Diversity of Integration Sites of Bovine Leukemia Virus (BLV) and Roles of Genes Related to Development of BLV-Induced Lymphoma in a Large Cohort

Abstract

Bovine leukemia virus (BLV) causes enzootic bovine leukosis (EBL), yet its pathogenic mechanisms remain largely unclear. In particular, the role of BLV genomic integration sites (IS(s)) in BLV-induced leukemogenesis has not been fully elucidated. Here, we identified a total of 235 ISs from 99 BLV-infected cattle with lymphoma, of which 4.3% and 46.8% were located within exon and intron, respectively, while no preferential integration into CpG islands or repetitive regions was observed. All identified ISs were distinct, and no identical sites were detected among the samples. We identified 246 genes related with these ISs and protein–protein interaction analysis of these genes demonstrated one “IS-Clustered genes” composed of 85 among 246 genes. This “IS-Clustered genes” contains 12 cancer genes (14.1%) with high significantly proportion. Notably, with 55 among 99 cattle tested (55.6%) harboring ISs within this cluster, suggesting its crucial involvement in BLV-induced pathogenesis. Furthermore, integrated analysis of known lymphoma- and IS-related genes and the 85 “IS-Clustered genes” showed that key genes formed a shared cluster, indicating a potential “common EBL-associated cluster.” These findings provide important insights into the role of BLV integration in EBL development and may contribute to elucidating its molecular mechanisms underlying onset of EBL. In addition, these findings may also aid in the development of therapeutic strategies and facilitate early diagnosis.

1. Introduction

Bovine leukemia virus (BLV), which is closely related to human T-cell leukemia virus type 1 (HTLV-1), is the causative agent of enzootic bovine leukosis (EBL), the most common neoplastic disease among cattle [1,2]. Approximately 70% of BLV-infected cattle are asymptomatic carriers; the remaining 30% of infected cattle show persistent lymphocytosis, which is characterized by polyclonal expression of non-neoplastic CD5-positive B lymphocytes [1,3]. Only 1–5% of cattle develop B-cell leukemia/lymphoma after a prolonged latency period [1].

EBL is a multifactorial disease; however, the mechanisms of its pathogenesis are not fully understood. As a viral factor, the proviral load of BLV is associated with disease progression [4,5]. In addition, the nucleotide at position 175 in its long terminal repeat (LTR) and the amino acid at position 233 in the Tax protein are associated with the onset of EBL [6,7]. As a host factor, the major histocompatibility complex, which plays a crucial role in immune regulation, also contributes to the onset of EBL. In particular, some bovine leukocyte antigen DRB3 alleles confer resistance and susceptibility to proviral loads and lymphoma [8,9,10,11,12,13,14,15,16]. Wide distribution of mutations in the tumor suppressor gene TP53 has been reported in cattle with lymphoma [17,18,19,20,21]. Additionally, somatic mutations in TP53 as well as various other cancer-associated genes have been reported [22]. Furthermore, changes in the expression levels of certain genes, such as Protein Arginine Methyltransferase 5 (PRMT5) and those associated with DNA Mismatch repair, have been reported [23,24].

Similar to other retroviruses, the BLV genome integrates into the host genome as a provirus and induces lifelong infections [1]. Many studies have investigated the association between BLV integration sites (IS(s)) and the onset of EBL [25,26,27,28,29,30,31,32]. BLV proviruses are distributed across every chromosome, and there are no integration hotspots [25]. Indeed, some studies have shown that the BLV provirus does not favor integration near transcription start sites, CpG islands, or repetitive sequences such as transposons [25,26]. In contrast, it is reported that BLV proviruses were integrated near CpG islands and into long interspersed nuclear elements (LINE) more frequently in cattle with lymphoma under 3 years old than in those over 3 years old [28]. Therefore, the impact of BLV integration into specific regions of the genome on the development of EBL remains unclear.

Furthermore, integration of the BLV genome into exons or introns is presumed to exert a substantial influence on gene expression. In addition, proviruses can disrupt the mRNA expression of host genes located in their vicinity through gene interruption [27]. Indeed, BLV/HTLV-1 proviruses in the tumor genomes of lymphoma animals or adult T-cell leukemia/lymphoma patients are preferentially integrated near cancer driver genes [27,33,34,35,36]. Additionally, clone abundance positively correlates with the proximity of the provirus to the transcribed region [26,28]. Notably, integration of the BLV genome has been identified near the genes that play a role in cancer development, such as in the intergenic region of RTN4IPI and ATG5, and in the intron of the regulatory-associated protein of mTOR (RPTOR) in two B-cell lymphoma lines [31]. Additionally, integration events within an intron of the checkpoint kinase 2 (CHEK2) gene and in the intergenic region of clarin 1 (CLRN1) and siah E3 ubiquitin protein ligase 2 (SIAH2) were observed at the onset of lymphoma in naturally infected BLV individuals who progressed from a premalignant stage to terminal disease [32]. Nevertheless, the interrelationships between these genes remain unclear, and the specific contributions of individual genes to disease onset are yet to be elucidated. Therefore, although ISs are believed to contribute to the onset of EBL, the mechanisms underlying their involvement in disease pathogenesis remain largely unresolved.

To elucidate the mechanisms underlying the contribution of ISs to the development of EBL, we previously developed a proviral DNA capture sequencing (proviral DNA-capture-seq) method [31], involving a target-enrichment high-throughput sequencing system for the characterization of BLV ISs. In this study, we firstly collected samples from 99 naturally BLV-infected cattle with lymphoma nationwide and identified ISs. Secondly, 235 ISs identified across 99 EBL cattle revealed that the BLV genome exhibited a preference for integration into exon and intron regions but not into repetitive regions. Thirdly, we successfully extracted 246 IS-Related genes and their functional annotations using Gene Ontology (GO) analysis. Fourth, through protein–protein interaction network analysis, we detected one cluster comprising 85 genes, and 55 of the 99 cattle with lymphoma possessed one or more ISs associated with these clustered genes. Finally, an integrated analysis of these 85 clustered genes using previously reported IS- and EBL-related genes was performed and one “common EBL-related cluster” comprising 132 genes was found and this “common EBL-related cluster” was strongly associated with cancer pathways. Because monoclonal expansion has been observed prior to the onset of EBL, and ISs are maintained from the premalignant stage through terminal disease [32], our results demonstrate that this “common EBL-related cluster” may be useful for the early detection of the onset of EBL.

2. Results

2.1. Distribution Analysis Revealed That BLV Proviruses Exhibit a Preference for Integration into Exon and Intron

To elucidate the contribution of BLV ISs to the development of EBL, DNA was extracted from blood or tissue samples obtained from 99 cattle with lymphoma collected in a nationwide survey across Japan (Figure 1A). Distribution of the BLV provirus was screened using the proviral DNA-capture-seq method (Figure 1A). Sequencing reads were mapped to the bovine reference genome (ARS-UCD1.2/bos Tau9) (Figure 1A). Genomic regions displaying mapped reads exhibiting a single peak or regions confirmed to contain chimeric sequences of BLV and host DNA through PCR and Sanger sequencing were defined as ISs (Figure 1A).

