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. 2020 Sep 11;15(9):e0238940. doi: 10.1371/journal.pone.0238940

Novel genes associated with folic acid-mediated metabolism in mouse: A bioinformatics study

Jianwen Zhao 1, Wen Zou 2, Tingxi Hu 1,*
Editor: Jishnu Das3
PMCID: PMC7485790  PMID: 32915913

Abstract

Folic acid plays an essential role in the central nervous system and cancer. This study aimed to screen genes related to folic acid metabolism. Datasets (GSE80587, GSE65267 and GSE116299) correlated to folic acid were screened in the Gene Expression Omnibus. Weighed gene co-expression network analysis was performed to identify modules associated with sample traits of folic acid and organs (brain, prostate and kidney). Functional enrichment analysis was performed for the eigengenes in modules that were significantly correlated with sample traits. Accordingly, the hub genes and key nodes in the modules were identified using the protein interaction network. A total of 17,252 genes in three datasets were identified. One module, which included 97 genes that were highly correlated with sample traits (including folic acid treatment [cor = -0.57, P = 3e-04] and kidney [cor = -0.68, p = 4e-06]), was screened out. Hub genes, including tetratricopeptide repeat protein 38 (Ttc38) and miR-185, as well as those (including Sema3A, Insl3, Dll1, Msh4 and Snai1) associated with “neuropilin binding”, “regulation of reproductive process” and “vitamin D metabolic process”, were identified. Genes, including Ttc38, Sema3A, Insl3, Dll1, Msh4 and Snai1, were the novel factors that may be associated with the development of the kidneys and related to folic acid treatment.

Introduction

Folic acid (vitamin B9), as a necessary micronutrient, has a crucial role in DNA biosynthesis and integrity, controlling the homocysteine level and inflammation response, and reducing the risk of cancers such as colon cancer [1–5]. This also plays an essential role in the central nervous system, and prevents a neural-tube defect (NTD), which is a major birth defect of the brain and spine that occurs early during the embryonic period [3, 6].

Folic acid can reduce the elevated level of serum homocysteine, which is recognized as a risk factor for several diseases, such as cardiovascular and neurological diseases [7]. In addition, it acts as a risk factor for dementia and Alzheimer’s disease (AD), and a predictor of cognitive decline [8–10]. In addition to the influence on the central nervous system in the brain, plentiful evidence has revealed the influence of folic acid and homocysteine on the health of other organs, such as the kidneys and prostate [11–14]. Significant evidence has indicated that high homocysteine level is associated with increased risk of atherosclerosis, coronary artery disease, and chronic kidney disease (CKD) [13, 14]. The role of folic acid can be well-recognized, because folic acid is an essential cofactor for homocysteine metabolism, and its homeostasis disruption may be directly correlated to cardiovascular risk and CKD progression [15, 16]. A meta-analysis research reported that folic acid therapy can reduce cardiovascular disease risk in patients with CKD by 15% [17]. Cohen et al. [13] conducted a cross-sectional study that involved a large cohort of 17,010 subjects, and concluded that higher homocysteine concentration is correlated with a lower estimated glomerular filtration rate (eGFR) and increased renal impairment. However, the biological mechanism of folic acid in the brain, prostate and kidneys remains unclear, especially in the kidneys. Therefore, it is necessary to distinguish the action mode of folic acid in different organs.

In order to fill this gap, the present bioinformatics study was performed based on the public datasets deposited in online databases. The datasets correlated to folic acid were screened in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/), and the genes related to folic acid metabolism and organ development were identified. The related mechanisms were discussed.

