Proteomic analysis of peripheral blood hematopoietic stem cells in children with acquired non-severe aplasia anemia

Proteomic analysis of peripheral blood hematopoietic stem cells in children with acquired non-severe aplasia anemia | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 14 July 2025 V1 Latest version Share on Proteomic analysis of peripheral blood hematopoietic stem cells in children with acquired non-severe aplasia anemia Authors : Lilan Wu , Lu Zhu , Wenling Guo , Sha Liu , Tao Xu , Xiaoyan Hu , Jieying Wu , Hua Jiang 0000-0002-4552-0785 , Liya He , and WenXian Zheng 0009-0005-2274-2843 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175249638.82993889/v1 184 views 142 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Objective: Acquired aplastic anemia is a bone marrow failure disease caused by various factors, particularly immune disorders. In order to understand the the pathogenesis of non-severe aplastic anemia (NSAA), we employed label-free proteomic methods to characterize the proteomic features of hematopoietic stem cells (HSCs) derived from peripheral blood samples of pediatric patients with NSAA. Method: Through comprehensive and individual analyses, we divided the proteomic results of 6 NSAA patients and 6 healthy controls into 7 analysis groups. Group T compared the proteomes of all 6 NSAA patients with those of the healthy controls, while Groups 1-6 individually compared each NSAA patient with their respective control. Result: Our findings indicate that differentially expressed proteins (DEPs) in each group are primarily enriched in GO-BP and KEGG pathways associated with essential cellular functions such as replication, transcription, translation, RNA metabolism, protein degradation, carbohydrate and lipid metabolism, amino acid synthesis, etc. Notably, DEPs identified through protein-protein interaction (PPI) analysis were also predominantly linked to housekeeping functions relevant to NSAA pathogenesis. Additionally, Group R represents an analysis subset comprising DEPs recurrently observed in at least three independent analysis groups. The enrichment analysis, node protein analysis, and transcription factor analysis of the DEPs from Group R are highly consistent with the pathogenesis of NSAA. A majority of the DEPs from Group R exhibit downregulation, with MRPL23, IDH2, and ZNF880 being the most recurrent ones. These proteins should be further investigated as potential biomarkers for NSAA. Additionally, this study simultaneously detected and analyzed a patient with myelodysplastic syndrome (MDS), revealing similarities in the proteome of their hematopoietic stem cells (HSCs) compared to NSAA patients. Both MDS and NSAA showed a decline in housekeeping function. Conclusion: The proteomic detection technology employed in this study has demonstrated its capability to assess peripheral blood HSC functional failure to some extent and holds promise for disease monitoring of NSAA in future applications. The pathogenesis of NSAA may involve impaired or diminished HSC housekeeping function, which could manifest as changes in HSC housekeeping function during disease progression. Proteins exhibiting high reproducibility such as MRPL23, IDH2, and ZNF880 have potential to serve as biomarkers for NSAA. Proteomic analysis of peripheral blood hematopoietic stem cells in children with acquired non-severe aplasia anemia Abstract Objective: Acquired aplastic anemia is a bone marrow failure disease caused by various factors, particularly immune disorders. In order to understand the the pathogenesis of non-severe aplastic anemia (NSAA), we employed label-free proteomic methods to characterize the proteomic features of hematopoietic stem cells (HSCs) derived from peripheral blood samples of pediatric patients with NSAA. Method: Through comprehensive and individual analyses, we divided the proteomic results of 6 NSAA patients and 6 healthy controls into 7 analysis groups. Group T compared the proteomes of all 6 NSAA patients with those of the healthy controls, while Groups 1-6 individually compared each NSAA patient with their respective control. Result: Our findings indicate that differentially expressed proteins (DEPs) in each group are primarily enriched in GO-BP and KEGG pathways associated with essential cellular functions such as replication, transcription, translation, RNA metabolism, protein degradation, carbohydrate and lipid metabolism, amino acid synthesis, etc. Notably, DEPs identified through protein-protein interaction (PPI) analysis were also predominantly linked to housekeeping functions relevant to NSAA pathogenesis. Additionally, Group R represents an analysis subset comprising DEPs recurrently observed in at least three independent analysis groups. The enrichment analysis, node protein analysis, and transcription factor analysis of the DEPs from Group R are highly consistent with the pathogenesis of NSAA. A majority of the DEPs from Group R exhibit downregulation, with MRPL23, IDH2, and ZNF880 being the most recurrent ones. These proteins should be further investigated as potential biomarkers for NSAA. Additionally, this study simultaneously detected and analyzed a patient with myelodysplastic syndrome (MDS), revealing similarities in the proteome of their hematopoietic stem cells (HSCs) compared to NSAA patients. Both MDS and NSAA showed a decline in housekeeping function. Conclusion: The proteomic detection technology employed in this study has demonstrated its capability to assess peripheral blood HSC functional failure to some extent and holds promise for disease monitoring of NSAA in future applications. The pathogenesis of NSAA may involve impaired or diminished HSC housekeeping function, which could manifest as changes in HSC housekeeping function during disease progression. Proteins exhibiting high reproducibility such as MRPL23, IDH2, and ZNF880 have potential to serve as biomarkers for NSAA. 1. Introduction Acquired aplastic anemia is a hematological disorder characterized by bone marrow failure, primarily attributed to immune dysregulation. It manifests as a reduction in peripheral blood cell counts across multiple lineages, including anemia, thrombocytopenia, and leukopenia (1-2). This condition can be classified into severe acquired