Two-hundred and thirty-five ISs were identified from the samples of 99 cattle with lymphoma, with the number of ISs per animal ranging from 1 to 12 (mean, 2.4) (Figure 1B). In particular, all the identified ISs were distinct, and no shared sites were detected among the samples. To elucidate the distribution of ISs within the bovine genome, ISs were categorized into exons, introns, and intergenic regions. Among 235 ISs, 10 (4.3%) were mapped to exons, 110 (46.8%) to introns, and 115 (48.9%) to inter-genes (Figure 2A). Exons, introns, and intergenic regions constitute approximately 1%, 24%, and 75% of the human genome [36], respectively. The ISs identified in EBL cattle were significantly enriched in exons and introns compared with those in the human genome (p < 0.001). Human genomic data were used for this analysis because corresponding bovine genomic data were not available. Using human gene composition as a reference for mammals is justified because less than 1% of genes lack detectable orthologs between humans and mice [37]. The proportion of ISs located within repetitive regions was analyzed. Among the 235 ISs, none were located within CpG islands, which comprise approximately 1% of the bovine genome (Figure 2B). This observation is consistent with the expected theoretical distribution. In addition, among the 235 ISs, 3 (1.3%) were mapped to simple repeats, 7 (3.0%) to LTR, 7 (3.0%) to DNA repeat elements (DNA), 33 (14.0%) to short interspersed nuclear elements (SINEs), 70 (29.8%) to LINE, and 115 (48.9%) to non-repeat regions (Figure 2C). Simple repeats, LTRs, DNA, SINEs, LINEs, and non-repeat regions constitute approximately 0%, 8%, 3%, 13%, 21%, and 55% of the cattle genome identified by repeat maskers, respectively. The proportion of ISs within repetitive regions appeared to be consistent with the expected theoretical value. These results suggest that ISs tend to be inserted near genes but lack a preference for CpG islands and repetitive regions.

2.2. Extraction of “IS-Related Genes” and Genetic Analysis Revealed That the Effects of BLV Integration near Cancer-Related Genes on the Onset of EBL Were Limited

To examine the potential direct role of ISs of BLV in tumorigenesis, genes harboring BLV provirus insertions within their exons or introns and the closest upstream and downstream genes to each IS were designated “IS-Related genes” (Figure 1B). From a total of 350 genes (including 10 ISs located in exons, 110 ISs located in introns, and 115 ISs located in intergenic regions), the 246 genes with functional annotations were extracted using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) and defined as “IS-Related genes” (

Table 1. 246 “IS-Related Gene” List ¹.

(1) Exon
ASB11	ATXN3	C25H16orf96	ENPP3	GABRB1	IL10	LPO	MEFV
(2) Intron
ADK 2	AFAP1	AHCYL2	ANKRD17	CAMK2A	CAMK2D	CARTPT	CCDC178	CLSTN2 2,3	DNASE1L2
DPYSL3	DPYSL5	DYSF	E2F5	ELOVL6	EPHB1	ERBB4	ETFDH	EXOC4	FAM151B
FARP1	FER	FNDC3B	GFPT1	GON4L	GPM6B	GRID2	GRM5	HDAC4	HIBADH
IBTK	IP6K3	ITK	KCNQ5	KUF27	LDB2	LOC789384	LRP2	MAST2	MUCR1
MFSD12	MKLN1	MORC2	MRAS 2	MROH2B	MRPL18	MSH3	MYO1E	MYO10	NAV2
NAV3	NFAT5	NFYC	NIDT9	OSBPL3	OUTD7A	PAFAH2	PEBP4	POT1 2	PPP1R1C
PPP2R2B	PRCP	PRDM16	PTEN	PTPRM	PYROXD1	RNF144A	RNGTT	RRM1	RSF1
RTN4	SBSPON	SEC24A	SEC24B	SGK1	SHLD1	SLC25A14	SMARCA4	SNED1	SRBD1
SRPK2 2	SYN3	TAFA2	TBX19	TCERG1L	TEX14	TIAM1	TMEM178B	TMEM260	TPR
TRHDE 2,3	TSPAN17	UBE3D	VCL	ZNF407
(3) Intergenic
ACAN	ADGRL2	AKAIN1	ALKAL2	ALPK2	ALYREF	APIP	ARHGDIA	ARHGEF10	ARL14
ARMC1	ARMCX4	ATF2	ATXN7L3B	BAZ1	C10H14orf39	C23H6orf141	C5AR1	C5AR2	CA8
CCDC57	CCDC62	CDH7	CDH10	CDH19	CDK5RAP2	CEP68	CHN1	CHRM1	CLSTN2 2,3
CMPK2	CNTNAP2	COL22A1	CSMD1	CYLC1	CYSTM1	DLGAP1	DMRT2	DMRT3	DPYD
DRD1	DTL	DUSP10	EFNA5	ETV3	FAM83B	FBXL17	FBOX4	FOXR2 2	GHR
GLYATL3	GNAI1	GNAT3	GPR158	GRIA1	GSTT4	GSTZ1 2	HECTD2	HIP1R	HNMT
HNRNPH2	HOXA2	HYOU1	IGFBP2	IGIP	KCNE4	KLF7	KLHDC9 2	LOC519202	LOC540403
LCO613444	LOC782977	LOC790004	LRRC31	LRRK2	LUZP2	MAGEH1 2	MALT1	MED27	MID1IP1
MIF	MIIP	MIR135A-2	MIR2902	MYO3A	NDUFV3	NECTIN4 2	NELL2	NTNG2	NUDCD1
PARVA	PCDH7	PHACTR1	PLOD2	PON1	PON3	POU3F3	PPIL4	PPM1A	PPP1R3C
PRIM2	PTBP2	PTTG1	RAB23	RAMP1	RFX7	RPL37A	RSAD2	SAMD7	SATB1
SKAP2	SLC1A4	SLC2A13	SLC16A3	SLC30A9	SLC37A4	SPDYC	STPG2	STUM	SYNCRIP
SYVN1	TBC1D7	TBL1XR1	TEAD1	TEF	TEX9	TFAP2A	THSD7A	THSD7B	TIFAB
TINAG	TMED8 2	TMEM33	TMEM106B	TMEM117	TMPO	TOB2	TOX	TRHDE 2,3	TRHR
TSNAX	TTLL7	USP24	WDR4	ZNF365

¹ Italic indicates gene names, red text indicates cancer genes and bold text indicates “IS-Clustered genes”. ² Boxes text indicates genes related to multiple IS. ³ CLSTN2 and TRHDE were related; both IS located in introns and intergenic regions.

) (Figure 1B,C). These 246 genes were classified into groups containing 8, 95, and 145 genes related to IS, located in exons, introns, and intergenic regions, respectively. Interestingly, only 12 genes (genes with five ISs, PUT1; genes with two ISs, ADK, CLSTN2, POXR2, GSTZ1, KLHDC9, MAGEH1, MRAS, NECTIN4, SPRK2, TMED8, and TRHDE) were associated with multiple IS (Table 1, shown in the boxes, and Figure 3A).

