scholarly journals Multi-omics integration identifies key upstream regulators of pathomechanisms in hypertrophic cardiomyopathy due to truncating MYBPC3 mutations

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
J. Pei ◽  
M. Schuldt ◽  
E. Nagyova ◽  
Z. Gu ◽  
S. el Bouhaddani ◽  
...  

Abstract Background Hypertrophic cardiomyopathy (HCM) is the most common genetic disease of the cardiac muscle, frequently caused by mutations in MYBPC3. However, little is known about the upstream pathways and key regulators causing the disease. Therefore, we employed a multi-omics approach to study the pathomechanisms underlying HCM comparing patient hearts harboring MYBPC3 mutations to control hearts. Results Using H3K27ac ChIP-seq and RNA-seq we obtained 9310 differentially acetylated regions and 2033 differentially expressed genes, respectively, between 13 HCM and 10 control hearts. We obtained 441 differentially expressed proteins between 11 HCM and 8 control hearts using proteomics. By integrating multi-omics datasets, we identified a set of DNA regions and genes that differentiate HCM from control hearts and 53 protein-coding genes as the major contributors. This comprehensive analysis consistently points toward altered extracellular matrix formation, muscle contraction, and metabolism. Therefore, we studied enriched transcription factor (TF) binding motifs and identified 9 motif-encoded TFs, including KLF15, ETV4, AR, CLOCK, ETS2, GATA5, MEIS1, RXRA, and ZFX. Selected candidates were examined in stem cell-derived cardiomyocytes with and without mutated MYBPC3. Furthermore, we observed an abundance of acetylation signals and transcripts derived from cardiomyocytes compared to non-myocyte populations. Conclusions By integrating histone acetylome, transcriptome, and proteome profiles, we identified major effector genes and protein networks that drive the pathological changes in HCM with mutated MYBPC3. Our work identifies 38 highly affected protein-coding genes as potential plasma HCM biomarkers and 9 TFs as potential upstream regulators of these pathomechanisms that may serve as possible therapeutic targets.

2020 ◽  
Vol 21 (9) ◽  
pp. 3040 ◽  
Author(s):  
Jun Gao ◽  
John Collyer ◽  
Maochun Wang ◽  
Fengping Sun ◽  
Fuyi Xu

Hypertrophic cardiomyopathy (HCM) is an inherited disorder of the myocardium, and pathogenic mutations in the sarcomere genes myosin heavy chain 7 (MYH7) and myosin-binding protein C (MYBPC3) explain 60%–70% of observed clinical cases. The heterogeneity of phenotypes observed in HCM patients, however, suggests that novel causative genes or genetic modifiers likely exist. Here, we systemically evaluated RNA-seq data from 28 HCM patients and 9 healthy controls with pathogenic variant identification, differential expression analysis, and gene co-expression and protein–protein interaction network analyses. We identified 43 potential pathogenic variants in 19 genes in 24 HCM patients. Genes with more than one variant included the following: MYBPC3, TTN, MYH7, PSEN2, and LDB3. A total of 2538 protein-coding genes, six microRNAs (miRNAs), and 1617 long noncoding RNAs (lncRNAs) were identified differentially expressed between the groups, including several well-characterized cardiomyopathy-related genes (ANKRD1, FHL2, TGFB3, miR-30d, and miR-154). Gene enrichment analysis revealed that those genes are significantly involved in heart development and physiology. Furthermore, we highlighted four subnetworks: mtDNA-subnetwork, DSP-subnetwork, MYH7-subnetwork, and MYBPC3-subnetwork, which could play significant roles in the progression of HCM. Our findings further illustrate that HCM is a complex disease, which results from mutations in multiple protein-coding genes, modulation by non-coding RNAs and perturbations in gene networks.


2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 178-179
Author(s):  
S. Alehashemi ◽  
M. Garg ◽  
B. Sellers ◽  
A. De Jesus ◽  
A. Biancotto ◽  
...  