Materials and methods

Microarray data resource

Next-generation sequencing datasets related to folic acid were screened in the NCBI GEO database (http://www.ncbi.nlm.nih.gov/geo/) using the following search strategy: ‘folic acid’ OR ‘folate’ AND ‘Mus musculus [Organism]’. Three datasets, including GSE80587, GSE65267 and GSE116299, were selected. GSE80587 (GPL13112, Illumina HiSeq 2000) consisted of 11 samples isolated from the hippocampi of six control mice fed with control dietary (14-week old, F1; three males and three females) and five mice fed with folic acid dietary at 40 mg/kg of chow (14-week old, F1, two males and three females) [18]. GSE65267 (GPL13112, Illumina HiSeq 2000) included 18 samples isolated from mouse kidney tissues before and at various time points (1, 2, 3, 7 and 14 days) after a single intraperitoneal injection of folic acid (250 mg/kg, n = 3/time-point) [19]. GSE116299 (GPL21493, Illumina HiSeq 3000) comprised of 23 samples isolated from prostate tissues from intact or castrated male mice (3, 10 and 14 days post-castration) fed with a control (4 mg of folic acid/kg of feed) and folic acid supplemented diet (24 mg of folic acid/kg of feed) from conception [20]. The data from the prostate tissues of intact male mice (n = 4/group) were used in the present study. The data files were downloaded from the GEO (GSE80587 and GSE65267) or European Nucleotide Archive (ENA) database (GSE116299) in the European Bioinformatics Institute (EBI; https://www.ebi.ac.uk/ena).

Data preprocessing

The raw data in GSE80587 and GSE65267 were processed using the Limma package (version 3.10.3, http://www.bioconductor.org/packages/2.9/bioc/html/limma.html) in R [21]. The workflow is shown in S1 Fig. The data (fastq) downloaded from GSE116299 was processed using fastQC to filter the low quality data [22]. The HISAT2 software (version 2.1.0; http://ccb.jhu.edu/software/hisat2) [23] was used to align to the reference genome of the mouse (mm10), with the default parameters. The raw counts of genes were calculated using featurecounts (Version 1.6.0) [24], and the transcripts per million (TPM) value of each gene was calculated for each sample. The Limma package was used for the batch effect correction [25]. Genes with averaged TPM from duplicates over 0.1 [26] and the coefficient of variation that ranked the top 75% were reserved [27]. According to the TPM values, the sample cluster diagram was drawn, and the outliers were removed.

Selection of gene modules through weighed gene co-expression network analysis (WGCNA)

The weighed gene co-expression network analysis (WGCNA) is usually applied for integrating the gene expression and identifying modules that are associated with sample traits [28]. All samples were pooled and assigned into two groups, according to the treatment strategy (with and without folic acid) or organ (brain, kidneys and prostate). The modules related to the organ and folic acid were analyzed using the WGCNA package in R (version 1.61, https://cran.r-project.org/web/packages/WGCNA/), according to the scale-free network theory [29]. Stable gene modules were screened according to the TPM values of all genes, with the thresholds of minModuleSize = 30, dissimilarity = 0.25, softPower = 11, and cutHeight = 0.95. The module significance criteria were set as P<0.05, and the correlation coefficient (cor) was >0.5. The eigengenes in the selected modules were used for further analysis.

Functional enrichment analysis

In order to investigate the functional processes and pathways associated with the eigengenes in the significant modules, the functional enrichment analysis was separately conducted for the eigengenes in each module. Database for Annotation, Visualization, and Integrated Discovery (DAVID) online tool (version 6.8, https://david.ncifcrf.gov/) [30] was utilized to extract the meaningful Gene Ontology (GO) functional terms, including the biological processes (BP), molecular function (MF) and cellular components (CC), and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Significant items were selected according to the criteria of P-value <0.05.

Protein-protein interaction (PPI) network construction

First, the eigengenes in the significant modules were subjected to the STRING (Version 10.0, http://string-db.org/) database, and the protein interaction pairs (score >0.4) were extracted. The PPI network was separately constructed for eigengenes in each module. The PPI network of each significant module was constructed using the Cytoscape software (Version 3.2.0, http://www.cytoscape.org/) [31]. The genes for coding the nodes in the PPI networks were regarded as hub genes.

Results

Data processing and batch effect correction

After data processing, a total of 17,252 genes with TPM >0.1 were identified from the samples in the three datasets (GSE80587, GSE65267 and GSE116299). The Pearson’s correlation analysis revealed that samples had a high correlation intra-dataset (Fig 1A). The batch effect correction for the 37 samples from three datasets indicated an outlier in GSE65267, which was named as, FA3dayrep1 (Fig 1B). The sample FA3dayrep1 was removed, accordingly.

Fig 1. Sample correlation and batch effect correction.

Fig 1

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(A) The Pearson’s correlation analysis for the samples included in the GSE80587, GSE65267 and GSE116299 datasets. (B) The batch effect correction of samples in the GSE80587, GSE65267 and GSE116299 datasets.