Comparison between the reference genes in the OncoKBTM Cancer Gene List (https://www.oncokb.org/cancer-genes, accessed on 8 October 2025) and the 246 “IS-Related genes” clearly showed that only 17 genes (6.9%), namely IL10, BPHB1, ERBB4, HDAC4, ITK, MSH3, POT1, PRDM16, PTEN, SGK1, SMARCA4, TPR, CHN1, DPYD, LRRK2, MART1, and TBL1XR1, as shown in red in Table 1 among the 246 “IS-Related genes” cancer genes (Figure 3B and Table 1, shown in red). Notably, five ISs were detected within the introns of POT1, a major cancer gene. In contrast, among the 21,306 known human genes [38], 1203 are classified as cancer genes according to the OncoKBTM Cancer Gene List, representing approximately 5.6% of all genes. Thus, the proportion of cancer genes among the “IS-Related genes” was 6.9% (Figure 3B and Table 1, shown in red), suggesting no significant deviation from the expected theoretical value. In addition, the distribution of IS located in exons, introns, and intergenic regions was examined among the cancer-related and non-cancer genes, revealing no significant differences between the two groups (Figure 3C). Furthermore, among 99 cattle with lymphoma, only 17 (17.2%) possessed one or more ISs associated with cancer-related genes (Figure 3D). These results demonstrate that the influence of ISs located near cancer-related gene on BLV-induced leukemogenesis is limited.

2.3. GO Analysis of “IS-Related Gene” Revealed That a Subset of Related Genes Is Involved in Pathways for Oncogenesis

GO analysis was conducted to clarify the mechanisms of BLV-induced leukemogenesis from the functional perspective of “IS-Related genes” (Figure 1C). As shown in

Table 2. Gene ontology analysis of “IS-Related gene” for biological processes.

Term 1	Gene 2	p-Value
homophilic cell adhesion via plasma membrane adhesion molecules	CDH10, CDH19, CDH7, CLSTN2, NECTIN4, PTPRM, PCDH7	0.00344
nervous system development	EPHB1, LDB2, SMARCA4, ENFA5, NAV2, NAV3, PTEN, TFAP2A	0.00432
positive regulation of receptor signaling pathway via JAK-STAT	CAMK2A, ERBB4, GHR, IL10	0.00843
cell–cell adhesion mediated by cadherin	FER, CDH10, CDH19, CDH7	0.00843
modulation of chemical synaptic transmission	DLGAP1, GRIA1, GRID2, NTNG2, RTN4	0.0104
regulation of focal adhesion assembly	EFNA5, GPM6B, VCL	0.011
protein polyubiquitination	FBXL17, FBXO4, HECTD2, DTL, RNF144A, UBE3D	0.0173
regulation of neuron migration	CAMK2A, NTNG2, PHACTR1	0.0175
carboxylic acid catabolic process	PON1, PON3	0.0214
protein dephosphorylation	RNGTT, DUSP10, PTEN, PPM1A, PTPRM	0.0285
positive regulation of transcription by RNA polymerase II	LDB2, POU3F3, SMARCA4, TBL1XR1, TEAD1, TEF, CCDC32, HDAC4, HOXA2, IL10, RFX7, SLC30A9, TOX, TFAP2A, ZNF407	0.0298
cell adhesion	EPHB1, FER, ACAN, CNTNAP2, NECTIN4, PARVA, PCDH7, TINAG, VCL	0.0308
actin cytoskeleton organization	FER, ARHGEF10, MKLN1, PARVA, THSD7A, THSD7B	0.0309
regulation of neuron projection development	GRID2, NTNG2, ZNF365	0.0316
negative regulation of mast cell activation involved in immune response	FER, ENPP3	0.0319
regulation of branching morphogenesis of a nerve	LRRK2, RTN4	0.0319
lactone catabolic process	PON1, PON3	0.0319
synaptic membrane adhesion	CDH10, EFNA5, NTNG2	0.0362
chemical synaptic transmission	CARTPT, CHRM1, EXOC4, GABRB1, GRM5	0.0418
dendrite arborization	PHACTR1, ZNF365	0.0423
Neurogenesis	CDK5RAP2, ERBB4, NAV2, NAV3	0.0426

¹ Underlined text indicates GO terms processes potentially involved in tumorigenesis. ² Red text indicates cancer genes and bold text indicates “IS-Clustered genes”.

and

Table 3. Gene ontology analysis of “IS-Related gene” for KEGG pathway.

Term	Gene 1	p-Value
Dopaminergic synapse	GNAI1, ATF2, CAMK2A, CAMK2D, DRD1, GRIA1, PPP2R2B	0.00394
Amphetamine addiction	ATF2, CAMK2A, CAMK2D, DRD1, GRIA1	0.00708
Axon guidance	EPHB1, GNAI1, CAMK2A, CAMK2D, DPYSL5, EFNA5, NTNG2	0.0151
Pyrimidine metabolism	CMPK2, DPYD, ENPP3, RRM1	0.0274
Cholinergic synapse	GNAI1, CAMK2A, CAMK2D, CHRM1, KCNQ5	0.0386
Long-term potentiation	CAMK2A, CAMK2D, GRIA1, GRM5	0.0424

¹ Bold text indicates “Clustered genes”.

, 21 GO terms associated with biological processes and 6 KEGG pathways were identified (Table 2 and Table 3). In particular, GO terms related to biological processes potentially involved in tumorigenesis, such as “positive regulation of receptor signaling pathway via JAK-STAT,” “protein polyubiquitination,” “protein dephosphorylation,” “positive regulation of transcription by RNA polymerase II,” and “cell adhesion,” were observed (Table 2, shown in underlined). These GO terms are related to signal pathways and transcription. These findings suggest that integration of the BLV provirus may disturb these biological processes, thereby contributing to lymphoma development. However, no KEGG pathways associated with cancer were identified (Table 3). Thus, 38 “IS-Related genes” (CAMK2A, ERBB4, GHR, IL10, FBXL17, FBXO4, HECTD2, DTL, RNF144A, UBE3D, RNGTT, DUSP10, PTEN, PPM1A, PTPRM, LDB2, POU3F3, SMARCA4, TBL1XR1, TEAD1, TEF, CCDC32, HDAC4, HOXA2, RFX7, SLC30A9, TOX, TFAP2A, ZNF407, EPHB1, FER, ACAN, CNTNAP2, NECTIN4, PARVA, PCDH7, TINAG, and VCL) were associated with these five GO terms, and only 6 genes, such as ERBB4, IL10, SMARCA4, TBL1XR1, HDAC4 and EPHB1 were cancer genes. These results suggest that non-oncogenes, as well as oncogenes, may be involved in the onset of EBL. However, this finding is unlikely to entirely facilitate the development of lymphoma, as only 6 cancer genes and 32 non-cancer genes were associated with five GO terms (Table 2, shown in underlined).