aplastic anemia (SAA) and non-severe acquired aplastic anemia (NSAA). SAA is defined by absolute reticulocyte count below 20x10 9 /L, platelet count below 20x10 9 /L, and absolute neutrophil count below 0.5x10 9 /L. NSAA refers to cases of aplastic anemia that do not fully meet the criteria for SAA (3-4). The disease course tends to be prolonged and recurrent, posing significant physical and psychological risks to pediatric patients. To enhance therapeutic approaches for this condition, it is imperative to employ diverse methodologies in comprehending its pathogenesis. Our research group recently employed label-free proteomic methods to isolate hematopoietic stem cells (HSCs) from 3-5ml peripheral blood samples of patients, analyze their proteomic expression, and assess the functional status of HSCs in non-severe aplastic anemia (NSAA). We aim to utilize this approach for disease monitoring and identification of NSAA biomarkers. In previous studies, researchers extracted HSCs from patients’ bone marrow and investigated changes in their proteomics and transcriptome (5-6). However, this invasive examination is inconvenient for frequent sampling, testing, and disease monitoring. In the subsequent sections, we will explore the proteomics of peripheral blood HSCs in NSAA using label-free proteomic methods which offer a non-invasive alternative that allows for convenient sampling, testing, and disease monitoring. We hypothesize that differentially expressed proteins exhibited by peripheral blood HSCs would differ significantly from those observed in bone marrow HSCs. 2. Materials and Methods 2.1 Patients and volunteers All patients and volunteers were recruited from the outpatient or day ward of Guangzhou Women and Children’s Medical Center, and blood samples were collected between February 2022 and March 2023. The diagnostic criteria for NSAA were based on the ”Diagnosis and Treatment Standards for Childhood Aplastic Anemia” issued by the National Health Commission in 2019 (3). Patients underwent thorough evaluation including bone marrow biopsy, regular blood monitoring, as well as long-term follow-up to ensure accurate diagnosis. Volunteers were selected to match the age and gender of the patients. This study was conducted in accordance with the Helsinki Declaration by obtaining informed consent from parents or individuals of patients and volunteers, along with approval from the Ethics Committee of Guangzhou Women and Children’s Medical Center. 2.2 Isolation and Purification of Stem Cells All the following steps should be performed under sterile conditions. A peripheral blood monocyte isolation kit (Serumwerk Bernburg AG, Norway) was used to isolate monocytes from peripheral blood samples. The washed peripheral blood mononuclear cells were resuspended in 2ml of Percoll stratification solution (I) (density=1.080g/ml) (different densities of Percoll stratification solution were prepared according to the manufacturer’s instructions, item number P8370, Solarbio). Subsequently, 2ml of Percoll stratification solution (II) (density=1.069g/ml) was gently layered on top of this liquid layer, followed by gentle addition of 2ml of Percoll stratification solution (III) (density=1.060g/ml) onto the second liquid layer. The above density gradient was centrifuged at 1000 × g in a rotating rotor for 90 minutes at 20℃ without braking during speed increase. After centrifugation, monocytes were found at the interface between Percoll stratified solutions II and III (the less dense part). The monocytes were carefully collected using a capillary pipette and washed three times with 1 × PBS solution containing 1-5% fetal bovine serum before being centrifuged at 400 × g for 5 minutes at 4℃. Thereafter, the supernatant was discarded. The cells were then resuspended in 1 × PBS and incubated with CD34 antibody (Becton Dickinson and Company), adding slowly while gently pipetting for one minute before performing flow cytometry sorting to collect a total of 5000 cells. To verify cell count accuracy, the collected cells were dispersed into a concentration of 10000/mL using fresh aliquots of PBS and subjected to cell counting analysis. For future use, each tube contained approximately 200 cells suspended in20uL volumeand stored at -80℃. 2.3 Extraction and processing of proteins The 200 pre-packaged stem cell precipitates were dispersed in lysis buffer (1% protease inhibitor in an 8 M urea solution, Sigma) and sonicated under ice bath conditions for 240 seconds (sonication for 10 seconds, intermittent for 10 seconds). Subsequently, the cell samples were centrifuged at high speed at 20,000 rpm and 4℃ for 10 minutes to eliminate cell debris, and the supernatant was collected. Next, a final concentration of 10 mM DTT (item number 43819, Sigma) was achieved by adding it at a concentration of 100 mM followed by incubation at 56℃ for two hours. After cooling to room temperature, a final concentration of 20 mM IAA (item number I1149, Sigma) was obtained by adding it at a concentration of 200 mM and allowing it to react in the dark for forty minutes. The solution was then diluted tenfold with 50 mM NH4HCO3 before adding solid enzyme (synthesized according to the manufacturer’s instructions, Suzhou Nano Micro Technology Co., Ltd.) along with buffer solution (50 mM NH4HCO3 containing 10% acetonitrile), resulting in a total reaction volume of fifty microliters which was oscillated at 37℃ and 1200 rpm for ten minutes. Finally, the mixture was centrifuged again and the supernatant collected. The samples obtained above were subjected to desalination using pep-Tip following the manufacturer’s instructions (Guangzhou Kefu Technology). 