No abstract available.
Proteomics*

No abstract available.
Proteomics*

Objective To identify the differentially expressed proteins associated with cognitive impairment between the high-altitude population and the plain population， and to investigate the biological functions and signaling pathways of the differentially expressed proteins. Methods A total of 30 individuals living in the plain area （with an altitude of 400 m） and 30 individuals living in the high-altitude area （with an altitude of 3 960 m） were enrolled as plain group and high-altitude group， respectively， and general information was collected from all subjects. Montreal Cognitive Assessment （MoCA） was used to assess cognitive function. Blood samples were collected from each group， and the tims TOF Pro mass spectrometer was used to measure the serum levels of proteins after centrifugation. SPSS 25.0 was used for statistical analysis to investigate he association between proteomics and cognitive impairment. Results The results of MoCA assessment for both groups showed that the high-altitude group had a significantly lower MoCA score than the plain group， suggesting that there was significant cognitive impairment in the high-altitude group， with the main manifestation of impairment in visual space/executive ability， attention， delayed memory， and orientation. The proteomic analysis of serum samples from the subjects identified 169 differentially expressed proteins （84 upregulated proteins and 85 downregulated proteins）， among which 39 proteins were associated with cognitive impairment. The enrichment analysis of the differentially expressed proteins showed that these differentially expressed proteins were involved in the regulation of multiple signaling pathways and metabolic pathways. Conclusion Significant cognitive impairment is observed in the high-altitude population， and there are differentially expressed proteins associated with cognitive function between the high-altitude population and the plain population.
Proteomics

Objective To investigate the differential expression of related proteins in interosseous muscle tissue between patients with familial amyotrophic lateral sclerosis（FALS） and normal individuals in a family using the isobaric tags for relative and absolute quantitation （iTRAQ） technique， to identify the pathogenic proteins for this family， and to provide a basis for treatment. Methods Interosseous muscle tissue samples were collected from all subjects in this family， and the iTRAQ technique was used to perform qualitative and quantitative analyses for all proteins and obtain the expression profile of proteins in the disease group and the normal group. The bioinformatics methods were used to identify the proteins associated with the onset of FALS. A gene ontology（GO）analysis was performed for cell components， and a classification analysis was performed for related proteins. Results A total of 453 proteins were identified by mass spectrometry. The GO analysis obtained 14 differentially expressed proteins between the disease group and the normal group （P<0.05）， and compared with the normal group，the disease group had the low expression of 5 proteins （Ratio<1） and the high expression of 9 proteins （Ratio>1）. Conclusion This study identifies 8 proteins that are highly associated with FALS， i.e.， tripartite motif-containing protein 72， NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 1， annexin A1， decorin， glutathione peroxidase 3， collagen alpha-1 （Ⅻ） chain， collagen alpha-2 （Ⅰ） chain， and collagen type I alpha 1 isoform CRA-a. There are 6 proteins that might be associated with FALS， i.e.，26 S protease regulatory subunit 8， laminin subunit alpha-2，prolargin， fibrillin-1， myosin-8， and dermatopontin.
Proteomics

For more than past hundred years, Engels F. thought that the nature of life was protein. The human was decided by 2 factors, including genome and various influences of environment. The establishment of science of protein system (proteomics) is a break through of biology in 21st century.
Genomics ; Proteomics

Proteomics opened a complete new way in the medical and pharmaceutical sector. It helped detecting causes of unknown diseases, early diagnosing, contributing to optimal treatment of many diseases, and manufacturing new drugs as molecular mechanism. It is hoped that proteomics will be used to understand and treat cancer
Genomics ; Proteomics

In the 21st century, proteomics, a new science, appears in the biology. Proteomics studies the human protein, opens a new era of molecular pathology. The scientists suggest that the human being is determined by 2 factors: the genetic system or human genome and various influences of surrounding environment. From which, proteomics have been become the turning point of the biology at the beginning of 21st century.
Proteomics ; Biology

With wide applications of genomics, transcriptomics, proteomics and metabolomics in the post-genome era, functional explanation has become the central task in life science research, and multi-omics data integrative analysis has become an indispensable strategy for uncovering the underlying biological mechanism. This special issue aimed to introduce the related research advances and applications in multi-omics by inviting the domestic experts. In total, 28 papers have been collected in this issue, for researcher's reference in multi-omics.
Genomics ; Proteomics ; Metabolomics ; Transcriptome

Chromatography is a basic process in the current proteomics workflow, and the retention time alignment of the chromatogram is one of the important steps to effectively improve the identification and quantification accuracy. After years of development, a series of algorithms for retention time alignment have been developed. This review summarizes the advances of chromatographic retention time alignment algorithms and tools for proteomics analysis from the perspective of proteomics users, and discusses the development and future application directions.
Algorithms ; Proteomics/methods*

Humans ; Oral Medicine ; Proteomics