
==== Front
Comp Immunol Rep
Comp Immunol Rep
Comparative Immunology Reports
2950-3116
Elsevier

S2950-3116(24)00032-6
10.1016/j.cirep.2024.200165
200165
Article
Comprehensive profiling of lncRNAs in the immune response of largemouth bass to Nocardia Seriolae infection
Fu Xiaona a1
Zhang Longsheng a1
Li Keqi a1
Liu Zhigang c
Wu Jikui jkwu@shou.edu.cn
b⁎
Zhang Junling jlzhang@shou.edu.cn
a⁎
a Key Laboratory of Exploration and Utilization of Aquatic Genetic Resources, Ministry of Education, Shanghai Ocean University, Shanghai Engineering Research Center of Aquaculture, Shanghai 201306, PR China
b Laboratory of Quality and Safety Risk Assessment for Aquatic Product on Storage and Preservation, Ministry of Agriculture, Shanghai Ocean University, Shanghai 201306, PR China
c Key Laboratory of Tropical and Subtropical Fishery Resources Application and Cultivation, Ministry of Agriculture and Rural Affairs, Pearl River Fisheries Research Institute, Chinese Academy of Fishery Science, Guangzhou 510380, PR China
⁎ Corresponding authors. jkwu@shou.edu.cnjlzhang@shou.edu.cn
1 These authors contributed equally to this work.

07 8 2024
12 2024
07 8 2024
7 2001651 7 2024
30 7 2024
6 8 2024
© 2024 The Authors. Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Long noncoding RNAs (lncRNAs) are crucial regulators of various biological processes. However, it remains unclear how lncRNAs can be involved in the immune response to Nocardia seriolae invasion in largemouth bass (Micropterus salmoides). Here we leveraged the whole transcriptome RNA-seq technique to screen lncRNAs associated with the head kidney infected with Nocardia seriolae. A total of 5,661 lncRNAs were identified, comprising 2,427 known lncRNAs and 3,234 novel lncRNAs. Among these, 531 lncRNAs were found to be differentially expressed, with 213 showing increased expression and 318 showing reduced expression. In these differentially expressed lncRNAs, 176 lncRNAs may have cis-regulatory relationships with 243 protein-coding genes. For example, MSTRG.17818.1, MSTRG.10160.1 target mapk1 and rela, respectively, thereby regulating gene expression. We then exploited Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to analyze the enrichment of target genes of lncRNAs. These target genes were found to be significantly enriched in immune signaling pathways, including RIG-I-like receptor signaling and Toll-like receptor signaling. Furthermore, we harnessed a total of 12 mRNAs, 33 miRNAs, and 167 lncRNAs to construct two lncRNA-miRNA-mRNA network maps. CeRNA maps revealed that some lncRNAs can competitively bind to the same miRNA and affect the expression of immune-related genes. For instance, MSTRG.13668.1 and MSTRG.5180.1 can simultaneously bind to mir-155–5p to increase irak4 expression. These findings suggest that lncRNAs may participate in the immune response process of infected largemouth bass with Nocardia seriolae. Overall, this work presents a thorough lncRNAs expression profile of Nocardia seriolae-infected largemouth bass and offers insights into the ceRNA regulatory processes that influence fish's immune response to bacterial infections.

Keywords

Largemouth bass
Nocardia seriolae
Head kidney
lncRNA
CERNA, Immune response
==== Body
pmcIntroduction

Long non-coding RNAs (lncRNAs) are RNA molecules that are more than 200 nucleotides in length and have important functions in a wide range of biological activities, including reproduction, embryonic development, sexual differentiation, metabolism, immunity, cell proliferation, differentiation, metastasis, and apoptosis [[1], [2], [3]]. With the advancement of large-scale sequencing methods, a substantial amount of lncRNAs have been discovered and characterized. Meanwhile, it was predicted through bioinformatics that lncRNAs can not only regulate the expression of target genes in cis but also act as miRNA molecular sponges to indirectly affect target gene expression and signaling pathways. For example, Zhu et al. [4] found that there were 4611 differentially expressed lncRNAs after sequencing the skeletal muscles of largemouth bass with fast and slow growth, and many of the target genes with cis-regulation of lncRNAs were related to muscle growth and development. Meanwhile, the construction of the lncRNA-miRNA-mRNA network diagram revealed that some lncRNAs could target miRNAs closely related to muscle development. At the same time, lncRNAs have been studied in immune regulation in teleost fish. In Nile tilapia, 1281 lncRNAs were differentially expressed after infection with Streptococcus lactis invasion and detected that target genes with cis-regulatory roles with lncRNAs were mainly enriched in the propanoate metabolism and rheumatoid arthritis pathways, and an immune-related competing endogenous RNA (ceRNA) network map was also identified. Some lncRNAs have also been experimentally validated to demonstrate their functioning [5]. Zheng et al. found that lncRNA NARL altered NOD1-mediated immune responses by acting as a sponge for miR-217–5p in Miichthys miiuy [6]. lncRNA-ANAPC2 and lncRNA-NEFM had similar effects in grass carp infected with Aeromonas hydrophila [7]. These studies broaden our insights into the role of lncRNAs in the regulation of the immune response in fish.

Although there is significant evidence on the regulatory function of lncRNAs in immunity, limited research has been conducted on largemouth bass (Micropterus salmoides), also called California bass. The species is native to the Mississippi River Basin in North America, was brought to China in the 1980s, and has emerged as a valuable aquaculture species owing to its rapid growth and great meat quality [8]. However, in recent years, the increased culture density of largemouth bass has led to an increase in infectious diseases caused by Nocardia seriolae, which is a Gram-positive, aerobic bacterium that belongs to the genus Nocardia [9]. It can cause severe infections in fish, leading to fibrosis and the development of white nodules in numerous tissues, including gills, liver, kidney, spleen, and heart [10]. Recently Lei et al. have isolated and characterised Nocardia seriolae from diseased largemouth bass [11], and the disease has also been reported in other freshwater and marine fishes, such as large yellow croaker [12], snakehead [13], and trachinotus ovatus [14]. The incubation period and course of the disease are relatively long, and the infection and mortality rates are high. The head kidney of largemouth bass serves as a vital immune organ and plays an important role in innate immunity. Although several studies have characterized immune-related genes and the transcriptome of largemouth bass [15,16], little is known about the significance of lncRNAs and their associated competing endogenous regulation mechanisms in this species.