2.4 Mass spectrometry detection The sample was dissolved in solvent A Phase (98% H2O+2% ACN+0.1% FA) and injected with a volume of 2μL into the EASY-nano-LC 1200 chromatography system for separation on an analysis column (ac-50um-20cm) at a flow rate of 125 nL/min. The chromatographic gradient proceeded as follows: from 0 to 94 minutes, solvent B (acetonitrile containing 0.1% formic acid) linearly increased from 7% to 26%, from 94 to 106 minutes, solvent B linearly increased from 26% to 48%, from 106 to111 minutes, solvent B linearly increased from 48% to 98%, then within 10 minutes, solvent B was raised to 98%, and maintained until 120 minutes. Mass spectrometry data acquisition was performed using a Q Exactive mass spectrometer (Thermo Scientific). The specific parameter settings were as follows: the ion source spray voltage was set at 1.9 kV, the first scan range was 355-1700 m/z with a resolution of 70000 and maximum IT of 100 ms, the second scan range was 355-1700 m/z with a resolution of 17500 and maximum IT of 50ms, the second scan mode was DDA (data-dependent acquisition), which selected the top 20 ions for fragmentation, the fragmentation mode used was HCD with the fragment ions detected in the Orbitrap. Dynamic exclusion time was set to 2 seconds. AGC was set to 3e6 for the first scan and 1e5 for the second scan. 2.5 Database Retrieval and Definition of Differential Proteins The resulting MS/MS data were processed using the Protein Discovery search engine (v.2.5). Tandem mass spectra were searched against a concatenated database consisting of the Macaca mulatta protein sequences (90,797 entries) and a reverse decoy database. Trypsin/P was specified as the cleavage enzyme, allowing for up to 2 missed cleavages. The mass tolerance for precursor ions was set at 20 ppm in the first search and 5 ppm in the main search, while the mass tolerance for fragment ions was set at 0.02 Da. Carbamidomethyl on Cys was designated as a fixed modification, whereas acetylation on protein N-terminal and oxidation on Met were considered variable modifications. False discovery rate (FDR) was adjusted to be less than 1%. Protein quantification was performed using label-free quantification method: the fold change (FC) of each protein was determined by calculating the ratio of mean relative quantification values across multiple repeated samples. To assess significance, a T-test was applied to compare relative quantification values between comparative group samples, with P value serving as an indicator of statistical significance (<0.05 being considered significant). 2.6 Data Analysis Methods Functional enrichment and pathway analysis of differential proteins were primarily conducted using the DAVID database (https://david.ncifcrf.gov/). Protein-protein interaction (PPI) analysis on differential proteins was performed using the STRING database (https://cn.string-db.org/), followed by calculation of topological parameters for the differential protein PPI network and identification of node proteins through Cytoscape 3.8.2. The Gene Cards database (https://www.genecards.org/) was utilized to search for aplastic anemia-related proteins and annotate their functions, while JASPAR (https://jaspar.genereg.net/) provided a comprehensive list of human transcription factors. 3. Results 3.1 Characteristics of Observation Object A total of 6 patients with NSAA were recruited from our center’s outpatient or day ward between February 2022 and March 2023. The demographic characteristics of the patients were presented in Table 1. Among them, two patients were already undergoing relevant treatments, such as cyclosporine, at the time of enrollment into the research group. The blood routine parameters listed in the table represent the most recent measurements prior to peripheral blood collection for stem cell extraction. 3.2 Summary of protein detection numbers Our strategy for analyzing differential proteins involved dividing 12 patients and controls into seven analysis groups (as presented in Table 2), namely Group T (ALL six patients vs ALL six controls), Group 1 (patient one vs ALL six controls), Group 2 (patient two vs ALL six controls), Group 3 (patient three vs ALL six controls), Group 4 (patient four vs ALL six controls), Group 5 (patient five vs ALL six controls), and Group 6 (patient six vs ALL six controls). The total number of detectable proteins from HSCs in each analysis group ranged between 1408-1807 using this detection technique. Differential expression protein (DEP) was defined as a protein present in more than thirty percent of individuals, upregulated or downregulated by at least two-fold, with a P-value<0.05. The total number of DEPs in each analysis group varied between fifty-one to seven hundred fifty. With the exception of Group T, which had more upregulated proteins than downregulated ones, all other analysis groups had fewer upregulated proteins than downregulated ones. The significant differences observed among the number of DEPs across the various analysis groups indicated heterogeneity in protein expression among NSAA patients, therefore, interpreting DEPs results required considering both common patterns and individual differences. 3.3 Analysis of Group T The most significantly upregulated differentially expressed proteins, including ASNS, GRIN2C, SMC6, MINDY1, SURF6, KIF23, MEX3C, MINDY4B, DDX3Y, WDR17 and C12orf71 were observed as shown in Figure 1A. Conversely, the most obvious downregulation of DEPs was observed for IDH2, SLC16A1, MRPL23, DUSP11, ACTN1 and FLOT2 (Figure 1A). GO-BP enrichment analysis revealed that NSAA-related biological processes encompassed protein K48-linked denaturation and degradation , ribosomal large subunit biogenesis , protein deubiquitination , translation , mitochondrial translation , glutamine metabolic process , protein K63-linked deubiquitination and tricarboxylic acid cycle (Figure 1B and Table 3). Furthermore, KEGG pathway analysis indicated that pathways associated with NSAA included Biosynthesis of amino acid, Gluconeogenesis, HIF-1 signaling pathway, and Citrate cycle (Figure 1C and Table 3). Protein-protein interaction network (PPI) of DEPs was constructed using STRING database, and topological parameters were calculated using Cytoscape software. The ranking parameter ” Degree ” was utilized to generate a list of node proteins (Figure 1E), and their PPI relationships were visualized (Figure 1D). Importantly, ZNF880 emerged as the sole transcription factor among the DEPs in this study group, and its expression was found to be downregulated. 