Background:Systemic Autoinflammatory diseases present with sterile inflammation. NOMID (Neonatal-Onset Multisystem Inflammatory Disease) is caused by gain-of-function mutations inNLRP3and excess IL-1 production, presents with fever, neutrophilic dermatosis, aseptic meningitis, hearing loss and eye inflammation; CANDLE (Chronic Atypical Neutrophilic Dermatosis, Lipodystrophy and Elevated Temperature) is caused by loss-of-function mutations in proteasome genes that lead to type-1 interferon signaling, characterized by fever, panniculitis, lipodystrophy, cytopenia, systemic and pulmonary hypertension and basal ganglia calcification. IL-1 blockers are approved for NOMID and JAK-inhibitors show efficacy in CANDLE treatment.Objectives:We used proteomic analysis to compare differentially expressed proteins in active NOMID and CANDLE compared to healthy controls before and after treatment, and whole blood bulk RNA seq to identify the immune cell signatures.Methods:Serum samples from active NOMID (n=12) and CANDLE (n=7) before and after treatment (table 1) and age matched healthy controls (HC) (n=7) were profiled using the SomaLogic platform (n=1125 proteins). Differentially expressed proteins in NOMID and CANDLE were ranked after non-parametric tests for unpaired (NOMIDp<0.05, CANDLE,p<0.1) and paired (p<0.05) analysis and assessed by enriched Gene Ontology pathways and network visualization. Whole blood RNA seq was performed (NOMID=7, CANDLE=7, Controls =5) and RPKM values were used to assess immune cells signatures.Table 1.Patient’s characteristicsNOMIDN=12, Male =6CANDLEN=7, Male =6AgeMedian (range)12 (2, 28)16 (3, 20)Ethnicity%White (Hispanic)80 (20)100 (30)GeneticsNLRP3mutation(2 Somatic, 10 Germline)mutations in proteasome component genes(1 digenic, 6 Homozygous/compound Heterozygous)Before treatmentAfter treatmentBefore treatmentAfter treatmentCRPMedian (range) mg/L52 (16-110)5 (0-23)5 (0-101)1 (0-4)IFN scoremedian (range)0NA328 (211-1135)3 (0-548)Results:Compared to control, 205 proteins (127 upregulated, 78 downregulated) were significantly different at baseline in NOMID, compared to 163 proteins (101 upregulated, and 62 downregulated) in CANDLE. 134 dysregulated proteins (85 upregulated, 49 downregulated) overlapped in NOMID and CANDLE (Figure 1). Pathway analysis identified neutrophil and monocyte chemotaxis signature in both NOMID and CANDLE. NOMID patients had neutrophilia and active neutrophils. CANDLE patients exhibited active neutrophils in whole blood RNA. Endothelial cell activation was the most prominent non-hematopoietic signature and suggest distinct endothelial cell dysregulation in NOMID and CANDLE. In NOMID, the signature included neutrophil transmigration (SELE) endothelial cell motility in response to angiogenesis (HGF, VEGF), while in CANDLE the endothelial signatures included extracellular matrix protein deposition (COL8A) suggesting increased vascular stiffness. CANDLE patients had higher expression of Renin, 4 out of 7 had hypertension, NOMID patients did not have hypertension. Treatment with anakinra and baricitinib normalized 143 and 142 of dysregulated proteins in NOMID and CANDLE respectively.Conclusion:Differentially expressed proteins in NOMID and CANDLE are consistent with innate immune cell activation. Distinct endothelial cell signatures in NOMID and CANDLE may provide mechanistic insight into differences in vascular phenotypes. Treatment with anakinra and Baricitinib in NOMID and CANDLE leaves 30% and 13% of the dysregulated proteins unchanged.Acknowledgments:This work was supported by Intramural Research atNational Institute of Allergy Immunology and Infectious Diseases of National Institutes of Health, Bethesda, Maryland, the Center of Human Immunology and was approved by the IRB.Disclosure of Interests:None declared


2021 ◽  
Author(s):  
Chengang Guo ◽  
Zhimin wei ◽  
Wei Lyu ◽  
Yanlou Geng

Abstract Quinoa saponins have complex, diverse and evident physiologic activities. However, the key regulatory genes for quinoa saponin metabolism are not yet well studied. The purpose of this study was to explore genes closely related to quinoa saponin metabolism. In this study, the significantly differentially expressed genes in yellow quinoa were firstly screened based on RNA-seq technology. Then, the key genes for saponin metabolism were selected by gene set enrichment analysis (GSEA) and principal component analysis (PCA) statistical methods. Finally, the specificity of the key genes was verified by hierarchical clustering. The results of differential analysis showed that 1654 differentially expressed genes were achieved after pseudogenes deletion. Therein, there were 142 long non-coding genes and 1512 protein-coding genes. Based on GSEA analysis, 116 key candidate genes were found to be significantly correlated with quinoa saponin metabolism. Through PCA dimension reduction analysis, 57 key genes were finally obtained. Hierarchical cluster analysis further demonstrated that these key genes can clearly separate the four groups of samples. The present results could provide references for the breeding of sweet quinoa and would be helpful for the rational utilization of quinoa saponins.