WGCNA module selection

Before the WGCNA, the soft-thresholding power of the adjacency matrix was explored, according to the scale-free network theory. The value of soft-thresholding power was 11, when the square value of the correlation coefficient (r2) = 0.9 (Fig 2A). The mean connectivity = 1 when the soft-thresholding power was 11 (Fig 2B). According to the parameters and criteria mentioned above (minModuleSize = 30, dissimilarity = 0.25, softPower = 11, and cutHeight = 0.95), the module tree was constructed (Fig 2C), and a total of 40 co-expressed modules were identified (Fig 2D).

Fig 2. The network topology analysis for the soft threshold power of the adjacency matrix.

Fig 2

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(A) The soft-thresholding power corresponding to the correlation coefficient square value (r2, y-axis). The higher the r2 value, the closer this was to the scale-free topology. (B) The connectivity corresponding to the different soft-thresholding power. The higher the soft-thresholding power, the lower the mean connectivity. The mean connectivity was equal to 1 when soft-thresholding power = 11. (C) The WGCNA modules tree. (D) The WGCNA modules and number of nodes in the modules.

These modules consisted of 46–3,544 eigengenes, respectively. In addition, 2, 7 and 7 modules were correlated to the folic acid treatment, prostate and kidneys, respectively (Fig 3). It was noted that two modules (bisque4, gene count = 87; and brown4, gene count = 97) were significantly correlated with the folic acid treatment (cor = -0.58, p = 2e-04 and cor = -0.57, p = 3e-04, respectively; Fig 3) and the kidney trait (cor = -0.71, p = 1e-06 and cor = -0.68, p = 4e-06, respectively).

Fig 3. The correlation of modules with the sample trait.

Fig 3

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ME, module of eigengenes. Red and blue colors in the heatmap note the positive and negative correlations with the corresponding traits, respectively. *Indicates the significant correlation (absolute cor > 0.5 and P<0.05).

The sample expression profiles of the eigengenes in bisque4 and brown4 are shown in S1 Fig. The correlation analysis revealed that the 97 eigengenes in the brown4 module revealed significant and moderate correlation (cor = 0.51 and P < 9.5e-08, S2A Fig), while the 87 genes in bisque4 had a low correlation (cor = 0.40 and P = 0.00012, S2B Fig). Then, the brown4 module was identified as the key one in the present study.

Enrichment analysis

The GO functional analysis revealed that eigengenes in the brown4 module were associated with BPs such as “regulation of male gonad development” (including Semaphorin 3A, Sema3A; and insulin-like factor 3, Insl3), “cell surface receptor signaling pathway involved in heart development” (involves Snai1 and Dll1), “regulation of vitamin metabolic process”, and “vitamin D metabolic process” (Snai1; Fig 4A and S1 Table). Sema3A had the MFs of “semaphorin receptor binding”, “chemorepellent activity”, and “neuropilin binding”, and Dll1 acted its activity “Notch binding” as a component of “apical part of cell” (S1 Table). Sema3a, MutS homologue 4 (Msh4), and Snai1 genes were involved in “regulation of reproductive process”, “gonad development”, “reproductive system development”, “female gamete generation”, and “sex differentiation” (S1 Table).

Fig 4. Functional enrichment analysis and the protein-protein interaction (PPI) network of eigengenes in the brown4 module.

Fig 4

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(A) The GO biological processes that involve the eigengenes in the brown4 module. (B) The PPI network of eigengenes in the brown4 module. The node size indicates the interaction degree.

PPI network and hub genes

The PPI network of the eigengenes in the brown4 module consisted of 69 genes, including eight hub genes with Kmer >0.95, including tetratricopeptide repeat protein 38 (Ttc38), miR-185 and 4931431C16Rik, with the interaction degree of 68, 68 and 67 in the network, respectively (Fig 4B). The other five hub genes were solute transporter-β (Slc51b), Msh4, Gm14687, Gm23199, n-R5s69 and Gm20442, with a low interaction degree in the PPI network (degree = 3).