The objective of this research was to examine the impact of lncRNAs on the immune system in the head kidney of largemouth bass infected with Nocardia seriolae, and to thoroughly investigate the ceRNA regulatory network (lncRNA-miRNA-mRNA) in reaction to Nocardia seriolae infections. We defined the expression profiles of immune-related lncRNAs and validated their reaction to Nocardia seriolae using whole transcriptome sequencing and real-time qPCR. These findings will deepen our understanding of the genetic regulatory pathways of ceRNA implicated in the fish immune response to bacterial infections.

Materials and methods

Experimental fish, bacterial culture, and sample collection

A total of thirty largemouth bass juveniles, weighing 2 ± 0.5 g and measuring 6 ± 1 cm in length, were acquired from a fish hatchery located in Huzhou, Zhejiang, China. They were temporarily domesticated in a recirculating water system at a temperature of 28 ± 0.5 °C. Before the test, the fish were given a commercial diet twice a day for at least 2 weeks to ensure their health and acclimation. Nocardia seriolae from the Pearl River Fisheries Research Institute (Guangzhou, China), was cultivated in Brain Heart Infusion (BHI) media. The bacterial culture underwent incubation in a shaker at a speed of 180 rpm and a temperature of 28 °C for a duration of 5 days. The concentration of Nocardia seriolae in the culture was determined spectrophotometrically as colony-forming units (CFU) per milliliter. The juvenile largemouth bass were separated into two groups by a random process: the experimental group (n = 15) and the control group (n = 15). The experimental group was given a 100 μL intraperitoneal injection of Nocardia seriolae (1 × 107 CFU·mL−1). The control group got the same quantity of phosphate-buffered saline (PBS). The fish were kept at a controlled temperature of 28 °C throughout the experiment and were fed normally during the experimental period. After a duration of 14 days, MS-222 (100 mg L−1) was used to euthanize all fish in both groups. Their head kidneys were collected for further analysis. Due to the small size of the tissue, samples from three fish were pooled together to constitute one sample, immediately frozen in liquid nitrogen. The resulting samples were then kept at −80 °C.

All animal research in this experiment was authorized by the Research Ethics Committee of Shanghai Ocean University.

Total RNA extraction, cDNA synthesis

RNA was extracted and purified from harvested head kidney samples using Trizol reagent (Invitrogen, Carlsbad, CA, USA). The concentration and purity of RNA samples were determined with a NanoDrop ND-1000 spectrophotometer (NanoDrop, Wilmington, DE, USA). The Agilent 2100 Bioanalyzer was employed to evaluate the quality and integrity of the RNA samples, ensuring that the RNA Integrity Number (RIN) of each sample for each sample exceeds 7.0. The Ribo-Zero™ rRNA Removal Kit (Illumina, San Diego, USA) was utilized to eliminate ribosomal RNA from 5 μg of total RNA. After that, the remaining RNAs were broken down into tiny fragments with divalent cations at elevated temperatures. Subsequently, the segmented RNA underwent reverse transcription to form cDNA, which was then employed in the synthesis of U-labeled second-stranded DNAs using E. coli DNA polymerase I, RNase H, and dUTP.

Library construction, and sequencing

Each DNA strand's blunt ends receive an A-base addition, readying them for attachment to the indexed adapters. Every adapter is equipped with a T-base extension to attach it to the fragmented A-tailed DNA. Adapters with either a single or dual index are attached to the fragments, and the choice of size was executed using AMPureXP beads. Following the treatment of U-labeled second-stranded DNAs with heat-labile UDG enzymes, PCR amplification of the ligated products occurs under these parameters: 95 °C (3 min); 98 °C (15 s), 60 °C (15 s), 8 cycles; 72 °C (5 min). For the ultimate cDNA library, the mean size of the insert measured 300 bp (±50 bp). The prepared cDNA libraries were subjected to paired-end sequencing using an Illumina HiSeq 4000 sequencing platform (LC Bio, China). The sequencing was performed following the manufacturer's protocol recommended by Illumina.

Transcripts assembly

First, CutAdapt [17] was used to eliminate some reads with splice contamination, low-quality bases, and unclear bases. Then, FastQC was utilized to ensure sequence quality. The remaining reads were aligned to the largemouth bass genome using Bowtie2 [18] and Hisat2 [19]. StringTie was employed to compile the aligned readings for each sample [20]. To construct a full transcriptome, the transcripts from the control and experimental groups of largemouth bass head kidneys were combined using a Perl script. After the final transcriptome was generated, the abundance of all transcripts was estimated using StringTie [20] and edgeR [21].

LncRNA characterization

First, Transcripts less than 200 bp and those that overlapped with known mRNAs were eliminated. Then, the CPC [22] and CNCI [23] methods were used to determine transcripts having coding potential. Eliminate any transcripts that have a CPC score below −1 and a CNCI score below 0. Finally, the remaining transcripts were recognized as lncRNA.

Analysis of different expressions of mRNAs and lncRNAs

The expression levels of mRNA and lncRNA were estimated from the read counts and normalized as FPKM [24]. The R package edgeR [21] was used to identify differentially expressed mRNAs and lncRNAs that exhibited |log2 Fold Change| ≥ 1 and were found to be statistically significant (p-value <0.05). After that, the length, exon number, ORF length, and expression of differentially expressed lncRNA transcripts were counted and analyzed in comparison with mRNA.

