Osteosarcoma (OS) is the most common primary aggressive sarcoma of bone, with massive aberrant expression of oncogenes related to the development of OS. RALA, a kind of small Ras-like guanosine triphosphatases, has been identified as a potential therapeutic target in several types of tumor, but its role in OS remains largely unknown.  Abnormal expression of RALA was proven in the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and RNA-sequence of samples and cell lines. The role of RALA in OS was analyzed in terms of DNA methylation, immune cell infiltration, and patient survival. The cancer-promoting effect of RALA was demonstrated in cell lines and xenograft osteosarcoma models. A prognostic scoring model incorporating RALA as an indicator was established with the clinical samples that we collected.  The results showed that RALA was highly expressed in human OS tissues and cell lines. Survival analysis demonstrated that RALA was the sole independent risk factor for poor overall survival and disease-free survival in OS patients and impacted the proportion of infiltrating immune cells and DNA methylation in the OS tumor microenvironment. By gene-gene interaction analysis, we found that the expression of RALA was highly correlated to the expression of ABCE1. Similar to RALA, upregulated ABCE1 is correlated with poor survival outcome of OS patients. In addition, the functional experiment demonstrated that higher expression of RALA promoted the proliferation, migration, and invasion of OS cells. In vivo results were similar with the in vitro results. We examined m6a methylation-related genes and found that m6A methylation is responsible for the abnormal expression of RALA. Finally, the prognostic prediction model of RALA could be used to predict the long-term outcome of OS patients.  We identified RALA as an oncogene in OS, and RALA upregulation in a concerted manner with ABCE1 was significantly associated with worse outcomes of OS patients. Targeting RALA may prove to be a novel target for OS immunotherapy in future clinical practice. Osteosarcoma (OS) is the most common primary malignant tumor of bone occurring most frequently in adolescence, with a combined global incidence of 3-4 cases per million people . OS consRas-like guanosine triphosphatases  are members of the Ras superfamily of small GTPases . Previou2.The human osteoblast cell line hFOB1.19 and human osteosarcoma cell lines  obtained from the Cell Bank of Type Culture Collection of the Chinese Academy of Sciences  were cultured in DMEM supplemented with 10% fetal bovine serum  and maintained at 37°C in a humidified atmosphere containing 5% COFresh human osteosarcoma tissue samples and normal tissues were obtained from patients undergoing surgical procedures at Jinling Hospital  between 2007.01 and 2021.12. Normal bone or connective tissues that were within 2 cm from the edge of osteosarcoma tissue were collected as the cancer adjacent normal tissues. All patients in the study, or their guardians, provided written consent prior to participation. The study protocol was approved by the Ethics Committee of the said hospital .https://portal.gdc.cancer.gov/) dataset, Genotype-Tissue Expression  dataset, Therapeutically Applicable Research To Generate Effective Treatments  dataset, Tumor Immune Single-cell Hub , STRING (https://cn.string-db.org/), and the Timer (https://cistrome.shinyapp-s.io/timer/) dataset. Patient information for OS and sarcoma was downloaded from TARGET and TCGA. Information on normal tissues was downloaded from GTeX. Immunization-related information was downloaded from Timer. Single-cell sequence data were downloaded from TISH. Protein-protein data were downloaded from STRING.The raw public data were downloaded from the Cancer Genome Atlas , using an empty plasmid as control vector. Cells were harvested 24 h after transfection, or when the cell monolayer reached 100% confluence.Mammalian expression plasmids encoding the human RALA open reading frame (ORF) were purchased from GenScript . They were transfected into OS cells using Lipofectamine 3000Standard immunohistochemical procedures were used. The sample was deparaffinized with xylene, rehydrated with ethanol, and incubated with 3% H2O2 for 5 min to block endogenous peroxidase activity. Then, antigen retrieval was performed by incubating the samples with sodium citrate buffer (pH 6.0) for 20 min at 95°C. After blocking with 5% normal goat serum for 10 min at 20°C, the sections were incubated with polyclonal antibodies against RALA  and Ki67 . Then, a hematoxylin-eosin (H&E) staining kit  was used for H&E staining. The images were captured with the Olympus FSX100 microscope .n = 50) and low-expression group .RALA expression level was evaluated by two pathologists independently. The intensity of staining was scored as follows: 0 (negative), 1 (weakly positive), 2 (moderately positive), and 3 (highly positive). The proportion of staining was scored according to the percentage of positively stained cells in the field as follows: 0 (0%), 1 (1-10%), 2 (11-40%), 3 (40-80%), and 4 (80%-100%). RALA immunopositivity scores were calculated by IHC as the sum of the intensity and extent scores. According to their RALA expression level, OS patients were classified into two groups: high-expression group  supplemented with a protease and phosphatase inhibitor mixture  on ice for 30 min. Lysates were then centrifuged for 12 min , and the supernatant was collected. The protein concentration was measured using the Pierce BCA Protein Assay Kit , and proteins were separated via 10% SDS-PAGE. The separated proteins were subsequently transferred onto PVDF membranes , which were then blocked at room temperature for 1 h using BSA blocker . The membranes were then incubated with monoclonal anti-RALA  or anti-GAPDH  primary antibodies for 24 h at 4°C. Following the primary antibody incubation, the membranes were incubated with secondary antibody  at room temperature for 2 h. Protein bands were visualized with an enhanced chemiluminescence kit , and densitometric analysis was performed using ImageJ software .® reagent . The RNA concentration was measured using a microplate reader. To quantify RALA mRNA expression levels, 1 μg total RNA was reverse transcribed into cDNA using avian myeloblastic virus reverse transcriptase and Oligo(dT) primers . The reaction was incubated as follows: 16°C for 5 min, 42°C for 60 min, and 70°C for 10 min. Then, the RT-PCR mixture, consisting of specific primers for RALA and GAPDH and SYBR Green dye , was incubated under the following conditions: 95°C for 5 min, followed by 40 cycles of 95°C for 30 s, 60°C for 30 s, and 72°C for 1 min. The following primer sequences were used for the RT-qPCR: RALA (forward: 5′-ATGGCTGCAAATAAGCCCAAG-3′ and reverse: 5′-TGTCTGC--TTTGGTAGGCTCATA-3′); GAPDH (forward: 5′-CGAGCCACATCGCTCAGAC-A-3′ and reverse: 5′-GTGGTGAAGACGCCAGTGGA-3′). Expression levels were quantified using the 2−ΔΔCq method and normalized to GAPDH.Total RNA was extracted from cells and tissue samples for further study using TRIzolThe proliferative ability of MG63 cells was analyzed using 5-ethynyl-2'-deoxyuridine (EdU) Cell Proliferation Assay kit  to incorporate 5-ethynyl-2'-deoxyuridine (EdU) according to the manufacturer's protocol. Data were analyzed using Image Pro Plus software μl pipette tips. Wound closure was photographed using a photomicroscope  at 0 and 24 h after being incubated in serum-free medium.The migratory ability of OS cells was detected using wound healing assay. The treated cells (100% confluence) were cultured in 6-well cell culture plates and scraped with the tip of 200 2 atmosphere, which permitted MG63 cells to migrate and penetrate the gelatin-coated polycarbonate membranes towards the upper chamber containing more nutritious DMEM with a higher FBS content. The cells on the outboard surface of the inserts were washed, fixed , stained , and visualized using a photomicroscope.  (in five randomly selected fields of view).The invasive ability of OS cells was analyzed using 12-well Millicell cell culture inserts (Merck KGaA). MG63 cells suspended in DMEM with 1% FBS were added to the upper chamber 24 h after transfection, DMEM supplemented with 10% FBS was plated into the chambers, and the Transwell plates were incubated for 20 h in a 5% CO6 cells/mouse, six mice/group). The mice were sacrificed 24 days after injection with CO2 (17.5% cage volume/min for 5 min), and the formed tumors were removed. Tumor volumes were calculated using the following formula: tumor volume = [length × (width)2]/2. A portion of the tumor tissue was fixed in 4% paraformaldehyde for 24 h at 4°C and further processed for hematoxylin and eosin (H&E) and IHC staining for RALA and Ki67 expression. The remaining tissue was used for protein extraction. These experiments were repeated three times. The animal experimental protocol was approved by the Institutional Review Board of Jinling Hospital and performed in accordance with the Guide for the Care and Use of Laboratory Animals . The ethics approval was obtained in September 2019.Male SCID mice  were purchased from the Model Animal Research Center  and housed under specific pathogen-free conditions  in Nanjing University. MG63 cells transfected with RALA overexpression plasmid were subcutaneously injected into 6-week-old SCID mice . RT-qPCR and dual luciferase reporter assay were performed in triplicate. Data are presented as the mean ± SEM. Pairwise statistical differences between groups were determined using Student's http://org.Hs.eg.db package, and GOplot package; Kaplan-Meier analysis: survminer package and survival package; time-dependent ROC curve and timeROC package; calibration plot, nomogram graphs, and rms package; decision curve analysis (DCA), survival package, and stdca.R .Statistical analysis and visualization of clinically relevant data and sequencing results are all implemented in R (version: 3.6.3). The R packages that we used are as follows: volcano plots, correlation analysis plots, forest plots, and risk score plots: ggplot2 package; heat map: ComplexHeatmap package; GO/KEGG enrich analysis, clusterProfiler package , http://Because of the low incidence of osteosarcoma and lack of samples in online databases, we ran the bioinformatic analysis by using datasets of sarcoma including osteosarcoma. Compared with the normal tissues from GTEx, sarcoma tissues from TCGA showed a higher mRNA expression level of RALA . In addihttp://gepia2.cancer-pku.cn/) tool was used to combine all tumor expression data of TCGA, from which the top 100 genes that correlated with RALA expression were obtained. Among them, six genes  exhibited the highest expression level, and details are shown in the RALA coexpression heat map in R = 0.450, p = 0.007) between RALA and ABCE1 is shown in R value was 0.342 (p < 0.001) in TARGET dataset  theory has been acknowledged by more researchers, and immune infiltration is regarded as the most important part of TME. Previous studies found that RALA involved in immune response of NK cells . To deteThe mechanisms leading to aberrant BATF2 expression remain unclear. DNA methylation played a key factor in regulating gene expression. In eukaryotes, the modification of Cap at the 5′ end and poly A at the 3′ end plays a very important role in transcriptional regulation. Therefore, we analyzed the methylation within 5000 bp upstream and 5000 bp downstream of the RALA transcription start site (TSS) in OS tissues and found a total of 8 sites with negative correlations between methylation level and RALA expression level . Among t3 in size and 0.8 g in weight , age (p < 0.001), metastasis (p < 0.001), vital status (p < 0.001), disease-free survival (p < 0.001), and overall survival (p < 0.001) between the two groups  and overall