Platelets - Submitted to 5 days of publication 10+, dreaming are laughing awake
- Explanation from platelets
Hello everyone, I am platelets, a small circulating seedless cell that plays a key role in hemostasis, wound healing and disease progression. But today I am not here to do popular science lectures, recently a group of people used me to send a 10+ article, and from submission to publication is actually only 5 days! I couldn't sit still, so I decided to introduce this article myself
This is my selfie
Article introduction:
This article [1] was published in the journal American Journal of Hematology, which is a relatively high-quality journal in hematology. In the most recent year the impact factor was 10.047. Chinese Academy of Sciences Category: Medicine Region 1 Chinese Academy of Sciences Subcategory: Region 2 Hematology. This journal was also not found in the Chinese Academy of Sciences' International Journals Early Warning List (Trial) list (published in January 2021).
The journal also includes the following databases:
Science Citation Index
Science Citation Index Expanded
Current Contents - Clinical Medicine
Current Contents - Life Sciences
BIOSIS Previews
Literature Interpretation:
When I communicate with other types of cells or circulate in the bloodstream, my RNA profile may change. I may be involved in the development and metastasis of tumors, and my RNA, which has been educated about tumors, will alter expression characteristics and be able to detect tumors. Only a handful of databases provide my data, such as PlateletWeb, which is used to study my protein signaling network and my antigen detection in humans, plateletomics, which detect my RNA and miRNA expression. However, there is a lack of systematic research on the expression spectrum of big data on Me, which requires obtaining high-quality transcriptome data under different conditions. There is currently no comprehensive database of platelet expression for disease.
I don't currently have a comprehensive database of expressions for disease, and this article will be very helpful to the research community. To systematically study platelet RNA expression profiles, they selected my expression dataset from NCBI GEO's Gene Expression Aggregate and SRA database, including 1260 RNA-seqs, 358 RNA microarrays, 21 miRNA-seqs, and 430 miRNA microarray data from 27 disease and health controls. And, of course, there are datasets of peripheral blood mononuclear cells (PBMCs) and whole blood RNA-seq. They then removed the batch effects of RNA-seq expression data and divided them into four groups:
(1) Solid tumors: colorectal cancer (CRC), breast cancer (BRCA), pancreatic cancer (PC), hepatobiliary cancer (HCC), glioblastoma (GBM), non-small cell lung cancer (NSCLC) and low-grade glioma (LGG)
(2) Cardiovascular disease (CAD): unstable angina (UA), ST-segment elevation myocardial infarction (ST-segment elevation myocardial infarction), pulmonary hypertension (PH), no significant atherosclerosis (NSAth), stable angina
(3) Infection (virus): human immunodeficiency virus (HIV), dengue fever and influenza (H1N1)
(4) Others: chronic pancreatitis (CP), epilepsy, multiple sclerosis, diabetes.
My Reads distribution analysis shows that on average about 80% of Reads are mapped to exon (39.89%) and intron (39.18%) areas (Figure 1A). They found that 12.07 percent of the fragments were localized to mitochondrial DNA because intact mitochondria were key to my functioning and survival. Only 8.86 percent of the fragments were localized to the gene spacer. Next, they looked at my expression in healthy samples and found that the number of genes expressed varied widely from study to study (Figure 1B). Is this a bit strange? I speculate that this may be due to a different treatment or treatment. In healthy platelets, the distribution of the FPKM>3 expression gene is similar (Figure 1B), so they believe that the FPKM>3 gene is a high confidence gene. The results showed that I expressed an average of 4994 protein-coding genes and 2168 non-coding genes in healthy people, while the total number of protein-coding genes and non-coding genes in patients with disease ranged from 3069 to 13678 (Figure 1C).