2020 ◽  
Author(s):  
Siew Woh Choo ◽  
Yu Zhong ◽  
Edward Sendler ◽  
Anton Scott Goustin ◽  
Juan Cai ◽  
...  

Abstract BackgroundEstrogen is a hormone that is frequently essential in breast cancer to drive key transcriptional programs by interacting with the estrogen receptor alpha that upregulates proliferative and oncogenic genes and represses apoptotic and tumor suppressor genes. Protein-coding targets of estrogen regulation in breast cancer are well-defined. However, long non-coding RNA (lncRNA) genes account for the majority of human gene catalogs. The coding status of these genes – their accidental, or regulated, translation by ribosomes, under the influence of estrogen – remains a controversial topic. MethodsHere, we performed comprehensive transcriptome analysis using RNA-Seq, as well as ribosome profiling using Ribo-Seq, on the same samples: biological replicates of human estrogen receptor alpha (ERa) positive MCF7 breast cancer cells before and after estrogen treatment. We correlated these two datasets, globally highlighting protein-coding and lncRNA differentially expressed genes and transcripts that were positively as well as negatively responsive to estrogen, separately at the transcriptional level and the translational (as approximated by ribosome binding) level.ResultsOur data showed that some transcripts were more robustly detected in RNA-Seq than in the ribosome-profiling data, and vice versa, suggesting distinct gene-specific estrogen responses at the transcriptional and the translational level, respectively. Certain differentially expressed transcripts may point to the regulation of alternative splicing by estrogen. Several pseudogenes were co- and anti-regulated with their cancer-functional parental genes. Gene ontology analysis highlighted cancer-relevant pathways enriched after estrogen treatment in cells.ConclusionsOur study represents a significant advance in the estrogen receptor biology, because we demonstrated global effects of estrogen on splicing and translation that are distinct from, and not always correlated with, its effects on transcription, and that differ globally for protein-coding and lncRNA genes. We have also highlighted for the first time the transcriptional and translational response of expressed pseudogenes to estrogen, pointing to new perspectives for biomarker and drug-target development for breast cancer in future.


2020 ◽  
Vol 35 (5) ◽  
pp. 1230-1245 ◽  
Author(s):  
L C Poulsen ◽  
J A Bøtkjær ◽  
O Østrup ◽  
K B Petersen ◽  
C Yding Andersen ◽  
...  