2.4. Analysis of “IS-Clustered Genes” Revealed That the Clustered Genes Were Strongly Associated with the Onset of EBL

Not all ISs are implicated in lymphoma; therefore, to identify genes involved in oncogenic processes, the “IS-Related genes” were clustered using protein–protein interaction networks using STRING (https://string-db.org/, accessed on 8 October 2025) (Figure 1D). Remarkably, of the 246 “IS-Related genes”, 85 genes (Table 1, shown in bold) were found to form a single cluster within the protein–protein interaction network (Figure 4A). These 85 genes were defined as “IS-Clustered genes”. In addition, these “IS-Clustered genes” included 12 cancer genes (14.1%), namely CHN1, SGK1, IL10, PTEN, HDAC4, ERBB4, BPHB1, SMARCA4, TBL1XR1, ITK, MSH3 and POT1, as shown in red color in Figure 4. In addition, the proportion of cancer genes of these clustered genes was significantly (p < 0.01) higher than that of the 161 non-clustered genes (3.1%) (Figure 4B). Furthermore, this proportion was also significantly (p < 0.01) higher than the proportion of cancer genes in the human genome (5.6%) (Figure 4C). These results suggest that the “IS-Clustered genes” play an important role in lymphoma development. Indeed, although 84 (34.6%) of the 246 “IS-Related genes” were classified as “IS-Clustered genes,” 55 (55.6%) of the 99 cattle with lymphoma possessed one or more ISs associated with the “IS-Clustered genes,” and the proportion was significantly (p < 0.001) higher than that of the non-clustered genes (Figure 4D).

2.5. GO Analysis Revealed That the “IS-Clustered Genes” Have a Role in the Onset of EBL

To investigate the functional roles of the “IS-Clustered genes,” GO analysis was conducted. As shown in

Table 4. Gene ontology analysis of “IS-Clustered genes” for biological processes.

Term 1	Gene 2	p-Value
positive regulation of receptor signaling pathway via JAK-STAT	CAMK2A, ERBB4, GHR, IL10	0.000479
modulation of chemical synaptic transmission	DLGAP1, GRIA1, GRID2, RTN4	0.0037
signal transduction	ADGRL2, CAMK2A, CAMK2D, CHN1, DUSP10, FAM83B, GABRB1, MYO10, PTPRM	0.00394
nervous system development	EPHB1, SMARCA4, EFNA5, NAV2, PTEN	0.00639
ephrin receptor signaling pathway	EPHB1, CHN1, EFNA5	0.00785
positive regulation of synapse assembly	CLSTN2, EFNA5, GRID2	0.0135
positive regulation of miRNA transcription	SMARCA4, TEAD1, IL10	0.0158
NK T cell differentiation	ITK, ATF2	0.0195
protein phosphorylation	EPHB1, ITK, CAMK2A, CAMK2D, ERBB4, SGK1	0.0235
positive regulation of toll-like receptor 9 signaling pathway	RSAD2, RTN4	0.0271
monoatomic ion transport	GABRB1, GRIA1, GRID2	0.0277
cellular response to forskolin	GNAI1, EFNA5	0.0461
regulation of neuronal synaptic plasticity	CAMK2A, CAMK2D	0.0498

¹ Underlined text indicates GO terms processes potentially involved in tumorigenesis. ² Red text indicates cancer genes.

and

Table 5. Gene ontology analysis of “IS-Clustered genes” for KEGG pathway.

Term1	Gene 2	p-Value
Dopaminergic synapse	GNAI1, ATF2, CAMK2A, CAMK2D, GRIA1, PPP2R2B	0.000622
PI3K-Akt signaling pathway	ATF2, CHRM1, EFNA5, ERBB4, GHR, PTEN, PPP2R2B, SGK1	0.00356
Amphetamine addiction	ATF2, CAMK2A, CAMK2D, GRIA1	0.00522
Long-term potentiation	CAMK2A, CAMK2D, GRIA1, GRM5	0.00543
Adrenergic signaling in cardiomyocytes	GNAI1, ATF2, CAMK2A, CAMK2D, PPP2R2B	0.00852
cAMP signaling pathway	GNAI1, TIAM1, CAMK2A, CAMK2D, CHRM1, GRIA1	0.00888
Nucleotide metabolism	ADK, CMPK2, ENPP3, RRM1	0.0102
Axon guidance	EPHB1, GNAI1, CAMK2A, CAMK2D, EFNA5	0.0135
Circadian entrainment	GNAI1, CAMK2A, CAMK2D, GRIA1	0.0148
Proteoglycans in cancer	TIAM1, CAMK2A, CAMK2D, ERBB4, MRAS	0.0204
Cholinergic synapse	GNAI1, CAMK2A, CAMK2D, CHRM1	0.0209
Glutamatergic synapse	DLGAP1, GNAI1, GRIA1, GRM5	0.0209
Pyrimidine metabolism	CMPK2, ENPP3, RRM1	0.0365
Long-term depression	GNAI1, GRIA1, GRID2	0.0412
Retrograde endocannabinoid signaling	GNAI1, GABRB1, GRIA1, GRM5	0.044
Calcium signaling pathway	CAMK2A, CAMK2D, CHRM1, ERBB4, GRM5	0.0455
Cushing syndrome	GNAI1, ATF2, CAMK2A, CAMK2D	0.0468

¹ Underlined text indicates KEGG pathway processes potentially involved in tumorigenesis. ² Red text indicates cancer genes and bold text indicates “IS-Clustered genes”.

, 13 GO terms associated with biological processes and 17 KEGG pathways were identified. Compared with “IS-Related genes,” the number of GO terms associated with biological processes decreased in “IS-Clustered genes,” whereas the number of KEGG pathways increased. These results suggest that “IS-Clustered genes” were enriched for gene groups that function more cooperatively within specific biological pathways. In particular, GO terms related to biological processes involved in tumorigenesis, such as “positive regulation of receptor signaling pathway via JAK-STAT,” “signal transduction,” “positive regulation of miRNA transcription,” “NK T cell differentiation,” “protein phosphorylation,” and “positive regulation of toll-like receptor 9 signaling pathway” (Table 4, shown in underlined), and KEGG pathways involved in tumorigenesis, such as “PI3K-Akt signaling pathway” and “Proteoglycans in cancer” (Table 5, shown in underlined) were detected. In addition, these GO terms and KEGG pathways were related to signal transduction, transcriptional regulation, and immune regulation, leading to the regulation of cancer. Notably, “NK T cell differentiation” was detected, consistent with previous reports showing that BLV reduces antiviral cytokine activities and NK cell cytotoxicity by inducing TGF-β secretion from regulatory T cells [39]. Furthermore, theses 6 GO terms and 2 KEGG pathways contained a total of 26 (30.6%) genes (CAMK2A, ERBB4, GHR, IL10, ADGRL2, CAMK2D, CHN1, DUSP10, FAM83B, GABRB1, MYO10, PTPRM, SMARCA4, TEAD1, ITK, ATF2, EPHB1, ITK, SGK1, RSAD2, RTN4, CHRM1, EFNA5, PTEN, PPP2R2B, SGK1, TIAM1, and MRAS) among the 85 “IS-Clusterd genes.” In addition, these 26 genes included eight cancer genes, namely CHN1, EPHB1, ERB4, IL10, ITK, PTEN, SGK1, and SMARCA4 (Table 4 and Table 5, shown in red). These results suggest that the clustered genes possess multiple physiological functions that are critically important for the onset of EBL.