3.4 Analysis of DEPs overlap in each analysis group Overlap analysis was performed on the DEPs from the seven analysis groups mentioned above (Group T & Group 1-6), resulting in a list of proteins exhibiting a recurrence frequency of ≥3 times and consistent expression trends (Table 4, defined as Group R ). A total of 241 DEPs were identified, with only three showing upregulation while the remaining were downregulated. Notably, MRPL23 and IDH2 exhibited downregulation across multiple analysis groups with a recurrence frequency exceeding six times. These two proteins were found to be associated with NSAA pathogenesis based on annotations from the Gene Cards database, suggesting their potential as biomarkers for NSAA. GO-BP enrichment analysis revealed that biological processes related to NSAA encompassed rRNA processing, ribosomal small subunit biogenesis, mitochondrial translation, translation, mRNA splicing via spliceosome, RNA splicing, ribosomal large subunit biogenesis, maturation of SSU-rRNA, mRNA processing, nucleosome positioning , etc (Figure 2A and Table 5). Importantly, all enriched proteins showed downregulation consistently aligning with the stem cell exhaustion state observed in aplastic anemia. The KEGG pathway analysis of these DEPs indicated that pathways related to NSAA included Ribosome biogenesis in eukaryotes, Ribosome, Spliceosome, RNA polymerase, Basal transcription factors, Nucleotide excision repair, Biosynthesis of amino acids (Figure 2B and Table 5). Notably, all the enriched proteins were found to be downregulated in accordance with the state of stem cell exhaustion observed in aplastic anemia. Furthermore, a list of node proteins was obtained through protein-protein interaction (PPI) analysis and Cytoscape calculation (Figure 2D), and their PPI relationships were displayed (Figure 2C). Additionally, among the identified DEPs were several ranscription factors including ZNF880, DPF1, CTCF, HOXB9 SMAD2, ZNF121, all of which exhibited downregulation in expression levels (Figure 2E). 3.5 Analysis of Group 1 Comparing Patient 1 with six healthy controls, a total of 67 DEPs were identified, consisting of 9 upregulated DEPs and 58 downregulated DEPs (Table 2). Gene Ontology Biological Process (GO-BP) enrichment analysis revealed that the biological processes associated with NSAA included rRNA processing, canonical glycolysis, glycolytic process, mRNA transcription from RNA polymerase II promoter, positive regulation of transcription initiation from RNA polymerase II promoter, regulation of DNA repair, RNA polymerase II transcriptional preinitiation complex assembly, translation, mitochondrial translation , etc. (Table 6). Most enriched proteins were found to be downregulated except for SCYL1 and MRPL39 which showed upregulation. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that pathways related to NSAA encompassed Basal transcription factors, Biosynthesis of amino acids, Carbon metabolism, Glycolysis/Gluconeogenesis, etc.(Table 6), wherein all the enriched proteins exhibited downregulation. Similar to the methodology employed in section 3.3, node proteins were obtained through protein-protein interaction (PPI) analysis followed by Cytoscape calculation. The top thirty node DEPs based on ”Degree” ranking were selected and annotated using the Gene Cards database to identify their relevance in NSAA pathogenesis (Table 7). The reproducibility of each node DEP associated with NSAA was calculated across all seven analysis groups. The node DEPs with a reproducibility of 1 were exclusively observed in this analysis group, comprising four components: MRPL40, TAF6, YY1, and KEAP1. It is noteworthy that all the node proteins in DEPs with a reproducibility of ≥ 2 exhibited downregulation. Furthermore, among the transcription factors identified in this analysis group (BBX, DPF1, YY1, ZFP28), their expression levels were consistently downregulated, notably, YY1 was uniquely associated with this analysis group and showed a reproducibility of 1 (Table 7). 3.6 Analysis of Group 2 Comparing Patient 2 with 6 healthy controls, a total of 88 DEPs were identified, comprising 30 upregulated DEPs and 58 downregulated DEPs (Table 2). The GO-BP enrichment analysis revealed that NSAA-related biological processes encompassed proteasome-mediated ubiquitin-dependent protein catabolic process, protein deubiquitination, rRNA processing, ubiquitin-dependent protein catabolic process, regulation of mRNA stability, heterochromatin assembly, pre-replicative complex assembly, regulation of cellular amino acid metabolic process, etc. (Table 8), demonstrating the presence of both upregulated and downregulated DEPs. Furthermore, KEGG pathway analysis indicated the involvement of NSAA-associated pathways such as Proteasome, Nucleocytoplasmic transport, Biosynthesis of amino acids (Table 8), wherein all proteins enriched in pathways related to amino acid synthesis exhibited downregulation. Similar to the processing in section 3.3, node proteins were obtained through PPI analysis and Cytoscape calculation. The top 30 node DEPs were selected based on their ranking in terms of ”Degree”, and the node DEPs associated with NSAA pathogenesis were annotated using the Gene Cards database (Table 9). The reproducibility of each NSAA-related node DEP was calculated across seven analysis groups. Among these, there were five unique proteins (PSMA1, PSMA5, PSMC4, PSMD14, PSMD6) that exhibited upregulated expression and had a reproducibility score of 1 within this analysis group. Notably, it was observed that the majority of node proteins in DEPs with a reproducibility score ≥2 showed downregulation. Furthermore, no transcription factors were identified among the DEPs from this particular analysis group (Table 9). 