Abstract STUDY QUESTION How does the human granulosa cell (GC) transcriptome change during ovulation? SUMMARY ANSWER Two transcriptional peaks were observed at 12 h and at 36 h after induction of ovulation, both dominated by genes and pathways known from the inflammatory system. WHAT IS KNOWN ALREADY The crosstalk between GCs and the oocyte, which is essential for ovulation and oocyte maturation, can be assessed through transcriptomic profiling of GCs. Detailed transcriptional changes during ovulation have not previously been assessed in humans. STUDY DESIGN, SIZE, DURATION This prospective cohort study comprised 50 women undergoing fertility treatment in a standard antagonist protocol at a university hospital-affiliated fertility clinic in 2016–2018. PARTICIPANTS/MATERIALS, SETTING, METHODS From each woman, one sample of GCs was collected by transvaginal ultrasound-guided follicle aspiration either before or 12 h, 17 h or 32 h after ovulation induction (OI). A second sample was collected at oocyte retrieval, 36 h after OI. Total RNA was isolated from GCs and analyzed by microarray. Gene expression differences between the five time points were assessed by ANOVA with a random factor accounting for the pairing of samples, and seven clusters of protein-coding genes representing distinct expression profiles were identified. These were used as input for subsequent bioinformatic analyses to identify enriched pathways and suggest upstream regulators. Subsets of genes were assessed to explore specific ovulatory functions. MAIN RESULTS AND THE ROLE OF CHANCE We identified 13 345 differentially expressed transcripts across the five time points (false discovery rate, &lt;0.01) of which 58% were protein-coding genes. Two clusters of mainly downregulated genes represented cell cycle pathways and DNA repair. Upregulated genes showed one peak at 12 h that resembled the initiation of an inflammatory response, and one peak at 36 h that resembled the effector functions of inflammation such as vasodilation, angiogenesis, coagulation, chemotaxis and tissue remodelling. Genes involved in cell–matrix interactions as a part of cytoskeletal rearrangement and cell motility were also upregulated at 36 h. Predicted activated upstream regulators of ovulation included FSH, LH, transforming growth factor B1, tumour necrosis factor, nuclear factor kappa-light-chain-enhancer of activated B cells, coagulation factor 2, fibroblast growth factor 2, interleukin 1 and cortisol, among others. The results confirmed early regulation of several previously described factors in a cascade inducing meiotic resumption and suggested new factors involved in cumulus expansion and follicle rupture through co-regulation with previously described factors. LARGE SCALE DATA The microarray data were deposited to the Gene Expression Omnibus (www.ncbi.nlm.nih.gov/gds/, accession number: GSE133868). LIMITATIONS, REASONS FOR CAUTION The study included women undergoing ovarian stimulation and the findings may therefore differ from a natural cycle. However, the results confirm significant regulation of many well-established ovulatory genes from a series of previous studies such as amphiregulin, epiregulin, tumour necrosis factor alfa induced protein 6, tissue inhibitor of metallopeptidases 1 and plasminogen activator inhibitor 1, which support the relevance of the results. WIDER IMPLICATIONS OF THE FINDINGS The study increases our understanding of human ovarian function during ovulation, and the publicly available dataset is a valuable resource for future investigations. Suggested upstream regulators and highly differentially expressed genes may be potential pharmaceutical targets in fertility treatment and gynaecology. STUDY FUNDING/COMPETING INTEREST(S) The study was funded by EU Interreg ÔKS V through ReproUnion (www.reprounion.eu) and by a grant from the Region Zealand Research Foundation. None of the authors have any conflicts of interest to declare.


2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Mikhail Pomaznoy ◽  
Ashu Sethi ◽  
Jason Greenbaum ◽  
Bjoern Peters

Abstract RNA-seq methods are widely utilized for transcriptomic profiling of biological samples. However, there are known caveats of this technology which can skew the gene expression estimates. Specifically, if the library preparation protocol does not retain RNA strand information then some genes can be erroneously quantitated. Although strand-specific protocols have been established, a significant portion of RNA-seq data is generated in non-strand-specific manner. We used a comprehensive stranded RNA-seq dataset of 15 blood cell types to identify genes for which expression would be erroneously estimated if strand information was not available. We found that about 10% of all genes and 2.5% of protein coding genes have a two-fold or higher difference in estimated expression when strand information of the reads was ignored. We used parameters of read alignments of these genes to construct a machine learning model that can identify which genes in an unstranded dataset might have incorrect expression estimates and which ones do not. We also show that differential expression analysis of genes with biased expression estimates in unstranded read data can be recovered by limiting the reads considered to those which span exonic boundaries. The resulting approach is implemented as a package available at https://github.com/mikpom/uslcount.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Lars Gabriel ◽  
Katharina J. Hoff ◽  
Tomáš Brůna ◽  
Mark Borodovsky ◽  
Mario Stanke

