as genomics, transcriptomics, proteomics, metabolomics, epigenomics, and other well-established omics, which are increasingly more affordable and efficient, can be applied to the study of RDs promoting diagnosis, understanding of pathological mechanisms, biomarker discovery, and identification of treatments.

Expert opinion: RDs, despite their challenges, offer a niche where collaborative efforts and personalized treatment strategies might be feasible using omics technologies. Specialized consortia fostering multidisciplinary collaboration, data sharing, and the development of biobanks and registries can be built; multi-omics approaches, including so far less exploited omics technologies, along with the implementation of AI tools can be undertaken to deepen our understanding of RDs, driving biomarker discovery and clinical interventions. Nevertheless, technical, ethical, legal, and societal issues must be clearly defined and addressed.

Introduction: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been a leading method for proteomics for 30 years. Advantages provided by LC-MS/MS are offset by significant disadvantages, including cost. Recently, several non-mass spectrometric methods have emerged, but little information is available about their capacity to analyze the complex mixtures routine for mass spectrometry.

Areas covered: We review recent non-mass-spectrometric methods for sequencing proteins and peptides, including those using nanopores, sequencing by degradation, reverse translation, and short-epitope mapping, with comments on bioinformatics challenges, fundamental limitations, and areas where new technologies will be more or less competitive with LC-MS/MS. In addition to conventional literature searches, instrument vendor websites, patents, webinars, and preprints were also consulted to give a more up-to-date picture.

Expert opinion: Many new technologies are promising. However, demonstrations that they outperform mass spectrometry in terms of peptides and proteins identified have not yet been published, and astute observers note important disadvantages, especially relating to the dynamic range of single-molecule measurements of complex mixtures. Still, even if the performance of emerging methods proves inferior to LC-MS/MS, their low cost could create a different kind of revolution: a dramatic increase in the number of biology laboratories engaging in new forms of proteomics research.

Introduction: Ligand binding assays combining immunoaffinity enrichment steps with mass spectrometry (MS) readout have gained attention as a highly specific and sensitive tool for protein quantification. These techniques typically combine enzymatic fragmentation of the sample or enriched protein with capture on the protein or peptide-level for quantification. Antibodies ensure specific target recognition, while MS offers quantitative accuracy with isotopically labeled internal standards. This dual approach supports a broad dynamic range, enabling protein measurements from picomolar to nanomolar levels. These methods have diverse applications, from quantifying signaling proteins in basic research to biomarker monitoring in clinical trials and analyzing the pharmacokinetics of therapeutic proteins.

Areas covered: This review delves into the diverse workflows of immunoaffinity-MS, shedding light on the innovative strategies employed, their practical applications, efficacy, and inherent limitations in the realm of protein quantification.

Expert opinion: Immunoaffinity-MS has transformed protein analysis, but widespread adoption is hindered by complex workflows, high instrument costs, and limited capture molecule availability. Efforts to enhance automation, standardize workflows, and advance technological innovation aim to overcome these barriers. Improvements in mass spectrometer sensitivity, advances in recombinant capture technologies, and support from public initiatives are poised to further improve the reliability and accessibility of this method.

Introduction: The DeepMind's AlphaFold (AF) has revolutionized biomedical and biocience research by providing both experts and non-experts with an invaluable tool for predicting protein structures. However, while AF is highly effective for predicting structures of rigid and globular proteins, it is not able to fully capture the dynamics, conformational variability, and interactions of proteins with ligands and other biomacromolecules.

Areas covered: In this review, we present a comprehensive overview of the latest advancements in 3D model predictions for biomacromolecules using AF. We also provide a detailed analysis its of strengths and limitations, and explore more recent iterations, modifications, and practical applications of this strategy. Moreover, we map the path forward for expanding the landscape of AF toward predicting structures of every protein and peptide, and their interactions in the proteome in the most physiologically relevant form. This discussion is based on an extensive literature search performed using PubMed and Google Scholar.

Expert opinion: While significant progress has been made to enhance AF's modeling capabilities, we argue that a combined approach integrating both various in silico and in vitro methods will be most beneficial for the future of structural biology, bridging the gaps between static and dynamic features of proteins and their functions.

Objective: Our study presents a novel analysis of the oncogenes and tumor suppressor proteins directly modulated by E6/E7 of high-risk HPV types 16 and 18, in colorectal cancer (CRC).

Methods: HCT 116 (KRAS mutant) & HT-29 (TP53 mutant) cell models of CRC were transduced with E6/E7 of HPV16 and HPV18, individually and in combination. Further, we utilized a liquid chromatography mass spectrometry (LC-MS/MS) approach to analyze and compare the proteomes of both CRC cell models.

Results: We generated six stably transduced cell lines. Our data revealed a significantly higher, HPV-induced modulation of oncogenes and tumor suppressor proteins in the TP53 mutant model, as compared to the KRAS mutant model (p ≤ 0.01). Less than 1% of the genes were commonly modulated by HPV, between both models. We also report that HT-29 cells, expressing E6/E7 of both HPV types, significantly reduced the suppression of oncogenes as compared to cells expressing E6/E7 of either HPV types individually (p-value ≤0.00001).

Conclusion: Our data imply that HPV coinfections leads to the sustenance of a pro-oncogenic environment in CRC. HPV modulates different oncogenes/tumor suppressor proteins in CRC of varying mutational backgrounds, thus highlighting the importance of personalized therapies for such diseases with mutational heterogeneity.