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Involvement of Abnormal Gut Microbiota Composition and Function in Doxorubicin-Induced Cardiotoxicity

Objectives Doxorubicin (Dox), a chemotherapeutic anthracycline agent for the treatment of a variety of malignancies, has a limitation in clinical application for dose-dependent cardiotoxicity. The purpose of this study was to explore the relationship between the composition/function of the gut microbiota and Dox-induced cardiotoxicity (DIC). Methods C57BL/6J mice were injected intraperitoneally with 15 mg/kg of Dox, with or without antibiotics (Abs) administration. The M-mode echocardiograms were performed to assess cardiac function. The histopathological analysis was conducted by H&E staining and TUNEL kit assay. The serum levels of creatine kinase (CK), CK-MB (CK-MB), lactic dehydrogenase (LDH), and cardiac troponin T (cTnT) were analyzed by an automatic biochemical analyzer. 16S rRNA gene and metagenomic sequencing of fecal samples were used to explore the gut microbiota composition and function. Key Findings Dox caused left ventricular (LV) dilation and reduced LV contractility. The levels of cardiomyocyte apoptosis and myocardial enzymes were elevated in Dox-treated mice compared with the control (Con) group. 16S rRNA gene sequencing results revealed significant differences in microbial composition between the two groups. In the Dox group, the relative abundances of Allobaculum, Muribaculum, and Lachnoclostridium were significantly decreased, whereas Faecalibaculum, Dubosiella, and Lachnospiraceae were significantly increased compared with the Con group at the genus level. Functional enrichment with Cluster of orthologous groups of proteins (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that the Dox mice displayed different clusters of cellular processes and metabolism from the Con mice. The different species and their functions between the two groups were associated with the clinical factors of cardiac enzymes. Moreover, depletion of the gut microbiota could alleviate Dox-induced myocardial injury and cardiomyocyte apoptosis. Conclusions The study here shows that composition imbalance and functional changes of the gut microbiota can be one of the etiological mechanisms underlying DIC. The gut microbiota may serve as new targets for the treatment of cardiotoxicity and cardiovascular diseases.

INTRODUCTION Doxorubicin (Dox) is a chemotherapeutic anthracycline agent with broad-spectrum and high efficacy used for the treatment of a variety of malignancies (Wu et al., 2021). However, its clinical application is limited due to the dose-dependent cardiotoxicity, which may lead to acute pericarditis, irreversible cardiomyopathy, and congestive heart failure (HF) (Wenningmann et al., 2019). The pathogenesis of Dox may be associated with topoisomerase IIb (Top2b) inhibition, oxidative stress, inflammation, and apoptosis (Liu et al., 2020a;Tadokoro et al., 2020). Though a variety of strategies including limitation of cumulative Dox doses, use of antioxidant drugs, and common HF drugs have been proposed to prevent or attenuate Dox-induced cardiotoxicity (DIC), none of these strategies have obtained satisfying efficacy (Vejpongsa and Yeh, 2014;Varricchi et al., 2018;Yarmohammadi et al., 2021). Given that the mechanism of DIC is a complex disturbance system, an alternative hypothesis is required to explain DIC, and a novel therapeutic strategy needs to be established.
From the outcome of recent studies, the intestinal microbiota has been found to play an essential role in the health of the host organism. Multiple diseases, such as diabetes, obesity, cancer, and nervous system disease, are related to the changes of intestinal microorganisms (Cheng et al., 2020;Fan and Pedersen, 2020;Megur et al., 2020;Verhaar et al., 2020). Particularly, the gut microbiota and their metabolites have been implicated in the progression of cardiovascular diseases (CVDs) including hypertension, dyslipidemia, atherosclerosis, thrombosis, HF, and ischemic stroke (Kasahara and Rey, 2019). In addition, it has become evident that the gut microbiota affects the response to cancer therapy and susceptibility to toxic side effects (Roy and Trinchieri, 2017). Besides, drugs can alter the microbiome and create secondary effects independent of the drug molecule itself (Hitchings and Kelly, 2019). Recently, antitumor chemotherapy drugs, such as cisplatin and Dox, have been reported to cause alteration of the gut microbiota, which is involved in the pathogenesis of cardiotoxicity (Zhao et al., 2018;Wu et al., 2019). However, previous studies were based on a 16S rRNA gene sequencing method to focus on changes in microbial composition, which has the limitations such as the bias of PCR amplification (Silverman et al., 2021) and difficulty to identify most microbes at the strain and species levels (Peng et al., 2018). Compared to the amplicon, the shotgun metagenome can provide functional gene profiles directly and reach a much higher resolution of taxonomic annotation . Therefore, further studies conducting metagenome sequencing are required to analyze the functional activity of the gut microbiome linked with DIC.
Here, to investigate the key microbes related to cardiotoxicity and microbial functions from fecal samples, we constructed an explicit mouse model of DIC. Using a combination of 16S rRNA gene sequencing and metagenomic sequencing analysis, we strived to compare the composition and function of the gut microbiome between the normal control (Con) mice and Dox mice. We also conducted an antibiotic intervention in mice to further understand the role of the gut microbiome in DIC.

Animals and Treatments
Female C57BL/6J mice aged 6-8 weeks were obtained from Laboratory Animal Center, Xiangya School of Medicine, Central South University (Changsha, China). All the procedures in this research were operated in accordance with the National Institutes of Health Guide (NIH publications no. 8023) for the Care and Use of Laboratory Animals. The experimental protocol was approved by the Medicine Animal Welfare Committee of Xiangya School of Medicine (SYXK-2015/0017).
To establish the cardiotoxicity model, the mice were randomly divided into two groups with 10 animals in each group, including the Con group and Dox group. The Dox group (15 mg/kg) was administrated with 3 mg/kg every other day by intraperitoneal injection. For antibiotic treatment, the mice were randomly divided into three groups including the Con group, Dox group, and Dox+Abs group. The Dox+Abs group was treated with drinking water containing 500 mg/L of each antibiotic for 28 days. Dox was injected intraperitoneally after 14 days of Abs. On the 28th day, the mice were subjected to echocardiography for evaluating cardiac function. Besides, the fecal samples from the Con group and Dox group were collected into sterile cryopreservation tubes, frozen quickly in liquid nitrogen, and then refrigerated at −80°C for DNA extraction and amplification of the 16S rRNA gene. Subsequently, all animals were anesthetized with 1% pentobarbital sodium (50 mg/kg, i.p.). The venous blood was drawn from the orbit of mice. The heart tissues were acquired after perfusing and rinsing with cold saline and then preserved in 4% paraformaldehyde for histopathological analysis.

M-Mode Echocardiograms
M-mode echocardiograms were performed by a VisualSonics Vevo 2100 (VisualSonics, Toronto, ON, Canada). The mice were anesthetized with 1.5% isoflurane, and an appropriate amount of coupling agent was applied to the left anterior chest area after hair removal. Then the M-ultrasound changes of 10~20 cardiac cycles were recorded. Finally, the parameters of cardiac function including left ventricular (LV) ejection fraction (EF%) and LV fractional shortening (FS%) were calculated.

Histopathological Analysis
After being fixed in 4% paraformaldehyde at room temperature, the heart tissues were embedded in paraffin wax and cut into 3mm thin sections. After being dehydrated in a series of graded alcohols, the sections were stained with H&E and TdT-mediated dUTP Nick-End Labeling (TUNEL) kit assay according to the manufacturer's instructions.

