Overdose intake of Neu5Gc triggers colorectal inflammation and alters liver metabolism

ABSTRACT

A positive correlation between massive red meat consumption with colorectal cancer had been reported. Given the complexity of meat components, we focused on investigating the role of Neu5Gc in human healthy. Two groups, either feeding with normal and overdose concentration of Neu5Gc for two weeks, to mimic the normal and overconsumption of red meat conditions in human, respectively. The colorectal transcriptomes revealed that Neu5Gc promotes intestinal immune response and identified the hub genes positively correlated with colorectal cancer such as Tnf, Cd19, Muc13, and Nso2,. The colorectal cancer patients have a 25–30% chance of developing liver metastases, thus we sequenced the liver and revealed the role of Neu5Gc in regulating cell metabolism. Moreover, we found that Neu5Gc negatively regulates the expression of Cmah. We conclude that high Neu5Gc intake promotes colorectal inflammatory responses in Apc^Min/+ mice, and suppresses colorectal and hepatic metabolic and digestive processes through Cmah inhibition.

KEYWORDS:

• Apc^min/+ mouse
• colorectal cancer
• Neu5Gc
• transcriptome

1. Introduction

Colorectal cancer (CRC) is the third most commonly diagnosed cancer and the second cause of cancer-related deaths (Siegel et al., 2020). World Health Organization reported around 1,930,000 new cases are registered each year and more than 935,000 people die of CRC (https://gco.iarc.fr/today/data/factsheets/cancers/10_8_9-Colorectum-fact-sheet.pdf). Even after treatment, 30% to 65% of patients with CRC will develop recurrent disease (Van Der Stok et al., 2017). Median survival in colorectal cancer patients may be improved by a fluoropyrimidine (5-FU)-based regimen or in combination with oxaliplatin or irinotecan (Tournigand et al., 2004; Kirstein et al., 2014; Malka et al., 2017). Although systemic therapy is possible with these drugs, the majority of patients die prematurely due to tumour growth and spread due to colorectal cancer resistance to chemotherapy and targeted drugs (Transition, 2019). The 5-year survival rate in patients with stage IV colorectal cancer is less than 10% (Labianca et al., 2010b). The main non-genetic factor in the development of colorectal cancer is diet, and there is evidence that red meat, processed meat, and heavy use of alcoholic beverages all increase the incidence of colorectal cancer (Labianca et al., 2010a).

Sialic acids (Sias), which serve roles such as being receptors for pathogens or mediating cell–cell interactions or cell signaling by endogenous lectins, are nine-carbon sugars typically found at the terminal end of mammalian glycoconjugates (Hedlund et al., 2007). The two most common mammalian Sias are N-acetylneuraminic acid (Neu5Ac) and Neu5Gc (Ma et al., 2016), the latter Neu5Gc is generated by hydroxylation of CMP-Neu5Ac and catalyzed by CMP-Neu5Ac hydroxylase (CMAH, encoded by Cmah gene) (Kwon et al., 2015). Although the human loss of CMAH enzyme function due to mutations in the CMAH gene, Neu5Gc is detectable on the surface of human epithelial and endothelial cells and is more abundant in cancer tissues (Varki, 2010). In the absence of an alternate pathway for Neu5Gc biosynthesis (Bergfeld et al., 2012), dietary intake is the possible source for incorporation (Banda et al., 2012). Studies reported that dietary intake of Neu5Gc can be absorbed by the intestine and react with anti-Neu5Gc antibodies in the blood circulation (Hedlund et al., 2007; Padler-Karavani et al., 2008). Previous work compared and found higher level of Neu5Gc in red meat and dairy products such as pork (25.5 µg/g), beef (30.1 µg/g), and milk (7.74 µg/g), compared to white meat such as poultry (0.2–0.076 µg/g) and fish (0.032-1.47 µg/g) (Tangvoranuntakul et al., 2003). In 2015, the International Agency for Research on Cancer published the results of a study on the association of cancer with red or processed meat consumption, concluding the link of red meat consumption with human colorectal cancer and the “probably carcinogenic to humans” classification of red meat (https://www.who.int/news-room/questions-and-answers/item/cancer-carcinogenicity-of-the-consumption-of-red-meat-and-processed-meat). These growing research suggests a connection of the consumption of red meat-derived non-human Neu5Gc and various diseases, including cancer progression, cardiovascular disease, inflammation, and several autoimmune diseases (Hedlund et al., 2008; Samraj et al., 2015).

