Proteomics最新文献

Lung adenocarcinoma, a subtype of lung cancer, is produced by uncontrolled proliferation of somatic cells affected by some tumorigenic factors. The origin of this disease can be attributed to the concept of “cancer driver,” which links the occurrence of tumor with specific changes in some key genes. These key genes can be identified at various molecular levels. Our innovative method uses a groundbreaking computing technology called GenePlexus to mine new genes related to lung adenocarcinoma. Initially, a vast network was synthesized from protein-protein interactions. Utilizing GenePlexus, we traversed paths interlinking aberrant genes across different layers and pinpointed emerging candidate genes situated on these trajectories. Finally, the candidate genes that were obtained underwent a series of filtering processes, including a permutation test, interaction test, and enrichment test. Compared with the shortest path method, GenePlexus has identified previously neglected genes involved in lung adenocarcinoma. For example, genes such as EGR2, EPHA3, FGFR4, HOXB1, and HEY1 play key roles at multiple molecular levels, including methylation, microRNA, mRNA and mutation, which affect tumorigenesis and lung cancer progression. These genes regulate various processes, from gene expression and cell proliferation to drug resistance to therapeutic drugs and the progress of lung adenocarcinoma.

Advances in high-throughput omics technologies have enabled system-wide characterization of biological samples across multiple molecular levels, such as the genome, transcriptome, and proteome. However, as sample sizes rapidly increase in large-scale multi-omics studies, sample mix-ups have become a prevalent issue, compromising data integrity and leading to erroneous conclusions. The interconnected nature of multi-omics data presents an opportunity to identify and correct these errors. This review examines the potential sources of sample mix-ups and evaluates the methodologies and tools developed for detecting and correcting these errors, with an emphasis on approaches applicable to proteomics data. We categorize existing tools into three main groups: expression/protein quantitative trait loci-based, genotype concordance-based, and gene/protein expression correlation-based approaches. Notably, only a handful of tools currently utilize the proteogenomics approach for correcting sample mix-ups at the proteomics level. Integrating the strengths of current tools across diverse data types could enable the development of more versatile and comprehensive solutions. In conclusion, verifying sample identity is a critical first step to reduce bias and increase precision in subsequent analyses for large-scale multi-omics studies. By leveraging these tools for identifying and correcting sample mix-ups, researchers can significantly improve the reliability and reproducibility of biomedical research.

Teleost fishes are a highly diverse, ecologically essential group of aquatic vertebrates that include coho salmon (Oncorhynchus kisutch). Coho are semelparous and all ovarian follicles develop synchronously. Owing to their ubiquitous distribution, teleosts provide critical sources of food worldwide through subsistence, commercial fisheries, and aquaculture. Enhancement of hatchery practices requires detailed knowledge of teleost reproductive physiology. Despite decades of research on teleost reproductive processes, an in-depth proteome of teleost ovarian development has yet to be generated. We have described a coho salmon ovarian proteome of over 5700 proteins, generated with data independent acquisition, revealing the proteins that change through the transition from primary to secondary ovarian follicle development. This transition is critical during the onset of puberty and for determining egg quality and embryonic development. Primary follicle development was marked by differential abundances of proteins in carbohydrate metabolism, protein turnover, and the complement pathway, suggesting elevated metabolism as the follicles develop through stages of oogenesis. The greatest proteomic shift occurred during the transition from primary to secondary follicle growth, with increased abundance of proteins underlying cortical alveoli formation, extracellular matrix reorganization, iron binding, and cell–cell signaling. This work provides a foundation for identifying biomarkers of salmon oocyte stage and quality.

Plant disease resistance (PDR) proteins are critical in identifying plant pathogens. Predicting PDR protein is essential for understanding plant–pathogen interactions and developing strategies for crop protection. This study proposes a hybrid model for predicting and designing PDR proteins against plant-invading pathogens. Initially, we tried alignment-based approaches, such as Basic Local Alignment Search Tool (BLAST) for similarity search and MERCI for motif search. These alignment-based approaches exhibit very poor coverage or sensitivity. To overcome these limitations, we developed alignment-free or machine learning (ML)-based methods using compositional features of proteins. Our ML-based model, developed using compositional features of proteins, achieved a maximum performance area under the receiver operating characteristic curve (AUROC) of 0.91. The performance of our model improved significantly from AUROC of 0.91–0.95 when we used evolutionary information instead of protein sequence. Finally, we developed a hybrid or ensemble model that combined our best ML model with BLAST and obtained the highest AUROC of 0.98 on the validation dataset. We trained and tested our models on a training dataset and evaluated them on a validation dataset. None of the proteins in our validation dataset are more than 40% similar to proteins in the training dataset. One of the objectives of this study is to facilitate the scientific community working in plant biology. Thus, we developed an online platform for predicting and designing plant resistance proteins, “PlantDRPpred” (https://webs.iiitd.edu.in/raghava/plantdrppred).