3.7 Analysis of Group 3 Comparing Patient 3 with six healthy controls, we identified a total of 750 DEPs, comprising 21 upregulated DEPs and 729 downregulated DEPs (Table 2). GO-BP enrichment analysis revealed that NSAA-related biological processes encompassed rRNA processing, mRNA splicing via spliceosome, translation, RNA splicing, ribosomal small subunit biogenesis, mRNA processing, mitochondrial translation, cytoplasmic translation, ribosome biogenesis, U2-type prespliceosome assembly , etc. (Table 10). Notably, the enriched DEPs were predominantly downregulated. Furthermore, KEGG pathway analysis indicated the involvement of NSAA-associated pathways such as Spliceosome, Ribosome, Ribosome biogenesis in eukaryotes, mRNA surveillance pathway, Nucleocytoplasmic transport, Nucleotide excision repair, Protein export, RNA degradation , etc. (Table10), wherein the enriched proteins were primarily downregulated. Consistent with the methodology described in section 3.3, the identification of node proteins was accomplished through PPI analysis followed by Cytoscape calculation. The top 30 node DEPs were selected based on their ranking in the ”Degree” category, and the node DEPs associated with NSAA pathogenesis were annotated using information from the Gene Cards database (Table 11). The reproducibility of each NSAA-related node DEP was calculated across seven analysis groups. Among these, a unique set of 10 downregulated proteins (EIF4A3, EPRS1, GNL2, HSPA4, PES1, RPL7A, RPL8, RRP1B, SNRPE, SNU13) exhibited a reproducibility score of 1 within this particular analysis group. Notably interesting was the consistent downregulation observed for all node proteins in this specific set of DEPs. Furthermore, transcription factors identified within this analysis group included BBX, CTCF, DPF1, GATA6, HOXB9, MAZ, PHOX2B, SMAD2, YBX1, ZFP28, ZNF121, ZNF629, ZNF768 and ZNF787, among them GATA6, H0XB9, MAZ, PBOX2B, YBXI, ZNF629, ZNF768, and ZNF787 showed a reproducibility score of 1 unique to this analysis group (Table 11). 3.8 Analysis of Group 4 Comparing Patient 4 with 6 healthy controls, a total of 604 DEPs were identified, comprising 32 upregulated DEPs and 572 downregulated DEPs (Table 2). The GO-BP enrichment analysis revealed that NSAA-related biological processes encompassed rRNA processing, translation, mRNA splicing via spliceosome, cytoplasmic translation, ribosomal small subunit biogenesis, RNA splicing, mitochondrial translation, mRNA processing, ribosomal large subunit biogenesis, regulation of translation, etc. (Table 12), with predominantly downregulation observed among the enriched DEPs. Furthermore, the KEGG pathway analysis indicated that pathways associated with NSAA included Ribosome, Spliceosome, Ribosome biogenesis in eukaryotes, Nucleotide excision repair, Basal transcription factors, RNA polymerase, Nucleocytoplasmic transport, RNA degradation, mRNA surveillance pathway, etc. (Table12), wherein the enriched proteins were primarily downregulated. Similar to section3.3 processing, nodal proteins were obtained through PPI analysis and Cytoscape calculation. The top 30 DEPs were selected based on the ”Degree” ranking, and the DEPs associated with the pathogenesis of NSAA were annotated from the Gene Cards database (Table 13). The reproducibility of each NSAA-related DEP was calculated in seven analysis groups. Among these, there were 13 proteins (EFTUD2, MTREX, NIFK, NOP58, RPL14, RPL17, RPL35, RPL7, RPS13, RPS23, RPS8, RPS9 and RPSA) that exhibited downregulated expression exclusively in this analysis group. It is noteworthy that all node proteins within these DEPs showed downregulation. Furthermore, among the transcription factors identified within these DEPs (BBX，DPF1，HOXB9，SMAD2，ZFP28，ZNF121，ZNF629 and ZNF880), except for ZFP28 which was upregulated with a reproducibility of 1 unique to this analysis group (Table 13), all others displayed downregulation. 3.9 Analysis of Group 5 Comparing Patient 5 with 6 healthy controls, we identified a total of 92 DEPs, comprising 24 upregulated DEPs and 68 downregulated DEPs (Table 2). Our Gene Ontology Biological Process enrichment analysis revealed that the NSAA-related biological processes were protein deubiquitination, protein K48-linked deubiquitination, ubiquitin-dependent protein catabolic process, negative regulation of proteasomal ubiquitin-dependent protein catabolic process, RNA stabilization, primitive hemopoiesis, translation , etc. (Table 14). Notably, the majority of enriched DEPs were found to be downregulated. The KEGG pathway analysis of DEPs suggested that pathways related to NSAA included Glucagon signaling pathway, Biosynthesis of amino acids, Ribosome, Metabolic pathways, etc (Table 14). Among these pathways, the enriched proteins were predominantly downregulated. Similar to the approach described in section 3.3, node proteins were identified through PPI analysis and Cytoscape calculation. The top 30 node DEPs were selected based on their ”Degree” ranking, and those associated with NSAA pathogenesis were annotated using the Gene Cards database (Table 15). The reproducibility of each NSAA-related node DEP was calculated across seven analysis groups. Node DEPs with a reproducibility score of 1 were unique to this particular analysis group and consisted of one protein, namely RPL37, which exhibited downregulated expression. Notably, all node proteins within this analysis group showed downregulation. Furthermore, transcription factors identified among the DEPs from this group included CTCF, MAZ, ZNF880, all exhibiting downregulated expression. However, no transcription factor had a reproducibility score of 1 (Table 15). 3.10 Analysis of Group 6 Comparing Patient 6 with six healthy controls, a total of 528 DEPs were identified, consisting of 207 upregulated DEPs and 321 downregulated DEPs (Table 2). The GO-BP enrichment analysis revealed that NSAA-related biological processes encompassed translation, cytoplasmic translation, mRNA splicing via spliceosome, rRNA processing, ribosomal small subunit biogenesis, RNA splicing, mRNA processing, mitochondrial translation, translational initiation, protein stabilization, etc (Table 16). It was observed that both upregulation and downregulation of enriched DEP expression were not uncommon. Furthermore, the KEGG pathway analysis indicated the involvement of pathways associated with NSAA such as Ribosome, Spliceosome, Protein export, mRNA surveillance pathway, Ribosome biogenesis in eukaryotes, RNA polymerase, Nucleocytoplasmic transport , etc. (Table16), again highlighting the occurrence of both upregulation and downregulation in enriched DEP expression. Similar to section 3.3 processing steps, node proteins were obtained through PPI analysis followed by Cytoscape calculation. The top 30 node DEPs were selected based on their ”Degree” ranking, and the node DEPs associated with the pathogenesis of NSAA were annotated using information from the Gene Cards database (Table 17). The reproducibility of each NSAA-related node DEP was calculated across seven analysis groups. Among these, there was a unique set of DEPs with a reproducibility score of 1 in this particular analysis group, consisting of five proteins, namely RPS11, RPL11, RACK1, RPL6, NCL, except for RACK1, all other expression was downregulated. Notably, the number of NSAA-related nodal proteins in this specific analysis group was lower compared to other groups, however, it is worth mentioning that all except for RACK1 showed downregulation in expression levels. Furthermore, transcription factors identified within this analysis group included CTCF, DPF1, GATA6, HOXB9, SMAD2, THAP11, ZFP28, ZNF121, ZNF787, ZNF880, all exhibiting downregulation in expression levels. Interestingly enough, THAP11 was found to be unique to this particular analysis group and had a reproducibility score of 1 (Table 17). 