Expert Review of Proteomics最新文献

Introduction: Since the emergence of the cholinergic hypothesis of Alzheimer's disease (AD), acetylcholine has been viewed as a mediator of learning and memory. Donepezil improves AD-associated learning deficits and memory loss by recovering brain acetylcholine levels. However, it is associated with side effects due to global activation of acetylcholine receptors. Muscarinic acetylcholine receptor M1 (M1R), a key mediator of learning and memory, has been an alternative target. The importance of targeting a specific pathway downstream of M1R has recently been recognized. Elucidating signaling pathways beyond M1R that lead to learning and memory holds important clues for AD therapeutic strategies.

Areas covered: This review first summarizes the role of acetylcholine in aversive learning, one of the outputs used for preliminary AD drug screening. It then describes the phosphoproteomic approach focused on identifying acetylcholine intracellular signaling pathways leading to aversive learning. Finally, the intracellular mechanism of donepezil and its effect on learning and memory is discussed.

Expert opinion: The elucidation of signaling pathways beyond M1R by phosphoproteomic approach offers a platform for understanding the intracellular mechanism of AD drugs and for developing AD therapeutic strategies. Clarifying the molecular mechanism that links the identified acetylcholine signaling to AD pathophysiology will advance the development of AD therapeutic strategies.

Introduction: An increasing number of studies indicate that the microbiota-gut-brain axis is an important pathway involved in the onset and progression of depression. The responses of the organism (or its microorganisms) to external cues cannot be separated from a key intermediate element: their metabolites.

Areas covered: In recent years, with the rapid development of metabolomics, an increasing amount of metabolites has been detected and studied, especially the gut metabolites. Nevertheless, the increasing amount of metabolites described has not been reflected in a better understanding of their functions and metabolic pathways. Moreover, our knowledge of the biological interactions among metabolites is also incomplete, which limits further studies on the connections between the microbial-entero-brain axis and depression.

Expert opinion: This paper summarizes the current knowledge on depression-related metabolites and their involvement in the onset and progression of this disease. More importantly, this paper summarized metabolites from the intestine, and defined them as enterogenic metabolites, to further clarify the function of intestinal metabolites and their biochemical cross-talk, providing theoretical support and new research directions for the prevention and treatment of depression.

Introduction: An estimated 20,000 women in the United States will receive a diagnosis of ovarian cancer in 2023. Late-stage diagnosis is associated with poor prognosis. There is a need for novel diagnostic biomarkers for ovarian cancer to improve early-stage detection and novel prognostic biomarkers to improve patient treatment.

Areas covered: This review provides an overview of the clinicopathological features of ovarian cancer and the currently available biomarkers and treatment options. Two affinity-based platforms using proximity extension assays (Olink) and DNA aptamers (SomaLogic) are described in the context of highly reproducible and sensitive multiplexed assays for biomarker discovery. Recent developments in ion mobility spectrometry are presented as novel techniques to apply to the biomarker discovery pipeline. Examples are provided of how these aforementioned methods are being applied to biomarker discovery efforts in various diseases, including ovarian cancer.

Expert opinion: Translating novel ovarian cancer biomarkers from candidates in the discovery phase to bona fide biomarkers with regulatory approval will have significant benefits for patients. Multiplexed affinity-based assay platforms and novel mass spectrometry methods are capable of quantifying low abundance proteins to aid biomarker discovery efforts by enabling the robust analytical interrogation of the ovarian cancer proteome.

Introduction: Despite advancements in diagnostic methods, the classification of indeterminate thyroid nodules still poses diagnostic challenges not only in pre-surgical evaluation but even after histological evaluation of surgical specimens. Proteomics, aided by mass spectrometry and integrated with artificial intelligence and machine learning algorithms, shows great promise in identifying diagnostic markers for thyroid lesions.

Areas covered: This review provides in-depth exploration of how proteomics has contributed to the understanding of thyroid pathology. It discusses the technical advancements related to immunohistochemistry, genetic and proteomic techniques, such as mass spectrometry, which have greatly improved sensitivity and spatial resolution up to single-cell level. These improvements allowed the identification of specific protein signatures associated with different types of thyroid lesions.