Because I was circulating in the blood vessels, they found differences in gene expression profiles between me, PBMCs, and whole blood. The results found that the average number of genes expressed in me, PBMCs, and whole blood (FPKM>3) was 7162, 10521, and 9336, respectively. Among these genes, non-coding genes accounted for about 30.27%, 12.54%, and 12.18%, respectively (Figure 1D). In addition, they screened for genes expressed in more than 40 percent of samples from FPKM>3 and identified 4326 common genes (4168 and 158 noncoding genes, respectively) from these three sources. While exploring the highest-expressed genes from three sources (Figure 1E). They found that 27 of the first 100 genes were identical and 142 of the first 500 genes were identical. Interestingly, 35 of the 37 mitochondrial genes are in the first 100 positions of my genes, and 6 of the top 10 genes with high expression come from mitochondria (MT-RNR2, MT-RNR1, MT-ND1, MT-CO2, MT-ATP6, and MT-CO3) (Figure 1F). The other four genes are B2M, TMSB4X, FTH1, and PPBP, which are closely related to my formation and function. Meanwhile, the first 10 genes expressed in PBMCs and whole blood are present in both mitochondrial-associated genes and hemoglobin genes (Figure 1F).
In order to study the function of genes in different diseases, they excluded my related genes by screening the keyword "platelets" in gene ontology (GO), so that they could better study my role in different diseases, without focusing on how I produce and how to play a coagulation role. Go enrichment analysis of the remaining top 500 genes. As shown in Figure 1G, in both healthy samples and diseases, these genes are enriched with membrane-localized proteins, endoplasmic reticulum-located proteins, and membrane-localized co-translational proteins. Interestingly, HCC, BRCA, PC, CRC, DM, CP, and virus lack the GO terminology associated with ribosome biogenesis compared to healthy samples and other diseases. This suggests that my RNA profile is different under different conditions or diseases.
Next, they identified differentially expressed genes (DEGs) between disease and healthy samples using the difference multiples > 1.5, p<0.05, and FPKM >10 as the cut-off values. The number of DEGs varies widely (8 to 3298) among different CAD diseases, and there are no DEGs in infectious diseases. Compared with healthy platelets, CRC, BRCA, PC, HCC, GBM, and NSCLC were 1434, 1414, 1316, 1191, 1070, and 225, respectively, illustrating the differences between non-small cell lung cancer and other tumors. To further explore this difference, they intersected DEGs and found that there were 716 DEGs in 5 tumors (BRCA, CRC, GBM, PC, and HCC) and 143 unique DEGs in NSCLC (Figure 1H). At the same time, they found four common upregulation genes (ITGA2B, DEFA1, DEFA3, and TRNA1) in 6 tumors, four of which have been reported to be associated with the development of multiple cancers and 78 common downregulated genes that primarily encode ribosomal proteins (Figure 1I).
To explore my potential value in cancer diagnosis, they used the SEG tool to identify specifically expressed genes (SEG) in 11 solid tumors (Figure 1J). They found low-specific expression of FKBP1A in NSCLC and LGG, and also observed in TCG data that the expression of FKBP1A in lung and brain tumors was down-regulated compared to healthy controls. In addition, the remaining 7 platelet SEGs (GP1BB, PRR7, CYBA, NOP53, TYMP, STUB1 and ATP5D) in NSCLC and LGG and 3 SCGs (DUSP1, DUSP2 and DIDIT4) were reported to be associated with tumorigenesis.
To facilitate new discoveries using these high-quality datasets of mine, they organized expression data and analysis into a state-of-the-art platelet expression database, named Platelet Expression Atlas (PEA). PEA provides a complete set of gene and miRNA expression profiles and advanced analytical results for each dataset:
(1) Expression profile of platelets in different diseases;
(2) Average expression of specific genes (miRNAs) in health and various diseases;
(3) Differential expression analysis, including functional enrichment and protein interaction networks of DEGs;
(4) Platelet-specific expression of genes.
Figure 1 Map of platelet gene expression.
Summary of this article:
In summary, they first systematically studied the expression of my RNA for different diseases and showed its expression in me, PBMCs, and whole blood. In fact, they provided me with a complete database of expressions, and I would like to say here -
This can serve as an infrastructure for the research community. But in the future, there are still several problems that need to be solved, including the long-term maintenance of PEA. In fact, I have a role in inflammation, immunity, cancer onset and metastasis. Recent studies have shown that I can also carry disease-related biomolecules (such as RNA, proteins and metabolites) spilled by dysfunctional cells, which have a diagnostic effect on diseases, but there is not a lot of research in this area, and everyone has to continue to refuel
With such a valuable resource, how to use it? If you don't know, look at the second "Refueling Platelets!" If you want to be able to meet platelets, please contact us to communicate more!