Discussion

This study identified that a WGCNA module (brown4) and key hub genes were simultaneously associated with folic acid-related mechanisms in the kidney. Among the eigengenes in brown4, one hub gene Ttc38 and one key miRNA miR-185 were identified. Eigengenes in the brown4 module, including Sema3A, Insl3, Dll1, Msh4 and Snai1, were associated with “regulation of reproductive process”, “gonad development”, “sex differentiation”, “neuropilin binding” and “regulation of vitamin metabolic process”. These nodes are the novel factors associated with folic acid metabolism or kidney development.

Folic acid deficiency correlates with elevated homocysteine levels, which is a risk factor for colon cancer [1, 32]. Larriba et al. [33] reported that colon cancer tissues that co-express Snai2 and Snai1 downregulated the vitamin D receptor, which mediates the antitumoral action of vitamin D. Sema3A is a ligand of neuropilin-1 and a tumor suppressor in acute leukemia [34]. Neuropilin-1 is involved in diverse processes, including cancer and angiogenesis [35–37]. It is a transmembrane glycoprotein that is required for the development of embryonic neuron and vascular [37, 38]. Elevated neuropilin-1 in urinary and renal tissues is associated with the clinical response of renal lupus nephritis [35]. The present study identified that Sema3A, Insl3, Dll1, Msh4 and Snai1 were all negatively associated with folic acid treatment and kidney development, which reveals their potential roles in folic acid-mediated diverse functions.

Homocysteine is a product of the methylation cycle and is catalyzed to methionine by enzyme methionine synthetases (MSs) [39]. Methylation reactions involve almost all chemical reactions in body, and its disturbance has been linked to various body disorders, including brain atrophy, oxidative stress, increased apoptosis, DNA damage and neurodegenerative disorders, such as Parkinson’s disease, AD and depression [8, 40]. Increasing evidence has shown folic acid and vitamin B12 can significantly improve cognitive performance in patients with AD [41, 42]. A 5-year trial for a large cohort of postmenopausal women without symptoms of dementia (memory cognitive impairment) indicated that lower levels of folic acid than the recommended daily allowance (<400 μg/d) increased the risk of dementia and cognitive impairment [43]. The benefit of folic acid in the central nervous system is attributed to its effect on homocysteine [8–10]. Folic acid can markedly increase the serum S-adenosylmethionine (SAM), which is a key MS that catalyzes methylation reactions in cells [4, 44], and thereby declines the accumulation of homocysteine and mediates cytotoxicity, DNA damage and neurodegenerative disorders.

Homocysteine, which can induce cytotoxicity, apoptosis and autophagy, is correlated to various cell signaling pathways, such as phosphoinositide 3-kinase (PI3K)/Akt [45–47]. Liu et al. [46] conducted an in vitro study by treating human umbilical vein endothelial cells with homocysteine, with and without epigallocatechin gallate, and this prevented homocysteine-induced cell apoptosis by activating he PI3K/Akt/endothelial nitric oxide synthase (eNOS) pathway. Price et al [11] reported that higher folate concentration was associated with elevated risk of prostate cancer (95% confidence interval [CI], 1.02–1.26) and high-grade disease. Prostate-specific membrane antigen (PSMA) or folate hydrolase 1 (FOLH1) is overexpressed in prostate cancer, and correlates with the PI3K/Akt signaling in cells [48, 49]. The inhibition of PSMA conversely promotes tumor regression by inhibiting PI3K signaling in preclinical models [12]. MiR-185 acts as a tumor suppressor, and inhibits tumor progression by regulating its targets, including the Akt1 and PI3K/AKT pathway expression [50]. The present study revealed that Ttc38 is a target of miR-185. Ttc38 was included in the brwon4 module, which is associated with folic acid or the kidneys, while the direct association of Ttc38 with these was not identified via bioinformatics analysis. These data revealed that Ttc38 is a novel factor that may be associated with folic acid or kidney development.

High homocysteine level is associated with increased risk of chronic kidney disease [13, 14]. The present study revealed that the Ttc38 associated with folic acid was also negatively correlated with the kidney. Another tetratricopeptide repeat (TPR) member, Ttc36, has an organ-specific expression profile, and shows a high expression level in the kidneys and liver, and a low expression level in the testis [51]. The expression of Ttc36 in renal proximal tubules is spatially and temporally-specific [51]. A recent proteome of mouse with experimental autoimmune encephalomyelitis (EAE) identified the downregulation of Ttc38 in the brain [52]. The present study revealed that Ttc38 is kidney-specific and folic acid-related. The novel expression profile may suggest the interesting mechanism in the folic acid-related mechanism, which may be kidney-specific.

