RNA-Seq Analysis of the Key Long Noncoding RNAs and mRNAs Related to the Regulation of Hepatic Lipid Metabolism in Oreochromis niloticus

Round 1

Reviewer 1 Report

The present manuscript by Tao et al., investigated long noncoding RNAs which are related to hepatic lipid metabolism in Nile tilapia. The study and its findings are very relevant for the preset context in the field of aquaculture and in specific to nutritional studies in nile tilapia. It is of growing interest to study on non-coding genes and the present study will provide updates and importance of long coding in aquaculture species. Overall the manuscript is well written and conclusion is derived according to the results obtained in the study. However, I have few minor comments which can be incorporated before publication.

1.    Section 2.1 need to revise as the experimental design is confusing to the readers especially on the replicates and number of fish used in each tanks with the number of groups. Author mentioned in line 87- for molecular analysis but which samples were collected and how they were preserved  need to be highlighted.

2.    Figure 8. It is better to use lower alphabets for significant difference.

3.    Section 3.6. author mentioned that the DEG from RNA-seq were validated with qRT-PCR. This is valid and understood that the genes upregulated by RNA-seq is confirmed by qRT-PCR. However, it is noteworthy that the calculations for qRT-PCR and RNA seq to evaluate the gene regulation is different. So it is advisable to mention that the genes upregulated by RNA seq has been confirmed by qRT-PCR instead of validation.

Author Response

Dear editors and reviewers,

Thank you for your letter and comments on our manuscript titled "RNA-Seq Analysis of the Key Long Noncoding RNAs and mRNAs Related to the Regulation of Hepatic Lipid Metabolism in Oreochromis niloticus" (fishes-1974536). These comments helped us improve our manuscript and provided important guidance for future research.

We have addressed the editor’s and reviewers’ comments to the best of our abilities. We hope our manuscript now meets your requirements for publication.

We marked the revised portions in red in our manuscript. All comments and our specific responses are detailed below.

Reviewer #1: Comments on fishes-1974536：

The present manuscript by Tao et al., investigated long noncoding RNAs which are related to hepatic lipid metabolism in Nile tilapia. The study and its findings are very relevant for the preset context in the field of aquaculture and in specific to nutritional studies in nile tilapia. It is of growing interest to study on non-coding genes and the present study will provide updates and importance of long coding in aquaculture species. Overall the manuscript is well written and conclusion is derived according to the results obtained in the study. However, I have few minor comments which can be incorporated before publication.

Comment 1: Section 2.1 need to revise as the experimental design is confusing to the readers especially on the replicates and number of fish used in each tanks with the number of groups. Author mentioned in line 87- for molecular analysis but which samples were collected and how they were preserved  need to be highlighted.

Response: We revised and added more detailed information in section 2.1. The revised part is in page [2], lines [81–83] and lines [87–89] in our manuscript.

Comment 2: Figure 8. It is better to use lower alphabets for significant difference.

Response: In Figure 8, the lowercase letters represent the differences between high-fat diet groups at different sampling times, and the capital letters represent the differences between normal-fat diet groups at different sampling times. We think this representation will enable readers to better distinguish the intra-group differences at different sampling times. If they were all lowercase letters, the figure may be difficult to understand without looking at the figure legend; therefore, we retained the existing letter representation, and we hope the reviewer accepts this justification.

Comment 3: Section 3.6. author mentioned that the DEG from RNA-seq were validated with qRT-PCR. This is valid and understood that the genes upregulated by RNA-seq is confirmed by qRT-PCR. However, it is noteworthy that the calculations for qRT-PCR and RNA seq to evaluate the gene regulation is different. So it is advisable to mention that the genes upregulated by RNA seq has been confirmed by qRT-PCR instead of validation.

Response: We changed the term "data validation" to "data confirmation". The revised part is in page [9], line [1] in our manuscript.

Reviewer 2 Report

The study by Yi-Fan Tao et al. uses RNA-seq to characterize the mRNA and ncRNA repertoire in the liver of high fat diet-fed farmed tilapia and controls. The authors then identify differently expressed transcripts, validate them with qRT-PCR and use prediction algorithms to assess possible functional links between them in the context of lipid metabolism.

The study looks reasonably well-conducted, however there are several issues that need to be addressed before publication:

1) The methods are not exhaustively described. This is especially felt in the case of qRT-PCR. A reference to a published non-open-access article cannot replace the detailed description of the method.