survival  between the two groups were shown using Kaplan-Meier analysis  and ≥16 years ) and metastasis  and no ) , metastatic tumors , and high expression of RALA  all increased the overall risk of survival, while surgical treatment  was associated with decreased mortality (p < 0.001), and it was difficult to support a relatively reliable recurrence risk model  is the most frequently mutated gene family in cancers, especially lung, colorectal, and pancreatic cancers . RAS-relWe first demonstrated that the mRNA and protein expression levels of RALA in OS were higher than those in normal tissues through various methods including bioinformatics means (TCGA and TARGET), RNA-sequence, Western blotting, and RT-qPCR. Meanwhile, a significant correlation between this differential expression and clinical outcomes was verified in TCGA dataset and TARGET dataset. This means that RALA is a plausible trigger point for our study. Further, in vitro and in vivo assays showed that high RALA expression may promote OS cell proliferation, migration, and invasion. The core part of our experiment is our finding that RALA could be used as a prognostic indicator of OS. In the current mainstream treatment, the American Joint Committee on Cancer (AJCC) AJCC Cancer Staging Manual and The Musculoskeletal Tumor Society (MSTS) staging system are generally used to grade and evaluate the prognosis of OS . HoweverIn addition, we observed an interesting phenomenon that the differential expression of RALA not only featured in tumor and normal tissues but was present in immune cells. Based on the enrichment score provided by TIMER tool, we found that the expression of RALA was less expressed in several key immune cells. A single-cell sequencing result of sarcoma directly described the lower expression of RALA in different immune cells . People The Ral GTPases, directly or indirectly, appear to be implicated in triggering diverse immune signaling pathways , 23. LikDespite the strengths in this study, there were some limitations. Firstly, one of the distinguishing features of osteosarcoma is the heterogeneity of its gene expression, i.e., the gene expression of osteosarcoma tissues in different patients may be completely different. With the advancement of research methods, the results of single-cell sequencing and spatial transcriptome sequencing have shown that several completely different phenotypes can appear within a solid tumor. It is obviously far from enough to provide key molecules for the targeted therapy and immunotherapy of osteosarcoma only through research of RALA. Patients with osteosarcoma need more individualized treatment. Secondly, the precise molecular mechanisms upstream and downstream of RALA have not yet been fully understood and are yet to be systematically studied. RALA may be regulated by various molecular modalities: pretranscriptionally, posttranscriptionally, and posttranslationally, which deserves the next step study. Although the preliminary results showed that there was a high probability that m6A methylation is responsible for the abnormal expression of RALA in OS, this remains to be tested in trials. Thirdly, we found that expression of the ABCE1 gene was highly correlated to that of RALA, and there was significant difference in survival between the high- and low-ABCE1 groups by processing the patient clinical information and RNA-sequencing results from the TCGA and TARGET dataset. However, gene-gene interactions between ABCE1 and RALA were unclear, and the molecular details of the involved mechanisms are currently lacking. Finally, further clinical trials are needed to validate the prognostic prediction power of the RALA-related risk model for survival assessment of OS patients.In summary, we explored the identity of RALA in the biological process of OS, its potential therapeutic direction, and the possibility of using it as a prognostic indicator by means of bioinformatics, molecular biology, and clinical statistics. Our study provides a new way of thinking for the discovery of therapeutic targets for OS through the combination of biological prediction and molecular biological verification.


Increased temperatures due to global climate change can facilitate lake salinization, particularly in the regions with cold semiarid climate. However, the effects of salinity on the CH4 oxidation activity and diversity and composition of methanotrophic community in the sediment of natural lakes at a regional scale are still unclear. Therefore, we collected lake sediment samples from 13 sites in Mongolian Plateau; CH4 oxidation activities of methanotrophs were investigated, and the diversity and abundance of methanotrophs were analyzed using real-time quantitative polymerase chain reaction and high throughput sequencing approach. The results revealed that the diversity of methanotrophic community decreased with increasing salinity, and community structure of methanotrophs was clearly different between the hypersaline sediment samples  and hyposaline sediment samples . Types II and I methanotrophs were predominant in HRS and HOS, respectively. Salinity was significantly positively correlated with the relative abundance of Methylosinus and negatively correlated with that of Methylococcus. In addition, CH4 oxidation rate and pmoA gene abundance decreased with increasing salinity, and salinity directly and indirectly affected CH4 oxidation rate via regulating the community diversity. Moreover, high salinity decreased cooperative association among methanotrophs and number of key methanotrophic species . These results suggested that salinity is a major driver of CH4 oxidation in lake sediments and acts by regulating the diversity of methanotrophic community and accociation among the methanotrophic species.Methanotrophs oxidize methane (CH However, this study did not determine the effect of salinity on CH4 oxidation rate. To date, salt-stress-resistant methanotrophic strains have been isolated from various environments such as soda lakes  is from −0.4 to 9.5°C, and the annual rainfall is 22.9–460 mm. The sampling site locations and basic climate information are given in 4+-N and nitrate nitrogen (NO3−-N) from sediments were extracted using 2 mol L−1 KCl solution for 2 h and filtered. Their contents were determined using AMS automatic discontinuous chemical analyzer . TOC and total nitrogen (TN) were measured using elemental analyzer . Total phosphorus (TP) content was evaluated using an inductively coupled plasma emission spectrometer . Water content (WC) in the sediment samples was determined by drying the samples at 105°C. Soil and water were mixed in a ratio of 1:5 to determine the pH using a pH meter. The salinity of sediments was assessed using the residue drying method. Each analysis was performed using three replicates for each sample to ensure data accuracy.Physicochemical analysis of the sediment samples was performed as described in previous studies . NH4+-N pmoA gene was amplified using the barcode primer pair A189F/mb661R .Total genomic DNA from approximately 0.5 g of each sediment sample was extracted using FastDNA Spin Kit for Soil DNA Extraction  as per the manufacturers’ instructions. The DNA concentration was determined using NanoPhotometer P-class ultrafine photometric analyzer , and DNA samples were stored at −20°C for molecular biology analysis. F/mb661R  and reagpmoA were converted to amino acid sequences using the FunGene Pipeline of the Ribosomal Database Project . The CH4 oxidation rate was determined according to the slope of the linear regression equation between CH4 oxidation and culture time  and SYBR® Premix Ex Taq TMII . Using the default settings and recommended similarity thresholds, networks were constructed and visualized in Gephi 0.9.2  according to Akaike information criteria (AIC) value (p < 0.05) under the criteria that the data met the test of homogeneity of variance (p > 0.05). To evaluate the dissimilarity in methanotrophic community between samples, nonmetric multidimensional scaling (NMDS) and UniFrac tree cluster analysis were conducted based on Bray–Curtis distance and Pairwise weighted UniFrac distance, respectively, with the “Vegan” package of R Studio. Pearson’s correlation analysis between methanotrophic community and environmental variables was conducted based on Bray–Curtis with the R Studio “Vegan” package , root mean square error of approximation , and its associated p value (p > 0.05). To obtain a better fitted model, we calculated the comparative fit index (>0.95), Tucker–Lewis coefficient index (>0.90), and goodness of fit index  and chose the final model that exhibited the lowest AIC.The correlations among the Shannon index, Sobs index, CHC) value . The one package . Redunda4+-N (mg/kg) in Wusulangzi lake (WSLZ) was significantly higher than that in other samples. NO3−-N (mg/kg) was the highest in Tonggunaoer Lake (TGNE). The WC of the sediments was 10–67%. The pH of the sediments was alkaline (pH > 7.0); particularly, Hongjiannao Lake  and TGNE (pH = 9.94) were highly alkaline.The environmental variables in the sediments were evaluated . SalinitpmoA gene amplicon sequencing. Overall, 598,292 high-quality sequences were grouped into 110 representative OTUs. The diversity of methanotrophic community is summarized in 2 = 0.41, p < 0.001; 2 = 0.21, p = 0.016; A total of 776,859 sequences were obtained from 39 sediment samples by p = 0.001). With the change of the salinity gradient, the HRS and HOS exhibited clear differences.The results of NMDS and UniFrac tree cluster analysis revealed that the methanotrophic communities could be clearly distinguished into two groups: HRS  and HOS  and Gammaproteobacteria (relative abundance 51.79–64.53%) were the dominant classes in HRS and HOS, respectively (Methylosinus (relative abundance 57.56–97.06%) was dominant in HRS, and the dominant species (OTU486) similarity to Methylosinus sp. D28  were mainly abundant in HOS  and significantly negatively correlated with type I methanotrophs including Methylococcus , TOC (p < 0.05), and WC (p < 0.05) were positively correlated with CH4 oxidation rate , TN (p < 0.01), NH4+-N (p < 0.01), and AAT (p < 0.01) were significantly positively correlated with pmoA gene abundance  on community diversity was greater than that of NO3−-N and AAT . Overall, 75% variation in richness was accounted by NO3−-N, TOC, and AAT . Salinity indirectly affected the richness by affecting the TOC and diversity. Overall, 64% variation in CH4 oxidation rate was accounted by salinity, pH, and NO3−-N  and significantly positively correlated with pH (p < 0.01). However, the results of Pearson’s correlation analysis revealed no significant correlation of pH and NO3−-N with CH4 oxidation rate. Different analysis methods may lead to different results. In addition, ATT indirectly affected the CH4 oxidation rate by affecting the methanotrophic community diversity. TOC had the highest effect on pmoA gene abundance (path coefficient = 0.47), and salinity indirectly affected pmoA gene abundance by affecting the TOC. Moreover, community diversity had a direct effect on CH4 oxidation rate and richness, and diversity was significantly positively correlated with CH4 oxidation rate and abundance (p < 0.001). Combining all the observations, the SEM results indicated that salinity was the most important environmental variable responsible for the variation in methanotrophic diversity and CH4 oxidation rate. The effect of salinity on the richness and abundance of methanotrophic community was secondary to that of NO3−-N and TOC.The environmental factors influencing the methanotrophic community diversity, richness, abundance, and CHour data , NO3−-N,The network property results revealed that negative correlations (58.84%) were higher than positive correlations 