Biochemical Analysis
After Abs and Dox treatment, the collected blood samples were centrifuged at 3,000 rpm at 4°C for 15 min to obtain serum. The serum biochemical parameters including creatine kinase (CK), CK-MB (CK-MB), lactic dehydrogenase (LDH), and cardiac troponin T (cTnT) were analyzed by using kits with an automatic biochemical analyzer according to the manufacturers' instructions.

16S rRNA Gene Sequence Analysis
Fecal bacterial DNA was extracted using the E.Z.N.A. ® Stool DNA Kit (Omega Biotech, Norcross, GA, USA) according to the manufacturer's protocol. The extracted genomic DNA was detected by 1% agarose gel electrophoresis. To assess bacterial diversity, the V3-V4 hypervariable regions of the bacterial 16S rRNA gene were amplified with a set of primers (338F 5′ACTCC TACGGGAGGCAGCAG-3′, 806R 5′GGACTACHVGGGT WTCTAAT-3′). Then, the PCR products were detected using 2% agarose gel electrophoresis, recovered using AxyPrepDNA Gel Recovery Kit (Axygen Biosciences, Union City, CA, USA), and quantified using QuantiFluor ™ -ST (Promega, Madison, WI, USA) according to the quantitative results of electrophoresis. Subsequently, purified amplicons were mixed in appropriate proportions and paired-end sequenced on an Illumina MiSeq platform (Illumina, San Diego, CA, USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China).
OTUs were assigned to the closest taxonomic neighbors and relative bacterial species by the RDP Classifier algorithm (http:// rdp.cme.msu.edu/) using a confidence threshold of 70%. The species alignment database of 16S bacteria is the Silva database (https://www.arb-silva.de/). The relative abundance of each taxonomic level was calculated using the QIIME tool. The indices of alpha diversity including Chao, Shannon, and Ace were analyzed by MOTHUR (version 1.30.2, https://www. mothur.org/). Principal coordinate analysis (PCoA) projections were used to describe the beta diversity.

Metagenome Sequence Analysis
We also selected a subset of specimens (6 from the Con group and 6 from the Dox group) for metagenome sequencing following 16S rRNA gene sequence analysis. In brief, the extracted genomic DNA was detected by 1% agarose gel electrophoresis and fragmented to the appropriate length (approximately 400 bp) by using Covaris M220 (Gene Company Limited, Shanghai, China). The paired-end library was constructed by the NEXTFLEX ™ Rapid DNA-Seq Kit (Bioo Scientific, Austin, TX, USA) according to the manufacturer's protocols. Then, the blunt-ends of fragments were ligated to adapters. Hiseq X sequencing systems (Illumina Inc., San Diego, CA, USA) were used for metagenomic sequencing. The raw data were trimmed and decontaminated to obtain optimized reads, which were assembled using MEGAHIT (http://www.l3-bioinfo.com/products/megahit.htm). The assembled contigs not less than 100 bp were used for further gene prediction and annotation.
Subsequently, the open reading frames (ORFs) of the assembled sequences were predicted by using the MetaGene platform (http://metagene.cb.k.u-tokyo.ac.jp/). The redundant genes were filtered out to construct non-redundant gene sets. Genes with sequence identity greater than 95% were clustered together using CD-HIT (http://www.bioinformatics.org/cd-hit/), and the longest sequences from each cluster were the representative gene. The genetic functions were annotated and classified by the functional databases. Cluster of orthologous groups of proteins (COG) annotation was conducted by using BLASTP (version 2.3.0) against the eggNOG database (version 4.5.1, http://eggnogdb.embl.de/#/app/home) with an e-value cutoff of 1e−5. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation was conducted by using BLASTP (version 2.3.0) against the KEGG database (http://www.genome. jp/kegg/) with an e-value cutoff of 1e−5.
The discrimination in COG and KEGG categories between the Con and Dox groups was identified by using linear discriminant analysis (LDA) effect size (LEfSe; http:// huttenhower.sph.harvard.edu/galaxy/root?tool_id=lefse_ upload). Only LDA values >2.0 at a p-value <0.05 were considered to be significantly enriched.

Statistical Analysis
The data are expressed as mean ± SEM. Unpaired Student's t-test for two comparisons or ANOVA followed by the Student-Newman-Keuls test for multiple comparisons was conducted to perform statistical analysis. A p-value less than 0.05 was considered statistically significant.

Myocardial Injury and Cardiomyocyte Apoptosis Induced by Doxorubicin
First, we constructed the heart injury model induced by Dox in mice. After treatment with Dox, the M-mode echocardiograms showed LV dilation ( Figure 1A). The EF% and FS% of the Dox group were decreased as compared with the Con group, indicating that Dox reduced LV contractility in mice ( Figure 1B). To further determine the histological changes of the heart induced by Dox, H&E staining and TUNEL staining were conducted. As shown in Figure 1C, we observed regular cell distribution and normal morphology in the myocardium of the Con group. However, the Dox-treated group showed inflammatory cell infiltration, myocardial fragmentation, and disorder of cardiac fiber arrangement. TUNEL assay was used to assess the cardiomyocyte apoptosis, which is an important event in the process of DIC. The results revealed that mice with Dox treatment showed obvious cardiomyocyte apoptosis as compared with the Con group ( Figures 1D, E). Myocardial injury was also determined by the serum levels of CK, CK-MB, LDH, and cTnT, and all of these myocardial enzymes were elevated in Dox-treated mice ( Figure 1F). The above results suggested that Dox caused severe myocardial lesions.

Similar Gut Microbial Diversity Between Control and Doxorubicin Mice
To characterize the effect of Dox on gut microbial communities, we initially used 16S rRNA gene sequencing. After size filtering, quality control, and chimera removal, a total of 968,758 highquality reads, ranging from 39,221 to 61,778 per sample, with an average length of 421.05 bp (421.05 ± 1.35 bp), were obtained from fecal samples of mice. These reads were matched into 827 OTUs (defined based on 97% sequence similarity) including 10 phyla, 272 species, and 165 genera of gut microbes. A Venn diagram showed that the two groups shared 699 OTUs, whereas 85 and 43 OTUs were unique to the Con and Dox mice, respectively ( Figure 2A). The relative abundance in the two groups on phylum, family, and genus levels is displayed in the bar plot ( Figures 2B-D). The results showed that the gut microbiome compositions of the Con and Dox mice were different. The phyla Bacteroidota, Firmicutes, and Verrucomicrobiota were predominant in the gut microbiota of mice. Muribaculaceae and Lactobacillaceae were the most abundant families in fecal samples from both the Con and Dox mice, but group Dox showed an upward trend compared with group Con.
The microbial alpha diversity indices, including microbial community richness (Chao and Ace) and diversity (Shannon), were used to illustrate the changes in the microbiota community structure. All p-values >0.05 (Wilcoxon rank-sum test) excluding Chao (p = 0.0392) at the phylum level (Supplementary Table 1), which showed almost no significant differences between the Con and Dox groups.