Real-time qPCR analysis

We randomly selected 10 differentially expressed lncRNAs for real-time fluorescence quantitative qPCR validation. The internal reference gene was β-actin. Quantitative primers were designed using Premier 5.0 software. All primer sequences for LncRNAs are shown in Table 1. The cDNA was obtained by reverse transcription. The quality and concentration of qualified total RNA were tested using the HiScript III RT SuperMix for qPCR (Vazyme Biotech, Nanjing, China) kit. The expression of each lncRNA was detected using the ChamQ Universal SYBR qPCR Master Mix (Vazyme Biotech, Nanjing, China) kit with cDNA as a template. 10 μL of SYBR qPCR Master Mix, 0.4 μL of upstream and downstream quantitative primers, and 1 μL of cDNA made up the reaction system. The reaction procedure was 95 °C (3 min), 95 °C (10 s), 56∼60 °C (30 s), 40 cycles. Each well was set up with 3 replicates. Each sample was replicated three times. The relative expression levels of lncRNAs were calculated based on Ct values (2 −ΔΔCt).Table 1 Primer information for differentially expressed lncRNAs.

Table 1Primer name	Forward name (5′−3′)	Reverse name (5′−3′)	Annealing temperature ( °C)	
MSTRG.13668.1	ATGGGGAATGGCACTTTAC	GCAGGACGCACTACTCTGA	60	
XR_005442644.1	TTTTGGAGGGAATGATAAG	AAAGCCAGGATAAAGAGGT	58	
MSTRG.5798.4	CTCGGGGCGAAGGTGGCTA	TTGGGGCGCACTGAGGACA	60	
MSTRG.10859.1	TGGTGATCAGTGTCTTTGT	TCTCTCTCTAGCTTTCCTT	58	
MSTRG.7556.2	CCTTCGCCATCACTGCCAT	CCACACGCTCAACCTCCTC	60	
MSTRG.17646.1	AACGGGGTGAATGTGGAGG	AGCTGAGCAGGGAGTGGAA	58	
MSTRG.7324.3	TGAATGTCAAAGTGAAGAA	CGCTAGATAGTAGGTAGGG	58	
MSTRG.10572.4	CCACAACTCAACTAACTAA	CAACTAAAAATACCAAACA	56	
MSTRG.7324.11	GTCGTCCCGTCGCCCTCCT	GCCGTACCCGATCCCCTCC	60	
MSTRG.9168.1	TCCCTATCTTTTCCACATT	CTAACAGCCACTCCATCTC	56	
β-actin	CCACCACAGCCGAGAGGGAA	TCATGGTGGATGGGGCCAGG	60	

Predicting target genes and analyzing the functions of lncRNAs

Possible target genes for lncRNAs in cis-regulatory relationships were predicted to explore the biological roles of lncRNAs. Using Python programs, upstream and downstream coding genes within 100 kbp of the differentially expressed lncRNAs were selected for our study. Then, BLAST2GO [25] was used to functionally analyze the target genes of lncRNAs. They were classified and annotated using the 3 aspects of biological processes, cellular components, and molecular functions, inside the GO as well as KEGG. P < 0.05 was considered significantly enriched.

The regulatory network of ceRNA

We used miRanda [26] and TargetScan software to predict the targeting relations between differentially expressed mRNA-miRNAs and lncRNA-miRNAs through the sequence complementarity situation. Then correlation analysis of lncRNA-miRNA-mRNA triplets by positive correlation between lncRNA and mRNA expression. Finally, the expression correlation coefficients between genes were obtained by a person correlation algorithm, and two networks were constructed and visualized using Cytoscape software. The research used both miRNA and mRNA data acquired from this high-throughput sequencing dataset.

Results

Identification of lncRNAs

Total RNA extraction, library building, and sequencing were performed on samples from both the control and experimental groups of the head kidney. The control group produced 81,347,226 raw data, and the experimental group produced 81,513,716 raw data. After filtering the raw data using the cut adapter tool, 78,530,942 and 78,814,018 valid data points were obtained. The data quality was evaluated by considering the proportion of Q20 bases (above 99.98 %) and Q30 bases (above 98 %), as well as the GC content (48 % and 50 %) (Table S1).

This analysis yielded a total of 5661 lncRNAs, containing 2427 known and 3234 new lncRNAs (Table S2). The screened lncRNAs were classified according to their genomic position in relation to protein-coding genes. The largest number of intergenic lncRNAs and the smallest number of antisense lncRNAs were found (Fig. 1).Fig. 1 Classification of lncRNAs.

Fig 1

lncRNA characterization

To gain an understanding of the characterization of lncRNA, various characteristics of lncRNA transcripts were compared with mRNA. The analysis revealed that lncRNA transcript lengths were primarily concentrated within the range of ≤300 bp and ≥1000 bp, with an average length of 2492 bp. In contrast, mRNA lengths were more concentrated on ≥1000 bp, with only 2116 mRNAs being under 1000 bp in length, while the remaining 33,555 mRNAs were all over 1000 base pairs in length (Fig. 2A). Moreover, lncRNAs had an average of 2.4 exons. Analyzing 5661 lncRNAs, it was found that 4795 lncRNAs predominantly had 1 to 3 exons. Interestingly, a majority of lncRNAs had 2 exons, accounting for 22.1 %, 39.7 % and 22.9 % of the lncRNA population, respectively. Conversely, most mRNA exons numbered 9, which is significantly higher than the average number of exons in lncRNAs. This discrepancy suggests that mRNA may have a broader functional range (Fig. 2B). Additionally, lncRNA ORF lengths were noticeably shorter than mRNAs. The majority of lncRNA ORFs were distributed within the 0–100 bp range (Fig. 2C). Lastly, mRNA expression levels were found to be higher than those of lncRNAs (Fig. 2D). In summary, the analysis indicated that lncRNAs exhibit shorter transcript lengths, fewer exons, shorter ORF lengths, and lower levels of expression compared to mRNAs.Fig. 2 Characterization of lncRNAs and mRNAs in largemouth bass. (A)Transcript length; (B) Number of exons; (C) Length distribution of ORFs; (D) Level and quantity of expression.