Here, we intend to investigate the short-term feed effect of Neu5Gc on colorectal cancer in Apc^Min/+ mice. Although the relationship between red meat consumption and colorectal cancer is most prominent in humans, we focus here on colorectal cancer in male Apc^Min/+ mice as a proof-of-principle, as these animals have a higher incidence of spontaneous colorectal adenomas and are well suited for short-term feeding.

2. Results

2.1. Effects of Neu5Gc on blood routine and colorectal tissue in ApcMin/+ mice

The natural survival period of Apc^Min/+ mouse is 120–150 days, and it is often accompanied by the characteristics of chronic anemia and manifested as increased reticulocytes, decreased red blood cells. To evaluate the effect of Neu5Gc in the real context of colorectal cancer development, we applied the Apc^Min/+ mouse colorectal cancer model. The 8-week-old male Apc^Min/+ mice in the two groups ingested different concentration of Neu5Gc for two weeks and underwent blood routine detection and histopathological diagnosis, as shown in Figure 1(A). After two weeks of Neu5Gc feeding, orbital blood was collected for routine blood test. The data showed that compared with the control group, the red blood cells, platelets, and lymphocytes of the treatment group showed a downward trend, and the number of white blood cells showed an upward trend (Figure 1D and Figure S2). Mice were euthanized and the colorectum was stained and analysed for the number and size of intestinal polyps. The data showed that the number of tumours in the treatment group was not significant compared with the control group (Figure 1B,C, and Figure S1).

Figure 1. Routine blood test and histopathological diagnosis of Apc^Min/+ mice ingested different concentration of Neu5Gc. (A) Experimental process of ingested different concentrations of Neu5Gc in Apc^Min/+ mice; (B) Histopathological diagnosis of colorectal in Apc^Min/+ mice; (C) Representative H&E staining histological sections in the colorectal in different groups: left: Control group; right: Treatment group; top panel: bar = 250 µm; bottom panel: bar = 100 µm; (D) Routine blood analysis of Apc^Min/+ mice, red blood cells (RBC), platelets (PLT), Lymphocyte count (Lymph), white blood cells (WBC).

2.2. Neu5gc alters the colorectal transcriptome

We next performed RNA-seq to obtain the global transcriptomic profile of Apc^Min/+ mice colorectal tissue under Neu5Gc treatment. After low-quality data filtrationand normalization, t-SNE were performed for dimensionality reductionusing the R package Rtsne (version 0.16), clustering into two groups as expected (Figure 2(A)). We obtained 1011 DEGs (323 and 688 for up- and down-regulated genes, respectively) in the treated group compared with the control group by differential analysis (Figure 2(B)). Running GO enrichment analysis, the up-regulated DEGs were significantly enriched into immune related biological processes such as: Regulation of cell–cell adhesion, Leukocyte cell–cell adhesion, and Lymphocyte proliferation (Figure 2C, Figure S3A). By KEGG annotation, these genes can be enriched into several disease-related pathways, for example, NF-kB signaling pathway, Hematopoietic cell lineage, Tuberculosis, Rheumatoid arthritis, Inflammatory bowel disease, Type I diabetes mellitus (Figure 2(D), Figure S3B). While, the down-regulated genes are more related to nutritional metabolism and transportation (Figure 2(E,F), Figure S3C,D). To verify the accuracy of RNA-seq, we screened out inflammation-related genes from DEGs based on the KEGG and GO pathway analysis results for RT-qPCR analysis and verification, and found that Il17f, Ccl17, Tnf, and Cd80 were significantly up-regulated in the treatment group, is consistent with the RNA-seq results (Figure S4).

Figure 2. RNA-seq analysis of the colorectal of Apc^Min/+ mice ingested different concentration of Neu5Gc. (A) t-SNE plots of relative gene expression in the colorectal; (B) Volcano plot for DEGs; (C) Top 10 GO terms of the up-DEGs; (D) Top 10 enriched pathways of the up-DEGs; (E) Top 10GO terms; (F) Top 10 enriched pathways.

To identify the hub genes related Neu5Gc treatment, we constructed PPI using all the DEGs by String (Figure 3). The network includes 88 nodes connected by 262 edges. Among the up-regulated central genes, Tnf, Cd19, Cyp2c68, Muc13, Adh1, Cd14, and Cd79a were directly or indirectly associated with colorectal cancer.

