Transcriptome Analysis of the Transdifferentiation of canine BMSCs into Insulin Producing Cells

doi:10.21203/rs.3.rs-32137/v1

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Research article

Transcriptome Analysis of the Transdifferentiation of canine BMSCs into Insulin Producing Cells

https://doi.org/10.21203/rs.3.rs-32137/v1

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published 25 Feb, 2021

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Background Bone marrow mesenchymal stem cells are a potential resource for the clinical therapy of certain diseases. Canine, as a companion animal, living in the same space with human, is an ideal new model for human diseases research. Because of the high prevalence of diabetes, our study in the alternative source of islets from bone marrow mesenchymal stem cells appears to be important.

Result In this study, we completed the transdifferentiation process and achieved the transcriptome profiling of five samples with two biological duplicates, namely, “BMSCs”, “islets”, “stage1”, “stage2” and “stage3”. A total of 11,530 differentially expressed transcripts were revealed in the profiling data. The enrichment analysis of differentially expressed genes revealed several signaling pathways that are essential for regulating proliferation and transdifferentiation, including focal adhesion, ECM–receptor interaction, tight junction, protein digestion and absorption, and the Rap1 signaling pathway. Meanwhile, the obtained protein–protein interaction network and functional identification indicating involvement of three genes, somatostatin receptor 2, ribosomal protein S6 kinase A6, and vasoactive intestinal peptide, could act as a foundation for further research.

Conclusion In conclusion, to the best of our knowledge, this is the first survey of the transdifferentiation of canine BMSCs into insulin-producing cells according with the timeline using next-generation sequencing technology. The three key genes we pick out may regulate decisive genes during the development of pancreas.

Epigenetics & Genomics

BMSC

sequencing

insulin producing cells

transdifferentiation

PPI

VIP

SSTR2

RPS6KA6

Stem cells in particular for BMSCs have been used for decades for the treatment of many diseases. Numerous reports about the therapeutic potential of BMSCs have been published. BMSCs can be used for the regeneration of cartilage and osteochondral tissue defects [1], craniofacial tissue [2], and spinal cord [3]; moreover, type 1 diabetes can be treated using BMSC-derived insulin-producing cells [4]. The number of patients with diabetes is continuing to increase; according to the WHO (https://www.who.int/diabetes/global-report/en/), in 2016, 422 million individuals were affected by diabetes globally.

Pancreatic islet transplantation is available as an alternative therapy for diabetes, but it has the limitation of insufficient availability of islets; as such, many research teams have searched for other cells that could substitute for islets. Human embryonic stem cell (hESC)- [5] and induced pluripotent stem cell (iPSC)-derived islet-like cells [6] have primarily been used to form islet-like clusters, but this is associated with a relatively high risk of neoplasia and other ethical issues. Against this background, induced β cells derived from BMSCs are a promising option given that they are easy to obtain and immunoregulation, and they can differentiate into osteoblasts, chondrocytes, and adipocytes in vitro. There are many ways to obtain insulin-producing cells using BMSCs. For example, it is possible to directly convert BMSCs into β-like cells by the lentiviral transduction of NGN3, PDX1, and MAFA [7]. Moreover, the addition of pancreas extract to the culture medium can be effective [8]. Another option is reprogramming, which can be achieved via the supplementation of small molecules such as conophylline [9].

Other groups reported that BMSC-derived vascular endothelial growth factor (VEGF) [10], epidermal growth factor (EGF) [11], and insulin-like growth factor 1 (IGF-1) [12] exhibited protective effects in many disease models and that the overexpression of several factors could indirectly mediate tissue repair [13]. The procedure for inducing pancreatic islet-like cells that we used in this study requires various factors, including pathway inhibitors and components EGF, bFGF, activin A, exendin-4, betacellulin, and nicotinamide.

Different signaling pathways are involved when reprogramming occurs. For example, the AKT signaling pathway influences hypoxic stress and STZ stimulation [14], while ERK1/2 signaling pathway regulation confers resistance to apoptosis [15]. Many animal experiments have shown that co-transplantation of pancreatic islets and BMSCs can boost the survival rate of islets, which improves the efficiency of surgery [16]. Research has also demonstrated the essential role played by the extracellular matrix, directly interacting with cells, in determining the direction and fate of cell differentiation [17]. In summary, signaling pathways, the extracellular matrix, and transplantation methods can influence the rate of islet-like cell mass acquisition and the therapeutic efficiency of transplantation.