Fig 2

Differential expression lncRNAs analysis

Using edgeR software, we identified a sum of 531 lncRNAs that showed differential expression in the head kidney. Among them, 318 lncRNAs had a drop in expression and 213 lncRNAs had an increase (Table S3). Fig. 3A and 3C show the differential expression of lncRNAs. Furthermore, 3662 differentially expressed mRNA (2265 up-regulated and 1397 down-regulated) were identified. Fig. 3B and 3D show the mRNAs that were differently expressed. Table 2 listed the top 10 most significantly expressed lncRNAs (P < 0.001).Fig. 3 LncRNAs and mRNAs expressed differentially in the head kidney of largemouth bass, after 14 d of Nocardia seriolae infection. (A) Volcano plots of differentially expressed lncRNAs; (B) Volcano plots of differentially expressed mRNAs; (C) Horizontal clustering analysis of differentially expressed lncRNAs; (D) Horizontal clustering analysis of differentially expressed mRNAs.

Fig 3

Table 2 Top 10 most significantly up- and down-regulated lncRNAs.

Table 2lncRNA ID	Chromosome location	Gene name	Log2(B/A)	Regulation	
MSTRG.7324.3	NW_024040735.1	LOC119906488	5.77	Up	
MSTRG.5328.1	NW_024040485.1	LOC119902980	5.35	Up	
MSTRG.10572.4	NW_024041150.1	——	4.64	Up	
MSTRG.20421.2	NW_024044691.1	——	4.20	Up	
MSTRG.8176.2	NW_024040817.1	LOC119906982	4.20	Up	
MSTRG.14160.1	NW_024042261.1	rflnb	3.56	Up	
MSTRG.5390.1	NW_024040485.1	——	3.36	Up	
MSTRG.14700.1	NW_024042603.1	LOC119884242	3.34	Up	
MSTRG.11829.1	NW_024041262.1	fasn	3.33	Up	
MSTRG.10572.3	NW_024041150.1	——	3.28	Up	
MSTRG.7324.11	NW_024040735.1	LOC119906488	−11.21	Down	
MSTRG.7324.8	NW_024040735.1	LOC119906488	−8.97	Down	
MSTRG.9168.1	NW_024041039.1	——	−4.83	Down	
MSTRG.17948.4	NW_024044459.1	LOC119892522	−4.17	Down	
MSTRG.9854.2	NW_024041075.1	LOC119911169	−4.16	Down	
MSTRG.10440.4	NW_024041150.1	capn1	−3.36	Down	
MSTRG.9312.4	NW_024041039.1	srsf5a	−3.25	Down	
MSTRG.1275.1	NW_024040040.1	——	−3.18	Down	
MSTRG.11.2	NC_008106.1	COX1	−3.18	Down	
MSTRG.10859.1	NW_024041150.1	LOC119912429	−3.11	Down	

Real-time qPCR validation of differentially expressed lncRNAs (DElncRNAs)

To validate the accuracy of the lncRNA data acquired by sequencing, we chose 10 lncRNAs that showed differential expression (DElncRNAs) and subjected them to Real-time qPCR verification. These lncRNAs expression was evaluated in the same sequencing samples. Fig. 4 shows that the variations in fluorescence quantification findings of the ten lncRNAs concur with those of the RNA-seq results, indicating that the sequencing has a high degree of reliability and accuracy.Fig. 4 Validation of DElncRNAs by qPCR and comparative analysis with RNA-seq.

Fig 4

Predicting target genes for differentially expressed lncRNAs (DElncRNAs)

To explore the cis-regulatory role of lncRNAs, we searched for lncRNAs and mRNAs that are co-differentially expressed upstream and downstream of the chromosome in the 100 kbp range. Through this analysis, we identified 243 protein-coding genes and 176 lncRNA transcripts that were nearby (within 100 kbp) to each other (Table S4). We found that some immune genes, such as il6r, mapk1, rela, ripk1l, and ulk1b, were predicted to be targets of lncRNAs MSTRG.2901.1, MSTRG.17818.1, MSTRG.10160.1, MSTRG.5273.2, and MSTRG.11596.3, respectively. These findings indicate that lncRNAs may have a function in promoting mRNA cis-regulated inflammatory responses.

GO research on target genes revealed 50 significantly enriched GO entries (P < 0.05), including 34 entries linked to biological processes, 7 to cellular components, and 9 to molecular functions (Table S5). We selected the top 20 entries for mapping, and the top five entries ranked by significance were: B-cell proliferation (GO:0,042,100), T-cell activation involved in the immune response (GO:0,002,286), natural killer cell activation involved in the immune response (GO:0,002,323), type I interferon receptor binding (GO:0,005,132), and positive regulation of serine phosphorylation of the STAT proteopeptide (GO:0,033,141) (Fig. 5A, B).Fig. 5 GO and KEGG enrichment analysis of target mRNAs differentially expressed by lncRNAs (A) GO enrichment histogram; (B) GO enrichment scatter plot; (C) KEGG enrichment scatter plot.