Figure 3. PPI analysis of DEGs. DEGs between Treatment and Control groups were investigated using String. The interaction between each protein pair is represented by a line, and the size of the circle is proportional to the degree of interaction. Proteins that are closer to the concentric circles have higher interactions with other proteins.

2.3. Neu5gc alters the liver transcriptome

Since 25-30% of colorectal cancer patients develop liver metastases, and our colorectal RNA-seq results identified alcoholic liver disease pathways affecting the liver (Figure S3B), we performed RNA-seq on liver tissue of Apc^Min/+ mice. Differential analysis of TPM values by edgeR package (Rees et al., 2008), we obtained 137 up-regulated and 71 down-regulated genes in the treated group compared with the control group by setting a threshold of FDR < 0.01 and |log₂FC| > 1 (Figure 4(A)). Running GO enrichment analysis, the up-regulated DEGs were significantly enriched into biological processes such as Muscular system processes, Muscle contraction, and Muscle cell differentiation striated (Figure 4(B), Figure S5A). GO analysis of the down-DEGs found several metabolic-related biological processes are involved (Figure 4(C), Figure S5B).

Figure 4. RNA-seq of the liver of Apc^Min/+ mice ingested different concentrations of Neu5Gc. (A) Volcano plot of DEGs in livers; (B) Top 10 enriched GO pathways of the up-DEGs involved the liver of Apc^Min/+ mice; (C) Top 10 enriched GO pathways of the down-DEGs involved the liver of Apc^Min/+ mice.

Neu5Gc is generated from CMP-Neu5Gc and catalyzed by Cmah (Leviatan Ben-Arye et al. 2017). Therefore, we explored the effects of the absorption of Neu5Gc in Apc^Min/+ mice on Cmah in the colorectum and liver. We found that the TPM value of the Cmah gene in the colorectal treatment group was extremely significantly higher than that in the control group, and the Cmah gene showed a downward trend in the colorectal RT-qPCR results, but it was not significant (Figure 5(A,B)). The TPM value and RT-qPCR results in the liver also decreased, but not significantly (Figure 5(C,D)).

Figure 5. Comparison of the TPM value and RT-qPCR of the Cmah gene in the colorectal and liver in the Treatment group and the Control group. (A) Histogram comparing the TPM value of the Cmah gene in the colorectal Treatment group and Control group; (B) The relative expression of the Cmah gene in the RT-qPCR results was compared between the colorectal Treatment group and the Control group; (C) Histogram comparing the TPM value of Cmah gene in the Treatment group and the Control group in the liver; (D) Comparison of the relative expression of Cmah gene in the RT-qPCR results of liver Treatment group and Control group. * indicates p < 0.05, ** indicates p < 0.01.

3. Discussion

Accumulation of Neu5Gc-glycans has been detected in human colorectal cancer (Samraj et al., 2014). Red meat is rich of Neu5Gc in bound form. Numerous epidemiological studies have concluded the association between red meat consumption with atherosclerotic cardiovascular disease and cancer that red meat consumption is (Pan et al., 2011; Chen et al., 2014). Findings have demonstrated that increased intake of red meat is associated with a significantly higher risk of cancer, especially colorectal cancer (Pan et al., 2012). Both human anti-Neu5Gc IgG and red meat have been reported to increase cancer risk, but the mechanism by which ingested Neu5Gc affects colorectal cancer is largely unknown (Bashir et al., 2020).