41.16%; . HoweverMethylosinus , Methylosarcina (OTU129), Methylobacter , Methylomonas (OTU1239), and Methyloumidiphilus (OTU1219) were the key members , Methylococcus , OTU1295, OTU1322, OTU1197, OTU1329, OTU1341, OTU1194, and OTU1278), and Methylosinus . Moreover, the key members contained some species with relatively low abundance such as Methylosarcina, Methylomicrobium, Methylomonas, Crenothrix, Methyloparacoccus, Methyloumidiphilus, Methylovulum, and Methylocaldum. Thus, high salinity reduced the number of key methanotrophic species.The Zi quantifies the degree of connectivity between the nodes. The Pi quantifies the extent to which the nodes are connected to various modules . As show members . In HOS  members , the key4 emission from wetlands into atmosphere. However, the key factors controlling the diversity, composition, and CH4 oxidation rate of methanotrophic community in the lake sediments across Inner Mongolia in Mongolian Plateau are unclear. In this study, we reported that salinity is the major factor controlling the CH4 oxidation rate by affecting methanotrophic community.Methanotrophs play an important role in reducing CHMethylomicrobium is the most dominant in salt lakes (Methylobacter (type I methanotroph) and Methylocystis (type II methanotroph) are the predominant methanotrophs in freshwater lakes (Methylosinus (type-II methanotroph) was the absolute dominant taxa. This salinity level appears to be the threshold, with the highest relative abundance of Methylococcus (type-I methanotroph) and Methylosinus (type-II methanotroph) in samples with salinity <0.69%  and type I  methanotrophs. In addition, phylogenetic analysis revealed that major OTUs were close to Methylosinus trichosporium OB3b (Methylosinus trichosporium OB3b could grow at <2% NaCl (Methylocystis sp. strain SC2 (type-II methantoroph) could cope with salt stress at <1% NaCl, and this salt stress induced expression of differentially expressed genes in this strain SC2. The products of the transcriptional stress response major controller  can control the stress response or confer stress tolerance under salt stress  was higher than that of the sediment with lower temperature (winter and spring seasons). However, pmoA gene abundance in summer was lower than that in winter and spring. The different responses of pmoA gene abundance and CH4 oxidation rate to salinity or temperature may be related to gene abundance at DNA level. Determining RNA level is often better than determining DNA level because it is targeted at functional bacteria such as methanotrophs or diazotrophs with physiological activity . In case of >0.6 M NaCl , the CH4 oxidation rate of Methylosinus decreased significantly and was even completely abolished  may influence grassland soil CH4 oxidation rate through their effects on methanotrophic community structure  effects of salinity on CH4 oxidation rate. In addition, CH4 oxidation rate was significantly negatively correlated with NO3−-N. This result was consistent with the previous research showing that the increase of nitrogen concentration such as NO3−-N inhibited CH4 oxidation rate in eutrophication lake sediments  increased and that of Methylococcus (type I methanotroph) decreased. In addition, high salinity reduced the CH4 oxidation rate, accociations among methanotrophic species, and number of key members of methanotrophs. Salinity was the major environmental factor controlling CH4 oxidation rate, and it acted by regulating methanotrophic community structure and accociations among methanotrophic species. Therefore, increased salinity in lake sediments reduced CH4 oxidation and may influence one-carbon cycle, aggravating global warming. Future studies should analyze the effect of change in salinity on global carbon cycle, particularly on CH4 emission, in natural environment.In conclusion, the results of this study revealed the key environmental factors affecting diversity, abundance, and CHhttps://www.ncbi.nlm.nih.gov/genbank/, SRR18332830.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: ZB, LW, RY, BP, and KY designed the study. SZ and LY performed the experiments. SZ, JC, KW, YL, JZ, and ZB analyzed the data. SZ and ZB wrote the paper. All authors contributed to the article and approved the submitted version.This study was funded by the Science and Technology Major Project on Lakes of Inner Mongolia grant (No. ZDZX2018054), National Natural Science Foundation of China grants , and Major Science and Technology Projects in Inner Mongolia Autonomous Region (No. 2022YFHH0086 and 2022YFHH0017).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.https://www.frontiersin.org/articles/10.3389/fmicb.2022.1067017/full#supplementary-materialThe Supplementary material for this article can be found online at: Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.


Vigna subterranea L.) improvement as a newly introduced legume in Malaysia. It has become a “miracle lucrative” legume for Asia and Africa because of its drought resilience, excellent nutritional profiles, and versatile uses. This crop's progress has been limited owing to a lack of extensive research, marginalization, inadequate knowledge, and a lack of accessible funds, among other concerns. The expansion of this crop is reliant on the assessment and selection of unique and reliable breeding lines in various circumstances. Consequently, the goal of this work is to determine genetic diversity and the relationship between yield-contributing components in 44 Bambara groundnut accessions sourced from the Genebank of Institute of Tropical Agriculture and Food Security (ITAFoS) at Universiti Putra Malaysia (UPM). Three replications were used in the experiment, which was done using a randomized complete block design (RCBD). The data were subjected to ANOVA, PCA, correlation, and heat map cluster analysis; also, genetic parameters and broad-sense heritability estimation were carried out on recorded phenotypic descriptors. All of the investigated variables had a significant variance (p ≤ 0.05 or 0.01) according to the ANOVA results. Yield per hectare showed a positively strong to perfect significant correlation  with the yield components viz. fresh pod weight, hundred seed weight, dry pod weight, and dry seed weight. Interestingly, these traits had heritability ≥ 60% and genetic gain ≥ 20%, which can be beneficial for direct selection to this crop improvement. The UPGMA clustering revealed five distinct clusters in which genotypes under cluster I, cluster II, and cluster IV produce a greater yield of 5.96%, 7.12%, and 15.05%, respectively, than the grand mean yield of 1927.01 kg/ha. The PCA biplot estimated that PC1 (32.9%) and PC2 (12.9%) would cover 45.8% of the total variance. We discovered 30 promising lines that provide yields per hectare more than 1.8 ton/ha and might be used as parental lines in future breeding operations aimed at improving the grain yield in tropical areas or comparable agroecological zones.Addressing genetic diversity and application of appropriate breeding strategies are imperative for Bambara groundnut ( Vigna subterranea (L) Verde.) is a tropical legume with subterranean pods that belongs to the Fabaceae family and the Faboideae subfamily [The Bambara groundnut  to determine genetic inheritance and yield performance by defining genetic components, heritability, genetic advance, and ordination analysis, as well as advancing the generation as S4. Moreover, our findings will provide evidence of the genetic variation of the accessions studied, enrich the data pool, and provide an idea for better cultivation techniques for future breeding programmes by optimizing the use of limited resources and conserving biodiversity.The study was conducted in Ladang 15, Agricultural Research Park, Institute of Tropical Agriculture and Food Security (ITAFoS), Universiti Putra Malaysia (UPM), whose GPS location is 3°02 N latitude, 101° 42 E longitude, and 31 meters above sea level. The field experiments were conducted in Malaysia's tropical humid environment throughout the September to February cropping seasons of 2019-2020.Following periodic selfing and intense selection based on desirable characteristics, the seeds of chosen Bambara groundnut genotypes were kept in GenBank, ITAFoS, and UPM with proper institutional rules and guidelines. To carry out this study, seeds from 44 accessions of selfed generation three (S3) were employed. The soil was mechanically cultivated and harrowed prior to sowing. The seeds were planted directly into the soil at field 15 with a gap of 30 cm between plants, 50 cm between rows, 1.5 m between plots, and 2.0 m between replications using a randomized complete block design (RCBD) with three replications . The expTwenty-seven quantifiable parameters  were exaStatistical analysis was done using Statistical Analysis System (SAS) version 9.4 for all of the morphological criteria specified by . The meaThe phenotypic and genotypic coefficient of variation (PCV and GCV) was calculated as described by Singh and Chaudhary  as folloThe variance components, such as genotypic and phenotypic variance (GV and PV), were computed for each character using SAS's proc varcomp and the Restricted Maximum Likelihood (REML) approach. σp2 is the phenotypic variance, σg2 is the genotypic variance, and σg2) to phenotypic variance (σp2). The formula for broad-sense heritability is as follows:(ii) Heritability broad sense was calculated as described by Falconer and Mackay  which iswhere Comstock . (ii) Heσg2 is the genotypic variance, σp2 is the phenotypic variance, and hB2 is the broad-sense heritability characterized as low (0-30%), moderate (30-60%), and high (≥60%) as given by Johnson et al. [K) was estimated to be 5%. Following Johnson et al.'s [(iii) Estimated and expected genetic advance (GA). The amount of anticipated GA (as a percentage of mean) was calculated using the method outlined by Johnson et al. , and selet al.'s  proposalwhere n et al. . (iii) EK is the selection intensity , hB2 is the heritability, and NCSS 2021 Statistical Software. NCSS, LLC.) reported by Khan et al. [Additionally, a dendrogram and PCA two-dimensional (2D) graph were constructed during cluster analysis based on the UPMGA (SHAN) method using NTSYSpc ver 2.0  reportedn et al. . To shown et al. . Eigen vn et al. . For estn et al. . Scattern et al. .Most of the plant breeders considered yield and other yield-contributing characteristics as highly important crop improvement criteria. However, in this work, we evaluated twenty-seven quantitative traits of 44 Bambara groundnut accessions to figure out how traits are inherited down over the generations and which genotypes are the best. For perfect judgments, mean square of analysis of variance for all the evaluated traits is provided in a unique table as Supplementary Table p ≤ 0.01) difference exist in the variables. Plant height, fresh pod weight, dry pod weight, and yield per hectare exhibited highly significant (p ≤ 0.01) variance within the replication, whereas total number of pods, shelling percent, and harvest index indicated significant variation at the p ≤ 0.05 level. The analysis of variance indicated that among the genotypes, there was a highly significant (The mean comparison based on Duncan's New Multiple Range Test (DNMRT = 0.05) shows the performance of 44 accessions of 10 major quantitative traits . Howeverr ≤ 0.25) as well as nonsignificant correlation with yield . Days to maturity (r = −0.118) exhibited a weak (0.0 ≤ r ≤ 0.25), negative, and nonsignificant association with yield per hectare whereas a positive and nonsignificant correlation was reported for plant height with yield. The attribute shelling percent (r = −0.17) was shown to have a weak but significant (p ≤ 0.05) negative association with yield. A