Fig 5

In addition, KEGG signaling pathway enrichment analysis revealed that the target genes of differentially expressed lncRNAs were mainly enriched in the immune system and biosynthesis (Table S6). The top five ranked pathways by significance were: The toll-like receptor signaling pathway (ko04620), RIG-I-like receptor signaling pathway (ko04622), N-glycine biosynthesis (ko00510), spliceosome (ko03040), and apoptosis (ko04210) (Fig. 5C). Notably, the rela and ripk1l genes were found to be involved in both the Toll-like receptor signaling pathway and the RIG-I-like receptor signaling pathway.

lncRNA-miRNA-mRNA network graph construction

Through the RNA-Seq analysis of Nocardia seriolae-infected largemouth bass head kidney, we obtained a total of 531 differentially expressed lncRNAs, 3662 differentially expressed mRNAs, and 346 differentially expressed miRNAs. We integrated and analyzed the differential expression data of these lncRNAs, mRNAs, and miRNAs to gain further insights. Based on the functional annotation and expression data of mRNAs, we selected 12 genes with relatively large differential changes and associated with inflammation (Table S7, S8). These genes included up-regulated genes such as irak4, pin1, ddit4, il13ra2, rbx1, map2k2b, and down-regulated genes such as dhx58, wnt5a, tgfb2, il1rl1, notch3, tnfrsf21. We then constructed two ceRNA network maps. In Fig. 6A, the network diagram includes 6 mRNAs, 11 miRNAs, and 76 lncRNAs. Fig. 6B contains 6 mRNAs, 12 miRNAs, and 91 lncRNAs. It is interesting to note that one mRNA can compete with multiple lncRNAs to bind miRNAs, forming a complex interaction network. The immune signaling pathways involved in the ceRNA network are mainly the Toll-like receptor signaling pathway, MAPK signaling pathway, RIG-I-like receptor signaling pathway, mTOR signaling pathway, and Wnt signaling pathway. These pathways are known to play crucial roles in immune responses and inflammatory processes. These findings can provide valuable insights into the regulatory mechanisms involved in the immune response to Nocardia seriolae infection.Fig. 6 Immuno-associated lncRNA-miRNA-mRNA competitive endogenous (ceRNA) network diagram. Blue rectangles represent lncRNAs, green diamonds represent miRNAs, and orange v-shapes represent mRNAs. (A) 'up-down-up' ceRNA network diagram; (B) 'down-up-down' ceRNA network diagram.

Fig 6

Discussion

Nocardia seriolae infection is a significant concern in aquaculture due to its high infection and mortality rates. It mainly affects species like largemouth bass and snakehead, particularly in China. Understanding the mechanism of Nocardia seriolae infection is crucial for controlling outbreaks and preventing the damage caused to fish populations, such as largemouth bass and other species. In the past, the focus has primarily been on studying host-encoded protein genes and their interactions with pathogens during infection. However, with the advancements in large-scale sequencing and bioinformatics, non-coding RNAs (miRNAs, lncRNAs, and circRNAs) have received increased attention. LncRNAs, in particular, have been recognized as crucial regulators of gene expression involved in various biological processes. They have been found to have substantial relationships with the incidence, development, and prevention of human diseases [27,28]. In recent years, the function of lncRNAs in the immune response of fish to bacterial infections, following the proposal of the competing endogenous RNAs (ceRNA) hypothesis [29], has gained more attention. Several research has been undertaken to investigate the comprehensive expression profiles of fish immune responses to bacterial infections through the lens of lncRNAs. For instance, in turbot's immune response to Vibrio eel infection, 36 differently expressed lncRNAs were identified as ceRNAs, regulating 37 differentially expressed target genes (DETGs) across 10 immune pathways by interacting with 16 differentially expressed target miRNAs (DETmiRs) through a lncRNA-miRNA-mRNA regulatory network [30]. Moreover, lncRNAs have also been shown to have a role in Atlantic salmon's immunological response to Aeromonas salmonicida infection [31].

However, the immune response of lncRNAs in the head kidney of largemouth bass after infection with Nocardia seriolae has not been extensively studied. As a result, it is critical to detect and analyze lncRNAs in the head kidney of Nocardia seriolae-infected largemouth bass. During this investigation, we examined lncRNAs in the control and infected groups using whole transcriptome RNA-seq. The results revealed a total of 5661 lncRNAs and 35,671 mRNAs. Compared to mRNAs, lncRNAs were found to be less conserved and exhibited lower lengths, exons, and expression levels. These results align with previous research done on other fish species, such as Atlantic salmon [31], tilapia [5], and turbot [32]. Furthermore, we identified 531 differentially expressed lncRNAs in response to Nocardia seriolae infection. Among them, lncRNAs MSTRG.7324.3 and MSTRG.7324.11 were the most significantly up- and down-regulated lncRNAs, respectively. This suggests that these lncRNAs might be involved in immune regulation. However, further molecular experiments are required to confirm their specific roles. We conducted a random selection of 10 lncRNAs and quantified their expression levels using real-time qPCR to validate the precision of the sequencing results. The findings showed a strong correlation with the sequencing data, providing further validation for the dependability of our sequencing methodology.

To comprehensively understand the potential functions of the lncRNAs that are differentially expressed, we performed target gene prediction and performed functional enrichment analyses using GO and KEGG databases. The target genes of the differentially expressed lncRNAs were mostly enriched in immune-related pathways, according to the GO analysis, including B-cell proliferation, natural killer cell activation in the immune response, and T-cell activation in the immune response. Additionally, it was shown that STAT protein polypeptides' serine phosphorylation is positively regulated, implying that these lncRNAs play a crucial role in innate immunity against bacterial invasion in fish. These results are consistent with previous reports [33] and demonstrate the importance of lncRNAs in fish immune regulation during bacterial infections. Moreover, the KEGG analysis indicated that the target genes of the lncRNAs were highly concentrated in various immune-related signaling pathways, such as Toll-like receptor signaling, cytokine-cytokine receptor interaction, RIG-I-like receptor signaling, amino acid metabolism, spliceosome signaling, and apoptosis. Studies of other fish species with infections have also shown comparable immune-related KEGG enrichment findings [34,35]. These results indicate that lncRNAs may function to regulate mRNA expression and activate downstream immune signaling pathways in response to fish innate immunity. Furthermore, studies on tilapia infected with Streptococcus agalactiae revealed the involvement of pattern recognition receptors (PRRs), including Toll-like receptors (TLRs), NOD-like receptors, and retinoic acid-inducible gene I (RIG-I)-like receptors, in early natural immune responses to pathogens [35]. Similarly, our results suggest that PRR-mediated signaling pathways, such as TLR and NOD signaling, may be activated during mid- to long-term infections, indicating the persistence of early infection signaling. This provides further evidence in favor of the theory that lncRNAs regulate mRNA expression and the ensuing immunological signaling pathways in fish innate immunity.