Expert commentary: Among all the proteomics approaches, spatial proteomics stands out due to its unique ability to capture the spatial context of proteins in both cytological and tissue thyroid samples. The integration of multi-layers of molecular information combining spatial proteomics, genomics, immunohistochemistry or metabolomics and the implementation of artificial intelligence and machine learning approaches, represent hugely promising steps forward toward the possibility to uncover intricate relationships and interactions among various molecular components, providing a complete picture of the biological landscape whilst fostering thyroid nodule diagnosis.

Objectives: Mycobacterial adherence plays a major role in the establishment of infection within the host. Adhesin-related proteins attach to host receptors and cell-surface components. The current study aimed to utilize in-silico strategies to determine the adhesin potential of conserved hypothetical (CH) proteins.

Methods: Computational analysis was performed on the whole Mycobacterium tuberculosis H37Rv proteome using a software program for the prediction of adhesin and adhesin-like proteins using neural networks (SPAAN) to determine the adhesin potential of CH proteins. A robust pipeline of computational analysis tools: Phyre2 and pFam for homology prediction; Mycosub, PsortB, and Loctree3 for subcellular localization; SignalP-5.0 and SecretomeP-2.0 for secretory prediction, were utilized to identify adhesin candidates.

Results: SPAAN revealed 776 potential adhesins within the whole MTB H37Rv proteome. Comprehensive analysis of the literature was cross-tabulated with SPAAN to verify the adhesin prediction potential of known adhesin (n = 34). However, approximately a third of known adhesins were below the probability of adhesin (P_ad) threshold (P_ad ≥0.51). Subsequently, 167 CH proteins of interest were categorized using essential in-silico tools.

Conclusion: The use of SPAAN with supporting in-silico tools should be fundamental when identifying novel adhesins. This study provides a pipeline to identify CH proteins as functional adhesin molecules.

Introduction: Determination of urinary gluten immunogenic peptides (GIP) has emerged as one of the most attractive test to monitor the adherence to the gluten-free diet (GFD) of patients with celiac disease (CD), being a simple, noninvasive and direct method to detect gluten contamination of the GFD.

Areas covered: We conducted a scoping review in Medline (PubMed) of articles published up to April 2023 that analyzed any aspect of the clinical relevance of the use of urinary GIP measurement in patients with CD. A total of 17 articles reporting the clinical use of urinary peptidomics for the follow-up of CD patients were finally included.

Expert opinion: Available data suggest that a negative urinary GIP result is a reliable noninvasive predictor of intestinal mucosa healing in CD patients treated with the GFD, especially if testing three urine samples on different days including the weekend. Due to conflicting results about the sensitivity and the specificity of the urinary GIP determination, additional in-depth information is needed, particularly related to (1) the relationship between the amount of ingested gluten and the quantity of urinary GIP excreted in treated CD patients, (2) the GIP kinetics and best timing for sample collection.

Introduction: Continuous advances in mass spectrometry (MS) technologies have enabled deeper and more reproducible proteome characterization and a better understanding of biological systems when integrated with other 'omics data. Bioinformatic resources meeting the analysis requirements of increasingly complex MS-based proteomic data and associated multi-omic data are critically needed. These requirements included availability of software that would span diverse types of analyses, scalability for large-scale, compute-intensive applications, and mechanisms to ease adoption of the software.

Areas covered: The Galaxy ecosystem meets these requirements by offering a multitude of open-source tools for MS-based proteomics analyses and applications, all in an adaptable, scalable, and accessible computing environment. A thriving global community maintains these software and associated training resources to empower researcher-driven analyses.

Expert opinion: The community-supported Galaxy ecosystem remains a crucial contributor to basic biological and clinical studies using MS-based proteomics. In addition to the current status of Galaxy-based resources, we describe ongoing developments for meeting emerging challenges in MS-based proteomic informatics. We hope this review will catalyze increased use of Galaxy by researchers employing MS-based proteomics and inspire software developers to join the community and implement new tools, workflows, and associated training content that will add further value to this already rich ecosystem.