However, all of the available reprogramming methods are limited by the low efficiency of transformation [18, 19] [20, 21], which may lead to an insufficiency of pancreatic islets for transplantation surgery. This problem is associated with our limited understanding of the transdifferentiation mechanisms.

In this study, BMSCs were converted to islet-like cells by a three-step induction procedure and samples were collected at the end of each phase along with BMSCs and islets as negative and positive controls. Then, they were analyzed by transcriptome analysis, followed by enrichment analysis and PPI network analysis with three genes chosen for further research. The results obtained here could provide a foundation for future work to understand the mechanisms underlying the transdifferentiation of canine BMSCs into islet-like structures.

Isolation and identification of BMSCs

When we transferred the mixture of cells extracted from bone marrow into dishes, we could only see the blood cells floating around, which is a necessary initial niche for the development of BMSCs. After 24 h of culture, the canine BMSCs presented an adherent state and fiber-like or irregular morphology; they also turned into spiral clusters when approaching confluence, which took approximately 6 days (Fig. 1A–C).

The results of flow cytometry assay confirmed that the BMSCs were positive for CD29, CD44, and CD166, and negative for CD11a, CD14, and CD34 (Fig. 1I).

To confirm the differentiation ability of BMSCs, we performed a three-lineage differentiation experiment; here, cell status was determined by staining tests, namely, Alizarin Red for osteoblasts (Fig. 1D), Alcian Blue for chondroblasts (Fig. 1E), and Oil Red O for adipocytes (Fig. 1F).

Induction And Characterization Of Islet-like Spheroids

At the nonadherent stage, BMSCs formed many spheroids floating in the medium (Fig. 1G). After the last induction stage, the spheroids were all positive for DTZ staining (Fig. 1H). The results of GSIS, 8.76 µIU/mL for DMEM low glucose (10 mM) and 45.22 µIU/mL for DMEM high glucose (25 mM) (Fig. 7K), confirmed that BMSCs had been transformed to pancreatic islet-like state.

De Novo Assembly

Pearson’s correlation coefficients (R²) were all around 0.9 for each biological replication of samples, which meant that these data were repeatable (Fig. 2B). The raw reads for each sample numbered around 100 million and the clean reads numbered about 90 million. Q20 and Q30 ranged from 96.67–97.12% and 91.87–92.76%, respectively, while the GC content ranged from 44.83–56.19% (Additional file 1). All clean reads, filtered by HISAT2, were used for comparison with the reference genome; in order for the reference genome to be considered suitable for this analysis and samples not to be contaminated, the rate of mapped reads should exceed 70%. The rate of total mapped reads was over 90% and the rates of uniquely mapped reads were all above 80% as prediction. (Additional file 2).

The quantitative analysis of common types of genes, such as miRNAs, tRNAs, and SRP-RNAs, using the software HTSeq, provided information on the status of different gene types based on the gene expression volume. The majority of genes were “protein-coding genes,” representing 67.60% of the total on average.

Overview Of Degs

Based on the thresholds of |log2FC| > 1 and FDR < 0.05, we subjected all types of transcripts to differential expression analysis. Differential transcript cluster analysis of mRNAs showed that there were 11,530 differentially expressed transcripts (Fig. 2A). The comparison of “BMSCs” and “stage1” showed that there were 555 upregulated genes and 569 downregulated ones (Fig. 2C), while there were 201 upregulated genes and 213 downregulated ones for the comparison between “stage 1” and “stage 2” (Fig. 2D), 232 and 132 for “stage 2” and “stage 3” (Fig. 2E), and 2,331 and 2,758 for “islets” vs “stage 3,” respectively (Fig. 2F).

