ABSTRACT Intrauterine growth restriction (IUGR) impairs neonatal weight and causes multiple organ dysplasia. IUGR not only threatens human health but is also a significant constraint to the

development of animal husbandry. However, the molecular mechanism underlying IUGR remains to be further elucidated. tRNA-derived small RNA (tsRNAs) is a regulative non-coding RNA, which has

recently been reported to correlate with the onset and progression of several diseases. In this study, we investigated the tsRNAs expression profiles of IUGR pigs. A tsRNAs dataset for

multiple organs in normal and IUGR pigs was generated, including muscle, liver, spleen and intestine. We further analyzed the characteristics of tsRNAs in different organs of pigs, and KEGG

27 July 2021 TRANSCRIPTOME ANALYSIS REVEALS THE LONG INTERGENIC NONCODING RNAS CONTRIBUTED TO SKELETAL MUSCLE DIFFERENCES BETWEEN YORKSHIRE AND TIBETAN PIG Article Open access 29 January

2021 DYNAMIC CHANGES IN THE TRANSCRIPTOME OF TRNA-DERIVED SMALL RNAS RELATED WITH FAT METABOLISM Article Open access 14 October 2023 BACKGROUND & SUMMARY Intrauterine growth restriction

(IUGR) broadly refers to a fetus’s slow growth and development caused by adverse factors, which usually induces low weight of newborns and multiple hypoplastic organs1. IUGR remains an

intractable public health concern worldwide and a major problem restricting animal husbandry development2,3. As a multiparous mammalian animal, pigs have exhibited a naturally high incidence

of IUGR4. IUGR permanently negatively affects mortality, postnatal growth and development for newborns. Available research shows that IUGR piglets exhibit abnormal development features,

including disrupted muscle development5,6, immune dysfunction7, insulin resistance8, abnormal glucolipid metabolism9 and other diseases. These abnormal physiological changes involve the

dysplasia of multiple tissues and organs. Hence, exploring the molecular mechanisms of multiple organs is vitally essential to deepen the understanding of IUGR. In epigenetics, non-coding

RNA-dependent mechanisms are essential for gene expression regulation. Recently, a tRNA-derived small non-coding RNA (tsRNAs) have been identified by high-throughput RNA sequencing10. tsRNAs

are produced by specific nuclease cutting different sites of parental tRNA. Several nucleases, such as angiogenin, Dicer, RNase P, RNase Z, and RNase L, have been shown to cleavage tRNAs11.

tsRNAs can be categorized into several subtypes, including tRF-1, tRF-2, tRF-3, tRF-5, tiRNA-3, tiRNA-5 based on the break site of parental tRNAs12. In early studies, tsRNAs were considered

solely a tRNA degradation product13. Many studies suggests that this novel ncRNA has several important functions, including ribosome biogenesis regulation14, intergenerational

inheritance15, RNA silencing16, and translational regulation17. tsRNAs are widely involved in various biological processes through the above mechanisms, such as cell proliferation,

migration, apoptosis, differentiation, and cell cycle18,19. Recently, the role of tsRNAs in the occurrence and development of diseases has attracted significant attention. However, studies

about tsRNA associated with the occurrence of IUGR are still lacking. Thus, the present study aimed to characterize the expression profiles of tsRNAs in muscle, liver, spleen and intestine

in the IUGR pigs model. A flow chart of this study is shown in Fig. 1. METHODS ANIMALS AND SAMPLE COLLECTION The study used 12 paternal half-sib female Duroc × Landrance × Yorkshire (DLY)

piglets. They were divided into two groups according to birthweight: Normal piglets (mean birth weight 1.60 ± 0.05 g, n = 6) and IUGR piglets (mean birth weight 1.07 ± 0.04 g, n = 6). The

body weight of IUGR piglets was significantly lower than the weight of normal piglets. IUGR is commonly defined as a birth weight less than two standard deviations below the normal1. The

piglets were raised following standard commercial practice. Body weight measurements were taken at 1, 23, 26, 30, and 37 days (Fig. 2A). At 37 days, piglets were slaughtered according to a

standard commercial procedure. The weight of longissimus dorsi muscle, liver, spleen, kidney and pancreas were measured separately (Fig. 2B). Longissimus dorsi muscle, liver, spleen and

intestine (jejunum) samples were collected in cryopreservation tubes and stored at −80 °C until used. RNA EXTRACTION AND LIBRARY CONSTRUCTION Tissue samples were ground in liquid nitrogen.