Bardor et al. described the mechanism by which human epithelial cells isolated from primary colon carcinomas uptake and incorporate Neu5Gc primarily via the pinocytotic/endocytic pathway (Bardor et al., 2005). In the present study, no significant effect of Neu5Gc blood contents was detected by blood routine analysis and the number of tumours by Histopathological diagnosis analysis, and we hypothesis the mainly reason may that the ApcMin + mouse was the spontaneous tumour model with the characteristics of significant chronic anemia. However, we found pathways related to immune response and nutrient metabolism/absorption in colorectal tissue by transcriptomic analysis. KEGG and GO analysis of up-DEGs in the colorectal of Apc^Min/+ mice ingested different doses of Neu5Gc showed that most pathways are closely related to immune cell proliferation, negative regulation of cell activation, and disease. The proliferation of lymphocytes is essential of immune response triggered by cytokine stimulation and the pathogenesis of chronic inflammatory diseases (Shi & Pamer, 2011; Kraus & Arber, 2009). It had been reported that the activation of pro-inflammatory transcription factor NF-kB can increase the risk of CRC in patients (Wang et al., 2009) and leukocyte activation and inflammation in colorectal tumour tissue were enhanced (Rainis et al., 2007; Bedi et al., 1995). Moreover, TNF has been reported to activate the NF-kB signaling pathway in colorectal cancer cells, thereby promoting immunosuppression and favouring the tumour microenvironment (Wang et al., 2017). GO and KEGG analysis of down-DEGs showed that they were mainly enriched in digestion and metabolism-related pathways, which may reveal the function of Neu5Gc uptake affecting colorectal digestion and metabolism. To obtain hub genes, we constructed PPI through DEGs in the colorectal of Apc^Min/+ mice and found Tnf, Cd19, Cyp2c68, Muc13, Adh1, Cd14, and Cd79a. Tnf is a potent pro-inflammatory cytokine that promotes tumour development in colorectal cancer model mice (Kraus & Arber, 2009). Cd19 and Cd79a are markers for B cells, and Cd14 is a marker for monocytes, macrophages, and granulocytes. Numerous clues have shed the light the important role of immune system in the occurrence and development of cancer (Schreiber et al., 2011). Immune cells present in the tumour microenvironment, including B cells, T lymphocytes, macrophages, mast cells, and neutrophils, can inhibit or enhance tumour growth (Shimabukuro-Vornhagen et al., 2014). Cyp2c68 is a specific gene in liver which is the most common metastatic site of colorectal cancer (Oh et al., 2017; Huang et al., 2021). High expression of Muc13 promotes colorectal tumours development (Sheng et al., 2019), protecting cancer cell death by promoting NF-kB activation and silencing. Both in vitro and in vivo, Muc13 had been validated to sensitize colorectum and renal cancer cells to multiple therapeutic kills, making Muc13 a potential therapeutic target (Sheng et al., 2017a, 2017b). The up-regulated gene Adh1 is involved in fatty acid metabolism pathways, involved in cell growth, cell membrane synthesis, and energy production in cancer cells (Yang et al., 2020). After that, we verified the inflammation-related genes in the colorectal of Apc^Min/+ mice and found that Nos2, Cxcl9, Il17f, Ccl17, Tnf, and Cd80 were significantly up-regulated, Inflammation-related genes with an up-regulated trend but not significant were Il1b and Ccl22. Growing evidence suggests that a variety of pro-inflammatory molecules and growth factors secreted by inflammatory cells in the intestinal tumour microenvironment can stimulate the proliferation, survival, migration, angiogenesis, apoptosis of tumour and tumour cells (Puppa et al., 2011). Inflammatory cell products promote intestinal tumour progression, including the overproduction of reactive oxygen and nitrogen species, upregulation of arachidonic acid biosynthetic pathway products and enzymes, intestinal immune system dysfunction, etc. (Landskron et al., 2014). For example, Nos2 induces NO synthesis, which leading to tumorigenesis by acting on DNA damage and DNA repair inhibition (Edwards et al., 1996).

We performed RNA-seq on the liver because 25–30% of colorectal cancer patients develop liver metastases, and colorectal cancer RNA-seq identified alcoholic liver disease pathways affecting the liver (Engstrand et al., 2018). Qiu et al found that liver metastases in mice with colon cancer resulted in decreased storage of glycogen and glucose in the remnant liver, as well as liver injury and hepatocyte death (Qiu et al., 2014). The liver is the main metabolic organ and the centre of various substance metabolism. GO and KEGG analysis of liver DEGs found that the main affected biological processes were concentrated in metabolic processes. Pan et al. found that the levels of lipid metabolites were significantly lower in Apc-induced colonic adenomas in Apc^Min/+ mice compared with WT mice, and 32 lipid metabolites were reduced in colonic mucosal specimens (Pan et al., 2015). Their subsequent studies also found that loss of free fatty acid receptor 2 enhanced the development of colonic adenomas in Apc^Min/+ mice (Pan et al., 2017). Therefore, we speculate that the intake of high concentrations of Neu5Gc will cause a decrease in lipid metabolism in Apc^Min/+ mice, thereby promoting the development of colorectal cancer.