Enrichment analysis of DEGs from transcriptome sequencing and GEO data

All of the DEGs, including upregulated and downregulated transcripts, were annotated to signaling pathways related to the GO terms. For the KEGG pathway analysis, the “BMSCs vs. stage 1” DEGs were mapped to 230 KEGG pathways, while the “stage 1 vs. stage 2,” “stage 2 vs. stage 3,” and “islets vs. stage 3” DEGs were annotated to 163, 148, and 273 pathways, respectively. Considering RF and Q-value, we obtained the top 20 KEGG pathways for each comparison group, and they shared several of the same pathways, including tight junction, protein digestion and absorption, pancreatic secretion, focal adhesion, ECM–receptor interaction, Rap1 signaling pathway, and cell cycle (Fig. 3A–D). These pathways are significantly related to cell proliferation and differentiation, which control the fate of BMSCs when induce to differentiate.

The GO analysis of “BMSCs vs. stage 1” was dominated by CC and MF, which included the categories of nucleus, extracellular region, intracellular membrane-bound organelle, membrane-bound organelle; and protein binding, binding, ion binding, metal ion binding, cation binding, and transition metal ion binding (Fig. 4A). For “stage 1 vs. stage 2,” the DEGs were mainly annotated to the categories of cellular protein modification process, protein modification process, and phosphate-containing compound metabolic process for BP; cytoskeleton, non-membrane-bound organelle, and intracellular non-membrane-bound organelle for CC; and ion binding, cation binding, metal ion binding, binding, zinc ion binding, and ATP binding for MF (Fig. 4B). The comparison between “stage 2” and “stage 3” showed that binding activity still formed the majority of MF and that CC was still dominated by organelle; however, for BP, the main categories were the regulation of transcription, regulation of RNA biosynthetic process, and regulation of RNA metabolic process (Fig. 4C). Then, for the pair “islets vs. stage 3,” annotations included binding activities like those mentioned above for MF; additional extracellular region part, extracellular matrix, and mitochondrion for CC; and intracellular signal transduction, carbohydrate metabolic process, single-organism carbohydrate metabolic process, and small GTPase-mediated signal transduction for BP (Fig. 4D).

To further verify the results, we downloaded similar sample sources from GEO database for the comparison with our results. They were chosen from two datasets, GSE20113 and GSE52063, which belong to the same platform, GPL3738. We also chose three normal pancreas samples, namely, GSM502601, GSM502602, and GSM502603, from the first dataset, along with four BMSC samples, namely, GSM1258129, GSM1258130, GSM1258131, and GSM1258132, from the second dataset. Because of the different backgrounds of the samples, the normalization of these data was performed before enrichment analysis using R packages limma and gplots(Fig. 5A, B). There were 1,431 DEGs between those two series, 771 downregulated and 660 upregulated, showed in the heatmap (Fig. 5C). The top 20 KEGG pathways acquired from DAVID (Additional file 3), including focal adhesion, ECM–receptor interaction, and PI3K–Akt signaling pathway, represented the pathways with the highest enrichment scores. In terms of the GO results, chaperone-mediated protein folding and cellular zinc ion homeostasis for BP; and ion channel activity and oxidoreductase activity for MF (Additional file 4) are essential processes of the pancreas. All these results are analogous to the findings of our samples from the transition of “BMSCs” to “islets.”

Ppi Network

The PPI network (Fig. 6) contained 1,406 nodes. Insulin, glucagon, and somatostatin represented key roles in this network based on the node size, so we analyzed the PPI network focusing on these three and those who linked to them by the first and second edges. Under these conditions, 168 nodes were shown in this network, and 22 nodes directly interacted with the three key genes.

Verification And Quantification Of Key Genes

The expression quantity of these 20 genes were verified by qRT-PCR, which was in accordance with the sequencing data (Fig. 7A). Among these genes, SSTR2, RPS6KA6, and VIP were picked out, transfected by lentivirus which were built for overexpression and knockdown of these three genes. It could be found that there were more spheroids in better shape than those who were knockdown (Fig. 7B-G). After the induction procedure, it was showed that the expression level of PDX1, NGN3 and NKX2.2 were upregulated in the overexpression groups of VIP and RPS6KA6 (Fig. 7H, J), which was contrary for OE-SSTR2 groups (Fig. 7I). The result gave us a hint that VIP and RPS6KA6 might regulate the expression level of pancreas key genes PDX1, NGN3 and NKX2.2 through directly interaction or participating the signaling pathway such as MAPK signaling pathway in terms of RPS6KA6 was a member of it. SSTR2, as a receptor of somatostatin, could have an effect on proliferation and differentiation via lowering cAMP [22]. Meanwhile, the GSIS (Fig. 7K) result was the same outcome.