The number of samples per group was three. We selected the three lightest piglets in the IUGR group and the three heaviest piglets in the normal group to extract RNA. Each sample was added 1

 ml RNAiso reagent (TaKaRa, Japan) to extract the total RNA according to the manufacturer’s instructions. Isolated total RNA was measured concentrations and purities using the NanoDrop 2000

spectrophotometer (Thermo Scientific, USA). The absorbance ratio of the sample at 260 nm and 280 nm is used to evaluate RNA purity. RNA samples with a ratio between 1.8 and 2.0 are used for

sequencing analysis. RNA modifications are abundant in tsRNAs and interfere with small RNA-seq library construction. Before library preparation, the detailed processing flow of total RNA is

as follows: 3′ - aminoacyl (charged) deacylation to 3′ –OH for 3′ adaptor ligation, 3′ -cP (2′, 3′ -cyclic phosphate) removal to 3′ -OH for 3′ adaptor ligation, 5′ -OH (hydroxyl group)

phosphorylation to 5′ -P for 5′ adaptor ligation, m1A and m3C demethylation for efficient reverse transcription. The total RNA from each sample was pretreated and then utilized to prepare

the tsRNA-seq library. NEBNext® Multiplex Small RNA Library Prep Set for Illumina (NEBNext®, USA) was used for library construction. Library construction steps were carried out as follows.

Firstly, the RNA was ligated to 3′ and 5′ small RNA adapters. Next, cDNA was synthesized from the ligated RNA using Illumina’s proprietary reverse transcription (RT) primers and

amplification primers. Subsequently, PCR amplification was performed to generate fragments ranging in size from 134–160 bp, which were extracted and purified from the polyacrylamide gel

electrophoresis (PAGE) gel. Finally, the completed libraries were quantified using the Agilent 2100 Bioanalyzer to determine the concentration and quality of the libraries. The purified

libraries were mixed in equal amounts and then used for sequencing. SEQUENCING PROCEDURES The libraries were denatured with 0.1 M NaOH to generate single-stranded DNA molecules and diluted

to a loading volume of 1.3 ml and loading concentration of 1.8pM. Diluted libraries were loaded onto reagent cartridges and forwarded to sequencing run on Illumina NextSeq500 system using

NextSeq 500/550 V2 kit (#FC-404-2005, Illumina), according to the manufacturer’s instructions. For standard small RNA sequencing on Illumina NextSeq instrument, the sequencing type is 50 bp

single-read. DATA ANALYSIS Raw sequence data in FASTQ format generated from the Illumina NextSeq500 sequencing platform were used for further analysis. First, the FastQC (v0.11.7) was used

to assess the quality scores of sequencing reads (Table 1). tRNA cytoplasmic sequences were downloaded from the Genomic tRNA Database (GtRNAdb)20. The reference genome used was Sscrofa11.1.

tRNA sequences of mitochondria were predicted with tRNAscan-SE21 software. Raw sequence were trimmed 5′, 3′ -adaptor sequence and discarded reads (length < 14nt or length > 40nt) to

generate trimmed reads using Cutadapt. Trimmed reads were aligned to mature tRNA sequences, allowing onely one mismatch, and then reads that did not map were aligned to precursor tRNA

sequences, allowing one mismatch with Bowtie software. The expression level of each tsRNA is evaluated using sequencing counts and is normalized as counts per million of total reads (CPM).

The count and CPM of tsRNAs can be calculated with the following formula: $${\rm{Count}}=\mathop{\sum }\limits_{i=1}^{n}\frac{{c}_{i}}{{m}_{i}},\quad \quad

{\rm{CPM}}=\frac{1{0}^{6}Count}{N}$$ i: the i-th read aligned to the tsRNA region; n: the number of the reads aligned to the tsRNA region; ci: the count of the i-th read; mi: the number of

tsRNA generated from the i-th read (mi possibly occur great than one, only when allowing for more than 1 mismatch); N: the total number of reads mapped onto all of the mature or precursor

tRNA. The obtained counts and CPM data of tsRNA were used for subsequent analysis. CHARACTERISTICS OF TSRNAS EXPRESSION PROFILE Based on the tsRNAs expression level (CPM), we evaluated

Spearman’s correlations coefficients between 24 samples (Fig. 3A). Principal Component Analysis was performed based on read counts (Fig. 3B) and CPM (Fig. 3C) of tsRNAs for each sample. The

number of tsRNAs identified from each group was depicted in the petal diagram, and 364 core tsRNAs were shared among all groups (Fig. 3D). The length distribution of the identified tsRNAs

was analyzed (Fig. 3E). The majority of tsRNAs were 31–32 nt in length. According to the cleavage site of parental tRNAs, tsRNAs were classified into 9 subtypes, as shown in Fig. 3F.