significantly positive and moderate (0.25 ≤ r ≤ 0.75) association was found between the trait total number of pods  with yield. A strongly (0.75 ≤ r < 1.00) positive and highly significant correlation was found between the yield and its highly related traits such as fresh pod weight , dry seed weight , and hundred seed weight . A correlation heat map and the circular plot were constructed based on evaluated quantitative traits  ranged from 2.02 (PH) to 126876 (yield), whereas the phenotypic variance (σp2) ranged from 5.70 (PH) to 130749 (yield). However, phenotypic variance appears to be greater than the genotypic variance for all traits. The fresh pod weight had greater PCV and GCV values of 25.18% and 25.07%, accordingly while shelling percent showed the lowest calculated PCV and GCV of 6.24% and 4.62%, respectively. Both the estimated PCV and GCV values were found more than 20% for the trait fresh pod weight (PCV = 25.18% and GCV = 25.07%). However, the traits including D50%F (PCV = 14.40% and GCV = 12.29%), TNP (PCV = 17.57% and GCV = 16.74%), DPW (PCV = 18.76% and GCV = 18.48%), HSW (PCV = 15.63% and GCV = 13.33%), HI (PCV = 12.87% and GCV = 12.46%), and yield (PCV = 18.76% and GCV = 18.48%) had GCV and PCV values of <20% , and phenotypic coefficient of variation (PCV), relative differences (RD), broad-sense heritability (Hb), and genetic advance (GA) as a percentage of mean are shown in  of <20% . This inRelative difference (RD) is an estimate of the proportion of GCV in relation to the corresponding PCV, and the calculated RD values ranged from 0.43% (fresh pod weight) to 48.15% for the plant height  heritability values were noted for the trait shelling percent (54.51%) whereas the rest of the traits exposed high (hb2 ≥ 60%) heritability values, which indicate that the degree of traits' inheritance is less affected by the environment.b2 ≥ 30% . Among tGenetic advance (GA) for plant height and fresh pod weight was recorded as a minimum (6.09%) and maximum (51.42%), respectively . The traGenotypic delineation is one of the key statistics in plant breeding for determining suitable genotypes for crop improvement, indicating the degree of divergence among the evaluated accessions. Clustering offers a very strong and compact illustration of the extent and pattern of genetic variation, which is crucial for selecting the desired genotype. The UPGMA (average linkage) cluster analysis revealed distinct clusters suggesting relationships among tested accessions, as represented by a dendrogram . As showHierarchical clustering may be shown in two directions using the clustered heat maps (double dendrograms) technique. The heat map is a two-dimensional depiction of a data matrix, with individual cells shown as colored rectangles. A cell's color is related to its position along a color gradient. The difference in color might be seen as a difference in hue or intensity, delivering the reader with visual information about how the phenomenon of clusters are varied throughout space. It illustrates the relative patterns of high-abundance traits on a backdrop of low-abundance or nonexistent traits. Heat map analysis of morphological features based on Euclidian distance was done using the simple average clustering (SAC) approach to offer a chromatic evaluation of the Bambara groundnut genotypes. The heat map analysis produced two dendrograms: one in the vertical direction, representing traits that influenced this dispersion, and another one in the horizontal direction, representing Bambara groundnut accessions . DendrogH′ = 1.61) whereas the majority of the traits had the greatest (H′ = 1.64). Likewise, for almost all parameters, maximum (E = 1.00) evenness values were recorded, whereas biomass fresh weight per plant had the lowest (E = 0.98) values.Principal component analysis (PCA) has been widely used in agricultural research for sorting traits and classifying genotypes. The first seven principal components (PC) accounted for 80.17% variation observed in the current research . PC1 andThe lower angle between two vectors  implies Z (PC3) variable values for X (PC1) and Y (PC2) variable combinations are depicted in the graph (X) and PC2 (Y) axes, respectively, while the PC3 value is represented by contour lines and bands. The contour lines link to PC1 and PC2 variable combinations that generate identical PC3 values. Contour plots are very useful when you need to find PC1 and PC2 combinations that result in advantageous or needed PC3 values.A contour plot can be used to show how a response variable is related to two predictor variables . A contohe graph . The PC1Vigna subterranea (L.) that confirm our results in this tropical environment. Aliyu et al. [According to the results of this study, there is a significant amount of genetic diversity across Bambara groundnut accessions for the quantitative parameters evaluated. Several earlier researchers namely Khan et al. , Khan etu et al.  conducteu et al. . Massaweu et al.  reportedu et al.  reportedu et al.  reportedu et al. . Long phu et al. . There wu et al.  supporteu et al.  who repou et al. , Ntundu u et al. , and Sheu et al. . We recou et al.  who noteu et al.  varied fThe correlations study made a relationship between yield and the variables that contribute to it. When selecting which factors to include in the genotype selection procedure, researchers can utilize the correlation measure as a helpful tool . This reWhen the variance components such as phenotypic variance and genotypic variance were computed, it was discovered that phenotypic values were somewhat higher than the corresponding genotypic values for all traits. This implies that the trait's expression is controlled by the environment, and a previous study by Malek et al.  agreed wCluster analysis, a multivariate approach, was used to evaluate the genetic diversity of quantitative features by statistically grouping individuals with comparable descriptions into the same cluster. This technique is established on the distance, similarity, and relatedness of the varieties. In the distance-based strategy, there are two groups: hierarchical and nonhierarchical. Similar types are placed into one cluster based on their similarities in the hierarchical group known as “agglomerative hierarchical”. Dissimilar variations, on the other hand, are divided into various clusters. Among the several agglomerative hierarchical approaches, the unweighted pair group method with arithmetic means (UPGMA) is the most commonly employed . K-meansH′ ≥ 1.63, which is supported by Aliyu et al. [H′ index values ranging from 1.60 to 2.07, whereas Olukolu et al. [H′ Index is 1.5 to 3.5, reported by Khan et al. [H′ index values for evaluated traits in our study showed moderate genetic diversity and this is due to the cleistogamous type of reproductive system in Bambara groundnut [Because a few principal components initially controlled all of the information of the original variables, principal component scores were used to cluster genotypes into groups and subgroups . The priu et al. , who repn et al. . Howeverroundnut . As a seroundnut . PCA biproundnut  and Alkaroundnut . The conroundnut  use a coH-index (1.63 to 1.64). Clustering and heat map analysis revealed that the majority of the high-producing accessions were grouped together in the same cluster, i.e., cluster I, based on the Euclidian distance. Moreover, 20 accessions from cluster I, 9 accessions from cluster II, and 1 accession from cluster IV were identified as high-performing accessions and can be recommended as high-yield potentials. We recommend that seeds from the S3 generation can be grown for further confirmation as well as advancing the generation and selection of the best accessions from the mega environmental trials along with molecular characterizations. However, our findings will provide evidence on the genetic variation of the accessions studied and expand the data pool for the upcoming breeding program through optimum uses of limited genetic materials.The current study revealed significant levels of diversity across the accessions as well as yield and its contributing components in terms of all statistical parameters. It is evident that the boosting yield and other yield-related attributes may be achieved by efficient selection based on heritability and genetic advance estimations and recommended that direct selection based on these traits will be beneficial for crop improvement. The first two main components accounted for 45.8% of the total variance, and owing to persistent selfing, the genotypes become homozygous and had a moderate degree of divergence as measured by the


Background and Objectives: Intradialytic hypotension (IDH) complicates 4 to 39.9% of hemodialysis (HD) sessions. Vessels’ reactivity disturbances may be responsible for this complication. Two-dimensional speckle tracking is used to assess arterial circumferential strain (CS) as a marker of the effectiveness of the cardiovascular response to the reduction of circulating plasma. Materials and Methods: The common carotid artery (CCA) and common iliac artery (CIA) CSs were recorded using ultrasonography in 68 chronically dialyzed patients before and after one HD session. Results: In patients with IDH episodes (n = 26), the CCA-CS was significantly lower both before (6.28 ± 2.34 vs. 4.63 ± 1.74 p = 0.003) and after HD (5.00 (3.53–6.78) vs. 3.79 ± 1.47 p = 0.010) than it was in patients without this complication. No relationship was observed between CIA-CS and IDH. IDH patients had a significantly higher UF rate; however, they did not differ compared to complication-free patients either in anthropometric or laboratory parameters. Conclusions: Patients with IDH were characterized by lower pre- and post-HD circumferential strain of the common carotid artery. The lower CCA-CS showed that impaired vascular reactivity is one of the most important risk factors for this complication’s occurrence. Intradialytic hypotension (IDH) is a common complication during hemodialysis (HD) sessions, occurring with a frequency of 4 to 39.9% ,3,4,5,6.Qualified for this cross-sectional study were 68 (26 F and 42 M) clinically stable, non-smoking, adult patients undergoing chronic 3 times weekly HD sessions in one dialysis center for at least 3 months.Data regarding patients’ age, sex, etiology of ESRD, dialysis vintage, as well as comorbidities, were collected from patients’ medical histories and medical records.2); signs of active infection on the day of the examination; active bleeding or a hemoglobin concentration below 8.0 mg/dL during the 3 months before a patient’s assessment; a bilateral arteriovenous (AV) fistula; wounds or dressings of the examined neck and groin areas; and lack of consent. The exclusion criteria were: a history of major CV complications within the preceding 3 months ; morbid obesity  with Kt/V urea above 1.2. The blood flow was 200–380 mL/min, and the dialysate flow was 500–600 mL/min with bicarbonate dialysate . The ultrafiltration (UF) rate was constant for individual patients during HD, and the dialysate temperature was 36.5 °C. Target dry weight was clinically evaluated based on the occurrence of peripheral oedema, pulmonary congestion and muscle cramps. Examinations were performed before and after the mid-week HD sessions. Before the examinations, patients were asked to rest at least 15 min in the supine position on their own dialysis beds. After the HD session (10 min at the latest), patients were examined in the supine position on the same dialysis bed. During the image acquisition of distal CCA and proximal CIA, patients were instructed to refrain from swallowing, and to hold their breath. All measurements were performed by the same radiologist, who was not aware of the HD courses. For all examinations, a GE Vivid I ultrasound machine with a linear probe 8 L RS in short-axis view and a frequency of 10 Mhz in the “carotid” preset, at an average of 43.7 frames per second, was used. The tracking quality was assessed and revised, as needed, during the examination. For each image acquisition, a concurrent ECG was recorded. For the purpose of analysis, at least two consecutive cardiac cycles were stored in the cine-loop format.A total of 8 recorded sequences of dilation and contraction were analyzed per patient (2 sequences for each artery before and after the HD session). EchoPac software was used for the analysis . DetaileBP was measured on a dialysis bed with the patient in the supine position using an automated oscillometric device and optimal-sized cuff at the beginning (not earlier than 10 nor later than 5 min before HD began) and the end (not later than 10 min after HD ended) of HD, every 30 min of HD or in cases with IDH symptoms. BP was measured 5 min before ultrasonography measurements. IDH was defined for pre-dialysis as SBP < 160 mmHg, nadir < 90 mmHg, and for pre-dialysis as SBP ≥ 160 mmHg, nadir < 100 mmHg, during hemodialysis sessions while ultrasonography examinations were performed. Patients were divided into two groups: those with IDH episodes (IDH) and those without (nIDH). 