We found that downregulation of Cmah gene expression was detected in the colorectum and liver after the high dose of Neu5Gc intake, where the Cmah TPM value of RNA was extremely significantly downregulated in the colorectum, which was also confirmed by RT-qPCR. A previous study revealed that Neu5Gc can negatively regulate the proliferation of B cell, consisted with the finding that Neu5Ac increased and Neu5Gc decreased after spleen T cell activation in Cmah^−/− mice (Naito et al., 2007; Naito-Matsui et al., 2014). Collectively, we speculate that excessive intake of Neu5Gc may inhibit the expression of Cmah, thereby hindering the hydroxylation of Neu5Ac to Neu5Gc by the Cmah enzyme. Taken together, our data suggest that high Neu5Gc intake promotes colorectal inflammatory responses in Apc^Min/+ mice, and suppresses colorectal and hepatic metabolic and digestive processes through Cmah inhibition. Our findings suggest that dietary glycan may be associated with inflammation-driven diseases, such as colorectal cancer. However, the immunomodulatory mechanism of Neu5Gc and the role of Neu5Gc glycosylation in colorectal cancer need further study.

4. Materials and methods

4.1. Animal experiment

The experiments were conducted following these protocols and were approved by the Experimental Animal Ethics Committee of Sichuan Agricultural University under permit number of 20220012. Ten 8-week-old male Apc^Min/+ mice were purchased from GemPharmatech Co., Ltd. and divided into two groups, 4 for the control group and 6 for the treatment group. Neu5Gc (G8530, Solarbio Life Sciences, Beijing, China) was dissolved and supplied in water. To mimic the concentration of normal daily consumption of Neu5Gc, we calculated the amount of Neu5Gc needed for mouse through daily water drunk, based on average body weight (He et al., 2018) and daily consumed Neu5Gc from food (Jahan et al., 2021). the Control group and the Treatment group were allowed access to water freely but supplied with Neu5Gc at the final concentration of 0.0127 and 0.2535 mg/ml, respectively, detected by high performance liquid chromatography. The Neu5Gc consumed by the treatment group was 20 times than that of the control group. All mice were maintained on a 12-hour light cycle and provided water and standard rodent chow ad libitum. After two weeks of feeding, the Apc^Min/+ mice were weighed (Supplementary Table S11), and then sacrificed by cervical dislocation. The blood was collected for routine blood tests, and the colorectal and liver tissues were collected for RNA-seq.

4.2. Blood routine test

Blood samples obtained by removing Apc^Min/+ mice eyeballs were analysed directly with the VetScan HM5 (Abaxis) automated hematology analyzer. The following parameters were analysed: percentage of monocytes (Mon%), red blood cells (RBC), hematocrit (HCT), mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean platelet volume (MPV), hemoglobin (HGB), percentage of lymphocytes (Lymph%), lymphocytes (Lymph), white blood cells (WBC), monocytes (Mon), red blood cell distribution width (RDW), platelet distribution width (PDW), Platelets (PLT), percentage of granulocytes (Gran%), granulocytes (Gran).

4.3. Histopathological diagnosis

H&E staining was applied to detect colorectal cancer histopathological diagnosis (Lang et al., 2013). Colorectal fixed in 4% paraformaldehyde as described above were embedded in paraffin blocks. Tissue was cut into 5 µm sections using a Leica RM2235 microtome. Light microscopy of paraffin-embedded tissue sections of Swiss coil whole colorectal with H&E staining to assess precancerous lesions. Samples from the entire colorectal were examined by a pathologist unaware of the treatment conditions to count the number of intestinal polyps.

4.4. Total RNA isolation

Total RNA was extracted fusing Trizol reagent (Invitrogen, CA, USA), according to the manufacturer's instructions. RNA quantity and integrity were detected using the Agilent 2100 Bio-analyzer (Agilent Technologies, CA, USA) and qualified RNA samples are stored at −80°C until further use.