Furthermore, we investigated the hypothesis of ceRNAs, which suggests that RNAs with binding sites to miRNAs compete for binding, resulting in the up- and down-regulation of target genes regulated by miRNAs [29]. We selected 12 immune genes and constructed a lncRNA-miRNA-mRNA regulatory network based on lncRNA-miRNA and mRNA-miRNA relationships. Among these, 12 lncRNAs were found to regulate the expression of irak4 by competitively binding miR-155–5p, while 10 lncRNAs targeted miR-33b-3p to modulate the expression of il1rl1 and notch3. It was first concluded that multiple lncRNAs can compete with mRNAs to bind miRNAs, which is similar to the experimental results in grass carp [7]. Secondly, previous studies on irak4, il1rl1, and notch3 in mammals have shown their involvement in adaptive immune processes and NF-κB activity [36,37]. Studies conducted on larimichthys crocea and cynoglossus semilaevis infected with pathogens have also indicated the upregulation of irak4 expression during infection [38,39]. Similarly, in mammals, lncRNA-Gm9866 has been found to regulate the TGFβ/Smad and Notch pathways to control liver fibrosis [40]. Additionally, miR-144–3p has been shown to regulate the proliferative and invasive capacity of lung adenocarcinoma cells and inhibit inflammation in Mycobacterium tuberculosis-infected macrophages [41,42]. In teleost fish, miR-144–3p has been found to target multiple immune-related genes in species such as bighead and silver carp, highlighting its importance in immune defenses [43]. In our study, we identified 10 lncRNAs that bind miR-144–3p competitively to regulate the expression of map2k2b. However, further tests are required to validate the binding of all these lncRNAs. These findings provide evidence for complex interactions between multiple genes during pathogen infection in largemouth bass. The sequencing analyses conducted in our study offer non-coding resources for future functional studies, facilitating a more comprehensive comprehension of the molecular processes of lncRNAs implicated in pathogen infection resistance.

Conclusions

This study presented a comprehensive analysis of lncRNAs in largemouth bass head kidneys infected with Nocardia seriolae. A total of 5661 lncRNAs were identified, of which 531 exhibited differential expression in response to the infection. These differentially expressed lncRNAs were found to be associated with immune-related pathways, indicating their potential regulatory roles in immune responses. Functional enrichment analyses revealed the enrichment of immune-related pathways such as Toll-like receptor signaling and RIG-I-like receptor signaling pathways. Furthermore, the construction of ceRNA networks highlighted complex interactions between lncRNAs, microRNAs, and mRNAs, suggesting their involvement in immune response regulation. Our findings underlined the significance of lncRNAs in modulating immune responses to Nocardia seriolae in largemouth bass and provided a foundation for future exploration of the specific functions and mechanisms of lncRNAs in host-pathogen interactions.

CRediT authorship contribution statement

Xiaona Fu: Data curation, Validation, Visualization, Writing – original draft. Longsheng Zhang: Data curation, Validation. Keqi Li: Data curation, Validation. Zhigang Liu: Validation, Investigation. Jikui Wu: Conceptualization, Writing – review & editing. Junling Zhang: Conceptualization, Supervision, Project administration, Funding acquisition, Writing – review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix Supplementary materials

Image, application 1

Data availability

The raw RNA-seq reads are available in the NCBI SRA (accession number: PRJNA1103740).

Acknowledgments

This research was supported by the National Key Research and Development Program of China (2023YFD2401001 ) and the National Natural Science Foundation of China (No. 31972772 ).

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.cirep.2024.200165.
==== Refs
References