2).Each patient’s weight was measured before and after their HD session, and their height was measured before their HD session. BMI was calculated regarding body mass after HD as the mass in kilograms divided by the square of height in meters  assessed during the past month and serum phosphate, total calcium and PTH levels from the 3 months preceding examination, were obtained from patients’ medical records. t-test. Continuous non-normal distribution and ordinal data were compared using the Mann–Whitney U test. Nominal data are presented as numbers with percentages and were compared using the Chi2 test (if the number of cases in each subgroup exceeded 15) and the Chi2 test with Yates correction (if <5 cases were present in any subgroup). Correlations between continuous or ordinal variables were tested using Pearson or Spearman rank correlation tests, respectively. A logistic regression model with a stepwise backward feature selection was fitted to create a simple model predicting the occurrence of IDH that could be of use in clinical settings; to check its goodness of fit we performed ROC curve analysis. p values lower than 0.05 were considered statistically significant. All analyses were performed using Microsoft Excel  or Statistica 13.3 software .Continuous data are presented as means with standard deviations (SD) or as medians with interquartile ranges, depending on the normality of the distribution, which was tested using Shapiro–Wilk’s test. Continuous data with normal distribution were compared using the unpaired Student’s n = 23), diabetic kidney disease (n = 13), hypertensive nephropathy (n = 9), polycystic kidney disease (n = 5), obstructive uropathy (n = 4), nephrocalcinosis (n = 1) and unknown (n = 11). Thirty-three patients were diagnosed with CV complications, e.g., coronary heart disease (n = 21), cerebrovascular disease (n = 5) and peripheral arterial disease (n = 17). Fifty-nine patients were hypertensive and were treated with angiotensin-convertase enzyme or sartans (n = 25), beta-blockers (n = 38), calcium blockers (n = 43) and other medications (n = 29). Thirty-one patients were treated with statins.The characteristics of 66 analyzed patients are shown in During HD sessions, 26 patients had an incidence of IDH (IDH group); nine of these were associated with symptoms; in two cases there were two IDH incidents during the session. In 14 cases, placing patients in the Trendelenburg position was applied as a rescue therapy, and nine patients were additionally infused with concentrated saline solution. IDH patients and those without this complication (nIDH) did not significantly differ in terms of age, dialysis vintage, systolic and diastolic BP before and after the HD session, BMI, or dry mass, but IDH patients had a significantly higher UF rate, as shown in CCA-CS before and after HD differed significantly between IDH and nIDH groups. There were no significant differences in CIA-CS before or after HD sessions . None of the evaluated biochemical results were significantly different in patients with or without IDH.Multiple logistic regression analysis with forward stepwise variable selection showed that UF rate, height, dry mass BMI and CCA-CS post HD were factors associated with the occurrence of IDH. All model parameters and regression coefficients are presented in In our study, IDH patients differed from those who did not experience this complication  whereas the CS measured on CIA did not differ. To our knowledge, this is the first study investigating changes in CCA-CS and CIA-CS and the incidence of IDH. The observed phenomenon in IDH patients can be related to the processes taking place during HD sessions. It is postulated that the cause of hypotension is a reduced cardiac filling , primariConsidering all the mentioned factors that have a satisfactory association between CCA-CS and VC , it appeIts small sample size from a single center and being a cross-sectional study with only a one-time measurement were the main limitations of our study. In addition, we used clinical assessment of the hydration status rather than bioimpedance. The definition of IDH we adopted has a significant clinical impact on dialysis patients; hence, the decision to use this definition. We undertook to eliminate or minimize deviations of modifiable factors affecting the frequency of IDH by selecting a group of patients using the same dialysate fluid composition (except potassium concentration), the same sodium concentration without profiling, at the same temperature; additionally, patients abstained from food ingestion during their HD sessions. Our results are in line with previous observations regarding the incidence of IDH in patients with higher UF rates ,16,19.Patients with IDH were characterized by lower pre- and post-HD circumferential strain of the common carotid artery. The lower CCA-CS showed that impaired vascular reactivity is one of the most important risk factors for this complication’s occurrence.


Six donkeys of similar age were selected according to age factors and divided into two groups, the high (H) and low (L) fat groups, according to their IMF content.Donkey meat has low fat and high protein contents and is rich in various unsaturated fatty acids and trace elements that are beneficial to human digestion and absorption. IMF (intramuscular fat), also known as marbling, is an important indicator of the lean meat to fat ratio, which directly affects the tenderness and juiciness of the meat. At present, the underlying molecular variations affecting IMF content among donkey breeds are unclear. The Guangling donkey is an indigenous species in China. This study explored candidate regulatory genes that affect IMF content in Guangling donkeys. The IMF content of the SCD, LEPR, CIDEA, DLK1, DGAT2, ITGAL, HMOX1, WNT10B, and DGKA, had significant roles in adipocyte differentiation and adipogenesis. The selected DEGs were further validated by qRT–PCR.RNA-seq technology was used to compare the muscle transcriptome between the two groups. More than 75.0% of alternative splicing (AS) events were of the skipped exon (SE) type. A total of 887 novel genes were identified; only 386 novel genes were aligned to the annotation information of various databases. Transcriptomics analysis revealed 167 differentially expressed genes (DEGs), of which 64 were upregulated and 103 were downregulated between the H and L groups. Gene ontology analysis showed that the DEGs were enriched in multiple biological processes and pathways that are related to adipocyte differentiation, lipid synthesis, and neutral lipid metabolism. KEGG pathway analysis suggested that arachidonic acid metabolism, the HIF-1 signalling pathway, fructose and mannose metabolism, glycerophospholipid metabolism, and the AMPK signalling pathway were involved in lipid deposition. In addition, a gene–gene interaction network was constructed that revealed that the DEGs, including This study improves the in-depth understanding of gene regulation and protein expression regarding IMF deposition and lays a basis for subsequent molecular breeding studies in Guangling donkeys.The online version contains supplementary material available at 10.1186/s12864-022-08857-2. Guangling donkeys are distributed in Guangling County, Shanxi Province, China, and they are a local dominant breed that is carefully reared by local people using traditional production practices . Guanglilongissimus dorsi and visceral adipose tissue (VAT) of Dezhou donkeys. Compared with VAT, 267 genes were upregulated and 311 genes were downregulated in the longissimus thoracis. KEGG enrichment analysis revealed that glycerolipid and glycerophospholipid metabolism are key metabolic pathways regulating lipid deposition [Next-generation sequencing (NGS) for transcriptome analysis (RNA-seq) has been widely used to examine complex traits, including fat deposition. Using RNA-seq technology, a total of 578 differentially expressed genes (DEGs) were identified in the position . Althouglongissimus dorsi tissues to assess different IMF contents among Guangling donkeys by analysing the DEGs and their associated pathways. The findings provide a theoretical basis for the genetic breeding of Guangling donkeys.This study used RNA-seq for transcriptome analysis of longissimus dorsi in 30 Guangling donkeys varied from 2.23 to 9.00%. The 6 selected donkeys (three in each group) with the lowest and highest IMF contents were named the L group and H group, respectively. Then, RNA-seq was performed. The individual samples in the groups were named L1, L2, L3, H1, H2, and H3 , and 64 genes were downregulated (38.32%) (Supplementary Table SSCD (log2 Fold-Change = 3.718), DGAT2 (log2 Fold-Change = 2.394), and CIDEA (log2 Fold-Change = 2.702) genes were differentially expressed and are considered to be associated with a high IMF content. Figure To screen out candidate genes that are significantly associated with changes in the IMF content in donkeys, the DEGs between the L and H groups were obtained using DESeq2. Notably, the gene density distribution between the H and L groups was almost the same without a significant difference Fig. A. HoweveP < 0.05). The biological processes included intercellular adhesion (GO: 0098609), the regulation of fat cell differentiation (GO: 0045598), smooth muscle cell proliferation, negative regulation (GO: 0048662), cellular response to lipids (GO: 0071396), the neutral lipid metabolic process (GO: 0006638), the neutral lipid biosynthetic process neutral lipid biosynthesis (GO: 0046460), and the negative regulation of MAPK cascade (GO: 0043409). The molecular functions included protein kinase C binding (GO: 0005080), carboxylic acid binding (GO: 0031406), organic acid binding (GO: 0043177), the triglyceride biosynthetic process (GO: 0019432), the acylglycerol biosynthetic process (GO: 0046463), the response to fatty acids (GO: 0070542), and the acylglycerol metabolic process (GO: 0006639). The cellular composition included lipid droplets (GO: 0005811) and actin cytoskeleton (GO: 0015629). These data showed that the DEGs are closely related to cell development and fat metabolism.Next, GO functional enrichment analysis was performed to explore the functions of the DEGs. GO terms included three categories: cellular composition, molecular function, and biological processes (Supplementary Table SP < 0.05), such as arachidonic acid metabolism, the HIF-1 signalling pathway, fructose and mannose metabolism, glycerophospholipid metabolism, and