Significant Differences in the Microbial Composition Between the Two Groups
The beta diversity analysis was carried out to reveal the difference in the microbial composition between the Con and Dox samples. As shown in PCoA, the first two principal coordinates explained 36.74% and 52.22% of the total variance for unweighted (analysis of similarities (ANOSIM) R = 0.3811, p = 0.001) and weighted (ANOSIM R = 0.2420, p = 0.004) UniFrac, respectively ( Figures 3A, B). The partial least squares discriminant analysis (PLS-DA) showed that the bacterial communities of the two groups clustered separately ( Figure 3C). Thus, Dox-treated mice possessed an obvious difference in a distinct clustering of fecal microbial structure as compared to the Con mice. To further identify the significant difference in specific bacterial taxa between the Con and Dox groups, the LEfSe analysis based on discriminative features cladogram and histogram was performed, and the effect size cutoff of the LDA score was set to 2.0. This analysis identified two phyla including p_Actinobacteriota and p_Campilobacterota, and 31 genera, which were responsible for this discrimination ( Figure 3D). In the Dox group, the relative abundances of g_Allobaculum, g_Muribaculum, and g_Lachnoclostridium were significantly decreased, whereas g_Faecalibaculum, g_Dubosiella, and g_Lachnospiraceae_NK4A136_group were significantly increased as compared with the Con group at the genus level ( Figure 3E).

The Microbial Correlation Networks Between the Two Groups Were Different
To investigate the microbial correlation network, we calculated Spearman's correlations among the 50 most abundant bacterial genera from each group. As shown in Figure 4, the Dox group featured more phyla (7 vs. 9) and displayed a stronger positive correlation among genera. The microbial community of the Dox group featured a more complicated network. In addition, the network constructed from the Dox group displayed fewer edges (334 vs. 125) (Supplementary Table 2) and lower transitivity (0.5650 vs. 0.4127), suggesting that the correlation among the microbiota in the Dox group was distinctly decreased compared to that of Con group. Moreover, we computed degree (DC), closeness (CC), and betweenness (BC) centrality to evaluate the taxa importance at the genus level within the network. According to the total scores of these coefficients (Supplementary Table 3), the top three nodes from each group were selected as putative keystone genera within this network (g_Prevotellaceae_ NK3B31_group, g_Alistipes, and g_Allobaculum for the Con group and g_norank_f_Oscillospiraceae, g_Bacteroides, and g_Faecalibaculum for the Dox group). Taken together, the above analyses suggested that the correlation structure of the microbial community in the Dox group was distinctly different from that of the Con group.

Correlations Between Significantly Different Species and Clinical Factors in Two Groups of Mice
Spearman's correlation heatmap showed that the relationship between significantly different species ( Figure 3E) and serum biochemical parameters (CK, CK-MB, LDH, and cTnT) was different. Bacterial genera enriched in the Dox group were positively correlated with these clinical factors, while those enriched in the normal mice showed a negative correlation. Among them, Coriobacteriaceae_UCG-002 and Dubosiella had significant positive correlations with all the clinical parameters, and Family_XIII_AD3011_group, Alloprevotella, Erysipelatoclostridium, Lachnoclostridium, and Eubacterium_ fissicatena_group showed extremely significant negative correlations with all these biochemical parameters ( Figure 5). Therefore, it is speculated that these different species may be involved in the process of cardiotoxicity.

Alternations of the Microbial Functional Profiles Were Revealed by Metagenomic Analysis
In this study, we used metagenomic sequencing analysis of the gut microbiomes to investigate the differences in the microbial functional composition between the Dox-treated and Con mice. Genomic DNA from the fecal specimens was extracted to obtain a total of 506,191,910 clean reads resulting in 2,646,123 contigs. A total of 4,092,442 ORFs predicted from the contigs were used for functional annotation in the COG and KEGG databases.
To identify protein function annotation, COG analysis was performed using LEfSe analysis between the Con and Dox mice. Based on the threshold LDA values >2.0 and p < 0.05, we identified 4 functional COG categories that showed high enrichment in the Dox group, which were related to the translation, ribosomal structure, and biogenesis [J]; cell cycle control, cell division, and chromosome partitioning [D]; intracellular trafficking, secretion, and vesicular transport [U]; and extracellular structures [W] ( Figure 6A). These functions could be classified into two categories: information storage and processing (Function J) and cellular processes and signaling (Functions D, U, and W). The remaining COG categories have no biologically significant differences. Overall, the cluster of cellular processes and signaling was the predominant COG category associated with the Dox mice.
To further explore the functions of differentially expressed genes, the KEGG pathways were analyzed also by LEfSe analysis (LDA > 2.0, p < 0.05). At KEGG level 1 (Figure 6B), the LEfSe bar showed that metabolism was the dominant signaling pathway in the Con group, and genetic information processing and environmental information processing were significantly enriched in the Dox group. At KEGG level 2 ( Figure 6C), 10 differential KEGG pathways (including membrane transport, replication and repair, signal transduction, and others) were identified in the gut microbiome of Dox-treated mice, while 8 KEGG pathways (including global and overview maps, amino acid metabolism, glycan biosynthesis, and metabolism and others) were significantly increased in the Con mice. At KEGG level 3 ( Figure 6D), we found a total of 38 statistically different functional KEGG pathways between the two groups. Half of these functions were highly enriched in the Dox group, including two-component system, quorum sensing, and ribosome. In contrast, the KEGG functions of the Con mice were enriched in the biosynthesis of amino acids, other glycan degradation, sphingolipid metabolism, and more. Thus, the Con mice and Dox mice represented completely different multiple functional pathways in the gut microbiome. Furthermore, to visualize the association between the gut microbiome and functional properties, we determined the top ten genera that mainly contributed to differences at KEGG level 1 pathways between the Con and Dox mice (Figure 7). The main functions involved in these species were metabolism, genetic information processing, environmental information processing, and cellular processes. G_Duncaniella was the main contributor of these functions and contributed significantly more to the Dox samples than the Con samples. A reduced contribution by taxa belonging to G_Prevotella and G_Bacteroides was also observed.

Correlations Between Microbial Functions and Clinical Factors in the Two Groups of Mice
We next combined the bacterial functions and serum biochemical parameters into annotated heatmaps that provided several insights into the correlation between the microbial functional profiles and DIC (Figure 8). At the COG level ( Figure 8A), carbohydrate transport and metabolism [G] showed a negative significant correlation with LDH level. Inorganic ion transport and metabolism [P] revealed a significant negative correlation with the CK level. Translation, ribosomal structure, and biogenesis [J] were significantly positively correlated with CK-MB, LDH, and cTnT levels. Intracellular trafficking, secretion, and vesicular transport [U] demonstrated a significant positive correlation with CK-MB and LDH levels. Cell cycle control, cell division, and chromosome partitioning [D] were significantly positively correlated with all the biochemical factors. Extracellular structures [W] were significantly positively correlated with the CK level. Notably, the COG categories including J, U, D, and W were enriched in the Dox group ( Figure 6A). At KEGG level 1 (Figure 8B), the metabolism pathway tended to be negatively, but not significantly, correlated with these biochemical factors. Genetic information processing showed a significant positive correlation with CK-MB, LDH, and cTnT levels. Environmental information processing and cellular processes were significantly positively correlated with CK-MB and LDH levels. At KEGG level 2 ( Figure 8C), several pathways enriched in the Con group such as amino acid metabolism, glycan biosynthesis, and metabolism and lipid metabolism were negatively correlated with the clinical factors, while several pathways enriched in the Dox group such as replication and repair, translation, and signal transduction were positively correlated with the clinical factors. Collectively, gut microbiota dysfunction may be at least partially related to the DIC.