These findings may also show the relationship between kidney disease and folic acid. Xu reported that enalapril-folic acid therapy, compared with enalapril alone, can significantly delay the progression of chronic kidney disease (CKD) in patients with mild-to-moderate CKD [53]. In addition, Matsumoto et al. reported that treatments with folic acid in phase G3b and G4 may reduce renal disease progression by enhancing antioxidant defenses [54]. These evidences further support that folic acid is correlated to the kidneys. The expression levels of these candidate genes may be regulated by folic acid, which in turn participates in kidney functions. Although no further cellular or animal experiments were carried out on candidate genes in this study, the present study can provide new insights for kidney development and kidney disease research.

Conclusion

In conclusion, the present study revealed a WGCNA module that consisted of genes associated with folic acid and the kidneys. Hub genes, such as Ttc38 and miR-185, are key factors that have a significant relationship with the development of the kidneys after folic acid treatment. In addition, genes such as Sema3A, Insl3, Dll1, Msh4 and Snai1 may have potential roles in regulating metabolisms correlated to folic acid and kidney development. These evidences further support that folic acid is correlated to the kidneys. The expression levels of these candidate genes may be regulated by folic acid, which in turn participates in kidney function.

Supporting information

S1 Table. The GO functional enrichment analysis of the eigengenes in the brown4 module.

(XLSX)

Click here for additional data file. (22KB, xlsx)
S1 Fig. Heatmap of the expression of eigengenes in the bisque4 (A) and brown4 (B) module, respectively.

(TIF)

Click here for additional data file. (872.8KB, tif)
S2 Fig. Gene significance of eigengenes in the brown4 (A) and bisque4 (B) module, respectively.

(TIF)

Click here for additional data file. (1.3MB, tif)

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

This research was funded by the Shenyang Medical College Doctoral Research Startup Fund (No. 20195068), and the Plan of Rejuvenating the Talents of Liaoning (No. XLYC1808012).