the AMPK signalling pathway  were related to fat metabolism  were randomly selected and confirmed by qRT–PCR. The results showed a similar trend with a correlation of 0.992 (R2 = 0.984), indicating the reliability of the RNA-seq results  type accounted for 75% of AS sites, indicating its higher presence in the longissimus dorsi of the Guangling donkey. These results provide a good reference for AS events in donkey muscle as they relate to IMF deposition.A total of 887 novel genes were obtained after comparing the spliced transcripts with the genome annotation data, and only 386 genes were aligned with the annotation information in the databases. Fig. , Fig. 7BPPARγ and C/EBPs induces adipocyte differentiation. During terminal differentiation, certain genes involved in the synthesis and degradation of triacylglycerol, such as the DGAT2 gene, are upregulated, which converts progenitor cells into mature white adipocytes [Fat deposition is a dynamic accumulation process that results from a higher energy intake than required . Commitmipocytes . Advanceipocytes . Notablyipocytes . These rSCD, LEPR, CIDEA, DLK1, DGAT2, ITGAL, HMOX1, WNT10B, and DGKA, that might have critical roles in IMF deposition.GO enrichment analysis indicated that many DEGs were enriched in biological pathways related to the regulation of fat cell differentiation, lipid biosynthesis, and metabolic processes. This indicated that there was a significant difference in lipid metabolism between the L and H groups. Moreover, we must admit that there were changes in the IMF contents of the Guangling donkeys aged between 24 and 36 months, and therefore, age, may be a significant factor. To obtain accurate inferences about IMF content, more follow-up experiments are needed in the future. Furthermore, KEGG pathway enrichment analysis revealed that the DEGs were significantly enriched in arachidonic acid metabolism, glycerophospholipid metabolism, fat digestion and absorption, and the AMPK signalling pathway. Among them, arachidonic acid metabolism and the AMPK signalling pathway play important roles in fat metabolism. Arachidonic acid (AA), a polyunsaturated fatty acid, is a precursor for the synthesis of bioactive mediators and plays a complex role in fat synthesis . AMPK siWNT10B and DLK1 were upregulated. The Wnt signalling pathway is closely related to adipocyte development and differentiation. The WNT10B gene, a member of the WNT gene family, inhibits adipogenesis by inhibiting key transcription factors, such as C/EBPα and PPARγ [WNT10B in the H group suggests that maintaining a balance between anabolic and catabolic lipid metabolism is highly complex. DLK1 , also known as preadipocyte factor l (pref-l), serves as a marker for preadipocytes. The overexpression of Pref-1 inhibits adipogenic differentiation through the downregulation of PPARγ and C/EBPα [DLK1 gene may enhance the differentiation of adipocytes and is positively correlated with fat content [HMOX1 gene) was downregulated in the H group; haem oxygenase-1 is a widely expressed microsomal enzyme in mammals, including humans. HMOX1 exerts multiple effects, such as anti-inflammatory, anti-apoptotic, and antioxidant properties, through multiple signalling pathways [HMOX1 can upregulate the Wnt/β-catenin signalling pathway, which inhibits the activities of PPARγ and C/EBPa. In turn, adipogenesis is reduced [HMOX1 overexpression inhibits mesenchymal stem cell differentiation into adipocytes [Compared with the L group, the H group had more genes related to the first phase of adipocyte differentiation; among those, nd PPARγ . Interesd C/EBPα , 20. How content , 22. Thepathways . HMOX1 c reduced , 25. Simipocytes .DGAT2, CIDEA, and SCD genes are involved in fat synthesis and promote fat deposition. Diacylglycerol acyltransferase (DGAT) is a rate-limiting enzyme that controls triglyceride (TG) synthesis in adipocytes [DGAT positively correlates with fat deposition and was identified as a candidate adipose deposition gene in pigs [CIDEA) induces lipid storage (obesity) [PPARs, which then promote fat deposition. CIDEA also negatively regulates uncoupling protein 1 (UCP1), thereby reducing energy metabolism and increasing fat accumulation [SCD gene affect the composition of fatty acids; for instance, the T allele at g.2228 T > C increases the content of monounsaturated fatty acids [Compared with the L group, the expression of genes related to terminal differentiation (committing white adipocytes to mature white adipocytes) were upregulated in the H group. The ipocytes . DGAT po in pigs . Cell deobesity) . It is amulation , 31. Stemulation . The SNPty acids . In summDGKA was downregulated in the H group. Diacylglycerol kinase (DGKA) phosphorylates diacylglycerol (DAG) to phosphatidic acid, thus removing DAG [DGKA is necessary for the VEGFA-triggered  angiogenic program, and its activation requires Src tyrosine kinase activity [DGKA gene may be a candidate gene for fat deposition. In addition, compared with the L group, the LEPR (leptin receptor) gene, which is an essential regulator of lipid metabolism, was downregulated in the H group [ITGAL gene was upregulated in the H group compared with the L group, which is consistent with the DEGs identified in the analyses of cattle adipose tissues with different IMF contents. In cattle with a high IMF content, certain ECM protein components  were significantly upregulated [In this study, compared with the L group, ving DAG . In addiactivity . In agreactivity . Therefo H group . Accordi H group , 39. In egulated . Consequlongissimus pectoralis muscle, which could be further exploited to find the specific roles of candidate genes  in IMF deposition.This study used RNA-seq technology to compare transcriptome differences that determine IMF content. In total, 167 DEGs were identified, revealing new information related to gene networks and metabolic pathways that may play an important role in IMF deposition in Guangling donkeys. In addition, a large number of novel genes and AS events were detected in the Animal welfare and all animal experimental procedures were performed in accordance with ARRIVE guidelines. Animal sample collection and experimental protocols were all approved by the Animal Ethics Committee of Shanxi Agricultural University . Throughout the study, the animals were reared in the same environment and with the same diet conditions.longissimus dorsi samples at the 13th rib were aseptically and quickly obtained within 30 min of harvest. The collected samples were stored in liquid nitrogen for immediate storage, and long-term storage was carried out at − 80 °C. According to the China National Standard GB5009.6–2016 “Determination of Fat in Foods in National Food Safety Standard”, the IMF content was determined by the Soxhlet method. A Soxhlet extraction apparatus was used to remove fat and dry the ground meat samples for fat extraction. Petroleum ether was used as a solvent. This was recycled and dried for 8 h. Then, it was weighed to obtain the weight of the bottle containing fat. The IMF content was calculated by a formula. The three longissimus dorsi samples with the highest IMF contents and the other three with the lowest IMF contents were selected for transcriptome analysis.To improve data reliability and reduce individual differences, a total of 30 Guangling donkeys were raised on a commercial donkey farm in Fanshi County, Xinzhou City, Shanxi Province, China, and 6 donkeys with IMF differences and similar ages were selected  for use in this study. All Guangling donkeys were reared under the same natural conditions of uncontrolled room temperature and light with unrestricted access to food and water. The Total RNA was extracted from muscle samples using TRIzol reagent (Life Technologies Corp.) following the manufacturer’s instructions. RNA purity, concentration, and integrity were measured using a Nano Photometer spectrophotometer, Qubit 2.0 fluorometer , and Agilent 2100 Bioanalyzer , respectively.Oligo (dT) magnetic beads were used to isolate polyA+ mRNA, and the first strand of cDNA was synthesized by reverse transcription. Then, the RNA template was removed to obtain double-stranded RNA. The cDNA library was subjected to AMPure XP bead  purification and PCR enrichment. The constructed library was subjected to preliminary quantification and concentration estimation (effective library concentration > 2 nM) using an Agilent Bioanalyzer 2100, Qubit 2.0 Fluorometer, and Q-PCR. Sequencing (100 bp read) was performed using the Illumina NovaSeq sequencing system .Based on the Illumina NovaSeq 6000 platform, transcriptome analysis of 6 samples of the Guangling donkey longissimus dorsi muscles from the H group and L group was carried out. Fastp v 0.19.3 was used to filter the original data, mainly to remove reads with adapters. When the N content in any sequencing reads exceeded 10% of the base number of the reads, the paired reads were removed. When the number of low-quality (Q < =20) bases contained in the reads exceeded 50% of the bases of the reads, the paired reads were removed. Clean reads of 55.79 Gb were obtained after filtering the data, and all subsequent analyses were based on the clean reads. RNA sequencing data have been submitted to the National Center for Biotechnology Information (NCBI) BioProject database (SRA: PRJNA658642).Equus asinus ASM130575v1) and its annotation files were downloaded from the NCBI database. HISAT v2.1.0 was used to construct the index and compare clean reads to the reference genome. FeatureCounts v1.6.2 was used to calculate the gene alignment and FPKM. FPKM is currently the most commonly used method to estimate gene expression levels.The reference genome  program annotation tools .GeneMANIA  was used2O, and 0.4 μL of ROX Reference Dye (50X). The qRT–PCR program was as follows: 95 °C for 1 min; 95 °C for 15 s; 60 °C for 1 min; and a final extension at 72 °C for 20 s, for a total of 40 cycles. Each amplification had three replicates. The relative expression levels of the genes were quantified by the 2-△△CT method; the β-actin gene was used as the internal reference gene. The significant differences in gene expression levels between the H (high) and L (low) groups were statistically identified using the t test (GraphPad Prism 7).Seven DEGs were validated by qRT–PCR using the TB Green® Premix Ex Taq™ II (TaKaRa) kit to verify the reliability and accuracy of the sequencing data. Primers, designed by the GenScript Primer Design online tool, were synthesized by Beijing Qingke Xinye Technology Table . The 20 StringTie v1.3.4d was used for novel gene prediction. StringTie applies network streaming algorithms to identify de novo splice transcripts. Compared with Cufflinks and other software, StringTie can splice a more complete and accurate transcript, and the splicing speed is faster. CPC2, PLEK and CNCI software were used to jointly predict gene coding potential. If the three crossover results showed “Non-Coding”, it indicated that the predicted genes or transcript had no coding ability. Blastx was used to align the novel genes with KEGG (Kyoto Encyclopedia of Genes and Genomes), GO (Gene Ontology), NR (NCBI nonredundant protein sequences), Swiss-Prot , trEMBL (Translation of EMBL), and KOG (Eukaryotic Orthologous Groups of proteins) databases, setting the e-value as 1e-5. The Pfam database uses HMMER software to perform the alignment and extract all annotation results.AS  events were identified by rMATS v4.0.2  softwareAdditional file 1: Table S1. Gene names/information about different genes screend.Additional file 2: Table S2. Differential genes GO functional enrichment analysis results.Additional file 3: Table S3. 38 differential genes related to fat metabolism.Additional file 4: Table S4. Details sheet for novel genes.