Abstract Background BRAKER is a suite of automatic pipelines, BRAKER1 and BRAKER2, for the accurate annotation of protein-coding genes in eukaryotic genomes. Each pipeline trains statistical models of protein-coding genes based on provided evidence and, then predicts protein-coding genes in genomic sequences using both the extrinsic evidence and statistical models. For training and prediction, BRAKER1 and BRAKER2 incorporate complementary extrinsic evidence: BRAKER1 uses only RNA-seq data while BRAKER2 uses only a database of cross-species proteins. The BRAKER suite has so far not been able to reliably exceed the accuracy of BRAKER1 and BRAKER2 when incorporating both types of evidence simultaneously. Currently, for a novel genome project where both RNA-seq and protein data are available, the best option is to run both pipelines independently, and to pick one, likely better output. Therefore, one or another type of the extrinsic evidence would remain unexploited. Results We present TSEBRA, a software that selects gene predictions (transcripts) from the sets generated by BRAKER1 and BRAKER2. TSEBRA uses a set of rules to compare scores of overlapping transcripts based on their support by RNA-seq and homologous protein evidence. We show in computational experiments on genomes of 11 species that TSEBRA achieves higher accuracy than either BRAKER1 or BRAKER2 running alone and that TSEBRA compares favorably with the combiner tool EVidenceModeler. Conclusion TSEBRA is an easy-to-use and fast software tool. It can be used in concert with the BRAKER pipeline to generate a gene prediction set supported by both RNA-seq and homologous protein evidence.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 3467-3467
Author(s):  
Douglas RA Silveira ◽  
Prodromos Chatzikyriakou ◽  
Olena Yavorska ◽  
Sarah Mackie ◽  
Roan Hulks ◽  
...  

Abstract Differentiation arrest in acute myeloid leukaemia (AML) results in accumulation of leukaemic progenitors (L-Prog) and bone marrow failure. Mutant isocitrate dehydrogenase enzyme produces d-2-hydroxyglutarate (2HG), which inhibits α-ketoglutarate-dependent dioxygenases, including Jumonji histone demethylases (JKDM) and TET2, but how this causes AML is unclear. Inhibitors of mutant IDH enzyme (mIDHi) restore differentiation in IDH-mutant (mIDH) AML (Amatangelo et al., 2018). Here, we studied transcriptional networks involved using single-cell (SC) gene expression (GEX) and transcription factor (TF) motif accessibility in primary AML treated with the mIDH2 inhibitor enasidenib (ENA) and found that ENA activates cell cycle (CC) and pro-differentiation programmes through increased promoter accessibility of granulocyte-monocyte (GM)-TF targets. We treated patient L-Prog in vitro with ENA or vehicle, and performed SC RNA-seq (Chromium 10x) in 4 responsive (R), and one non-responsive (NR) patient samples in early, mid and late timepoints. GEX signatures were used to annotate cells according to function (undifferentiated [U], early and late GM [EGM and LGM]) and CC states. In R samples, ENA yielded more dividing late-GM at mid-late timepoints than DMSO (18% vs 6.5%), and more terminally differentiated neutrophils at late timepoints (46% vs 16%). Using SCENIC (Aibar et al., 2017) to assign highly differentially-expressed genes to TF motifs, we computed regulatory networks (regulons, 'R'). Expression of the SP1 R was strongly correlated with active proliferation and ENA conditions led to generation of more cells that co-expressed CEBPA R or CEBPE R with SP1 R, emphasising simultaneous engagement of CC and GM programmes. SP1 function is associated with CC and GM differentiation, and silencing of its binding to its targets contributes to AML pathogenesis (Maiques-Diaz et al., 2012). Control and NR samples failed to produce neutrophils, had reduced co-expression of CEBPE/SP1 R and yielded more poorly differentiated cells expressing GATA2 R. At the individual gene level, ENA stimulated downregulation of GATA2, GFI1B, IKZF1/2, and RUNX3 together with upregulation of immediate early genes which respond to cytokine and mitogenic stimuli (EGR1, IER2, AP-1) in early-mid phase. Later there is upregulation of CEBP TFs and effector genes FUT4, ELANE, AZU1 and PRTN3. Interestingly, expression of some GM-TFs (RUNX1, SPI1/PU.1, GFI1) was similar between ENA and DMSO, indicating that gene expression alone was insufficient for GM differentiation. Given the effects of 2-HG on JKDM, we assessed chromatin accessibility and TF binding using SC ATAC-seq. Overall, we had 25% of differentially accessible (DA) peaks, from which 75% were more accessible in ENA than in DMSO. ENA DA peaks were highly enriched in promoters. Using ArchR (Granja et al., 2021), we clustered cells and used ELANE expression levels to compute trajectories in parallel with SC RNA-seq data. ENA peaks were sequentially enriched for CBF/RUNX and GATA families, followed by AP-1 (JUN/FOS) and EGR/CEBP/KLF motifs. Footprinting analysis showed sequential decrease and increase of TF binding for GATA2 and CEBPA/E respectively during ENA-induced differentiation. Although it did not cause higher expression of SPI1/PU.1, ENA induced increased accessibility of its target binding sites at promoters, which included CEBPA/E and GM effectors (MPO, FUT4, PRTN3). This provides a novel mechanism by which ENA induces differentiation of L-prog. Regulatory network analysis around active, differentially expressed TFs at different phases of ENA-induced differentiation showed a switch from a repressive transcriptional landscape driven by stem-progenitor TFs, to one where AP-1 and GM-TFs activate expression of GM-effector genes. We postulate a model where MYC, E2F8 and EGR1 upregulate the CEBP family in early-mid differentiation. In addition to stimulation of promoter accessibility of TFBS, we find that ENA increases accessibility of cis-regulatory elements of CEBP TFs, adding another mechanism by which differentiation of L-Prog occurs. Our data on the mechanism of action of ENA suggest that differentiation arrest in IDHm AML involves suppression of CC and GM differentiation programs in a repressive chromatin landscape, likely via inhibition of KDM6A and demethylation of repressive H3K27me3 marks. Disclosures Silveira: Astellas: Speakers Bureau; Abbvie: Speakers Bureau; Servier/Agios: Research Funding; BMS/Celgene: Research Funding. Hasan: Bristol Myers Squibb: Current Employment. Thakurta: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company, Patents & Royalties. Vyas: Gilead: Honoraria; Astellas: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria; Takeda: Honoraria; Bristol Myers Squibb: Consultancy, Honoraria, Research Funding; Janssen: Honoraria; Daiichi Sankyo: Honoraria; Jazz: Honoraria; Pfizer: Honoraria; Novartis: Honoraria. Quek: BMS/Celgene: Research Funding; Servier/Agios: Research Funding.