2) The correlation between RNA-seq and qRT-PCR data (Fig. 5) is problematic. The scale of differential expression differs by many orders of magnitude (!) between the two methods. Either there is an error in the calculations, or the correlation cannot be considered viable. Are there really transcripts with expression changing 30,000X fold between NFD and HFD? If so, why do they only change 8X fold when measured by qRT-PCR?

3) English style needs work. Examples: " absent from protein-coding ability ",  "qRT- PCR were run..." (which should really be "qPCR was performed", as the RT stage was described in the previous sentence), etc. I suggest a general language editing.

 

Author Response

Dear editors and reviewers,

Thank you for your letter and comments on our manuscript titled "RNA-Seq Analysis of the Key Long Noncoding RNAs and mRNAs Related to the Regulation of Hepatic Lipid Metabolism in Oreochromis niloticus" (fishes-1974536). These comments helped us improve our manuscript and provided important guidance for future research.

We have addressed the editor’s and reviewers’ comments to the best of our abilities. We hope our manuscript now meets your requirements for publication.

We marked the revised portions in red in our manuscript. All comments and our specific responses are detailed below.

Reviewer #2: Comments on fishes-1974536：

The study by Yi-Fan Tao et al. uses RNA-seq to characterize the mRNA and ncRNA repertoire in the liver of high fat diet-fed farmed tilapia and controls. The authors then identify differently expressed transcripts, validate them with qRT-PCR and use prediction algorithms to assess possible functional links between them in the context of lipid metabolism.

The study looks reasonably well-conducted, however there are several issues that need to be addressed before publication:

Comment 1: The methods are not exhaustively described. This is especially felt in the case of qRT-PCR. A reference to a published non-open-access article cannot replace the detailed description of the method.

Response: We revised and added more detailed information in section 2 Materials and Methods, especially in section 2.8. The revised part is in page [2], lines [81–83] and lines [87–89]; page [3], lines [96–98], line [105], line [108], lines [115-116], lines [119-120], lines [123-124], lines [128-130]; page [4], lines [149-158] in our manuscript.

Comment 2: The correlation between RNA-seq and qRT-PCR data (Fig. 5) is problematic. The scale of differential expression differs by many orders of magnitude (!) between the two methods. Either there is an error in the calculations, or the correlation cannot be considered viable. Are there really transcripts with expression changing 30,000X fold between NFD and HFD? If so, why do they only change 8X fold when measured by qRT-PCR?

Response: High-throughput sequencing technology is also known as the "second generation sequencing technology". With this technology, hundreds of thousands or even millions of DNA fragments can be sequenced at the same time. It has several advantages, including high speed, high accuracy, and low cost. qRT-PCR technology measures the total number of products after each PCR cycle with fluorescent chemicals in the DNA amplification reaction, and it is an indirect quantitative analysis of specific DNA sequences in the tested samples. Both technologies are effective for detecting gene expression, but there are certain differences between the two technologies [1, 2]. High-throughput sequencing technology is used to compare or assemble approximately 150-bp reads into transcripts, count the number of reads compared with transcripts, and obtain expression level through standardization. qRT-PCR is used to quantify the relative expression of target genes by detecting the expression levels of internal reference genes. Therefore, the different calculation methods of expression level may be a possible reason for the differences in transcript expression level. In addition, RNA-seq mostly covered the exon region of the gene, so the obtained gene expression level actually accounted for the expression level of all exon regions of the gene. However, qRT-PCR quantification is typically used to design primers and amplify local regions, and does not take into account the full length of the gene. This may have also led to differences between the two test results. In other research papers on lncRNAs, the detection trends of individually selected lncRNA or mRNA expression using the two methods were the same, but the fold changes differed by approximately 100–500 times [3, 4], which is similar to our study. Therefore, in this experiment, the fold change differences detected between two methods may be caused by the differences in the detection principles and calculation methods of the two technologies. In this experiment, it was reliable to use qRT-PCR technology to confirm the key factors obtained by RNA-seq.

Comment 3: English style needs work. Examples: " absent from protein-coding ability ",  "qRT- PCR were run..." (which should really be "qPCR was performed", as the RT stage was described in the previous sentence), etc. I suggest a general language editing.

Response: The English writing of our manuscript was checked and modified by a native English-speaking expert.