Depletion of Gut Microbiota Attenuated Doxorubicin-Induced Cardiotoxicity
Next, we depleted the gut microbiota with a cocktail of Abs in Dox-treated mice to determine whether the progression of DIC is related to gut microbiota dysbiosis. Results displayed that Abs treatment attenuated the LV dilation ( Figure 9A) and the decrease of EF% and FS% caused by Dox injection ( Figure 9B). H&E staining results showed that inflammatory infiltrations and disorder of cardiac fiber arrangement in Dox-treated mice, while Abs administration could ameliorate this myocardial damage ( Figure 9C). We also found that the Dox-induced cardiomyocyte apoptosis was ameliorated by treatment with Abs ( Figures 9D, E). Moreover, Abs inhibited the effects of Dox on the serum levels of myocardial enzymes including CK, CK-MB, LDH, and cTnT ( Figure 9F). These data suggested that depletion of the gut microbiota using a cocktail of Abs could alleviate Dox-induced myocardial injury and cardiomyocyte apoptosis.

DISCUSSION
The gut microbiota has been shown to have a greater impact on multiple diseases including CVDs (Kasahara and Rey, 2019) and the therapeutic effects of drugs (Tarasiuk and Fichna, 2019). This study firstly integrated the 16S rRNA gene and metagenomic sequencing to explore the association between DIC and the gut microbiota. Sequencing information not only can identify bacteria at different taxonomic levels but also can obtain functional information on the microbiome. In the current study, the mice with Dox treatment had markedly different structural compositions and functional networks on the gut microbiota as compared with the normal mice. Moreover, the cardiomyocyte apoptosis and myocardial damage caused by Dox could be suppressed by depleting the gut microbiota. Therefore, it is reasonable for us to propose that the gut microbiota and their functions, at least in part, contribute to DIC development. According to the results of microbial alpha diversity analysis, the Chao index showed a statistical difference with a downward trend in Dox-treated mice. Another research on a rat model indicated that Dox significantly decreased the species diversity of fecal bacteria, which was consistent with our results. However, the relative abundance of Firmicutes-to-Bacteroidetes ratio, contrary to the trend of this research, was decreased in Doxtreated rats (Wu et al., 2019). Lactobacillus, one of the Firmicutes bacteria, is frequently either positively or negatively related to human disease and chronic conditions (Heeney et al., 2018;  Slattery et al., 2019). Cisplatin was found to decrease the relative abundance of Lactobacillus in the fecal bacterial community, and supplementation with Lactobacillus could prevent cisplatininduced cardiotoxicity (Zhao et al., 2018). In contrast to our study using Dox-treated mice, we observed a slight increase in the proportion of this bacterium, but there was no significant difference compared to the Con mice. Given the conflicting reports, intestinal Lactobacillus level and its role in DIC need further investigation.
The correlation network analysis also revealed the disorder of gut microbiota structure in Dox-treatment mice, which performed fewer relationships but more complex networks. It should be noted that the harmful bacterium Bacteroides was chosen as one of the putative keystone genera in the Dox network. It is an obligate anaerobic, gram-negative rod-shaped bacterium that is usually symbiotic and a common opportunistic pathogen in clinical infections (Rocha and Smith, 2013). A 16S rRNA sequencing study showed that, compared with the notreatment Wistar rats, Dox treatment caused intestinal flora disorder, increasing the harmful flora Bacteroides fragilis (Zhao et al., 2021).
In line with our bacterial difference analysis, Liu et al. reported that phylum Actinobacteriota becomes abundant in the Dox mice (Liu et al., 2020b), suggesting that Actinobacteriota may be a minus factor in the cardiotoxicity process. A previous study demonstrated that the combination of multi-walled carbon nanotubes with Dox increased the abundance of family Coriobacteriaceae within the phylum Actinobacteria in mice, polarized colonic macrophages to an M1-like pro-inflammatory phenotype, and thus upregulated proinflammatory factors TNFa and IL-1b in DIC (Liu et al., 2020b). Here, we found that genus Coriobacteriaceae_UCG-002 not only significantly increased in the Dox group but also positively correlated with the serum levels of myocardial enzymes. Further experiment at this genus level seemed to be necessary. Genus Dubosiella is a member of shortchain fatty acid (SCFA) producers (Mao et al., 2019;Bojovic et al., 2020). A previous study showed that chlorogenic acid increased the abundance of Dubosiella and improved metabolic endotoxemia . In contrast, the protective effects of yellow wine polyphenolic compounds were associated with a lower abundance of Dubosiella in Dox-treated rats (Lin et al., 2021). Here, we demonstrated that Dubosiella increased in the Dox-treated mice and positively correlated with all the myocardial enzyme levels. Further experiments are being carried out to investigate the role of these microbes (e.g., Coriobacteriaceae_UCG-002, Dubosiella, Family_XIII_ AD3011_group, and Alloprevotella) in DIC.
At the functional level with metagenomic sequencing, we used COG and KEGG analyses to annotate the functional discrimination of the gut microbiota between the two groups. For COG functional annotation, cellular processes and signaling were the predominant categories associated with the Dox mice, which included cell cycle control, cell division, intracellular trafficking, secretion, vesicular transport, and extracellular structures. The KEGG pathway analysis further acknowledged the gut microbiota functions might contribute to DIC pathogenesis through cellular processes, such as membrane transport, replication and repair, and signal transduction. Correlation heatmaps also revealed those cellular processes were significantly positively correlated with the clinical factors. Extracellular structures of Gram-negative bacteria contain an endotoxin called lipopolysaccharide (LPS) (Cheng et al., 2018).
Cancer chemotherapy, such as Dox, can induce intestinal mucositis and damage (Kaczmarek et al., 2012). Thus, LPS can enter the bloodstream through the impaired intestinal barrier and lead to the expression of a wide array of inflammatory downstream products (such as tumor necrosis factor (TNF), IL-1, and IL-6) via the toll-like receptor 4 (TLR4) pattern recognition receptor (Lu et al., 2008;Tang et al., 2019). Dysregulation of the cellular structure and function of the microbiota may lead to increased LPS transport, which is involved in the process of cardiotoxicity. Edematous patients with chronic HF were also found to have higher blood levels of endotoxin and cytokines (Niebauer et al., 1999). Therefore, it can be inferred that the alleviation of DIC after depletion of the gut microbiota may be attributed to the lower endotoxin levels. Moreover, our functional enrichment analysis revealed obvious variation of metabolism processes such as amino acid metabolism, glycan biosynthesis and metabolism, lipid metabolism, and other secondary metabolites between the two groups of mice. The correlation heatmaps at the KEGG level also demonstrated the relationship between altered metabolic functions of the gut microbiota and DIC. Accumulating evidence has suggested that gut microbial metabolites, including bile acids, SCFAs, trimethylamine N-oxide (TMAO), and amino acid metabolites are mechanistically linked to the pathogenesis of CVD (Mamic et al., 2021). Bile acids can activate the bile acid receptor (known as FXR) and G-protein-coupled receptors (Tang et al., 2019). FXR modulates metabolism and inflammation and is involved in myocardial apoptosis and fibrosis (Calkin and Tontonoz, 2012;Pu et al., 2013). Several SCFAs exert anti-inflammatory effects through regulatory T-cell activation to mitigate cardiac hypertrophy and fibrosis (Bartolomaeus et al., 2019). Butyric acid is beneficial to the DIC models, and its derivative phenylalanine-butyramide could reduce Dox cardiotoxicity in human cellular models, thereby attenuating Dox-induced reactive oxygen species production (Russo et al., 2019). TMAO is a gut microbiotadependent metabolite of specific dietary nutrients, which is mainly produced from the bacterial phyla Firmicutes and Actinobacteria (Romano et al., 2015). DOX-induced cardiac fibrosis could be aggravated by TMAO through activation of the NLRP3 inflammasome . Here, we found that the proportions of Firmicutes and Actinobacteria had an upward trend in the Dox mice compared with the Con mice. These findings facilitate our understanding of Dox-related cardiac changes or diseases. However, further research needs to be conducted for exploring the link between metabolites of the gut microbiome and DIC.
It has been reported that the heart is a priority target for Dox toxicity. However, this anticancer drug also damages other organs like the brain, kidney, and liver (Carvalho et al., 2009). For example, Dox administration can induce the decline of cognitive function (Jansen et al., 2008) and liver injury (Greupink et al., 2006). An experiment in a high-sugar and high-fat diet model showed that the abundance of Coriobacteriaceae involved in cholesterol metabolism was increased, and the altered gut microbiota and their metabolites resulted in systemic impacts on both hepatic metabolism and cognitive function (Jena et al., 2020). In our study, Coriobacteriaceae level was also found to be elevated in the Dox mice and positively correlated with clinical factors. We speculate that those increased bacteria in the Dox group were not only associated with cardiac toxicity but may also affect other comorbidities induced by Dox.
This study proved the correlations between the composition/ function of the gut microbiota and DIC in the mouse model, but further rigorous experimental models depleted or colonized with a specific microbiota should be performed to identify the key bacteria. Even though several biological functions and pathways appear involved in the DIC process have been explained, we still need to further clarify these results of the biometric analysis by molecular signaling experiments. Furthermore, metabolomics and metatranscriptomics are ultimately required to explore the changes in the levels of metabolites of the gut microbiome and understand the metabolism mechanism of DIC.