2.6. Discovery of “Common EBL-Related Clusters” Through Integrated Analysis of the “IS-Clustered Genes” and Previously Reported Genes

Numerous ISs have been identified, and 65 genes associated with these IS have been reported (

Table 6. Previously reported integration site-associated genes.

Gene 1	Reference
ADRB2, AGTR1, APOH, BGLAP, BSP1, CD99, CHGB, FAM83D, MTOR	[28]
ARHGEF4, BRCC3, CDX1, CGGBP1, COL2A1, DDX10, ELF2, FAM168B, FOXR2, GAS6, HUWE1, ICA1, IRS2, KLHL14, KPNA3, KSR1, LHPP, MSH2, MTCP1, MYCBP2, N4BP2, NAPG, NF1, OSBPL8, PDGFRB, PPP1R12C, PRPSAP2, RAPGEF6, RASA3, RRAGB, SCAF8, SEPT11, SNIIP1, SPOCK1, STARD7, TCF4, TMEM67, TNKS, UBASH3B, UBE2D3, WDR82	[30]
ANK3, FAM135B, FAM92A	[32]
CCDC71, CITED4, CTPS1, LMBRD2, NAMPT, PGPEP1, SCFD2, SKP2	[33]
PRTN4IP1, ATG5, RPTOR	[34]
CHEK2	[35]

¹ Bold text indicates gene incorporated “common EBL-related cluster” and red text indicates “ IS-Related genes” identified in the present study.

). However, only FOXR2 was identified in the present study. This finding suggests that there are no apparent hotspots of BLV proviral integration. Furthermore, several BLV-induced lymphoma-related genes, including those harboring somatic mutations or showing altered expression levels in cattle with EBL, have been previously reported (

Table 7. Previously reported EBL-related genes.

Gene 1	References
TP53	[17,18,19,20,21]
TP53, KMT2D, CREBBP, KRAS, PTEN, NOTCH1, MYD88, CARD11	[25]
PRMT5	[26]
EXO1, MSH2	[27]

¹ Bold text indicates gene incorporated “common EBL-related cluster” and red text indicates “IS-Related genes” identified in the present study.

). Among these, only PTEN was identified in the present study. To elucidate the relationship between the “IS-Clustered genes” and previously reported genes, protein–protein interactions were analyzed using STRING (Figure 1E). Consequently, 49 of the 75 previously reported genes were incorporated into a single cluster (Table 6 and Table 7, shown in bold) and 132 genes were included in this cluster (Figure 5). Notably, all the genes listed in Table 7 were incorporated into this cluster (Figure 5 and Table 7). These results suggest that the cluster identified in the present study may play a central role in the onset of EBL. Therefore, we defined this cluster as “common EBL-related cluster.”

2.7. GO Analysis Revealed That “Common EBL-Related Clusters” Have Various Functions in the Onset of EBL

To investigate the functional roles of the “common EBL-related cluster,” GO analysis was conducted (Figure 1E). As shown in

Table 8. Gene ontology analysis of “common EBL-related cluster” for biological processes.

Term 1	Gene 2	p-Value
positive regulation of receptor signaling pathway via JAK-STAT	CAMK2A, ERBB4, GHR, IL10, NOTCH1	0.0000968
positive regulation of transcription by RNA polymerase II	CREBBP, MYD88, SMARCA4, TBL1XR1, TEAD1, ADRB2, HDAC4, IL10, KMT2D, NAMPT, SLC30A9, TNKS, TOX, TXF4, TP53	0.000198
Ras protein signal transduction	KRAS, RAPGEF6, KSR1, MRAS, TP53	0.000835
protein phosphorylation	EPHB1, ITK, CAMK2A, CAMK2D, CHEK2, ERBB4, KSR1, MTOR, PDGFRB, SGK1	0.00111
negative regulation of autophagy	RRAGB, IL10, MTOR, RPTOR	0.00179
positive regulation of miRNA transcription	SMARCA4, TEAD1, IL10, TP53	0.00345
TORC1 signaling	CARD11, MTOR, RPTOR	0.00378
cellular response to leucine starvation	RRAGB, ATF2, MTOR	0.00378
mismatch repair	EXO1, MSH2, MSH3	0.00544
Rac protein signal transduction	FARP1, KRAS, TIAM1	0.00605
nervous system development	EPHB1, SMARCA4, EFNA5, NAV2, PTEN, RTN4IP1	0.00629
circadian regulation of gene expression	HUWE1, MYCBP2, NAMPT, PRMT5	0.00639
signal transduction	KRAS, ADGRL2, ANK3, CAMK2A, CAMK2D, CHN1, DUSP10, FAM83B, GABRB1, MYO10, PTPRM	0.00719
positive regulation of peptidyl-tyrosine phosphorylation	EFNA5, GHR, MTOR, TP53	0.0073
positive regulation of calcium-mediated signaling	CA8, GRM5, PDGFRB	0.00957
protein polyubiquitination	FBXL17, HUWE1, DTL, RNF144A, TNKS	0.0109
oligodendrocyte differentiation	DUSP10, MTOR, NOTCH1	0.0112
intrinsic apoptotic signaling pathway in response to hypoxia	ATF2, TP53	0.0123
positive regulation of phosphatidylinositol 3-kinase/protein kinase B signal transduction	ERBB4, IRS2, PDGFRB, RTN4, TCF4	0.0129
modulation of chemical synaptic transmission	DLGAP1, GRIA1, GRID2, RTN4	0.013
outflow tract morphogenesis	SEC24B, ATF2, NOTCH1	0.0157
positive regulation of G1/S transition of mitotic cell cycle	ANKRD17, RPTOR, RRM1	0.0167
anterior head development	DDX10, COL2A1	0.0184
ephrin receptor signaling pathway	EPHB1, CHN1, EFNA5	0.0187
glucose homeostasis	CSMD1, BGLAP, PRCP, TCF4	0.02
positive regulation of epithelial to mesenchymal transition	MTOR, NOTCH1, TCF4	0.0208
protein import into nucleus	ATF2, KPNA3, NOTCH1, TP53	0.0223
Cognition	BGLAP, CHRM1, NF1	0.0231
maintenance of DNA repeat elements	MSH3, TCF4	0.0245
NK T cell differentiation	ITK, ATF2	0.0305
negative regulation of glial cell proliferation	NOTCH1, TP53	0.0305
regulation of blood vessel endothelial cell migration	NF1, PRCP	0.0305
positive regulation of synapse assembly	CLSTN2, EFNA5, GRID2	0.0316
mitotic recombination	MSH2, MSH3	0.0365
regulation of somitogenesis	DMRT2, NOTCH1	0.0365
cellular response to follicle-stimulating hormone stimulus	EFNA5, NOTCH1	0.0365
DNA-templated transcription termination	WDR82, PRMT5	0.0365
positive regulation of toll-like receptor 9 signaling pathway	RSAD2, RTN4	0.0425
coronary artery morphogenesis	SEC24B, NOTCH1	0.0425
skeletal system development	ACAN, COL2A1, HDAC4	0.047
cardiac muscle cell apoptotic process	ATG5, TP53	0.0484
positive regulation of transcription of nucleolar large rRNA by RNA polymerase I	SMARCA4, MTOR	0.0484
thymocyte apoptotic process	CHEK2, TP53	0.0484

¹ Bold text indicates common terms with “IS-Clustered genes”. ² Red text indicates cancer genes and bold text indicates “IS-Clustered genes”.

and

Table 9. Gene ontology analysis of “common EBL-related cluster” for KEGG pathway.