3.11 Analysis of a non-NSAA case To ascertain the disease specificity of the proteome obtained from peripheral blood stem cells using this detection technique, we conducted a proteomic analysis of peripheral blood HSCs derived from a non-NSAA patient who presented with pale complexion several months prior to hospital admission. Despite the similarity in peripheral blood routine parameters to NSAA at that time, HSC proteome testing was performed after blood sampling, which promptly led to the diagnosis of MDS and subsequent treatment involving peripheral blood HSC transplantation. The proteome of this MDS was compared with 6 healthy controls, resulting in the identification of 642 differentially expressed proteins (DEPs), comprising 15 upregulated and 627 downregulated DEPs. Notably, MRPL23 and IDH2, which had very high reproducibility in the seven analysis groups mentioned above, were also present in this case, with downregulated expression. Gene Ontology Biological Process (GO-BP) enrichment analysis revealed that biological processes associated with bone marrow failure encompassed rRNA processing, translation, mitochondrial translation, mRNA splicing via spliceosome, ribosome biogenesis, RNA splicing, positive regulation of telomerase RNA localization to Cajal body, ribosomal small subunit biogenesis, translational elongation, nucleosome disassembly, etc (Table 18). The enriched DEPs, except for SMARCE1, were all downregulated. Furthermore, KEGG pathway analysis indicated that pathways related to bone marrow failure included Nucleocytoplasmic transport, Ribosome biogenesis in eukaryotes, Ribosome, Spliceosome, Protein export, mRNA surveillance pathway, Protein processing in endoplasmic reticulum, Biosynthesis of amino acids, Motor proteins , etc (Table 18). The enriched DEPs were predominantly downregulated, except for SMARCE1, IPO5, and TPI1. Similar to the methodology described in section 3.3, node proteins were identified through protein-protein interaction (PPI) analysis and subsequent Cytoscape calculations. The top 30 node DEPs were selected based on their ”Degree” ranking, and DEPs associated with the pathogenesis of bone marrow failure were annotated using information from the Gene Cards database (Table 19). The reproducibility of each DEP related to bone marrow failure was assessed across the seven aforementioned analysis groups. Notably, four DEPs (MRPL24, HNRNPC, GRWD1, TSR1L) exhibited a reproducibility score of 0 exclusively in this patient with myelodysplastic syndrome (MDS), suggesting their uniqueness to this case. A reproducibility of 1 meant that the DEPs of the MDS patient only appeared once in the seven analysis groups mentioned above, which was considered relatively unique DEPs, with 8 DEPs in this patient, namely RPL12, HSPA4, SNRPE, MRPL12, NIFK, VARS1, RPLP2, EEF1B2. Intriguingly, all node proteins associated with bone marrow failure showed downregulation in this particular case. Furthermore, transcription factors BBX, GATA6, HOXB9, SMAD2, TBR1, THAP11, ZNF121 were found among these DEPs, notably all exhibiting downregulated expression levels. Of particular interest was TBR1 which demonstrated a recurrence rate of 0 unique to this case while THAP11 had a recurrence rate of 1 relatively unique to this case as well (Table 19). In terms of GO-BP enrichment analysis and KEGG pathway analysis, the DEPs of this MDS case exhibited a high degree of similarity to the enrichment patterns observed in the aforementioned seven analysis groups. Although there were a few unique node proteins and transcription factors, their proportion and quantity did not show a significant increase compared to the Group1-6 analysis group. Therefore, it can be concluded that the proteome of peripheral blood stem cells obtained through this detection technique is not disease-specific. 4. Discussion Two previous articles have reported on the expression profile of hematopoietic stem cells in patients with aplastic anemia, one focusing on proteomics study(5) and the other on transcriptomics study(6). However, this article differs from the aforementioned studies in several aspects. Firstly, it exclusively examines pediatric aplastic anemia samples and corresponding healthy control populations. Secondly, it specifically investigates patients with NSAA (non-severe aplastic anemia). Thirdly, it analyzes the proteome of HSCs in peripheral blood using trace proteome detection technology. Lastly, its analysis method considers both commonalities and individualities to account for overall and individual aspects. Consequently, some conclusions derived from this study significantly diverge from those presented in the two previously mentioned articles. The proteomic characteristics of peripheral blood HSC in NSAA patients were described using a novel proteomic detection technique in this study. However, due to the limited number of proteins detected per sample (approximately 1500-2000 proteins), it should be noted that the obtained results may not fully represent the entire HSC proteome and have certain limitations. Nevertheless, despite these limitations, the research team successfully conducted comprehensive detection and analysis of the HSC proteome, leading to several intriguing discoveries previously unreported. In terms of overall analysis , we primarily focused on Group T and identified 241 differentially expressed proteins (DEPs) with a recurrence frequency of ≥ 3 (referred to as the Group R analysis group). The analyses