CONCLUSION
Taken together, our results demonstrated that Dox modified the composition and function of the gut microbiome in mice. We provide important information that supports that the gut microbiota promotes DIC partially through influencing cell processes and biochemical metabolism. The gut microbiota might be a vital participant in a potential therapeutic strategy to attenuate the cardiotoxicity of chemotherapeutic drugs.

DATA AVAILABILITY STATEMENT
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 in the article/Supplementary Material.

ETHICS STATEMENT
The animal study was reviewed and approved by the Medicine Animal Welfare Committee of Xiangya School of Medicine (SYXK-2015/0017).

AUTHOR CONTRIBUTIONS
BZ and WL conceived and designed the experiments. JH, SW, CJ, and ZX performed the experiments. JH, JL, and WP analyzed the data. JH, BZ, and WL wrote the paper. All authors contributed to the article and approved the submitted version.


A comparison of capillary electrophoresis and next-generation sequencing in the detection of immunoglobulin heavy chain H and light chain κ gene rearrangements in the diagnosis of classic hodgkin’s lymphoma

ABSTRACT This study aimed to compare the application value of capillary electrophoresis and next-generation sequencing for immunoglobulin (IG) gene rearrangement in the diagnosis of classic Hodgkin’s lymphoma. Twenty paraffin-embedded specimens from patients with classic Hodgkin’s lymphoma were screened. For gene rearrangement detection, the ABI 3500 Genetic Analyzer and ABI Ion GeneStudio S5 Plus sequencing system were used, respectively, and the results were compared. Five cases with monoclonal rearrangements (25%, 5/20) were detected by Capillary Electrophoresis, and positivity for the FR1, FR2, FR3, and IGк loci was 5%, 10%, 10%, and 15%, respectively; 12 cases with monoclonal rearrangements (60%, 12/20) were detected by Next-generation Sequencing where the positivity of the above corresponding loci were 35%, 45%, 50%, and 30%, respectively. Among the 20 samples, 6 IGк clonal rearrangements were detected, and the usage frequency (66.7%) of IGкJ4 was the highest in the IGкJ subgroup. The usage frequency of IGкV1 and IGкV3 in the GкV sub-group was 33.3% and 33.3%, respectively. Twelve immunoglobulin heavy chain (IGH) clonal rearrangements were detected among the 20 samples, and the order of usage frequency in the IGH joining region J (IGHJ) subgroup was IGHJ4 > IGHJ5 > IGHJ6 > IGHJ3. The gene with the highest usage frequency in the IGH variable (IGHV) subgroup was IGHV3 (50%) and the percentage of IGHV mutations ranged from 0% ± 11.45% with an average frequency of 3.34%. Compared with Capillary Electrophoresis, Next-generation Sequencing showed a higher positivity in the detection of gene clonal rearrangements, was more accurate in the interpretation of results.

Introduction
Lymphoma originates from the lymphopoietic system and is a highly heterogeneous malignancy [1,2]. In 2016, the World Health Organization released a new lymphoma classification, in which a variety of new molecular types were introduced, marking the era of molecular diagnosis in the clinical diagnosis of lymphoma [3][4][5]. Currently, 10%-15% of cases with lymphoma cannot be diagnosed by histomorphological and immunological tests, and further molecular biology is needed to assist in diagnoses [6]. Therefore, a comprehensive evaluation approach combining clinical characteristics, histomorphology, imaging, immunohistochemistry, and molecular genetics has become necessary for the diagnosis of lymphoma [7].
During the development of lymphomagenesis, stimulated by tumor-specific antigens or tumorassociated antigens, one or several immunoglobulins (IGs) or T-cell receptor (TCR) genes in lymphocytes undergo targeted and selective rearrangement, resulting in the monoclonal expression of IG or TCR genes [8]. These may result in the clonal proliferation of lymphocytes, a process known as lymphoma monoclonal rearrangement [9,10]. Currently, the commonly used methods for the detection of lymphoma gene rearrangement include gel electrophoresis, capillary electrophoresis, and next-generation sequencing techniques. Capillary electrophoresis is considered the 'gold standard' for nucleic acid detection because of its low technical error rate [5] and is widely applied in clinical practice [11].
Several studies have shown that abnormal peaks often occur in the results of Capillary Electrophoresis detection of IG and TCR gene rearrangements [12][13][14][15], which affects the interpretation of the results and fails to provide a clear reference meaning for histological diagnosis. In contrast, Next-generation Sequencing technology is easy to operate, has a high throughput and uniform judgment criteria, and can detect specific loci in the case of gene clonal rearrangements [16,17]; it can also analyze the somatic hypermutations of the IGH and IGк variable V-region genes [15,16]. Schumacher et al. [18] used nextgeneration sequencing technology and traditional capillary electrophoresis to detect gene clonal rearrangement in 48 patients with T-cell lymphoma, and found that next-generation sequencing technology can be applied to clonality testing. A primary method of evaluation, especially when analysis by capillary electrophoresis techniques is difficult. In view of the differences between next-generation sequencing technology and capillary electrophoresis in the detection of gene rearrangement in lymphoma, especially for cases with difficult judgment criteria by capillary electrophoresis, it is necessary to compare the application value of the two methods.
Therefore, in order to clarify the application value of capillary electrophoresis and nextgeneration sequencing in the detection of immunoglobulin gene rearrangement in the diagnosis of classic Hodgkin's lymphoma, we screened 20 cases of classic Hodgkin's lymphoma as the research objects. Electrophoresis and next-generation sequencing methods were used to detect the rearrangement of the immunoglobulin heavy chain H and light chain kappa genes, and to analyze the differences between the two detection techniques, so as to provide more gene clonality for classic Hodgkin's lymphoma. Rearrange detection information and diagnostic basis.