Term 1	Gene 2	p-Value
PI3K-Akt signaling pathway	KRAS, ATF2, CHRM1, COL2A1, EFNA5, ERBB4, GHR, MTOR, PTEN, PDGFRB, PPP2R2B, RPTOR, SGK1, TP53	0.0000292
Glioma	KRAS, CAMK2A, CAMK2D, MTOR, PTEN, PDGFRB, TP53	0.0000554
Longevity regulating pathway	KRAS, ATF2, ATG5, IRS2, MTOR, RPTOR, TP53	0.000124
MicroRNAs in cancer	CREBBP, KRAS, EFNA5, HDAC4, IRS2, MTOR, NOTCH1, PTEN, PDGFRB, RPTOR, TP53	0.000227
Long-term potentiation	CREBBP, KRAS, CAMK2A, CAMK2D, GRIA1, GRM5	0.000338
Proteoglycans in cancer	KRAS, TIAM1, ANK3, CAMK2A, CAMK2D, ERBB4, MTOR, MRAS, TP53	0.000385
Growth hormone synthesis, secretion and action	CREBBP, GNAI1, KRAS, ATF2, GHR, IRS2, MTOR	0.00059
Autophagy—animal	KRAS, RRAGB, ATG5, IRS2, MTOR, MRAS, PTEN, RPTOR	0.000731
Pathways in cancer	CREBBP, GNAI1, KRAS, SKP2, AGTR1, CAMK2A, CAMK2D, MTOR, MSH2, MSH3, NOTCH1, PTEN, PDGFRB, TP53	0.000926
FoxO signaling pathway	CREBBP, KRAS, SKP2, IRS2, IL10, PTEN, SGK1	0.00109
MAPK signaling pathway	KRAS, MYD88, ATF2, DUSP10, EFNA5, ERBB4, MRAS, NF1, PDGFRB, TP53	0.00111
Prostate cancer	CREBBP, KRAS, MTOR, PTEN, PDGFRB, TP53	0.00164
Longevity regulating pathway—multiple species	KRAS, ATG5, IRS2, MTOR, RPTOR	0.00189
Adrenergic signaling in cardiomyocytes	GNAI1, ATF2, ADRB2, AGTR1, CAMK2A, CAMK2D, PPP2R2B	0.00244
Central carbon metabolism in cancer	KRAS, MTOR, PTEN, PDGFRB, TP53	0.00297
mTOR signaling pathway	KRAS, RRAGB, SKP2, MTOR, PTEN, RPTOR, SGK1	0.00303
Ras signaling pathway	KRAS, RASA3, TIAM1, EFNA5, KSR1, MRAS, NF1, PDGFRB	0.0055
EGFR tyrosine kinase inhibitor resistance	KRAS, MTOR, NF1, PTEN, PDGFRB	0.00551
cAMP signaling pathway	CREBBP, GNAI1, TIAM1, ADRB2, CAMK2A, CAMK2D, CHRM1, GRIA1	0.00599
Dopaminergic synapse	GNAI1, ATF2, CAMK2A, CAMK2D, GRIA1, PPP2R2B	0.00645
ErbB signaling pathway	KRAS, CAMK2A, CAMK2D, ERBB4, MTOR	0.00652
Nucleotide metabolism	CTPS1, ADK, CMPK2, ENPP3, RRM1	0.00707
Calcium signaling pathway	ADRB2, AGTR1, CAMK2A, CAMK2D, CHRM1, ERBB4, GRM5, PDGFRB	0.00784
Apelin signaling pathway	GNAI1, KRAS, AGTR1, HDAC4, MTOR, MRAS	0.00794
Colorectal cancer	KRAS, MTOR, MSH2, MSH3, TP53	0.00825
Phospholipase D signaling pathway	KRAS, AGTR1, GRM5, MTOR, MRAS, PDGFRB	0.0107
Rap1 signaling pathway	GNAI1, KRAS, RAPGEF6, TIAM1, EFNA5, MRAS, PDGFRB	0.011
Human papillomavirus infection	CREBBP, KRAS, COL2A1, MTOR, NOTCH1, PTEN, PDGFRB, PPP2R2B, TP53	0.0114
Cushing syndrome	GNAI1, ATF2, AGTR1, CAMK2A, CAMK2D, KMT2D	0.0119
Melanogenesis	CREBBP, GNAI1, KRAS, CAMK2A, CAMK2D	0.0122
Cellular senescence	KRAS, CHEK2, MTOR, MRAS, PTEN, TP53	0.0138
Pyrimidine metabolism	CTSP1, CMPK2, ENPP3, RRM1	0.0144
Mismatch repair	EXO1, MSH2, MSH3	0.0167
Long-term depression	GNAI1, KRAS, GRIA1, GRID2	0.0172
Cholinergic synapse	GNAI1, KRAS, CAMK2A, CAMK2D, CHRM1	0.0176
Axon guidance	EPHB1, GNAI1, KRAS, CAMK2A, CAMK2D, EFNA5	0.0203
Thyroid hormone signaling pathway	CREBBP, KRAS, MTOR, NOTCH1, TP53	0.0213
Amphetamine addiction	ATF2, CAMK2A, CAMK2D, GRIA1	0.0219
Sphingolipid signaling pathway	GNAI1, KRAS, PTEN, PPP2R2B, TP53	0.0231
Neurotrophin signaling pathway	KRAS, ARHGDIA, CAMK2A, CAMK2D, TP53	0.0231
T cell receptor signaling pathway	ITK, KRAS, CARD11, IL10, PPP2R2B	0.0249
Melanoma	KRAS, PTEN, PDGFRB, TP53	0.0262
Autophagy—other	ATG5, MTOR, RPTOR	0.033
Tuberculosis	CREBBP, MYD88, CAMK2A, CAMK2D, IL10, KSR1	0.0355
Chemical carcinogenesis—receptor activation	GNAI1, KRAS, ATF2, ADRB2, KPNA3, MTOR	0.0407
Breast cancer	KRAS, MTOR, NOTCH1, PTEN, TP53	0.042
Gap junction	GNAI1, KRAS, GRM5, PDGFRB	0.0456
Endocrine resistance	KRAS, MTOR, NOTCH1, TP53	0.0493
PD-L1 expression and PD-1 checkpoint pathway in cancer	KRAS, MYD88, MTOR, PTEN	0.0493

¹ Bold text indicates common terms with “IS-Clustered genes” and underlined text indicates terms directly involved in cancer. ² Red text indicates cancer genes and bold text indicates “IS-Clustered genes”.