Canine BMSCs are the most advantageous source of pancreatic islet-like clusters

To the best of our knowledge, this is the first study analyzing the transdifferentiation of canine BMSCs according to the timeline of induction. Screening for DEGs between different timepoints was performed, and referring to pancreatic development, we could tell what state the cells were in during the induction, enabling adjustment of the induction procedure to achieve optimal conditions. The reason why we chose canine BMSCs for this study was that they can be easily derived from bone marrow, along with having another advantageous immune escape mechanism compared with other cell types such as ESCs and iPSCs. Another advantage is that the usage of and access to these latter two types of cell are often limited by ethical concerns. Otherwise, canine has always been neglected to be a model animal with the increasing diabetes morbidity of companion dogs, and the same living environment with human can give more help for human diabetes treatment than mouse or any other animals.

For the first time induction procedure could be improved based on the profiling data

At present, existing methods for induction have low efficiency [23], and no consensus has yet been reached regarding the mechanism of transdifferentiation of BMSCs into insulin producing cells. However, surprisingly, besides our team, no other groups have focused on the timeline-based analysis of the whole transition process of canine BMSCs. The data was uploaded on SRA database (https://www.ncbi.nlm.nih.gov/sra) with submission number SUB7457848.

Through the enrichment analysis, we found that all of the transcriptome profiling data and GEO data provided the same findings. The DEGs from each set of data were enriched in pathways such as those related to cell junction, PI3K–Akt signaling pathway, ECM–receptor interaction, Rap1 signaling pathway, cell cycle, and cell adhesion molecules (CAMs). The increased binding activity including protein binding and ion binding reflected certain characteristics of the pancreas, generally related to vigorous secretory activity and calcium exchange [24].

The utility of GEO datasets is progressively expanding. In this research, several GEO-derived expression profiles obtained by array were considered. However, when limiting the selection to canine species, there were only 174 series selected from the database, which was too few for comparison with the total of 110,121 series of all GEO datasets (as of March 6, 2019). We screened out 71 series belonging to the same platform GPL3738, among which only three series had several samples related to the pancreas and BMSCs. Seven samples were chosen for the following data analysis as a reference and for verification of our profiling data.

PPI network analysis can screen out the genes among DEGs that are directly or indirectly regulated by each other, which can play an indispensable role in guiding future study of important regulatory factors in the process of induction. VIP, SSTR2 and RPS6KA6 are emerging from the network, after the overexpression and knockdown, we come to the conclusion that the two of them, VIP and RPS6KA6, have a positive effect on the induction of BMSCs, which means that they may interaction with PDX1, NGN3 and NKX2.2, so that the overexpression of them can increase insulin producing level. Furthermore, they are participating in the same pathway like MAPK signaling pathway [25], Rap1 signaling pathway and Ras signaling pathway, and it can be a lead for the next-step mechanism research of the induction process to obtain high quality islet-like clusters.

Animals

The two Chinese rural dogs used in this experiment were purchased from the Experimental Animal Center of Northwest Agriculture and Forest University (Yangling, China). All animal experiments were carried out in accordance with our institute’s laboratory animal requirements and the Guide for the Care and Use of Laboratory Animals (Ministry of Science and Technology of China, 2006).