conducted for these groups included GO-BP analysis, KEGG analysis, node protein analysis, transcription factor analysis, etc., revealing significant differences between the two groups. Notably, while both upregulated and downregulated DEPs were observed in Group T, the majority of DEPs in Group R were found to be downregulated. We believe that the analysis results from Group R align more closely with the pathogenesis of aplastic anemia. Due to the stringent definition criteria applied for DEPs in Group T and the high heterogeneity observed in HSC proteome among the six patients studied, only a limited number of DEPs were identified. This approach may have excluded several DEPs that are potentially relevant to aplastic anemia pathogenesis. Conversely, DEPs in Group R were defined as proteins exhibiting a recurrence frequency of ≥ 3 across all seven analyzed groups (Group T + Groups 1-6), resulting in a larger set of identified DEPs and greater inclusion of those associated with aplastic anemia for further investigation. Consequently, this approach better reflects commonalities among all six patients. The findings derived from the analysis of Group R (Figure 2 and Table 5) demonstrate a relative consistency with the pathogenesis of aplastic anemia. For instance, the GO-BP enrichment analysis of DEPs in Group R revealed that disease-associated DEPs were predominantly enriched in essential cellular functions, including RNA synthesis, splicing, maturation, ribosome assembly, translation, etc. Similarly, the KEGG pathway analysis identified enriched pathways primarily focused on crucial cellular processes such as transcription initiation, RNA synthesis and splicing, ribosome synthesis and assembly, amino acid synthesis, etc. Amongst the top 30 node proteins in Group R, 28 were found to be associated with aplastic anemia and their functions were also related to vital housekeeping activities like mitochondrial function maintenance (PHB), nuclear small RNA synthesis (SNRPF), and ribosome synthesis (MRPS12, IMP4, RPL7L1, BOP1, EMG1, NSA2, POLR1A, RPP38, RPL39, RPS28, MRPS14, RPS25, MRPL15, POLR1E, SDAD1, RPL36AL), ribosomal maturation (MPHOSPH10, WDR3, NOL10, RRP1, PNO1), copy initiation (NOC3L), RNA transcription initiation and splicing (DDX56), RNA synthesis (POLR2H, POLR2L), as well as RNA maturation and transport (DDX28). Interestingly enough, the DEPs enriched in the GO-BP analyses and KEGG pathways, as well as the node proteins and transcription factors, were all downregulated, which was highly consistent with the pathogenesis of aplastic anemia, which was caused by hematopoietic failure due to infection, immunity, gene mutations, and other factors. Currently, utilizing proteomics technology in this study and based on the analysis of differentially expressed proteins (DEPs) in hematopoietic stem cells (HSCs), our research demonstrates that despite no significant reduction in HSC numbers, NSAA leads to impaired or dysfunctional HSC function, resulting in hematopoietic failure. It is worth noting that this study solely focused on peripheral blood HSCs and did not investigate bone marrow HSCs. Furthermore, there was no comparison made between peripheral blood and bone marrow HSCs, hence further investigations are warranted to validate these findings. Additionally, two crucial proteins, MRPL23(7-8) and IDH2(9-10), were consistently observed across all analyzed groups. The former protein is a constituent of mitochondrial ribosomes while the latter is involved in energy production. To date, there have been no published studies exploring the association between these two proteins and NSAA. Further research can be conducted to ascertain their potential as biomarkers for NSAA. In terms of individual analysis , we conducted separate analyses for Groups 1-6. The analytical approach involved comparing each NSAA patient with a control group comprising six healthy individuals to identify the differentially expressed proteins (DEPs) in each analysis group. Subsequently, DEP enrichment analysis was performed using GO-BP enrichment and KEGG pathway analyses, as well as node protein and transcription factor analyses. Finally, the analysis results between the groups were compared to determine differences and similarities. In terms of DEPs enrichment analysis and pathway analysis, the enriched terms differed among these six analysis groups, however, there were still commonalities among terms related to NSAA, such as their enrichment in housekeeping functions, consistent with the overall analysis. The enriched GO-BP terms included replication, transcription, mRNA splicing and maturation, translation, translation regulation, protein degradation, amino acid synthesis etc. The enriched KEGG pathways encompassed DNA repair, transcription factors, RNA synthesis and degradation, ribosome synthesis, protein transport and degradation, amino acid metabolism, sugar metabolism etc. Among the DEPs that were enriched, except for Group 2 and Group 6 which exhibited a large number of upregulated proteins, the other four groups predominantly showed downregulation of DEPs. Regarding DEPs node protein analysis, each analysis group contained its own unique proteins while also sharing some overlapping proteins. These node proteins, whether unique or replicated, primarily encompassed housekeeping functions associated with NSAA, including replication and repair, transcription and regulation, RNA modification and degradation, ribosome synthesis and maturation, translation and regulation, protein maturation and degradation, cytoskeleton and transport, glucose metabolism, lipid metabolism, anaerobic fermentation, respiratory chain and energy production, and cell cycle regulation. The expression of node proteins in each analysis group was mainly downregulated, except for Group 2 which had a large number of upregulated proteins. Regarding DEPs transcription factor analysis, the number of covered transcription factors varied among different analysis groups, with most groups having unique ones. Except for Group 4 which showed an upregulated ZFP28, all other groups had downregulated transcription factors. Notably, ZNF880(11-12) exhibited high reproducibility and was present in almost all groups despite lacking any published study on