, 43 GO terms associated with biological processes and 49 KEGG pathways were identified. Ten GO terms, namely “positive regulation of receptor signaling pathway via JAK-STAT,” “modulation of chemical synaptic transmission,” “signal transduction,” “nervous system development,” “ephrin receptor signaling pathway,” “positive regulation of synapse assembly,” “positive regulation of miRNA transcription,” “NK T cell differentiation,” “protein phosphorylation,” and “positive regulation of toll-like receptor 9 signaling pathway” were identified in the same “IS-Clustered genes” and 33 GO terms were newly detected. Similarly, 14 KEGG pathways, including “Dopaminergic synapse,” “PI3K-Akt signaling pathway,” “Amphetamine addiction,” “Long-term potentiation,” “Adrenergic signaling in cardiomyocytes,” “cAMP signaling pathway,” “Nucleotide metabolism,” “Axon guidance,” “Proteoglycans in cancer,” “Cholinergic synapse,” “Pyrimidine metabolism,” “Long-term depression,” “Calcium signaling pathway,” and “Cushing syndrome were identified in the same “IS-Clustered genes” and 35 KEGG pathways were newly detected.

In particular, several KEGG pathways involved in cancer were detected. Briefly, seven KEGG pathways were detected, namely “MicroRNAs in cancer,” “Pathways in cancer,” “prostate cancer,” “Colorectal cancer,” “Central carbon metabolism in cancer,” “Breast cancer,” and “PD-L1 expression and PD-1 checkpoint pathway in cancer”, which are directly involved in cancer (Table 9, shown in underlined). In addition, “Proteoglycans in cancer” was shared across “IS-Clustered genes.” These eight pathways comprised 23 genes (CREBBP, KRAS, EFNA5, HDAC4, IRS2, MTOR, NOTCH1, PTEN, PDGFRB, RPTOR, TP53, TIAM1, ANK3, CAMK2A, CAMK2D, ERBB4, MRAS, GNAI1, SKP2, AGTR1, MSH2, MSH3, MYD88) and 10 genes (EFNA5, HDAC4, PTEN, TIAM1, CAMK2A, CAMK2D, ERBB4, MRAS, GNAI1, and MSH3), which were “IS-Clustered genes” (Table 9, shown in bold). Furthermore, 14 cancer-related genes were identified (CREBBP, KRAS, HDAC4, IRS2, MTOR, NOTCH1, PTEN, PDGFRB, RPTOR, TP53, ERBB4, MSH2, MSH3, and MYD88) among these 23 genes (Table 9, shown in red). These results suggest that the “common EBL-related cluster” possesses multiple functions that are critically important in the onset of EBL, and integration of the BLV genome into the nearby “common EBL-related cluster” may lead to the onset of EBL.

3. Discussion

In this study, a novel analysis of ISs in a cohort of 99 cattle with lymphoma was conducted. First, we detected 235 ISs from 99 BLV-infected cattle with lymphoma, and identified 246 associated genes as “IS-Related genes.” Next, we extracted a single cluster within the 246 genes, comprising 85 genes identified as “IS-Clustered genes.” Finally, a “common EBL-related cluster” including 132 genes was discovered through integrated analysis of previously reported EBL- and IS-related genes within the “IS-Clustered genes.” This study is the first to elucidate the mechanism of EBL onset through protein–protein interaction analysis of IS-Related genes. Notably, ISs associated with the cluster comprising 85 genes, including 12 oncogenes, were conserved in 55 of the 99 BLV-infected cattle with lymphoma, suggesting a potential role of IS in the onset of EBL. These findings offer significant insights into the relationship between BLV integration and EBL pathogenesis, which remains poorly understood.

In the present study, we found the “common EBL-related cluster” through the integration analysis of “IS-Clustered genes” and previously reported integration side-associated genes and EBL-related genes. This cluster included TP53, which has been reported to be associated with EBL development in numerous studies [17,18,19,20,21]. Additionally, in this study, TP53, which is similar to other cancer genes such as PTEN and SMARCA4, exhibits many interactions with other genes within the network of the “common EBL-related cluster.” Moreover, this cluster appears to represent a potential serve as a missing link between several genes that we previously reported as EBL-related genes, including PRMT5, MSH2, EXO1, RTN4IP1, ATG5, RPTOR, and CHEK2 [23,24,31,32]. Therefore, it is possible that integration of the BLV genome into cancer genes or genes that interact with cancer genes in the “common EBL-related cluster,” changes their expression and subsequently contributes to the onset of EBL.

In contrast, the 161 genes that were not included in “IS-Clustered genes” did not form clear clusters, and only five of them were cancer genes. This result suggests that integration sites associated with cancer-related genes may contribute to the onset of EBL, whereas other integration sites are less likely to be involved in the onset of EBL. Because factors of the onset of EBL other than integration sites, such as viral factors and host factors, are also important, further detailed analyses are required.

Consistent with the findings of previous studies, a high frequency of BLV genome insertions was observed within exons and introns in the present study [25,26]. In contrast, no preferential integration of the BLV genome into CpG islands or repetitive regions, which are considered to affect gene expression, was detected [25,26]. However, no significant integration was observed near cancer genes. This result conflicts with those of previous reports [27,33,34,35,36]. This is likely because the present study analyzed an unprecedented number of 99 cattle with lymphoma, and not all cattle developed lymphoma because of their ISs. However, cancer genes were enriched within the “IS-cluster genes” and integrated analysis detected the “common EBL-related cluster”, suggesting that the integration of the BLV genome near the “common EBL-related cluster” may be a factor in EBL onset.

In this study, a large number of “IS-Related genes” were identified. However, it remains unclear whether the expression of these genes is altered by BLV genome integration. Further studies are needed to investigate whether BLV integration directly affects the expression of these genes. Especially, to clarify the changes in RNA expression associated with ISs, it is necessary to perform RNA-seq on cattle with and without lymphoma and identify the genes that are differentially expressed between the two groups. Furthermore, integrative analyses combining RNA-seq data with the present IS data will be essential to clarify whether the “IS-Related genes” and “IS-Clustered genes” are truly involved in disease development.

Moreover, we identified a wide range of associated GO terms and KEGG pathways. In particular, the “common EBL-related cluster” was associated with KEGG pathways directly involved in tumorigenesis, such as “MicroRNAs in cancer,” “Pathways in cancer,” “prostate cancer,” “Central carbon metabolism in cancer,” “Colorectal cancer,” “Breast cancer,” “Proteoglycans in cancer,” and “PD-L1 expression and PD-1 checkpoint pathway in cancer.” However, the pathways that are critical for the development of lymphoma remain unknown. Although the onset of EBL is a multifactorial process involving not only host factors but also viral and environmental factors, exploring the pathways implicated in lymphoma development through the analysis of ISs is considered highly important for elucidating the mechanisms of EBL pathogenesis.