2020 ◽  
Author(s):  
Xiao Ma ◽  
Shuangshuang Cen ◽  
Luming Wang ◽  
Chao Zhang ◽  
Limin Wu ◽  
...  

Abstract Background: The gonad is the major factor affecting animal reproduction. The regulatory mechanism of the expression of protein-coding genes involved in reproduction still remains to be elucidated. Increasing evidence has shown that ncRNAs play key regulatory roles in gene expression in many life processes. The roles of microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) in reproduction have been investigated in some species. However, the regulatory patterns of miRNA and lncRNA in the sex biased expression of protein coding genes remains to be elucidated. In this study, we performed an integrated analysis of miRNA, messenger RNA (mRNA), and lncRNA expression profiles to explore their regulatory patterns in the female ovary and male testis of Chinese soft-shelled turtle, Pelodiscus sinensis.Results: We identified 10 446 mature miRNAs, 20 414 mRNAs and 28 500 lncRNAs in the ovaries and testes, and 633 miRNAs, 11 319 mRNAs, and 10 495 lncRNAs showed differential expression. A total of 2 814 target genes were identified for miRNAs. The predicted target genes of these differentially expressed (DE) miRNAs and lncRNAs included abundant genes related to reproductive regulation. Furthermore, we found that 189 DEmiRNAs and 5 408 DElncRNAs showed sex-specific expression. Of these, 3 DEmiRNAs and 917 DElncRNAs were testis-specific, and 186 DEmiRNAs and 4 491 DElncRNAs were ovary-specific. We further constructed complete endogenous lncRNA-miRNA-mRNA networks using bioinformatics, including 103 DEmiRNAs, 636 DEmRNAs, and 1 622 DElncRNAs. The target genes for the differentially expressed miRNAs and lncRNAs included abundant genes involved in gonadal development, including Wt1, Creb3l2, Gata4, Wnt2, Nr5a1, Hsd17, Igf2r, H2afz, Lin52, Trim71, Zar1, and Jazf1.Conclusions: In animals, miRNA and lncRNA as master regulators regulate reproductive processes by controlling the expression of mRNAs. Considering their importance, the identified miRNAs, lncRNAs, and their targets in P. sinensis might be useful for studying the molecular processes involved in sexual reproduction and genome editing to produce higher quality aquaculture animals. A thorough understanding of ncRNA-based cellular regulatory networks will aid in the improvement of P. sinensis reproductive traits for aquaculture.


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