Cases and samples
In this study, 20 paraffin-embedded specimens from patients with classic Hodgkin's lymphoma from the Department of Pathology in the First People's Hospital of Yunnan Province, China, were retrospectively collected from 2014 to 2019, and the diagnoses were confirmed by two pathologists. This study was conducted in accordance with the declaration of Helsinki and approved by the Ethics Committee of the First People's Hospital of Yunnan Province. Written informed consent was obtained from all participants.

Nucleic acid extraction and quantification
Ten 10-μm white films were cut from each wax block and deoxyribonucleic acid (DNA) was extracted using a nucleic acid extraction kit (Xiamen AmoyDx formalin-fixed paraffinembedded [FFPE] DNA Extraction Kit). The concentration and purity of DNA were determined by Nanodrop™.

Capillary electrophoresis
The DNA quality was verified using a specimen control size ladder mixture (California USA Invivoscribe Technologies) kit. The Master Mix (IGH Gene Clonality Assay -ABI Fluorescence/ IGк Gene Clonality Assay -ABI Fluorescence) clonality detection system was prepared and corresponding negative and positive controls were set up. Polymerase chain reaction (PCR) amplification was conducted (95°C, 7 min; 95°C, 45s; 60°C, 45s; 72°C, 90s; return to step 2 and repeat 34 more times; 72°C, 10 min; 15°C, 1 min). The ABI 3500 Genetic Analyzer was adopted for the detection of the amplification products. Clonality interpretation of the results was conducted according to the determination criteria (Table 1) [19].

Next-generation sequencing
The DNA quality was verified using the specimen control size ladder mix (California USA Invivoscribe). The LymphoTrack (LymphoTrack® IGH FR1/2/3 Assay -S5/PGM™/ LymphoTrack® IGк Assay -S5/PGM™) clonality assay system was prepared and the corresponding negative quality control (QC), positive QC, and blank QC were set up. The composition of the PCR amplification system was as follows: 45 μL of Master Mix, 0.2 μL of AmpliTaq Gold Taq, and 5 μL of sample or control DNA; the total volume was 50 μL. The program details of the amplification were as follows: 95°C, 7 min; 95°C, 45s; 60°C, 45s; 72°C, 90s; return to step 2 and repeat 29 more times; 72°C, 10 min; 15°C, 1 min. The PCR products were purified by the AMPure XP method, and the size and concentration of the purified product fragments were detected using the 4200 D1000 ScreenTape assay. Finally, the samples were diluted to 25 pM, and the gene libraries were constructed using the Ion PGMTM Hi-QTM Reagent Mix, Ion PGMTM Hi-QTM Enzyme Mix, nuclease-free water, and a diluted library system, and sequenced using the ABI Ion GeneStudio S5 Plus Next-generation Sequencing instrument. The LymphoTrack Dx PGM software was adopted for analysis of the sequencing results; clonality determination of the results was conducted based on the determination criteria ( Figure 1) [20].

Statistic analysis
The SPSS Statistics 23.0 software was employed for statistical analysis of the data. The Spearman's rank correlation test and the chi-squared (χ 2 ) exact test were used for comparison between groups; P < 0.05 was considered statistically significant.

Results
In this study, we used capillary electrophoresis technology and next-generation sequencing technology to detect gene rearrangement in paraffinembedded tissue of classical Hodgkin's lymphoma, and then performed correlation analysis between the results obtained by the two detection methods. The research shows that the detection results of the two technologies are quite different, and it is reasonable to infer that the detection effect of the next-generation sequencing method is better than that of the capillary electrophoresis method.

The clinical characteristics of the enrolled patients
The general clinical characteristics of the 20 cases with classic Hodgkin's lymphoma are demonstrated in Table 2. There were 15 males and 6 females with an average age of 38.6 years. The maximum age was 86 years and the minimum age was 14 years. In the present study, correlation analyses were conducted among various clinical and pathological indicators such as Ann Arbor staging, serum lactate dehydrogenase (LDH) levels, and the immunohistochemical results of tissue; no significant correlation was found between these general characteristics and various clinical and pathological indicators.

The DNA sample quality
The sample DNA was detected by ultraviolet spectrophotometer, and the optical density (OD) 260/ 280 ratio ranged between 1.7-2.0. The concentration of DNA extracted from the paraffinembedded tissue was between 46.2 and 147.2 ng/ μL, which was in accordance with the requirements for the library construction.

Results analysis of the Capillary Electrophoresis and Next-generation Sequencing
The detection results of IGH and IGк gene rearrangement in 20 samples are shown in Tables 3-5 Table 4). Among all the samples, 9 had consistent results for all loci detected by the two methods. In addition, there were 26 loci in 11 samples with inconsistent results, and the average consistency between the two methods was 67.5%. The detection rate of Capillary Electrophoresis was lower compared with Next-generation Sequencing, and the difference was statistically significant (P < 0.05) ( Table 5).
In the present study, correlation analyses were conducted between gene mutations and various clinically relevant indicators including gender, age, and Ann Arbor stage in patients. However, due to the limited sample size, no significant correlation was found between IGк, IGH gene usage frequency, or between IGHV mutations and various clinical indicators.