Conventional and specifically expressed tsRNAs between normal and IUGR groups are depicted in the Fig. 3F Venn diagram. The pie chart shows the number of each group’s tsRNAs subpopulation.

We further analyzed the percentages of per tsRNAs in each group. The percentage of the top 15 tsRNAs was also computed in all groups (Fig. 3H). Among them, tRF-Gly-GCC-037/038 was the

highest in abundance, the sum of two tsRNAs exceeded the 60%. Interestingly, the top 6 tsRNAs all originate from the same tRNA-Gly-GCC. Figure 3H illustrates the sequence of

tRF-Gly-GCC-037/038 and their parental tRNA-Gly-GCC cleavage site. We further analyzed the characteristics of tsRNAs identified in four tissues of pigs. Figure 4A demonstrates the tRNA types

from which the tsRNAs originate and the number of tsRNA subtypes. It indicated that the tRNA-Glu-TTC produced the most significant number of tsRNAs. As shown in both Figs. 3F, 4A diagrams,

tRF-5c was the most abundant tsRNA subtype in any one sample. Moreover, the tRNA cleavage sites corresponding to each subtype were analyzed in Fig. 4B. We calculated the proportion of four

bases in each break site of tRNA in Fig. 4B lower panel. IDENTIFICATION OF DIFFERENTIALLY EXPRESSED TSRNAS Differentially expressed tsRNAs analyses were performed with R package edgeR. The

P-value of the exact test was calculated by negative binomial distribution. Then, multiple testing using a FDR was applied to obtain the Q-values. No differentially expressed tsRNAs were

found with FDR correction. The threshold for screening differentially expressed tsRNAs was the absolute fold change > 1.5 and _P_-value < 0.05. Differentially expressed tsRNAs in four

tissues between normal and IUGR groups were visualized according to fold change and _P_-value. The red circle represents up-regulated tsRNAs, and blue circle indicates down-regulated tsRNAs

(Fig. 5 left panel). Heat map showing differentially expressed tsRNAs clustering for each tissues (Fig. 5 right panel). FUNCTIONAL ENRICHMENT ANALYSIS Multiple recent studies have

demonstrated that tsRNAs have similar regulation mechanisms to microRNAs. Thus, we used the publicly available miRanda and TargetScan tools to predict the target genes of tsRNAs. Targetscan

software threshold was set at 50 (context score percentile), and miRanda was set with a maximum binding free energy of less than −20. Those genes predicted jointly in miRanda and TargetScan

were used as the target genes of tsRNAs for the Kyoto Encyclopedia of Genes and Genomics (KEGG) analysis. All up-regulated and down-regulated tsRNAs in four tissues were performed KEGG

pathway enrichment analysis, respectively. The top 10 pathways for each tissue are shown in Fig. 6. Up-regulated tsRNAs were mainly enriched in the MAPK signaling pathway and metabolic

pathway. Down-regulated tsRNAs were mainly enriched in the insulin signaling pathway and ErbB signaling pathway. We also constructed the relationship between these pathways and up-regulated

and down-regulated tsRNAs. STATISTICAL ANALYSES.RSON Results in Fig. 2B were represented as means ± SD. Significant differences between normal and IUGR group were determined by the unpaired

t-tests. _P_-values < 0.05 (*) represent significant difference. _P_-values < 0.01 (**) represent highly significant difference. DATA RECORDS The RNA-Seq raw data were deposited in the

NCBI Sequence Read Archive (SRA) database under the accession number PRJNA97481722 and PRJNA80065423. The tRNA sequences, results of differential expression analysis and the functional

enrichment analysis are stored in figshare24. TECHNICAL VALIDATION SEQUENCING DATA QUALITY ASSESSMENT Raw data were obtained by Illumina platform. FastQC software (v0.11.7) was used to

assess quality scores of raw data for each samples. Quality score Q was used to predict the probability of base-calling error (_P_): Q = −10_log_10(_P_). Q30 means the incorrect base calling

probability to be 0.001 or 99.9% base calling accuracy. Table 1. show the proportion of bases (Q ≥ 30) number after quality filtering. VALIDATION OF EXPERIMENTAL SAMPLE Pearsons correlation

coefficient analysis was performed on all 24 samples. Strong correlations were seen between samples from the same tissue type (Fig. 3A). Principal component analysis (PCA) was also

performed with all samples and the distances between the sample points represent the similarity of samples. Obviously, the distance between samples from the same tissue type is closer (Fig. 

