Expert Review of Proteomics最新文献

Introduction: Hepatocellular carcinoma (HCC) represents a significant burden globally, which ranks sixth among the most frequently diagnosed cancers and stands as the third leading cause of cancer-related mortality. Glycoproteomics, as an important branch of proteomics, has already made significant achievements in the field of HCC research. Aberrant protein glycosylation has shown to promote the malignant transformation of hepatocytes by modulating a wide range of tumor-promoting signaling pathways. The glycoproteome provides valuable information for understanding cancer progression, tumor immunity, and clinical outcome, which could serve as potential diagnostic, prognostic, and therapeutic tools in HCC.

Areas covered: In this review, recent advances of glycoproteomics contribute to clinical applications (diagnosis and prognosis) and molecular mechanisms (hepatocarcinogenesis, progression, stemness and recurrence, and drug resistance) of HCC are summarized.

Expert opinion: Glycoproteomics shows promise in HCC, enhancing early detection, risk stratification, and personalized treatments. Challenges include sample heterogeneity, diverse glycans structures, sensitivity issues, complex workflows, limited databases, and incomplete understanding of immune cell glycosylation. Addressing these limitations requires collaborative efforts, technological advancements, standardization, and validation studies. Future research should focus on targeting abnormal protein glycosylation therapeutically. Advancements in glycobiomarkers and glycosylation-targeted therapies will greatly impact HCC diagnosis, prognosis, and treatment.

Introduction: Cancer is a disease of (altered) biological pathways, often driven by somatic mutations and with several implications. Therefore, the identification of potential markers of disease is challenging. Given the large amount of biological data generated with omics approaches, oncology has experienced significant contributions. Proteomics mapping of protein fragments, derived from proteolytic processing events during oncogenesis, may shed light on (i) the role of active proteases and (ii) the functional implications of processed substrates in biological signaling circuits. Both outcomes have the potential for predicting diagnosis/prognosis in diseases like cancer. Therefore, understanding proteolytic processing events and their downstream implications may contribute to advances in the understanding of tumor biology and targeted therapies in precision medicine.

Areas covered: Proteolytic events associated with some hallmarks of cancer (cell migration and proliferation, angiogenesis, metastasis, as well as extracellular matrix degradation) will be discussed. Moreover, biomarker discovery and the use of proteomics approaches to uncover proteolytic signaling events will also be covered.

Expert opinion: Proteolytic processing is an irreversible protein post-translational modification and the deconvolution of biological data resulting from the study of proteolytic signaling events may be used in both patient diagnosis/prognosis and targeted therapies in cancer.

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.