4.5. RNA-Seq

RNA-seq libraries were prepared following the NEBNext® UltraTM RNA Library Prep Kit (NEB, USA, 7530). The paired-end RNA-seq sequencing libraries were further sequenced by the Illumina Novaseq6000 platform (PE150, 219 Gb) to produce an average of 700 million 150-bp paired-end raw reads (Novogene, Beijing, China). The RNA-seq data were submitted in Gene Expression Omnibus (GEO) database: GSE218116. We applied fastp for adapter trimming and base quality filtering on raw data to obtain clean data (Chen et al. 2018). We performed read alignment and quantification on clean data using Kallisto v.0.48.0 (Bray et al. 2016). In this study, we pseudo-aligned the reads to the Mus_musculus transcriptome (Mus_musculus.GRCm39.105.gtf.gz) downloaded from the Ensembl website (https://ftp.ensembl.org/pub/release-105/gtf/mus_musculus/). Gene expression level was normalized using TPM (Transcripts Per Million). DEGs were identified using edgeR based on reading count data (Robinson et al. 2010). Significant DEGs were screened with a threshold of FDR < 0.01 and |log₂FC| > 1. Screened DEGs were subjected to Gene Ontology annotation and KEGG pathway enrichment analysis using the R package clusterProfiler (version 4.4.4) (Yu et al. 2012).

4.6. Quantitative real-time PCR (RT-qPCR)

The relative quantification of each putative reference gene was performed by RT-qPCR. 1 µg Total RNA was reverse transcribed using an HiScript III RT SuperMix (Vazyme, R323-01) for cDNA synthesis. Subsequently, RT-qPCR was performed with Taq Pro Universal SYBR qPCR Master Mix (Vazyme, Q712-02) in 96-well plates using the CFX Connect Real-Time PCR Detection System. The primers used for RT-qPCR are listed in Table 1.

Table 1. The Oligonucleotide primers used in RT-qPCR analysis.

Gene	Primer sequences
Cmah	Forward primer: TACCCTGGAGCTGGCAGATG
	Reverse primer: TTACCAGGTCCAGACTAATC
Cd80	Forward primer: CTGGGAAAAACCCCCAGAAG
	Reverse primer: TGACAACGATGACGACGACTG
Nos2	Forward primer: AATGGCAACATCAGGTCGGCCATCACT
	Reverse primer: GCTGTGTGTCACAGAAGTCTCGAACTC
Ccl17	Forward primer: CTCCTGGCTGCTCTGCTTCT
	Reverse primer: GGAACACTCCACTGAGGTCTTATAC
Ccl22	Forward primer: CCTGGTGGCTCTCGTCCTTCTT
	Reverse primer: TGGCAGAGGGTGACGGATGTAG
Cxcl9	Forward primer: CCGAGGCACGATCCACTACAA
	Reverse primer: CTTCCTTGAACGACGACGACTT
Il17f	Forward primer: CTCCGTCGCCATTCAGCAAGAA
	Reverse primer: TTGACACAGGTGCAGCCAACTT
Il1b	Forward primer: ACAGGCTCCGAGATGAACAACA
	Reverse primer: AGGCTTGTGCTCTGCTTGTGA
Tnf	Forward primer: GCCACCACGCTCTTCTGTCTAC
	Reverse primer: GACGGCAGAGAGGAGGTTGACT
β-actin	Forward primer: GGCTGTATTCCCCTCCATCG
	Reverse primer: CCAGTTGGTAACAATGCCATGT

4.7. Statistical analysis

The values are shown as the mean ± SEM. Statistical analyses were performed by the Wilcoxon-test using GraphPad Prism 8 software, in the comparison of two groups. A p < 0.05 was considered significant.

Institutional review board statement

All the experiments were conducted following these protocols and were approved by the Experimental Animal Ethics Committee of Sichuan Agricultural University under permit number of 20220012. All experimental steps were performed following relevant guidelines and regulatory requirements.

Informed consent statement

Not applicable.

Acknowledgements

Author contributions: Expreiment, Teng Yuan, Anjing Zhang, Jing Li, Peidong Yang, Ziyin Han, Dengke Pan, Shuqi Yang and Yihamu Jimu; Draft writing, Teng Yuan, Anjing Zhang and Lu Lu; Review & editing, Teng Yuan, Anjing Zhang, Jing Li, Dengke Pan, Fanli Kong, Keren Long, Lu Lu and Mingzhou Li.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data is contained within the article.

Additional information

Funding

This work was supported by the National Key R & D Program of China [grant number 2021YFD1300800], the National Natural Science Foundation of China [grant numbers 32225046 and 32202630], the Science Foundation of the Sichuan Province [grant numbers 2021YFS0008, 2022YFQ0022, and 2022NSFSC1781], the China Postdoctoral Science Foundation [grant number 2021M692329], the Major Science and Technology Projects of Tibet Autonomous Region grant number XZ202101ZD0005N] and the A'Ba Science and Technology Program [grant number 20YYJSYJ0073].