Discussion
The diagnosis and differential diagnosis of lymphoma is complex, with a high rate of misdiagnosis [21][22][23][24]. Since the late 1980s, molecular genetic analysis has gradually become an important adjunct for providing objective indicators for the diagnosis of lymphoma [25][26][27][28]. Currently, Capillary Electrophoresis technology is considered the gold standard for nucleic acid detection because of its low error rate [5] and it is widely applied in clinical practice [11]. However, abnormal results such as peaks out-ofrange or low peaks may occur in some cases and fail to guide clonality determination for histological and immunological diagnosis [29]. In recent years, Next-generation Sequencing has emerged as a high-throughput sequencing technology that is characterized by the ability to obtain information on all rearranged loci at one time [30]. The application of Nextgeneration Sequencing can further improve the detection rate of lymphoma and reduce the incidence of false positivity and false negativity in gene rearrangement assays. In the present study,  's lymphoma  Normal  III  -+  -2  Male  61  Classic Hodgkin's lymphoma  Normal  -+  +  +  3  Male  16  Classic Hodgkin's lymphoma  Normal  IV  --+  4  Male  48  Classic Hodgkin's lymphoma  Up  IV  ---5  Female  58  Classic Hodgkin's lymphoma  Normal  I  -+  -6 Male the gene rearrangement information from paraffin-embedded samples taken from 20 patients with classic Hodgkin's lymphoma in Yunnan Province, China, and detected by Capillary Electrophoresis and Next-generation Sequencing were compared. The investigation results showed that Next-generation Sequencing had a higher detection rate in the gene clonal rearrangements than Capillary Electrophoresis, and the detection rate of IGH and IGк clonal rearrangements in classical Hodgkin's lymphoma by Nextgeneration Sequencing were higher than those by Capillary Electrophoresis (35%/5% 45%/10% 50%/10% 30%/15%). The usage frequency of IGH and IGк genes and IGHV mutations are commonly reported in studies concerning chronic large B-cell lymphoma and chronic lymphocytic leukemia but not in classical Hodgkin's lymphoma. In the present study, the gene usage of IGH and IGк in classical Hodgkin's lymphoma and the frequency of IGHV mutations were statistically analyzed. Among the 12 samples analyzed, IGHV mutations occurred in 4 cases, with an average mutation rate of 3.34%. This study found that IGHV mutation frequency was not correlated with the usage frequency of the familial gene, while the correlation with tumor grade could not be assessed. The present study aimed to compare the application results of the two detection methods. To further exclude the abnormal results of Capillary Electrophoresis, particularly in cases where the results of Capillary Electrophoresis were close to the judging criteria and failed to provide sufficient evidence for interpretation, the sequences of the fragment were detected by Next-generation Sequencing; following validation, we found that the results of the two methods were inconsistent in 11 samples (Table 6). Sample 1: A monoclonal peak was detected at the IGк locus (190 bp) by Capillary Electrophoresis; conversely, monoclonal rearrangements at FR2, FR3, and IGк loci were detected by Next-generation Sequencing and involved the clonotypes of IGHV1-J5, IGHV4-J4, and IGкV3-J4.
Sample 3: Polyclonal rearrangements were detected at the FR1, FR2, and FR3 loci by Capillary Electrophoresis with no corresponding amplification peak at the IGк loci. However, the Next-generation Sequencing results showed that there existed monoclonal rearrangements at FR1, FR2, and IGк, and the clonal types included IGHV3-J3 and IGкV1-J4.
Sample 11: There existed polyclonal peaks at the FR2 and FR3 loci in the Capillary Electrophoresis detection but no corresponding amplification peaks at the FR1 and IGк loci. Conversely, the   Next-generation Sequencing results showed monoclonal rearrangement at the FR1 and FR3 loci, in which the clonotypes included IGHV3-J4. Sample 12: Next-generation sequencing detection revealed monoclonal rearrangements at the FR1, FR2, and FR3 loci.
Sample 17: Monoclonal peaks at the FR2 and FR3 loci were detected in the Capillary Electrophoresis assay, and there was no corresponding amplification peak at the FR1 and IGк loci. The Next-generation Sequencing results showed that there existed monoclonal rearrangements at the FR1, FR2, FR3, and IGк loci, in which the clonotypes included IGHV3-J6, IGHV1-J4, and IGкV3-J2.
Sample 20: A corresponding amplification peak was detected by Capillary Electrophoresis at the IGк locus, while Next-generation Sequencing found monoclonal rearrangements at the FR1, FR2, and FR3 loci.
High-quality nucleic acid is an important prerequisite for getting accurate sequencing results. Compared with the fresh specimens, paraffinembedded samples are abundant, easy to transport, can be stored for a long time, and represent the most commonly used preservation method [31]. With the development of precision medicine, paraffin-embedded specimens are playing an increasingly important role in tumor genetic testing; however, they are affected by reagents and other factors during the production process, resulting in DNA degradation by fragmentation [32]. Guyard et al. observed a decrease in both the quantity and quality of DNA extracted from paraffin-embedded samples that had been stored for many years [33]. The present study found that different sequencing methods resulted in a different positivity for the same batch of samples. When sequencing was conducted by Capillary Electrophoresis, monoclonal rearrangements were detected in 5 of 20 specimens (25%, 5/20), while the same DNA samples were found to have monoclonal rearrangements in 12 specimens when tested by Next-generation Sequencing (60%, 12/ 20). The results of the comparison showed that the sensitivity of Capillary Electrophoresis and Next-generation Sequencing techniques differed when adopting the same DNA. Notably, these results suggested that storage time influenced the paraffin-embedded specimens in terms of the adopted sequencing method; furthermore, as well as a difference between Capillary Electrophoresis and Next-generation Sequencing measurements, Next-generation Sequencing was better suitable for use in retrospective studies. Therefore, in cases of severe DNA fragmentation, Nextgeneration Sequencing technology can be adopted to further improve the accuracy of diagnosis and treatment.
In the present study, the comparison of Capillary Electrophoresis and Next-generation Sequencing results (Table 6) showed that the rearrangement results of different loci of the same sample were different. For example, in sample 18, a clonal rearrangement in FR2 and FR3 was detected by Capillary Electrophoresis, while the presence of FR1, FR2, FR3, and IG1 gene rearrangements was detected by Nextgeneration Sequencing (Table 7), where the total number of reads detected for FR1 was 88,105, including two clonotypes (IGVH3-J6 and IGHV1-J4) ( Figure 3). The comparison revealed that several loci in the Capillary Electrophoresis results failed to show the expected amplification peaks. Several similar studies have been conducted [34][35][36]. This phenomenon may have been the result of changes in the primer binding sites due to rearrangement, which affected the effective binding of genes to primers and resulted in PCR amplification failure. It was verified that in the detection of B-cell non-Hodgkin's lymphoma, somatic hypermutation of the IGH in the germinal center affected the positivity of monoclonal assays for lymphoma originating from the germinal center, and somatic hypermutation at the primer binding site may have led to the occurrence of the above phenomenon [37]. Table 6. Samples with non-conforming results for clonogenic rearrangement generation sequencing and next generation sequencing assays. Age   IGH-FR1  IGH-FR2  IGH-FR3  IGк   CE NGS CE NGS CE NGS CE NGS  1  Male  19  ---+  -+  +  +  3  Male  16  -+  -+  ---+  4  Male  48  ---+  -+  +  +  11 Male  37  -+  ---+  --12 Female 25  -+  -+  -+  --13 Female 67  -----+  -+  14 Female 41  -----+  --15 Male  35  -+  -+  ----16 Male  15  ---+  -+  --17 Male  27 - further clarify the gene rearrangement clones and provide more accurate information for clinical practice. It is worth noting that in the 20th sample (Table 6), the results of capillary electrophoresis showed that the IGк site was rearranged monoclonally. In the nextgeneration sequencing, the FR1, FR2, and FR3 sites were single-clonally rearranged, while the IGк site was a polyclonal rearrangement. Row. The possible reasons for this result are that, on the one hand, the diversity of the Vк-Jк junction region is low, and it is easy to form false positive clones, which is also the reason why a false dominant clone peak always appears around 150bp in normal lymphoid tissue. On the one hand, the capillary electrophoresis method distinguishes different clones based on the length of the rearranged sequence, and the primers in the Vк region are prone to mismatches. From the quality control results, it can be found that the quality of the nucleic acid of sample No. 20 is poor, resulting in the expansion of the advantage of small fragments. increased, and a dominant monoclonal peak appeared at the lower limit of the calling range of 120 bp. The next-generation sequencing method distinguishes different clones based on the rearrangement sequence, and can obtain complete sequence information. Compared with the simple fragment size of capillary electrophoresis, the diversity of Vк-Jк can be greatly enriched. Some clones are distinguished with higher accuracy. Therefore, IG gene rearrangement detected by next-generation sequencing is a better and more feasible method than capillary electrophoresis for the clonality assessment of paraffin-embedded samples of classical lymphoproliferative diseases.