3B,C). CODE AVAILABILITY Raw sequencing data were analyzed using publicly available bioinformatics softwares. We used common data analysis software packages and no custom code was created.

Software tools used are as follows: FastQC: v0.11.7, https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ Bowtie: v1.2.2, https://bowtie-bio.sourceforge.net/index.shtml GtRNAdb:

http://gtrnadb.ucsc.edu/ tRNAscan-SE: v2.0, http://lowelab.ucsc.edu/tRNAscan-SE/ Cutadapt: v1.17, https://github.com/marcelm/cutadapt/ edgeR: v3.24.3,

https://bioconductor.org/packages/release/bioc/html/edgeR.html R software: v3.5.1, https://www.r-project.org/ OmicStudio tools (https://www.omicstudio.cn/tool) was used for prediction of

target genes. GraphPad Prism 9 (GraphPad Software Inc., USA) was used for statistical analyses and data visualization. CHANGE HISTORY * _ 11 OCTOBER 2024 A Correction to this paper has been

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https://doi.org/10.6084/m9.figshare.24097857.v2 (2023). Download references ACKNOWLEDGEMENTS This work was supported by the National Natural Science Foundation of China (32372844), National

Key Research and Development Program of China (2021YFD1200801), Sichuan Science and Technology Program (2021YFYZ0007; 2021YFYZ0030; 2021ZDZX0008; scsztd-2023-08-09), China Agriculture

Research System of MOF and MARA (CARS-pig-35), National Center of Technology Innovation for Pigs. AUTHOR INFORMATION Author notes * These authors contributed equally: Ma Jianfeng, Gan

Mailin, Yang Yiting. AUTHORS AND AFFILIATIONS * Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China

Ma Jianfeng, Gan Mailin, Yang Yiting, Chen Lei, Zhao Ye, Niu Lili, Wang Yan, Zhang Shunhua, Zhu Li & Shen Linyuan * Key Laboratory of Livestock and Poultry Multi-omics, Ministry of

Agriculture and Rural Affairs, College of Animal and Technology, Sichuan Agricultural University, Chengdu, China Ma Jianfeng, Gan Mailin, Yang Yiting, Chen Lei, Zhao Ye, Niu Lili, Wang Yan, 

Zhang Shunhua, Zhu Li & Shen Linyuan * Chongqing Academy of Animal Science, Chongqing, China Wang Jingyong Authors * Ma Jianfeng View author publications You can also search for this

author inPubMed Google Scholar * Gan Mailin View author publications You can also search for this author inPubMed Google Scholar * Yang Yiting View author publications You can also search

for this author inPubMed Google Scholar * Chen Lei View author publications You can also search for this author inPubMed Google Scholar * Zhao Ye View author publications You can also search

for this author inPubMed Google Scholar * Niu Lili View author publications You can also search for this author inPubMed Google Scholar * Wang Yan View author publications You can also

search for this author inPubMed Google Scholar * Zhang Shunhua View author publications You can also search for this author inPubMed Google Scholar * Wang Jingyong View author publications

You can also search for this author inPubMed Google Scholar * Zhu Li View author publications You can also search for this author inPubMed Google Scholar * Shen Linyuan View author

publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS J.M., M.G. designed the experiments, wrote the manuscript, and generated the graphs. Y.Y. analyze the

most data and wrote the original manuscript. L.C., Y.Z., L.N., Y.W., S.Z. and J.W. helped to revise the manuscript. L.Z. and L.S. supervised the preparation of the draft and edited it and

worked as a corresponding author. All authors read and approved the final manuscript. CORRESPONDING AUTHORS Correspondence to Zhu Li or Shen Linyuan. ETHICS DECLARATIONS COMPETING INTERESTS

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ARTICLE Jianfeng, M., Mailin, G., Yiting, Y. _et al._ tRNA-derived small RNA dataset in multiple organs of intrauterine growth-restricted pig. _Sci Data_ 10, 793 (2023).

https://doi.org/10.1038/s41597-023-02715-w Download citation * Received: 29 May 2023 * Accepted: 01 November 2023 * Published: 10 November 2023 * DOI:

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