1 Mohammad F. Pandey G.K. Mondal T. Enroth S. Redrup L. Gyllensten U. Kanduri C. Long noncoding RNA-mediated maintenance of DNA methylation and transcriptional gene silencing Development 139 15 2012 2792 2803 10.1242/dev.079566 22721776
2 Yao Z. Y Zhang Xu D. Zhou X. Peng P. Pan Z. Xiao N. Yao J. Li Z. Research progress on long non-coding RNA and radiotherapy Med. Sci. Monit. 25 2019 5757 5770 10.12659/msm.915647 31375656
3 Zhou L. Li J. Liu J. Wang A. Liu Y. Yu H. Ouyang H. Pang D. Investigation of the lncRNA THOR in mice highlights the importance of noncoding RNAs in mammalian male reproduction Biomedicines 9 8 2021 859 10.3390/biomedicines9080859 34440063
4 Zhu W. Huang Y. Zhang Y. Ding X. Bai Y. Liu Z. Shen J. Identification and characterization of long non-coding RNAs in juvenile and adult skeletal muscle of largemouth bass (Micropterus salmoides) Biomedicines 9 8 2021 859 10.3390/biomedicines9080859 34440063
5 Shen Y. Liang W. Lin Y. Yang H. Chen X. Feng P. Zhang B. Zhu J. Zhang Y. Luo H. Single molecule real-time sequencing and RNA-seq unravel the role of long non-coding and circular RNA in the regulatory network during Nile tilapia (Oreochromis niloticus) infection with Streptococcus agalactiae Fish Shellfish Immunol. 104 2020 640 653 10.1016/j.fsi.2020.06.015 32544555
6 Zheng W. Chu Q. Xu T. The long noncoding RNA NARL regulates immune responses via microRNA-mediated NOD1 downregulation in teleost fish J. Biol. Chem. 296 2021 100414 10.1016/j.jbc.2021.100414
7 Pang Y. Li L. Yang Y. Shen Y. Xu X. Li J. LncRNA-ANAPC2 and lncRNA-NEFM positively regulates the inflammatory response via the miR-451/npr2/hdac8 axis in grass carp Fish Shellfish Immunol. 128 2022 1 6 10.1016/j.fsi.2022.07.014 35843524
8 Zhu X. Qian Q. Wu C. Zhu Y. Gao X. Jiang Q. Wang J. Liu G. Zhang X. Pathogenicity of aeromonas veronii causing mass mortality of largemouth bass (Micropterus salmoides) and its induced host immune response Microorganisms 10 11 2022 2198 10.3390/microorganisms10112198 36363790
9 Mcneil M. Brown J. The medically important aerobic actinomycetes: epidemiology and microbiology Clin. Microbiol. Rev. 7 3 1994 357 417 10.1128/cmr.7.3.357 7923055
10 Vu-Khac H. Duong V.Q. Chen S.C. Pham T.H. Nguyen T.T. Trinh T.T. Isolation and genetic characterization of Nocardia seriolae from snubnose pompano Trachinotus blochii in Vietnam Dis. Aquat. Organ. 120 2 2016 173 177 10.3354/dao03023 27409241
11 Lei X. Zhao R. Geng Y. Wang K. Yang P.O. Chen D. Huang X. Zuo Z. He C. Chen Z. Huang C. Guo H. Lai W. Nocardia seriolae: a serious threat to the largemouth bass Micropterus salmoides industry in Southwest China Dis. Aquat. Organ. 142 2020 13 21 10.3354/dao03517 33150871
12 Wang G.L. Yuan S.P. Jin S. Nocardiosis in large yellow croaker, Larimichthys crocea (Richardson) J. Fish Dis. 28 6 2005 339 345 10.1111/j.1365-2761.2005.00637.x 15960657
13 Zhou T. Cai P. Li J. Dan X. Li Z. Pathological variations and immune response in Channa argus infected with pathogenic Nocardia seriolae strain Fish Shellfish Immunol. 150 2024 109554 10.1016/j.fsi.2024.109554
14 Xia L. Cai J. Wang B. Huang Y. Jian J. Lu Y. Draft genome sequence of nocardia seriolae ZJ0503, a fish pathogen isolated from trachinotus ovatus in China Genome Announc. 3 1 2015 10.1128/genomea.01223-14 e01223-14
15 Ho P.Y. Byadgi O. Wang P.C. Tsai M.A. Liaw L.L. Chen S.C. Identification, molecular cloning of IL-1β and its expression profile during Nocardia seriolae infection in largemouth bass, Micropterus salmoides Int. J. Mol. Sci. 17 10 2016 1670 10.3390/ijms17101670 27706080
16 Byadgi O. Chen C.W. Wang P.C. Tsai M.A. Chen S.C. De Novo Transcriptome analysis of differential functional gene expression in largemouth bass (Micropterus salmoides) after challenge with Nocardia seriolae Int. J. Mol. Sci. 17 8 2016 1315 10.3390/ijms17081315 27529219
17 Kechin A. Boyarskikh U. Kel A. Filipenko M. cutPrimers: a new tool for accurate cutting of primers from reads of targeted next generation sequencing J. Comput. Biol. 24 11 2017 1138 1143 10.1089/cmb.2017.0096 28715235
18 Langmead B. Salzberg S.L. Fast gapped-read alignment with Bowtie 2 Nat. Methods 9 4 2012 357 359 10.1038/nmeth.1923 22388286
19 Kim D. Langmead B. Salzberg S.L. HISAT: a fast spliced aligner with low memory requirements Nat. Methods 12 4 2015 357 360 10.1038/nmeth.3317 25751142
20 Pertea M. Pertea G.M. Antonescu C.M. Chang T.C. Mendell J.T. Salzberg S.L. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads Nat. Biotechnol. 33 3 2015 290 295 10.1038/nbt.3122 25690850
21 Robinson M.D. McCarthy D.J. Smyth G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data Bioinformatics 26 1 2010 139 140 10.1093/bioinformatics/btp616 19910308
22 Kong L. Zhang Y. Ye Z.Q. Liu X.Q. Zhao S.Q. Wei L. Gao G. CPC: assess the protein-coding potential of transcripts using sequence features and support vector machine Nucleic Acids Res. 35 2007 W345 W349 10.1093/nar/gkm391 17631615
23 Sun L. Luo H. Bu D. Zhao G. Yu K. Zhang C. Liu Y. Chen R. Zhao Y. Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts Nucleic Acids Res. 41 17 2013 e166 10.1093/nar/gkt646 23892401