 

 

Reference:

1. Su, Z.; Labaj, P. P.; Li, S.; Thierry-Mieg, J.; Thierry-Mieg, D.; Shi, W.; Wang, C.; Schroth, G. P.; Setterquist, R. A.; Thompson, J. F.; Jones, W. D.; Xiao, W.; Xu, W.; Jensen, R. V.; Kelly, R.; Xu, J.; Conesa, A.; Furlanello, C.; Gao, H.; Hong, H.; Jafari, N.; Letovsky, S.; Liao, Y.; Lu, F.; Oakeley, E. J.; Peng, Z.; Praul, C. A.; Santoyo-Lopez, J.; Scherer, A.; Shi, T.; Smyth, G. K.; Staedtler, F.; Sykacek, P.; Tan, X.-X.; Thompson, E. A.; Vandesompele, J.; Wang, M. D.; Wang, J.; Wolfinger, R. D.; Zavadil, J.; Auerbach, S. S.; Bao, W.; Binder, H.; Blomquist, T.; Brilliant, M. H.; Bushel, P. R.; Cain, W.; Catalano, J. G.; Chang, C.-W.; Chen, T.; Chen, G.; Chen, R.; Chierici, M.; Chu, T.-M.; Clevert, D.-A.; Deng, Y.; Derti, A.; Devanarayan, V.; Dong, Z.; Dopazo, J.; Du, T.; Fang, H.; Fang, Y.; Fasold, M.; Fernandez, A.; Fischer, M.; Furio-Tari, P.; Fuscoe, J. C.; Caiment, F.; Gaj, S.; Gandara, J.; Gao, H.; Ge, W.; Gondo, Y.; Gong, B.; Gong, M.; Gong, Z.; Green, B.; Guo, C.; Guo, L.; Guo, L.-W.; Hadfield, J.; Hellemans, J.; Hochreiter, S.; Jia, M.; Jian, M.; Johnson, C. D.; Kay, S.; Kleinjans, J.; Lababidi, S.; Levy, S.; Li, Q.-Z.; Li, L.; Li, L.; Li, P.; Li, Y.; Li, H.; Li, J.; Li, S.; Lin, S. M.; Lopez, F. J.; Lu, X.; Luo, H.; Ma, X.; Meehan, J.; Megherbi, D. B.; Mei, N.; Mu, B.; Ning, B.; Pandey, A.; Perez-Florido, J.; Perkins, R. G.; Peters, R.; Phan, J. H.; Pirooznia, M.; Qian, F.; Qing, T.; Rainbow, L.; Rocca-Serra, P.; Sambourg, L.; Sansone, S.-A.; Schwartz, S.; Shah, R.; Shen, J.; Smith, T. M.; Stegle, O.; Stralis-Pavese, N.; Stupka, E.; Suzuki, Y.; Szkotnicki, L. T.; Tinning, M.; Tu, B.; van Deft, J.; Vela-Boza, A.; Venturini, E.; Walker, S. J.; Wan, L.; Wang, W.; Wang, J.; Wang, J.; Wieben, E. D.; Willey, J. C.; Wu, P.-Y.; Xuan, J.; Yang, Y.; Ye, Z.; Yin, Y.; Yu, Y.; Yuan, Y.-C.; Zhang, J.; Zhang, K. K.; Zhang, W.; Zhang, W.; Zhang, Y.; Zhao, C.; Zheng, Y.; Zhou, Y.; Zumbo, P.; Tong, W.; Kreil, D. P.; Mason, C. E.; Shi, L., A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat. Biotechnol. 2014, 32, (9), 903-914.
2. Everaert, C.; Luypaert, M.; Maag, J. L. V.; Cheng, Q. X.; Dinger, M. E.; Hellemans, J.; Mestdagh, P., Benchmarking of RNA-sequencing analysis workflows using wholetranscriptome RT-qPCR expression data. Sci. Rep. 2017, 7, 1559.
3. Yang, C.; Wang, Z.; Song, Q.; Dong, B.; Bi, Y.; Bai, H.; Jiang, Y.; Chang, G.; Chen, G., Transcriptome Sequencing to Identify Important Genes and lncRNAs Regulating Abdominal Fat Deposition in Ducks. Animals 2022, 12, (10), 1256.
4. Luo, M.; Wang, L.; Yin, H.; Zhu, W.; Fu, J.; Dong, Z., Integrated analysis of long non-coding RNA and mRNA expression in different colored skin of koi carp. Bmc Genomics 2019, 20, 515.