ID Gender
In a recent study that included 1,189 cases with B-cell and plasma cell tumors, both Capillary Electrophoresis and Next-generation Sequencing methods were shown to have good application for gene rearrangement detection, and it was concluded that the Next-generation Sequencing results were superior to Capillary Electrophoresis in the routine identification of IGH clonality [20]. The comparative data in the present study revealed a significant difference in the positivity of clonal rearrangements detected by the two methods (Table 6). In summary, it was feasible to adopt Next-generation Sequencing to detect IG gene rearrangements and apply to the paraffin-embedded specimens of patients with classical Hodgkin's lymphoma. Compared with Capillary Electrophoresis, Next-generation Sequencing had a higher positive detection rate for clonal rearrangements and may have been more accurate in the interpretation of results. In addition, when gene rearrangement analysis is used to diagnose lymphoma, these data should be combined with the PCR results, clinical data, the results of histomorphology, and immunohistochemistry to allow for a comprehensive evaluation of the disease.

Conclusion
In this study, 20 cases of classic Hodgkin lymphoma pathology were used as the research objects. The clonal rearrangement of immunoglobulin heavy chain H and light chain κ genes was detected by capillary electrophoresis and next-generation sequencing technology. The results show that the next-generation sequencing technology has higher sensitivity and specificity than capillary electrophoresis for detecting gene clonal rearrangements. In addition, the secondgeneration sequencing technology also has other advantages, such as higher resolution and more objective interpretation standards. The overall detection effect is better than that of capillary electrophoresis, and it can be used as an important auxiliary method for the diagnosis of classic Hodgkin lymphoma.

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

Funding
The present study was supported by the Yunnan Health Training Project of High Level Talents (grant no. H-2019001), Yunnan Province Clinical Center for Hematologic Disease (grant no. 2020LCZXKF-XY13), and Science and technology Planning project of Yunnan Science and Technology Department (grant no. 202101AT070238).

Data availability statement
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.


s2fieldsofstudy: Biology Engineering

text:
NIR‐II Excitation Phototheranostic Platform for Synergistic Photothermal Therapy/Chemotherapy/Chemodynamic Therapy of Breast Cancer Bone Metastases

Abstract To improve bone metastases treatment efficacy, current strategies are focused on the integration of chemotherapy with phototheranostic. However, the success of phototheranostic approaches is hampered by the limited tissue penetration depth of near‐infrared‐I (NIR‐I) light (700–900 nm). In this study, a NIR‐II (1000–1700 nm) excitation phototheranostic (BTZ/Fe2+@BTF/ALD) is presented for NIR‐II fluorescence imaging and NIR‐II photoacoustic imaging‐guided NIR‐II photothermal therapy (PTT), chemotherapy, and chemodynamic therapy (CDT) of breast cancer bone metastases. This phototheranostic is developed by integrating a dopamine‐modified NIR‐II absorbing donor–acceptor–donor small molecule (BBT‐FT‐DA), the boronate anticancer drug bortezomib (BTZ), and Fe2+ ions, as CDT catalysts, into an amphiphilic PEGylated phospholipid modified with the bone‐targeting ligand alendronate. In acidic and hydrogen peroxide (H2O2) over expression tumor microenvironment, the boronate–catechol linkage is cleaved and BTZ and Fe2+ ions are released to initiate the Fenton reaction, that is, chemotherapy and CDT, respectively, are initialized. It is confirmed using the murine 4T1 bone metastasis model that BTZ/Fe2+@BTF/ALD significantly suppresses the progression of tumor cells in the bone tissue via a synergistic NIR‐II PTT/chemotherapy/CDT effect. Overall, this work provides fresh insights to guide the development of NIR‐II phototheranostics for breast cancer bone metastases.

(164 mg, Yield: 56.37%). 1   was dropped into pre-prepared BTF-DA (1 ml) and stirred vigorously. After continuous agitation overnight, excess ferrous ions were removed by ultra-centrifugation at 2500 rpm for 25 min and then rinsed with water three times. Then the purified Fe 2+ @BTF-DA was diluted and filtered through a 220nm PVDF syringe driven filter, and the Fe 2+ @BTF-DA stock solution was obtained by ultra-centrifugation.
The preparation method was referred to Fe 2+ @BTF-DA.  In vitro Cellular Uptake. The 4T1 cells were used for the assessment of cellular uptake. First, cells, seeded 4 × 10 4 per well, were incubated in DMEM in a 6-well plate at 37 °C and 5% CO 2 condition for 24 h. Subsequently, the medium was replaced by one of the two groups, including 1 mL fresh DMEM medium alone and the 1mL mixture of fresh DMEM medium and BTZ/Fe 2+ @BTF-DA (0.1 mg mL -1 ), respectively. The treated cells were further incubated at 37 °C and 5% CO 2 condition for 4 h. Afterward, the upper supernatant was sucked out and 1 mL of PBS was added to clean twice and remove dead cells. Subsequently, PBS was removed and 500 μL trypsin digestion solution with EDTA was added, and the cells were dissolved in an incubator at 37 °C and 5% CO 2 for 90 s. Later 1 mL of DMEM was added and the cells were transferred into a 15 mL centrifuge tube and centrifuged for 3 min. The supernatant was then removed, and 100 μL of PBS was added to the centrifuge tube. Finally, the cells were transferred to a 96-well plate, where NIR-II fluorescence images of the two groups were captured under the 1064 nm laser excitation.

Cellular internalization. To track internalization of BTZ/Fe 2+ @BTF-DA in tumor cells, 4T1
cells (1 × 10 4 ) were grown on slices in a 6-well culture plate at 37 °C and 5% CO 2 condition for 24 h, and then incubated with FITC/BTZ/Fe 2+ @BTF-DA (0.1 mg mL -1 ) . FITC was used as a fluorescent marker to determine whether the material was uptaken by cells. After 4 h, the cells were washed with PBS for three times, followed by costaining with DAPI (nucleus indicator, 10 min). Then, the cells were imaged by . image was performed using the NIR-II in vivo imaging system software.
Mice are mounted on specially designed holders, which rotate 360 degrees with sophisticated rotating motors. Four short pulsed laser beams (biorthogonal, bioblique) are emitted by a laser the tissue absorbs light energy to generate ultrasonic signal, which is received by a professionally designed 125° are transducer. The signal was collected at 360°, and the photoacoustic image was reconstructed by special 3D reconstruction software through unit transformation of DAQ data.
Bone targeting assay. Two mice were injected into the tail vein with BTZ/Fe 2+ @BTF-DA (as a control group) and BTZ/Fe 2+ @BTF/ALD, the real-time in vivo NIR-II fluorescence imaging was performed at different post-injection times by using an in vivo NIR-II fluorescence imaging system. The analysis of the signal intensity of NIR-II image was performed using the NIR-II in vivo imaging system software.

4T1
bone metastasis models mice