24 Trapnell C. Williams B.A. Pertea G. Mortazavi A. Kwan G. van Baren M.J. Salzberg S.L. Wold B.J. Pachter L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation Nat. Biotechnol. 28 5 2010 511 515 10.1038/nbt.1621 20436464
25 Conesa A. Götz S. García-Gómez J.M. Terol J. Talón M. Robles M. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research Bioinformatics 21 18 2005 3674 3676 10.1093/bioinformatics/bti610 16081474
26 Enright A.J. John B. Gaul U. Tuschl T. Sander C. Marks D.S. MicroRNA targets in drosophila Genome Biol. 5 1 2003 R1 10.1186/gb-2003-5-1-r1 14709173
27 Jarroux J. Morillon A. Pinskaya M. History, discovery, and classification of lncRNAs Adv. Exp. Med. Biol. 1008 2017 1 46 10.1007/978-981-10-5203-3_1 28815535
28 Ping P. Wang L. Kuang L. Ye S. M F. B Iqbal T Pei A novel method for LncRNA-disease association prediction based on an lncRNA-disease association network IEEE/ACM Trans. Comput. Biol. Bioinform. 16 2 2019 688 693 10.1109/tcbb.2018.2827373 29993639
29 Salmena L. Poliseno L. Tay Y. Kats L. Pandolfi P.P. A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language Cell 146 3 2011 353 358 10.1016/j.cell.2011.07.014 21802130
30 Ning X. Sun L. Identification and characterization of immune-related lncRNAs and lncRNA-miRNA-mRNA networks of Paralichthys olivaceus involved in Vibrio anguillarum infection BMC Genomics 22 1 2021 447 10.1186/s12864-021-07780-2 34130627
31 Xia Y.Q. Cheng J.X. Liu Y.F. Li C.H. Liu Y. Liu P.F. Genome-wide integrated analysis reveals functions of lncRNA-miRNA-mRNA interactions in Atlantic salmon challenged by Aeromonas salmonicida Genomics 114 1 2022 328 339 10.1016/j.ygeno.2021.12.013 34933071
32 Cai X. Lymbery A.J. Armstrong N.J. Gao C. L Ma C Li Systematic identification and characterization of lncRNAs and lncRNA-miRNA-mRNA networks in the liver of turbot (Scophthalmus maximus L) induced with Vibrio anguillarum Fish Shellfish Immunol. 131 2022 21 29 10.1016/j.fsi.2022.09.058 36170960
33 Wu W. Li L. Liu Y. Huang T. Liang W. Chen M. Multiomics analyses reveal that NOD-like signaling pathway plays an important role against Streptococcus agalactiae in the spleen of tilapia Fish Shellfish Immunol. 95 2019 336 348 10.1016/j.fsi.2019.10.007 31586680
34 Fu Q. Li Y. Zhao S. Wang H. Zhao C. Zhang P. Cao M. Yang N. Li C. Comprehensive identification and expression profiling of immune-related lncRNAs and their target genes in the intestine of turbot (Scophthalmus maximus L.) in response to Vibrio anguillarum infection Fish Shellfish Immunol. 130 2022 233 243 10.1016/j.fsi.2022.09.004 36084890
35 Kumar H. Kawai T. Akira S. Pathogen recognition by the innate immune system Int. Rev. Immunol. 30 2011 16 34 10.3109/08830185.2010.529976 21235323
36 Suzuki N. Suzuki S. Millar D.G. Unno M. Hara H. Calzascia T. Yamasaki S. Yokosuka T. Chen N.J. Elford A.R. Suzuki J. Takeuchi A. Mirtsos C. Bouchard D. Ohashi P.S. Yeh W.C. Saito T. A critical role for the innate immune signaling molecule IRAK-4 in T cell activation Science 311 5769 2006 1927 1932 10.1126/science.1124256 16574867
37 Yoon S.B. Hong H. Lim H.J. Choi J.H. Choi Y.P. Seo S.W. Lee H.W. Chae C.H. Park W.K. Kim H.Y. Jeong D. De T.Q. Myung C.S. Cho H. A novel IRAK4/PIM1 inhibitor ameliorates rheumatoid arthritis and lymphoid malignancy by blocking the TLR/MYD88-mediated NF-κB pathway Acta Pharm Sin B 13 3 2023 1093 1109 10.1016/j.apsb.2022.12.001 36970199
38 Zou P.F. Huang X.N. Yao C.L. Sun Q.X. Li Y. Zhu Q. Yu Z.X. Fan Z.J. Cloning and functional characterization of IRAK4 in large yellow croaker (Larimichthys crocea) that associates with MyD88 but impairs NF-κB activation Fish Shellfish Immunol. 63 2017 452 464 10.1016/j.fsi.2016.12.019 27989863
39 Yu Y. Zhong Q. Li C. Jiang L. Wang Y. Sun Y. Wang X. Wang Z. Zhang Q. Identification and characterization of IL-1 receptor-associated kinase-4 (IRAK-4) in half-smooth tongue sole Cynoglossus semilaevis Fish Shellfish Immunol. 32 4 2012 609 615 10.1016/j.fsi.2011.12.011 22230843
40 X Liao Ruan X. Yao P. Yang D. Wu X. Zhou X. Jing J. Wei D. Liang Y. Zhang T. Qin S. Jiang H. LncRNA-Gm9866 promotes liver fibrosis by activating TGFβ/Smad signaling via targeting Fam98b J. Transl. Med. 21 1 2023 778 10.1186/s12967-023-04642-1 37919785
41 Song S. Li J. X Liu miR-144-3p Inhibits proliferation and invasion of lung adenocarcinoma cells and arrests cell cycle by Targeting E2F8 Life Sci. Res. 27 06 2023 479 487 10.16605/j.cnki.1007-7847.2022.09.0210
42 Zhao Y. Liang L. Jiang N. Impact of targeted regulation of miR-144-3p by LncRNAGAS5 on macrophage apoptosis and inflammatory response in Mycobacterium tuberculosis infected macrophages Chin. J. Anti-Tuberculosis 45 08 2023 744 751 10.19982/j.issn.1000-6621.20230105
43 Zhao Y. Gu J. Wu R. Liu B. Dong P. Yu G. Zhao D. Li G. Yang Z. Characteristics of conserved microRNAome and their evolutionary adaptation to regulation of immune defense functions in the spleen of silver carp and bighead carp Fish Shellfish Immunol. 144 2024 109312 10.1016/j.fsi.2023.109312