References

  • 1.Burr NE, Hull MA, Subramanian V. Folic acid supplementation may reduce colorectal cancer risk in patients with inflammatory bowel disease. Journal of clinical gastroenterology. 2017; 51(3): 247–253. [DOI] [PubMed] [Google Scholar]
  • 2.Sun S, He M, Wang Y, Yang H, Al-Abed Y. Folic acid derived-P5779 mimetics regulate DAMP-mediated inflammation through disruption of HMGB1: TLR4: MD-2 axes. PloS one. 2018; 13(2):e0193028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Murphy ME, Westmark CJ. Folic Acid Fortification and Neural Tube Defect Risk: Analysis of the Food Fortification Initiative Dataset. Nutrients. 2020; 12(1): 247. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Chen H, Liu S, Ji L, Wu T, Ji Y, Zhou Y, et al. Folic acid supplementation mitigates Alzheimer’s disease by reducing inflammation: a randomized controlled trial. Mediators of inflammation. 2016; 2016:1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Samblas M, Martínez JA, Milagro F. Folic acid improves the inflammatory response in LPS-activated THP-1 macrophages. Mediators of inflammation. 2018; 2018: 1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Fleming A, Copp AJ. Embryonic folate metabolism and mouse neural tube defects. Science. 1998; 280(5372): 2107–2109. [DOI] [PubMed] [Google Scholar]
  • 7.Tinelli C, Di Pino A, Ficulle E, Marcelli S, Feligioni M. Hyperhomocysteinemia as a Risk Factor and Potential Nutraceutical Target for Certain Pathologies. Frontiers in nutrition. 2019; 6:49. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Mikkelsen K, Stojanovska L, Tangalakis K, Bosevski M, Apostolopoulos V. Cognitive decline: A vitamin B perspective. Maturitas. 2016; 93: 108–113. [DOI] [PubMed] [Google Scholar]
  • 9.Seshadri S, Beiser A, Selhub J, Jacques PF, Rosenberg IH, D’Agostino RB, et al. Plasma homocysteine as a risk factor for dementia and Alzheimer’s disease. New England journal of medicine. 2002; 346(7): 476–483. [DOI] [PubMed] [Google Scholar]
  • 10.Oulhaj A, Refsum H, Beaumont H, Williams J, King E, Jacoby R, et al. Homocysteine as a predictor of cognitive decline in Alzheimer’s disease. International journal of geriatric psychiatry. 2010; 25(1): 82–90. [DOI] [PubMed] [Google Scholar]
  • 11.Price AJ, Travis RC, Appleby PN, Albanes D, Gurrea AB, Bjørge T, et al. Circulating folate and vitamin B12 and risk of prostate cancer: a collaborative analysis of individual participant data from six cohorts including 6875 cases and 8104 controls. European urology. 2016; 70(6): 941–951. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Kaittanis C, Andreou C, Hieronymus H, Mao N, Foss CA, Eiber M, et al. Prostate-specific membrane antigen cleavage of vitamin B9 stimulates oncogenic signaling through metabotropic glutamate receptors. Journal of experimental medicine. 2018; 215(1): 159–175. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Cohen E, Margalit I, Shochat T, Goldberg E, Krause I. The relationship between the concentration of plasma homocysteine and chronic kidney disease: a cross sectional study of a large cohort. Journal of nephrology. 2019; 32(5): 783–789. [DOI] [PubMed] [Google Scholar]
  • 14.Cianciolo G, De Pascalis A, Di Lullo L, Ronco C, Zannini C, La Manna G. Folic acid and homocysteine in chronic kidney disease and cardiovascular disease progression: which comes first. Cardiorenal medicine. 2017; 7(4): 255–266. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Cianciolo G, La Manna G, Colì L, Donati G, D’Addio F, Persici E, et al. 5-methyltetrahydrofolate administration is associated with prolonged survival and reduced inflammation in ESRD patients. American journal of nephrology. 2008; 28(6): 941–948. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Soohoo M, Ahmadi SF, Qader H, Streja E, Obi Y, Moradi H, et al. Association of serum vitamin B12 and folate with mortality in incident hemodialysis patients. Nephrology dialysis transplantation. 2017; 32(6): 1024–1032. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Qin X, Huo Y, Langman CB, Hou F, Chen Y, Matossian D, et al. Folic Acid Therapy and Cardiovascular Disease in ESRD or Advanced Chronic Kidney Disease: A Meta-Analysis. Clinical Journal of the American society of nephrology. 2011; 6(3): 482–488. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Henzel KS, Ryan DP, Schröder S, Weiergräber M, Ehninger D. High-dose maternal folic acid supplementation before conception impairs reversal learning in offspring mice. Scientific reports. 2017; 7(1): 1–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Craciun FL, Bijol V, Ajay AK, Rao P, Kumar RK, Hutchinson J, et al. RNA sequencing identifies novel translational biomarkers of kidney fibrosis. Journal of the American society of nephrology. 2016; 27(6): 1702–1713. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Joseph DB, Chandrashekar AS, Chu LF, Thomson JA, Vezina CM. A folic acid‐enriched diet attenuates prostate involution in response to androgen deprivation. The prostate. 2019; 79(2): 183–194. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Smyth GK. limma: Linear Models for Microarray Data. Bioinformatics and computational biology solutions using R and bioconductor. 2005: 397–420. [Google Scholar]