 

Round 2

Reviewer 2 Report

The manuscript has been improved in the revised version. Some of the issues previously raised were addressed. However, the issue with the disparity between NGS and qRT-PCR DE values was not properly addressed. Describing the principles of these methods in the response letter is not necessary, as the reviewer has ample experience with both. Instead, the authors should try to find a reasonable explanation for such a wild artifact. The possible reasons given (amplified fragment vs. full length of gene, etc) are not convincing in explaining a 4 orders of magnitude difference between the results of the 2 methods. In my view, this calls into question the QC of the RNA-seq results. The authors should revisit the expression data and try to make sense of it, and thoroughly address these disparities in the results and discussion sections.

Author Response

Reviewer #1: Comments on fishes-1974536 v1：

Comment: The manuscript has been improved in the revised version. Some of the issues previously raised were addressed. However, the issue with the disparity between NGS and qRT-PCR DE values was not properly addressed. Describing the principles of these methods in the response letter is not necessary, as the reviewer has ample experience with both. Instead, the authors should try to find a reasonable explanation for such a wild artifact. The possible reasons given (amplified fragment vs. full length of gene, etc) are not convincing in explaining a 4 orders of magnitude difference between the results of the 2 methods. In my view, this calls into question the QC of the RNA-seq results. The authors should revisit the expression data and try to make sense of it, and thoroughly address these disparities in the results and discussion sections.

 

Response: Firstly, we would like to express our sincere gratitude to the valuable comments and suggestions from the reviewer. We re-visited the expression results of RNA-seq data, and found that there was a potential reason for four orders of magnitude difference in "fold difference" between the two methods. Previously, when we were performing fold change calculation, in order to enable the software to calculate numerical results, we set the expression value of 0 in the sequencing result to 0.0001. Therefore, the fold change value was close to 10⁴. For example, the expression level (fpkm) of lncRNA-MSTRG. 14598.1 was 1.74 ± 0.17 in the HFD group, which was not detected in the NFD group. The expression level was 0. If the expression value was set to 0.0001, the fold change was calculated as 17,389. We realized that our previous calculation method was inappropriate after consulting the literature and experts in related fields. It is easy to cause misunderstanding for readers and is not convincing enough. Therefore, we re-analyzed our sequencing data using the true transcript expression value "0" instead of "0.0001". We referred to the calculation methods in some published papers and make the following settings: If the candidates were only expressed in HFD group, Log₂ fold change was expressed as "Inf"; If the candidates were only expressed in NFD group, Log₂ fold change was expressed as "Dam". We deleted Figure 5 and made the comparison results between qRT-PCR and sequencing, as shown in Table 3.

Sun et al [1] also selected several "Nam" and "Inf" lncRNAs from RNA-seq data for qRT-PCR verification. For example, "lnc_1708", its log₂ fold change in RNA-seq result was "Nam", while its log₂ fold change in qRT-PCR results was -2.82; "lnc_1369", its log₂ fold change in RNA-seq result was "Inf", while its log₂ fold change in qRT-PCR results was 1.34. It can be seen that the expression trend of lncRNAs verified by the two detection methods is consistent, but there is a certain discrepancy in the difference multiple, which is similar to the results in our study. In addition to the different detection methods, the possible reasons for these results may be related to the relatively low expression of selected DE-lncRNAs (no expression level in the HFD group or NFD group was detected by RNA-seq). Similar results have also been reported in other literatures [2], which means that it is meaningful to select "Inf" and "Nam" lncRNAs to verify the reliability of sequencing results. Therefore, we modified the Results and Discussion section, the revised part is in page [9], lines [1-4] and page [14], lines [17-24] in our manuscript.

 

Reference:

1. Sun, X.; Jia, B.; Qiu, X. L.; Chu, H. X.; Zhang, Z. Q.; Wang, Z. P.; Zhao, J. J., Potential functions of long non-coding RNAs in the osteogenic differentiation of human bone marrow mesenchymal stem cells. Mol. Med. Rep. 2019, 19, (1), 103-114.
2. Li, H.; Cui, P.; Fu, X.; Zhang, L.; Yan, W.; Zhai, Y.; Lei, C.; Wang, H.; Yang, X., Identification and analysis of long non-coding RNAs and mRNAs in chicken macrophages infected with avian infectious bronchitis coronavirus. Bmc Genomics 2021, 22, (1), 67.

Round 3

Reviewer 2 Report

The authors have addressed my concerns in a satisfactory manner.