Cultivation And Identification Of Primary Canine Bmscs

The BMSCs were extracted from the long bones of two female 3-month-old Chinese rural dogs, after anesthesia by injecting zoletil (0.1 ml/kg, Virbac Group, France). Followed by the experiment, they were committed to euthanasia with 200 mg/min intravenous infusion of propofol till no heartbeat. The bodies were collected by the Experimental Animal Center of Northwest Agriculture and Forest University (Yangling, China) for harmless disposal. For details about the separation of BMSCs, see our previous paper [26]. The cells were identified by flow cytometry and confirmed to undergo three-lineage differentiation the same as when proliferating in dishes [27]. Specifically, the potential for differentiating into the three lineages was assessed by a 7-day induction procedure. To induce osteogenic differentiation, a total of 2 × 10⁴ cells were seeded into 12-well plates with α-MEM culture medium consisting of 10% FBS, 100 nmol/L dexamethasone, 30 µg/mL ascorbic acid, and 10 mmol/L β-glycerophosphate (Sigma-Aldrich, St. Louis, MO, USA). The medium for adipogenic differentiation contained 10% FBS, 1 µmol/L dexamethasone, 0.5 mmol/L isobutylmethylxanthine, 1 µg/mL insulin, and 100 µmol/L indomethacin (all from Sigma Aldrich). Chondrogenic differentiation medium was supplied with 10% FBS, 40 ng/mL dexamethasone, 50 µg/mL ascorbic acid, 50 µg/mL L-proline, 1 mmol/L sodium pyruvate (all from Sigma Aldrich), insulin–transferrin–selenium X (Gibco, Carlsbad, CA, USA), and 10 ng/mL transforming growth factor-β3 (PeproTech, Rocky Hill, NJ, USA), which was changed every 2 days.

The Induction Of Bmscs To Transform Into Ipcs

The protocol used in this study involved three stages, while the BMSCs were placed in a suspended state to form spheroids, which mimicked the characteristics of islets in the pancreas. The dishes used in the protocol were all treated with 2-hydroxyethylmethacrylate (Sigma-Aldrich). In stage 1, passage 4 cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) high glucose (containing 17.5 mmol/L glucose; Hyclone, Logan, UT, USA), which contained 10 ng/ml basic fibroblast growth factor (bFGF; Invitrogen, Carlsbad, CA, USA), 10 ng/ml epidermal growth factor (EGF; Chemicon, CA, USA), 2% B27 supplement minus insulin (Gibco), 0.5% bovine serum albumin (BSA; Solarbio), and 0.1 mmol/L β-mercaptoethanol. Two days later, the medium was exchanged for stage 2 induction medium and supplied with DMEM high glucose with 10 ng/mL EGF, 20 ng/ml Activin A (Peprotech), 10 mmol/L nicotinamide (Sigma), 2% B27, 0.5% BSA, and 0.1 mmol/L β-mercaptoethanol for 3 days. Stage 3 also lasted 3 days, in which the spheroids were suspended in DMEM low glucose (containing 5.6 mmol/L glucose) with 10 ng/mL EGF, 10 nmol/L exendin-4 (Peprotech), 10 ng/mL betacellulin (Peprotech), 2% B27, 0.5% BSA, and 0.1 mmol/L β-mercaptoethanol. The medium was changed every 2 days.

Collecting those spheroids with a consistent size, we then performed DTZ staining as described previously and GSIS to determine whether a transition occurred after the induction procedure. The selected clusters were precultured in DMEM low glucose for 2 h to remove the insulin present, washed three times with PBS, and incubated in DMEM low glucose for 30 min. The supernatant was then collected and the clusters were transferred into DMEM high glucose stimulated for 30 min. The released insulin was tested by ELISA using Quantikine® ELISA kit, strictly in accordance with the operating instructions.

Rna Extraction Of Cell Clusters And Quality Control

Total RNA was extracted using TRIzol® reagent (Invitrogen, USA) following the manufacturer’s protocol, and RNA degradation and contamination were monitored on 1% agarose gels, as well as the purity being checked using the NanoPhotometer® spectrophotometer (IMPLEN, CA, USA). RNA concentration was measured using Qubit® RNA Assay Kit in a Qubit® 2.0 Fluorometer (Life Technologies, CA, USA). Finally, RNA integrity, assessed by RIN8.0, was determined using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA, USA).

Library Construction And Sequencing Processing

Ribosomal RNA was removed using the Epicentre Ribo-zero™ rRNA Removal Kit (Epicentre, USA), and an rRNA-free residue was obtained by ethanol precipitation. Subsequently, 3 µg of RNA per sample was used for library construction, which was generated using the rRNA-depleted RNA with the NEBNext® Ultra™ Directional RNA Library Prep Kit for Illumina® (NEB, USA), in accordance with the manufacturer’s recommendations. To preferentially select cDNA fragments of 150–200 bp in length, the library fragments were purified with the AMPure XP system (Beckman Coulter, Beverly, USA). PCR was performed with Phusion High-Fidelity DNA polymerase, universal PCR primers, and Index (X) Primer. Finally, products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system.