  • 22.Andrews S. FastQC: a quality control tool for high throughput sequence data Babraham bioinformatics, Babraham institute, Cambridge, United Kingdom: 2010. [Google Scholar]
  • 23.Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nature methods. 2015; 12(4): 357–360. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014; 30(7): 923–930. [DOI] [PubMed] [Google Scholar]
  • 25.Haghverdi L, Lun AT, Morgan MD, Marioni JC. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nature biotechnology. 2018; 36(5): 421–427. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Kagale S, Nixon J, Khedikar Y, Pasha A, Provart NJ, Clarke WE, et al. The developmental transcriptome atlas of the biofuel crop Camelina sativa. The plant journal. 2016; 88(5): 879–894. [DOI] [PubMed] [Google Scholar]
  • 27.Liao E. Challenges in High-throughput Data Analysis: Proteomic Data Pre-processing and Network Methods for Integrating Multiple Data Types: UCLA. 2012.
  • 28.Li J, Zhou D, Qiu W, Shi Y, Yang JJ, Shi C, et al. Application of Weighted Gene Co-expression Network Analysis for Data from Paired Design. Scientific reports. 2018; 8(1): 622. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Langfelder P, Horvath S. WGCNA: an R package for weighted gene co-expression network analysis. BMC bioinformatics. 2008; 9: 559. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature protocols. 2008; 4(1): 44–57. [DOI] [PubMed] [Google Scholar]
  • 31.Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research. 2003; 13(11): 2498–2504. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Park S-K, Youn C-H, Ko H-J, Kim A-S, Kim H-M, Kim H-S, et al. Homocysteine Concentration and the Risk of Developing Colorectal Neoplasm. Korean journal of family practice. 2016; 6(5): 404–409. [Google Scholar]
  • 33.Larriba MJ, Martín-Villar E, García JM, Pereira F, Pena C, García de Herreros A, et al. Snail2 cooperates with Snail1 in the repression of vitamin D receptor in colon cancer. Carcinogenesis. 2009; 30(8): 1459–1468. [DOI] [PubMed] [Google Scholar]
  • 34.Yang ZG, Wen RT, Qi K, Li J, Zheng GX, Wang YF, et al. The Neuropilin‐1 Ligand, Sema3A, Acts as a Tumor Suppressor in the Pathogenesis of Acute Leukemia. The anatomical record. 2019; 302(7): 1127–1135. [DOI] [PubMed] [Google Scholar]
  • 35.Torres-Salido MT, Sanchis M, Solé C, Moliné T, Vidal M, Vidal X, et al. Urinary Neuropilin-1: A Predictive Biomarker for Renal Outcome in Lupus Nephritis. International journal of molecular sciences. 2019; 20(18): 4601. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Li X, Bielenberg DR. Neuropilin 1 and Neuropilin 2: Cancer Progression and Biomarker Analysis. Biomarkers of the tumor microenvironment. 2017. 329–349. [Google Scholar]
  • 37.Mehta V, Fields L, Evans IM, Yamaji M, Pellet-Many C, Jones T, et al. VEGF (vascular endothelial growth factor) induces NRP1 (neuropilin-1) cleavage via ADAMs (a disintegrin and metalloproteinase) 9 and 10 to generate novel carboxy-terminal NRP1 fragments that regulate angiogenic signaling. Arteriosclerosis thrombosis and vascular biology. 2018; 38(8): 1845–1858. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Sano N, Shimogawa T, Sakaguchi H, Ioroi Y, Miyawaki Y, Morizane A, et al. Enhanced axonal extension of subcortical projection neurons isolated from murine embryonic cortex using neuropilin-1. Frontiers in cellular neuroscience. 2017; 11: 123. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Coleman DN, Vailati-Riboni M, Elolimy AA, Cardoso FC, Rodriguez-Zas SL, Miura M, et al. Hepatic betaine-homocysteine methyltransferase and methionine synthase activity and intermediates of the methionine cycle are altered by choline supply during negative energy balance in Holstein cows. Journal of dairy science. 2019; 102(9): 8305–8318. [DOI] [PubMed] [Google Scholar]
  • 40.Kruman II, Culmsee C, Chan SL, Kruman Y, Guo Z, Penix L, et al. Homocysteine elicits a DNA damage response in neurons that promotes apoptosis and hypersensitivity to excitotoxicity. Journal of neuroscience. 2000; 20(18): 6920–6926. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Remington R, Bechtel C, Larsen D, Samar A, Page R, Morrell C, et al. Maintenance of cognitive performance and mood for individuals with Alzheimer’s disease following consumption of a nutraceutical formulation: A one-year, open-label study. Journal of Alzheimer’s disease. 2016; 51(4): 991–995. [DOI] [PubMed] [Google Scholar]