The receptor tyrosine kinases (RTKs) family is well-recognized as vital targets for the treatment of hepatocarcinoma cancer (HCC) clinically, whereas the survival benefit of target therapy sorafenib is not satisfactory for liver cancer patients due to metastasis. EGFR and MET are two molecules of the RTK family that were related to the survival time of liver cancer patients and resistance to targeted therapy in clinical reports. However, the mechanism and clinical therapeutic value of EGFR/MET in HCC metastasis are still not completely clarified. The study confirmed that EGFR/MET was highly expressed in HCC cells and tissues and the phosphorylation was stable after metastasis. The expression of EGFR/MET was up-regulated in circulating tumor microemboli (CTM) to accelerate IL-8 production and resistance to the lethal effect of leukocytes. Meanwhile, highly expressed EGFR/MET effectively regulated the Ras/MAPK pathway and stabilized suspended HCC cells by facilitating proliferation and inhibiting apoptosis. Moreover, EGFR/MET promoted phosphorylation of hetero-RTKs, which was dependent on high-energy phosphoric acid compounds rather than their direct interactions. In conclusion, highly expressed EGFR/MET could be used in CTM identification and suitable for preventing metastasis of HCC in clinical practice. Liver cancer is one of the most lethal cancers, causing the 6th carcinoma incidence and 4th cancer mortality in the world . HepatocReceptor tyrosine kinases (RTKs) are currently considered as critical roles in malignant transformation and cancer metastasis . The humIn the present study, we determined the expression of RTKs proteins in CTM and cells and verified the important roles of EGFR/MET and the possibility of being used as a clinical treatment target. Furthermore, we identified the mechanism that EGFR/MET promotes metastasis and stabilizes HCC cell lines in hematogenous metastasis. These findings provide mechanistic evidence for us to design HCC medications targeting EGFR/MET, thereby benefiting patients with HCC in reducing the incidence of metastasis and prolonging survival time.We firstly explored the expressions of RTKs in HCC tissues and HCC cell lines. Interestingly, the expressions of RTKs in HCC tissues and HCC cell lines varied greatly. Certain RTKs namely M-CSFR were highly expressed in HCC tissues whereas poorly expressed in cell lines. However, other RTKs namely the HER family were highly expressed in both HCC tissues and cell lines. Compared to the other RTKs we detected, the concentration of EGFR/MET was high expressional RTKs both in HCC tissues and HCC cell lines. Especially in HCC cells, expressions of EGFR and MET in MHCC97H were 54,548.83 and 607,470.8 pg/mg, respectively, and those of SK-HEP1 were 103,099.9 and 38,652.84 pg/mg, respectively, higher in expression than other RTKs Fig. . AdditioNext, we verified whether the expressions of EGFR/MET using a xenograft model for lung metastasis of mice were stable during metastasis. In this model, Hepa1-6 cells were successfully metastasized from the left lobe of the liver to the lungs Fig. . CompareSince metastasis of CTM was closely related to anoikis resistance, we next observed the role of EGFR/MET in HCC cell lines of suspension. Phosphorylation RTKs array reported that phosphorylation of EGFR/MET in suspended HCC cell lines was enhanced and Western blot assays indicated that peak point appeared at 24–48 h Fig. . IL-8 syAs the Ras/MAPK pathway regulated by RTKs played an important role in tumor metastasis, the function of EGFR/MET in regulating the Ras/MAPK pathway will be further investigated in our subsequent research. Firstly, like phosphorylation of EGFR/MET, we found Ras-GTP was up-regulated in HCC cell lines in suspension Fig. . Both EGNext, we analyzed the activation of ERK in suspended HCC cell lines. The findings were identical to Ras-GTP indicating that the phosphorylation level of ERK was up-regulated in suspended HCC cell lines Fig. . FollowiBased on the high expression of EGFR/MET in HCC tissue/cell lines and stable expression in metastasis, the regulatory effects of EGFR/MET on the phosphorylation level of other RTKs were investigated. In this study, Lenvatinib, as an inhibitor of PDGFRβ/VEGFR/FGFR/RET, was effective in suppressing EGFR/MET and targeting RTKs in MHCC97H and HCCLM3, but less effective on PLC/PRF/5. In contrast, Sorafenib, as an inhibitor of PDGFRβ/VEGFR/Raf, had less effect on EGFR/MET, and barely suppressed the function of PDGFRβ in MHCC97H and LM3 Fig. . Also, wNext, we focused on possible mechanisms related to the phosphorylation of EGFR/MET. Interestingly, we found that phosphorylation of MET was affected by MET amplification Fig. . PhosphoRTKs nowadays are considered to be critical in tumorigenesis, intrahepatic metastasis, and distal metastasis of liver cancer . The corExtrahepatic metastasis is easily observed in patients with HCC . BecauseThe Ras/MAPK pathway is considered a survival factor for carcinoma cells, and RTKs produce a similar mechanism in regulating the Ras/MAPK pathway , 36. IncRTKs have a similar effect on the activation of downstream signaling pathway Ras/MAPK pathway . Our preIn conclusion, we have demonstrated that EGFR/MET are as the two of the most expressed in the RTKs family in both HCC tissue and cells. The study also clarifies the relationship between EGFR/MET and CTM. Activation of EGFR/MET caused by complicated factors helps suspended HCC cell lines to maintain cell stability and distal metastasis. Targeting EGFR/MET may provide opportunities for the prevention of metastasis of HCC.2. 100 mg/mL penicillin G and 50 μg/mL streptomycin  were added. All cell lines were identified by STR and mycoplasma tests and all were used within 3 months after thawing early passage cells.Human HCC cell lines MHCC-97H , PLC/PRF/5 , HCCLM3, SK-Hep1, and mouse HCC cell line  were cultured in DMEM containing high glucose  with 10% fetal bovine serum  at 37 °C in a humidified atmosphere containing 5% COOf 3% Poly-HEMA  was dissolved in ethanol for preventing HCC cell lines adhesion. Tissue culture plates were coated with 1 ml of Poly-HEMA solution and dried overnight at room temperature in a laminar flow hood. All cells were sub-cultured, re-plated in suspension condition, and harvested at indicated time points for detection.For EGFR/MET knockdown, siRNAs were transfected into MHCC97H/HCCLM3/PLC-PRF-5 cells using Lipofectamine 3000 . Cells were cultured for further experiments after 48 h. The sequences of siRNAs were listed in Table Sorafenib (VEGFR and PDGFRβ), Lenvatinib , Erlotinib (EGFR), Afatinib (EGFR), Crizotinib (MET), and AZ628 (Ras/MAPK) were purchased from Selleck  and dissolved in DMSO. Recombined EGF, HGF, FGF2, PDGF-BB, and VEGF were purchased from PeproTech , and recombined insulin was bought from Selleck .The composition of Co-IP buffer was 50 mM Tris, 150 mM NaCl, 1% NP40, 0.5% sodium deoxycholate, 1 mM EDTA, 1 mM PMSF and 1× protease inhibitor cocktail . Co-IP and Western blot were performed as described [Luminex Kit  was coated with RTKs  and RTKs ligands (EGF and HGF). ELISAs  were used to detect the concentration of RTKs which Luminex Kit didn’t contain. Phosphorylation of RTKs was performed by Human/Mice Phospho-RTKs Array Kit  according to the manufacturer’s instructions. Image pro plus 6.0 was used for intensity quantification. Ras Activation Assay Kit  was used for the detection of Ras activation.5/ml. Trypsin digestion was performed in cells with or without suspension at the same time to dissociate clusters into single cells and reduce interference from trypsin. PI/RNAse was used for detecting cell cycle and Annexin V/PI was used for detecting apoptosis of HCC cell lines. Flow cytometry was performed according to the manufacturer’s instructions.All HCC cell lines were harvested and resuspended at a concentration of 10Paraffin specimens from patients with HCC and carcinoma tissues of mice were performed according to the manufacturer’s instructions. Immunofluorescence assays were conducted in a manner blinded to the sample identity. Cells cultured with or without suspension were harvested and dried on coverslips with Poly-L-lysine  coated. EDU assay  was used for detecting DNA synthesis of HCC cell lines.Leukocytes were collected from healthy donor blood by gradient centrifugation with leukocytes separation . After being cultured with DMSO or inhibitors for 24 h, HCC cells lines were re-suspended and the supernatant was discarded. Then, equal leukocytes were added to the culture medium with MHCC97H cell line and were co-cultured for 24 h in suspension. Cell mixtures were collected, dried on coverslips with Poly-L-lysine coated, and observed by immunofluorescence with EpCAM + EGFR and CD45 staining.5 Hepa1-6 cells were injected into the left lobe of the liver in each mouse. The animals were sacrificed after 60 days and the presence of any extrahepatic metastasis except lungs was considered to be a failure, even lung metastasis model was successfully established. Tumor tissues from the liver and lung in the same mouse were collected for phosphorylation detection, immunohistochemistry, immunofluorescence, and scRNA-seq.5-week-old C57 male mice  were employed to construct xenograft tumor models. The mice were anesthetized with 50 mg/kg Zoletil®50  combined with 5 mg/kg Xylazine hydrochloride  by intraperitoneal injection, and 10p-value < 0.05; min. pct > 0.1) was used for marker gene identification.ScRNA-seq was performed by Annoroad Gene Tech . Carcinoma tissues were separated from the xenograft model in 48 h and a library was constructed as manufacturer’s instructions. CellRanger was applied to identify cell barcodes in reads1 and UMI marks in different transcripts, and the corresponding reads2 were compared referring to genome through STAR to determine source gene of the reads and complete quantification of gene expression. RBC in cells containing <200 expressed genes or mitochondria UMI rate >80% were excluded. Then, mitochondrial genes were removed in the expression table. After normalizing UMI data of gene expression in each cell by Seurat analysis and UMI data dimension reduction by PCA, the top 20 principal components were selected for cell classification and UMAP construction based on graph-based clustering method. FindAllMarkers function by the Wilcox rank-sum test algorithm  was used for data analysis. The statistical significance of differences between different experimental cell groups was determined by SupplementSupplement 1Supplemental Table 1Supplemental Table 2Supplemental Table 3Supplemental Table 4Reproducibility ChecklistAuthor Contribution FormWestern Blot Origin