Furthermore, no studies have analyzed the impact of BLV viral strain variability on the distribution of ISs. Similarly, investigations assessing ISs based on the breed or geographic origin of cattle that developed EBL have not been conducted. This study also only utilized Japanese cattle; thus, we were unable to analyze whether ISs differed based on viral strain, cattle breed, or origin. Accordingly, future studies are needed to investigate whether the “common-EBL-related cluster” identified in this study represents a concept that can be applied worldwide.

In recent years, monoclonal expansion has been observed prior to the onset of EBL [32,40]. This finding suggests that the critical BLV genome integration for the onset of EBL occurs prior to lymphoma development. Therefore, monitoring BLV integration into the “common EBL-related cluster” identified in this study may enable the early detection of cattle at risk of developing EBL before clinical onset.

4. Materials and Methods

4.1. Sample Collection and Extraction of Genomic DNA

Blood and tissue samples were randomly collected from 99 BLV-infected cattle diagnosed with EBL in a nationwide survey conducted in Japan. Lymphoma was diagnosed based on both gross and histological observations and by detecting atypical mononuclear cells in the slaughterhouse. All samples were handled by veterinarians and researchers in strict accordance with good animal practice. The Animal Experiments Committee of the University of Tokyo approved this study (Approval Number: p22-2-030, accepted on 27 May 2022).

Genomic DNA was extracted from ethylenediaminetetraacetic acid (EDTA)-treated blood and tissue samples using the Wizard Genomic DNA Purification Kit (Promega Corporation, Madison, WI, USA). The extracted DNA was stored at −20 °C until further use. DNA from a bovine lymphosarcoma cell line (BSLC-KU1) and naturally BLV-infected lymphoma cattle samples (Kas-3) were used as positive controls because they carry known BLV ISs, as reported in previous studies [31,32]. DNA from Madin-Darby Bovine Kidney (MDBK) was used as a negative control.

4.2. NGS DNA Library Preparation and Library Enhancement by Viral-DNA Capture

DNA libraries were prepared using the KAPA Hyper Plus kit (Roche, Basel, Switzerland) according to the manufacturer’s instructions. Briefly, genomic DNA (1 μg) was enzymatically fragmented to an average length of approximately 500–600 bp, followed by end repair and addition of adenosine at the 3′-ends of the fragmented DNA. Then, index adaptors (Roche Sequencing Solutions, Inc., Santa Clara, CA, USA) were ligated into the DNA 6 fragments of each sample. Adaptor-tagged library fragments were purified using Agencourt AMPure XP beads (Beckman Coulter, Inc., Brea, CA, USA) on an NGS Magna Stand Ch YS-Model (Nippon Genetics Co., Ltd., Tokyo, Japan) according to the manufacturer’s instructions. Double-sided size selection was performed to keep fragments of 250 to 450 bp in length, using Agencourt AMPure XP beads to remove unwanted fragment sizes and low-molecular-weight materials. The adaptor-tagged purified libraries were subjected to PCR amplification and post-PCR purification. 21-test samples plus DNA from two positive controls (BLSC-KU1 & Kas-3) and a negative control (MDBK) were mixed to construct an NGS DNA library with a final concentration of 1 μg total DNA. For library enrichment, we used the Viral-DNA capture library enhancement method, which was previously established in our laboratory [31] with small modifications. Three custom-designed biotinylated probes (100 bp in length, targeting LTR) (Thermo Fisher Scientific, Waltham, MA, USA) from our previous study were used to capture virus–host chimeric DNA fragments and BLV proviral genome-only fragments to enrich the DNA library. Detailed information on biotinylated probes has been described in our previous publications [31,32]. DNA library enrichment was performed using the SeqCap EZ Hybridization and a Wash Kit (Roche NimbleGen, Pleasanton, CA, USA) following the manufacturer’s instructions for DNA probe hybridization and target capture. After the recommended washing steps were performed, the captured DNA was amplified using PCR and purified using Agencourt AMPure XP beads. DNA libraries enriched for proviral sequences were quantified using qPCR with Illumina P5 and P7 primers before sequencing. The quality and quantity of the amplified libraries were assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Waldbronn, Germany) and a DNA 1000 Assay.

4.3. High-Throughput Sequencing Data Analysis

Multiplexed enriched libraries were subjected to cluster generation using the MiSeq Reagent Kit v3 (600 cycles) (Illumina Inc., San Diego, CA, USA). Raw sequences were generated as Fastq using Illumina MiSeq with 600 cycles of paired-end reads and validated by evaluating the distribution of quality scores. Validated FASTQ files were aligned using the Burrows-Wheeler Aligner tool (BWA v. 0.7.8-r455) [41] against a BLV reference sequence (accession number: EF600696), with or without the cattle reference genome (ARS-UCD1.2/bos Tau9) (ARS-UCD1.2–bosTau9–Genome–Assembly-NCBI (nih.gov)), using the BWA-MEM algorithm31. The resulting alignment map (SAM) format output is suitable for analysis using SAMTools (version1.15) [42]. Sequence quality, depth of coverage, and short-read alignment were analyzed using SAMTools [42]. PCR duplicates were removed using Picard (http://broadinstitute.github.io/picard/, accessed on 18 September 2025). The mapped data were visualized using Integrative Genomics Viewer software (IGV) (version2.12.3) [43]. To calculate the error rate of the sequencing step, we individually analyzed the sequencing quality score (Q-score) of each base read.

4.4. Confirmation of Provirus ISs Using Sanger Sequencing

To confirm the BLV ISs, the BLV-host genome junction was amplified using 148 PrimeSTAR GXL DNA Polymerase (Takara Bio Inc. Kusatsu, Japan). PCR was performed using two primers: one targeting the host genome and the other targeting the BLV region. Our previous publications [31,32] provide a detailed description of the PCR conditions used. The amplified PCR products were sequenced on an ABI3730xl DNA Analyzer with the same PCR primers, using an ABI PRISM Big Dye Terminator v. 3.1 Ready Reaction Cycle Sequencing Kit (Thermo Fisher Scientific, Waltham, MA USA). The obtained sequences were blasted against BLV 8 reference sequences (FLK-BLV (accession number: EF600696)) and the cattle genome (ARS-UCD1.2/bos Tau9) using NCBI BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi, accessed on 20 September 2025) and the Genome Browser (https://genome.ucsc.edu/, accessed on 20 September 2025).

4.5. Gene Ontology Analysis

Gene ontology terms were identified using the DAVID (version 2021).

4.6. Protein Interaction Analysis

Protein–protein interactions were identified using STRING (version 12).

4.7. Statistical Analysis

Data were analyzed using Fisher’s exact test. A p-value < 0.05 was considered statistically significant. All calculations were performed using R software (version 4.3.0; R Foundation for Statistical Computing, Vienna, Austria).

5. Conclusions

Our study elucidated the role of IS in the development of EBL and identified a “common EBL-related cluster,” which plays a particularly important role in the onset of EBL. These findings provide a foundation for future investigations into the detailed molecular mechanisms underlying EBL and may contribute to the development of therapeutic strategies and early diagnostic tools for EBL.