Cluster generation was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumina), in accordance with the manufacturer’s instructions. Subsequently, the sequencing data were processed on an Illumina Hiseq 4000 platform.

De novo assembly

Clean data were obtained after the processing of raw data through in-house Perl scripts. Q20, Q30, and GC content were calculated, with Q20 and Q30 representing the percentages of bases whose Phred [Phred = − 10log10(e)] equaled or exceeded 20 and 30 relative to all bases. For mapping to the reference genome, the genome and gene model annotation files were downloaded directly from the genome website. The software HISAT2 v2.0.4[28] was used in this field. Finally, the mapped reads were assembled by StringTie (v1.3.1) [29] in a reference-based approach.

Identification Of Degs

FPKM was calculated based on the length of the fragments and count of reads mapped to this fragment. This was performed using cuffdiff (v2.1.1) and used to define the expression levels of coding genes. Transcripts with an adjusted P-value < 0.05 were considered to be differentially expressed. As thresholds for defining significant differences in gene expression between samples, we used |log2FC| > 1 with a false discovery rate (FDR) < 0.05.

Enrichment Analysis Of Degs And Ppi Analysis

Gene ontology (GO) was applied to annotate the functions of DEGs and lncRNAs, which were divided into three biological modules: molecular function (MF), cellular component (CC), and biological process (BP). This was implemented using the GOseq R package, with correction for bias in gene length [30]. GO terms with a corrected P-value less than 0.05 were considered to be significantly enriched with differentially expressed genes.

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was also adopted to define the functions of the DEGs and lncRNAs. We used KOBAS software to test the statistical significance of enrichment of differentially expressed genes and lncRNA target genes in KEGG pathways. The significance formula was the same as that in the GO analysis. The significance, which was calculated using the Q-value derived from multiple hypothesis testing of the P-value, was presented using the magnitude of –log10 (Q-value). Meanwhile, RF (rich factor) was defined as the ratio of DEGs relative to the overall annotated transcripts enriched in the same pathway.

PPI analysis of differentially expressed genes was based on the STRING database, which features known and predicted protein–protein interactions. Canine data were included in the database, so we constructed the networks by extracting the target gene list from the database. We visualized the findings using Cytoscape (v3.6.1).

Lentivirus Vectors Construction And Transfection

The overexpression vector was Pcdh-CMV-MCS-CopGFP-T2A-Puro, and complete coding sequences of target genes were synthesized by GeneCreat biological technology Co. (Wuhan, China). The vector used for knockdown was CD513B-U6, and siRNAs were designed by BLOCK-iT™ RNAi Designer (https://rnaidesigner.thermofisher.com/rnaiexpress/). shRNAs derived from siRNAs were synthesized by TsingKe biological technology Co. (Beijing China).

Qrt-pcr

Total RNA was extracted as described above, and cDNA was generated from total RNA. Primers for quantitative real time PCR (qRT-PCR) were designed using Primer Premier 5.0 software (Premier, Canada), and synthesized by TsingKe biological technology Co (Beijing China). qRT-PCR was performed on a Step One Plus™ Real-Time PCR System (Thermo Fisher Scientific, USA). Each 20 µL reaction mixture contained 10 µL of Maxima SYBR Green/ROX qPCR Master Mix (2X) (Thermo Scientific, USA), 0.3 µL of each primer (10 µM), 0.8 µL of cDNA, and 8.6 µL of nuclease-free water. The qRT-PCR run protocol was as follows: 95 °C, 10 min; followed by 40 cycles of 95 °C, 15 s; 60 °C, 30 s; and 72 °C, 15 s in 96-well optical reaction plates. Three biological replicates with three technical replicates for each value determined the Ct values. Expression levels of the tested reference genes were determined by Ct values and calculated by 2^−△△Ct.

Bioinformatic analysis of data from the Gene Expression Omnibus (GEO) database using the DAVID database

The gene expression profiles GSE20113 and GSE52063, chosen from GPL3738, were downloaded from the GEO database; these two series included three normal pancreas samples and four normal bone marrow mesenchymal stem cell samples derived from canine. Because these data were from the same platform, after normalization of the microarray data, we used R (https://www.r-project.org/) to acquire the DEGs, and analyzed these data using the DAVID database (https://david.ncifcrf.gov/).