The role of B cell subsets remained to be elucidated in a variety of immune diseases, though which was used as an effective biomarker for anti-inflammatory or antiviral response. This study aimed to evaluate the early changes of B cell subtypes distribution in elderly patients with community acquired pneumonia (CAP), as well as the association between B cell subtypes and prognosis.+ B cells were further divided into naïve B cells (Bn), IgM/D+ memory B cells (IgM+ Bm), switched B cells (SwB), and transitional B cells (Btr).This prospective study included elderly patients with CAP, severe CAP (sCAP) and healthy elderly subjects between April 2016 and March 2018. Flow cytometry was used to detect CD3, CD20, HLA-DR, CD24, CD27, CD38, IgM, and IgD. CD20+ Bm was significantly higher. Heat map showed Bn and Btr on day 3 and day 7 was negatively correlated with activated partial prothrombin time (APTT), international normalized ratio (INR), sequential organ failure assessment score (SOFA) and Acute Physiology and Chronic Health Evaluation II (APACHE II). After 28-day follow-up, Btr percentage in survival group was significantly higher. Receiver operator characteristic (ROC) curve analysis found that Btr count showed sensitivity of 48.6% and specificity of 87.0% for predicting the 28-day survival, with an area under the ROC curves of 0.689 (p = 0.019).A total of 22 healthy controls, 87 patients with CAP and 58 patients with sCAP were included in the study. Compared to CAP, sCAP was characterized by significantly lower absolute number of B cells, Bn and Btr, significantly lower Btr and Bn subset percentage, while percentage of IgMSeverity and prognosis of CAP in elderly people is accompanied by changes in the B cell subsets. Btr subsets could play prognostic role for a short-term mortality of elderly CAP patients.The online version contains supplementary material available at 10.1186/s12890-022-01985-1. Community acquired pneumonia (CAP) is an infectious parenchymal lung disease with high morbidity . PreviouIn the current clinical practice, presence of significant risk factors for pneumonia, such as age and comorbidities, as well as clinical severity scores, such as the Pneumonia Severity Index (PSI) and Confusion, Urea, Respiratory rate, Blood pressure, aged 65 and older (CURB-65), assist to predict patient outcomes [Humoral immunity plays an important role in the prevention and recovery of CAP. B cells are the main types of cells involved in humoral immune response, playing the role of anti-infection by regulating production of antibodies, antigen presentation and immunization . Based oBased on the above, we carried out this study to evaluate the early changes of B cell subtypes distribution in elderly patients with CAP, as well as the correlation with disease severity. We hypothesized that the predictive value of B cell subtypes could be useful in clinical practice for the prognosis of these pneumonia patients.This prospective study included elderly patients with CAP and healthy elderly subjects comparable by age at Shanghai East Hospital affiliated to Tongji University between April 2016 and March 2018 , and all participants signed the informed consent form.Clinical data was recorded, including sex, age, Body-mass index (BMI) and etiology , Acute Physiology and Chronic Health Evaluation II (APACHE II), sequential organ failure assessment score (SOFA), PSI score and CURB-65 score. All patients with CAP and sCAP were followed up for 28 days, and divided into survivors and non-survivors according to 28-day outcome.Peripheral venous blood (8 ml) was collected from healthy controls at day 1 and from CAP and sCAP patients at days 1, 3 and 7 after admission. Peripheral blood mononuclear cells (PBMC) were separated from blood samples by standard Ficoll-Paque gradient centrifugation .+ T cells, cells were gated into CD20+ B cells and CD20− fraction. CD20+ B cells were further divided into naïve B cells (Bn) (CD20+IgM/D+CD24intCD38int), IgM/D+ memory B cells (IgM/D+ Bm) (CD20+IgM/D+CD24+CD38−), switched B cells (SwB) (CD20+IgM/D−), Transitional B cells (Btr) (CD20+IgM/D+CD24hiCD38hi)  were used to separate the B cell subsets. After gating out CD3hi) Fig. . Flow cy+ cells in peripheral blood lymphocytes multiplied by the total number of lymphocytes per microliter measured using a Coulter LH instrument . The absolute number of CD20+  by multiplying the total number of B lymphocytes previously calculated by the percentage of positive cells for each one of these antigens in CD20+ B cells. All absolute numbers were expressed as cells per milliliter.The percentage of cells expressing CD20 in the total lymphocytes gate was defined by forward and side scatter in PBMCs. The absolute number of circulatory B lymphocytes was calculated by determining the percentage of CD20p < 0.05 was considered significantly different.All data were analyzed using SPSS 20.0  and GraphPad Prism 7  software. Kolmogorov–Smirnov test was used to test the normality of continuous variables, and those consistent with normal distribution were expressed as mean ± SD. T test was used for comparison between two groups, ANOVA test was used for comparison between three groups, and Dunn's post hoc test was used for pairwise comparison. Non-normal continuous variables were represented by median (quartile). Mann–Whitney U test was used for comparison between two groups, Kruskal–Wallis test was for comparison between three groups, and Dunn's test was used for pairwise comparison. Counting variables were expressed using the number (percentage), and comparisons between groups were tested by either chi-square or Fisher exact test. Spearman or Pearson test was used for correlation analysis. Receiver operating characteristic (ROC) curve was drawn to calculate the AUC, sensitivity, specificity and cut-off values with the death rate at 28 days as the outcome. According to the cut-off value, the patients were divided into two groups. Kaplan–Meier (KM) curve was drawn and the differences between the two groups were compared by log-rank test. Two-sided A total of 22 healthy controls, 87 patients with CAP and 58 patients with sCAP were included in the study. The baseline information is shown in Table p = 0.14 for WBC, p = 0.078 for lymphocytes). However, WBC was significantly higher in patients with sCAP both compared to CAP group and healthy controls , while lymphocytes count was significantly lower , as demonstrated on Additional file We collected blood samples from healthy controls, CAP and sCAP and compared B cell subsets on the first day. A total white blood cells (WBC) and lymphocytes count did not significantly differ between healthy controls and CAP patients (p < 0.0001). Compared with healthy controls, percentage of Btr cells in CAP patients was lower (p < 0.05), and even more significantly reduced in sCAP . At the same time percentage of Bn was lower and percentage of IgM+ Bm cells significantly higher in sCAP compared to CAP. We made a further analysis of the differences between B cell subsets related to etiology of pneumonia in CAP and sCAP group. We found that both the absolute numbers and frequency of Btr in sCAP with bacterial and fungal were lower than those in CAP, while the absolute number of Btr in sCAP with viral was lower than that in CAP, and the frequency of Btr was not different from that in CAP , it was decreased significantly in sCAP patients on day 1 and showed further decrease tendency on day 7, while in CAP group, the percentage of Btr gradually increased from day 3 to day 7. Detailed data on peripheral B cell subsets changes on days 1, 3 and 7 are shown on Fig. Different B cell subsets, severity scores and different clinical parameters were used to generate the heat map demonstrated in Fig. After 28-day follow-up, the mortality rate among sCAP patients was analyzed. Results showed the Btr percentage in survival group was significantly higher than that in non-survival group at day 1. The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the discriminative ability of B cells subsets to predict the 28-days mortality for sCAP patients.p = 0.019). For the absolute counts of Bn, IgM+ Bm and SwB cells, ROC analysis did not find any significance for predicting the survival (All p > 0.05). Detailed information on B cell subsets according to the 28-day mortality is shown on Fig. For the absolute counts of Btr cells, a cut-off value of 0.58 showed sensitivity of 48.6%  = 31.38% to 66.01%) and specificity of 87.0% (95% CI = 66.41% to 97.22%) for predicting the survival. The area under the ROC curve was 0.689 . To the best of our knowledge, this is the first study to report in detail dynamical changes in B cell subsets according to the severity and prognosis of CAP in elder patients.After 28-day follow-up, Btr absolute count in survival group was significantly higher than that in non-survival group and ROC curve analysis further found that with a cut-off value of 0.58, Btr showed sensitivity of 48.6% and specificity of 87.0% for predicting the survival, with the area under the ROC curve of 0.689  was decreased significantly with the severity of the disease. Previous studies , 14 have+ B cells. Our study showed the Btr levels were significantly higher in survival group at day 1, suggesting that Btr may be another prognostic factor in patients with sCAP, possibly linked to the IgA deficiency [Recent study by Luchsinger et al. , undertaficiency , reporteficiency , 7.+ Bm and SwB differ significantly between CAP and sCAP patients. Moreover, subtypes of B cells were correlated with severity scores, and Btr percentage significantly correlated with survival of CAP patients. All this data indicates that changes in B cell subsets may predict the prognosis in older patients with severe pneumonia.Some previous CAP studies , 21 note+ Bm cells was significantly higher in CAP and especially sCAP compared to healthy controls. However, other reports indicate that total blood IgM, IgG2 and IgG levels are lower in CAP [Although human IgM + Bm cells represent a large subpopulation, their immunological functions are still poorly understood . Undoubtr in CAP , as wellr in CAP . This cor in CAP . Thus, tThis study has some limitations. The sample size for healthy control group was limited by the presence of other age-related diseases that could influence B cell subsets. However, we did our best to match our study group with controls by age, BMI and other characteristics. Secondly, we performed dynamic evaluation on 1st, 3rd and 7th days counting from the admission, without taking into account incubation period, and this could influence some of the B cell subsets development. Finally, single-center design of our study does not allow fully addressing the prognostic value of Btr in CAP, which should be validated in further studies.+ B cells, including IgM/D+ Bm cells and Btr cells. Those changes might serve as a biomarker in clinical practice for early stratification of CAP patients.In conclusion, the severity and prognosis of CAP in elderly people is accompanied by changes in the B cell subsets, in particular CD20Additional file 1: Primary data.Additional file 2: Peripheral B cell subsets absolute number in patients with CAP, sCAP and healthy control.Additional file 3: Frequency and absolute number of B cell subsets in CAP and sCAP patients with bacterial, virus, or fungal causes.Additional file 4: Dynamic changes of peripheral B cell subsets absolute number in CAP patients.Additional file 5: The prognostic value of the frequency of Btr, Bn, IgM+ Bm, and SwB in patients with sCAP.Additional file 6: ROC curve for peripheral blood B subset patients with sCAP on the first day of admission to predict prognosis at day 28.