its association with aplastic anemia, thus it could serve as a potential biomarker for NSAA pending further validation. The individual analysis results of DEPs in HSCs of the six analysis groups aforementioned, whether it was enrichment analysis, pathway analysis, or node protein analysis, mostly showed low housekeeping function or failure in housekeeping function, which was consistent with the pathogenesis of aplastic anemia. In order to investigate the disease specificity of the differentially expressed proteins (DEPs) identified by this proteomic detection technology, a non-NSAA case (MDS patient) was selected for analysis. The peripheral blood of MDS exhibited similar characteristics to NSAA, with a more severe decrease in all three lineages. By comparing the MDS patient with a control group consisting of 6 healthy individuals, we identified DEPs that were significantly altered. Conventional analysis of these DEPs revealed enrichment in GO-BP terms and KEGG pathways primarily associated with housekeeping functions. Additionally, node DEPs were found to be related to bone marrow failure and housekeeping functions (Tables 18 and 19). Although this analysis group contained four unique proteins among its node DEPs, there was no significant difference in the number of unique proteins compared to other analysis groups. Similar observations were made during transcription factor analysis within this group. Furthermore, whether it was enrichment analysis, node protein analysis or transcription factor analysis, most DEPs showed downregulation in expression levels which strongly correlated with HSC hematopoietic dysfunction or failure observed in MDS patients. Therefore, it can be concluded that this proteomic technique may lack disease-specificity when applied to diseases primarily characterized by decline in all three lineages, and showed the failure or low housekeeping function of HSCs. Conclusion The proteomic detection technology employed in this study can partially discern the functional failure status of peripheral blood hematopoietic stem cells (HSCs), and holds promise for future utilization in disease monitoring, such as NSAA. The pathogenesis of NSAA may involve HSC housekeeping function impairment or decline, with corresponding changes in disease manifestation being reflected by alterations in HSC housekeeping function. Highly reproducible differentially expressed proteins (DEPs) like MRPL23, IDH2, and ZNF880 are anticipated to serve as potential biomarkers for NSAA. Reference 1. Schoettler ML, Nathan DG. The Pathophysiology of Acquired Aplastic Anemia: Current Concepts Revisited. Hematol Oncol Clin North Am, 2018, 32(4): 581-594. 2. Yoshida N. Recent advances in the diagnosis and treatment of pediatric acquired aplastic anemia. Int J Hematol. 2024, 119(3): 240-247. 3. General Office of the National Health Commission. Diagnostic and Treatment Guidelines for Children with Aplastic Anemia (2019 Edition). Clinical Education of General Practice, 2019, Vol.17, No.11: 965-969. 4. Li H, Fu L, Yang B, Chen H, Ma J, Wu R. Cyclosporine Monotherapy in Pediatric Patients With Non-severe Aplastic Anemia: A Retrospective Analysis. Front Med (Lausanne). 2022, 9: 805197. 5. Qi W, Fu R, Wang H, Liu C, Ren Y, Shao Y, Shao Z. Comparative proteomic analysis of CD34(+) cells in bone marrow between severe aplastic anemia and normal control. Cell Immunol, 2016, 304-305: 9-15. 6. Zeng W, Chen G, Kajigaya S, Nunez O, Charrow A, Billings EM, Young NS. Gene expression profiling in CD34 cells to identify differences between aplastic anemia patients and healthy volunteers. Blood, 2004, 103(1):325-32. 7. Cheong A, Lingutla R, Mager J. Expression analysis of mammalian mitochondrial ribosomal protein genes. Gene Expr Patterns, 2020, 38:119147. 8. Mozhui K, Snively BM, Rapp SR, Wallace RB, Williams RW, Johnson KC. Genetic Analysis of Mitochondrial Ribosomal Proteins and Cognitive Aging in Postmenopausal Women. Front Genet, 2017, 8:127. 9. Chotirat S, Thongnoppakhun W, Wanachiwanawin W, Auewarakul CU. Acquired somatic mutations of isocitrate dehydrogenases 1 and 2 (IDH1 and IDH2) in preleukemic disorders. Blood Cells Mol Dis, 2015, 54(3): 286-91. 10. Wang N, Wang F, Shan N, Sui X, Xu H. IDH1 Mutation Is an Independent Inferior Prognostic Indicator for Patients with Myelodysplastic Syndromes. Acta Haematol, 2017, 138(3): 143-151. 11. Dong X, Zhang Y, Sun Y, Nan Q, Li M, Ma L, Zhang L, Luo J, Qi Y, Miao Y. Promoter hypermethylation and comprehensive regulation of ncRNA lead to the down-regulation of ZNF880, providing a new insight for the therapeutics and research of colorectal cancer. BMC Med Genomics, 2023, 16(1): 148. 12. Xu J, Zeng Y, Si H, Liu Y, Li M, Zeng J, Shen B. Integrating transcriptome-wide association study and mRNA expression profile identified candidate genes related to hand osteoarthritis. Arthritis Res Ther, 2021, 23(1) :81. Information & Authors Information Version history V1 Version 1 14 July 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords anemia aplastic bone marrow failure molecular biology Authors Affiliations Lilan Wu Guangzhou Women and Children's Medical Center Institute of Pediatrics View all articles by this author Lu Zhu Guangzhou Women and Children's Medical Center View all articles by this author Wenling Guo Guangzhou Women and Children's Medical Center View all articles by this author Sha Liu Guangzhou Women and Children's Medical Center View all articles by this author Tao Xu Guangzhou Women and Children's Medical Center View all articles by this author Xiaoyan Hu Guangzhou Women and Children's Medical Center View all articles by this author Jieying Wu Guangzhou Women and Children's Medical Center Institute of Pediatrics View all articles by this author Hua Jiang 0000-0002-4552-0785 Guangzhou Women and Children's Medical Center View all articles by this author Liya He Guangzhou Women and Children's Medical Center View all articles by this author WenXian Zheng 0009-0005-2274-2843 [email protected] Guangzhou Women and Children's Medical Center View all articles by this author Metrics & Citations Metrics Article Usage 184 views 142 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Lilan Wu, Lu Zhu, Wenling Guo, et al. 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