In this study, we have obtained an overview of the pathways involved in the regulation of BMSC transdifferentiation at different timepoints, which have not been shown in previous studies, and it makes a progress for learning the mechanism of BMSCs transdifferentiation. Simultaneously, VIP, SSTR2, and RPS6KA6 were selected out for their positive potential for the induction, and gain-and-lose function verification of them has been performed.

BMSCs bone marrow mesenchymal stem cells

DEGs differentially expressed genes

GSIS glucose-stimulated insulin secretion

PPI protein protein interaction

FPKM fragments per kilobase of exon per million fragments mapped

IPCs insulin producing cells

PDX1 pancreatic and duodenal homeobox 1

NGN3 neurogenin 3

NKX2.2 NK2 homeobox 2

VIP Vasoactive Intestinal Peptide

SSTR2 Somatostatin Receptor 2

RPS6KA6 Ribosomal Protein S6 Kinase A6

Ethics approval and consent to participate

The application named “The use of two Chinese rural dogs for the separation of BMSCs” were approved by the Animal Ethics Committee of Northwest Agriculture and Forest University (Animal Welfare Assurance no. NWAFU-AEC – 2018-11060076) at November 6, 2018. The consent to participate was permitted by the leader of institution Yihua Zhang.

Consent for publication

Not applicable.

Availability of data and material

Authors can confirm that all relevant data are included in the article and its supplementary information files, except for transcriptome data is uploaded on SRA with submission number: SUB7457848.

Author contributions

WJL and ZYH conceived the article, and WJL performed the experiments with data collection and analysis.

DPX, ZT, LYO, ZW and ZXK helped to collect and assemble data.

ZYH made the final approval of manuscript.

All authors have read and approved the manuscript.

Competing interests

The authors declare no conflict of interests/financial disclosure statement.

Funding

This work was supported by the National Natural Science Foundation of China (project number: 31872529) in the design of study, data collection, analysis .and writing of the manuscript.

Acknowledgments

We thank Novogene incorporated company at Beijing for their assistance in transcriptome analysis.

We thank Liwen Bianji, Edanz Group China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript.

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Additional file 1. xlsx -A summary of the quality of output data. “Q20” and “Q30” represented the percentage of bases with Phred value greater than 20 and 30 in the total base. “GC content” represented the percentage of total bases G and C.

Additional file 2. xlsx - A comparison of mapped reads and the reference genome alignment. “Total mapped” meant that the number of reads that can be mapped to the genome, and “Uniquely mapped” represented for the number of reads with a unique matched alignment position on the reference sequence. Our samples owned relatively high quality with over 90% mapped clean reads.

Additional file 3. xlsx - KEGG analysis of GEO data using DAVID. It was top 20 signaling pathways showed in this table.

Additional file 4. xlsx - Top 20 items of GO analysis of GEO data.

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Transcriptome Analysis of the Transdifferentiation of canine BMSCs into Insulin Producing Cells

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Results

Isolation and identification of BMSCs

Induction And Characterization Of Islet-like Spheroids

De Novo Assembly

Overview Of Degs

Enrichment analysis of DEGs from transcriptome sequencing and GEO data

Ppi Network

Verification And Quantification Of Key Genes

Discussion

Canine BMSCs are the most advantageous source of pancreatic islet-like clusters

For the first time induction procedure could be improved based on the profiling data

Methods

Animals

Cultivation And Identification Of Primary Canine Bmscs

The Induction Of Bmscs To Transform Into Ipcs

Rna Extraction Of Cell Clusters And Quality Control

Library Construction And Sequencing Processing

De novo assembly

Identification Of Degs

Enrichment Analysis Of Degs And Ppi Analysis

Lentivirus Vectors Construction And Transfection

Qrt-pcr

Bioinformatic analysis of data from the Gene Expression Omnibus (GEO) database using the DAVID database

Conclusion

Abbreviations

Declarations

References

Additional File Legends

Supplementary Files

Status:

Journal Publication

Version 1