  • 42.Horvat P, Gardiner J, Kubinova R, Pajak A, Tamosiunas A, Schöttker B, et al. Serum folate, vitamin B-12 and cognitive function in middle and older age: The HAPIEE study. Experimental gerontology. 2016; 76: 33–38. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Agnew-Blais JC, Wassertheil-Smoller S, Kang JH, Hogan PE, Coker LH, Snetselaar LG, et al. Folate, vitamin B-6, and vitamin B-12 intake and mild cognitive impairment and probable dementia in the Women’s Health Initiative Memory Study. Journal of the academy of nutrition and dietetics. 2015; 115(2): 231–241. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Luo H, Hansen ASL, Yang L, Schneider K, Kristensen M, Christensen U, et al. Coupling S-adenosylmethionine-dependent methylation to growth: Design and uses. Plos biology. 2019; 17(3): e2007050. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Cui S, Li W, Wang P, Lv X, Gao Y, Huang G. Folic acid inhibits homocysteine-induced cell apoptosis in human umbilical vein endothelial cells. Molecular and cellular biochemistry. 2018; 444(1–2): 77–86. [DOI] [PubMed] [Google Scholar]
  • 46.Liu S, Sun Z, Chu P, Li H, Ahsan A, Zhou Z, et al. EGCG protects against homocysteine-induced human umbilical vein endothelial cells apoptosis by modulating mitochondrial-dependent apoptotic signaling and PI3K/Akt/eNOS signaling pathways. Apoptosis. 2017; 22(5): 672–680. [DOI] [PubMed] [Google Scholar]
  • 47.Chen Y, Liu H, Wang X, Zhang H, Liu E, Su X. Homocysteine up-regulates endothelin type A receptor in vascular smooth muscle cells through Sirt1/ERK1/2 signaling pathway. Microvascular research. 2017; 114: 34–40. [DOI] [PubMed] [Google Scholar]
  • 48.Caromile LA, Shapiro LH. PSMA redirects MAPK to PI3K-AKT signaling to promote prostate cancer progression. Molecular and cellular oncology. 2017; 4(4): e1321168. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Caromile LA, Dortche K, Rahman MM, Grant CL, Stoddard C, Ferrer FA, et al. PSMA redirects cell survival signaling from the MAPK to the PI3K-AKT pathways to promote the progression of prostate cancer. Science signaling. 2017; 10(470): eaag 3326. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Zhang S, Li D, Jiao G-j, Wang H-l, Yan T-b. miR-185 suppresses progression of Ewing’s sarcoma via inhibiting the PI3K/AKT and Wnt/β-catenin pathways. OncoTargets and therapy. 2018; 11: 7967–7977. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Zhou Y, He Q, Chen J, Liu Y, Mao Z, Lyu Z, et al. The expression patterns of Tetratricopeptide repeat domain 36 (Ttc36). Gene expression patterns. 2016; 22(2): 37–45. [DOI] [PubMed] [Google Scholar]
  • 52.Lereim RR, Oveland E, Xiao Y, Torkildsen Ø, Wergeland S, Myhr K-M, et al. The brain proteome of the ubiquitin ligase peli1 knock-out mouse during experimental autoimmune encephalomyelitis. Journal of proteomics and bioinformatics. 2016; 9(9): 209–219. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Xu X, Qin X, Li Y, Sun D, Wang J, Liang M, et al. Efficacy of Folic Acid Therapy on the Progression of Chronic Kidney Disease: The Renal Substudy of the China Stroke Primary Prevention Trial. JAMA internal medicine. 2016; 176(10): 1443–1450. [DOI] [PubMed] [Google Scholar]
  • 54.Andressa, Matsumoto K, Maes M, Michelin A, Bonifacio K, De L, et al. Treatment With Folic Acid Increases Paraoxonase 1 Activity Thereby Improving Chronic Kidney Disease. 2020 March.

Decision Letter 0

Jishnu Das

27 May 2020

PONE-D-20-10567

Novel genes associates with folic acid-mediated metabolism in mouse: a bioinformatics study

PLOS ONE

Dear Dr. Hu,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

We would be willing to consider a revised manuscript that addresses all the comments raised by the reviewers. A key concern is the quality of presentation (especially for the figures); the manuscript should be revised to address the relevant issues raised by both reviewers. Additionally, there are many grammatical and typographical errors throughout the manuscript that should be fixed.

Please submit your revised manuscript by Jul 10 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

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If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Jishnu Das, Ph.D.

Academic Editor

PLOS ONE

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[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: I Don't Know

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: No

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This study aims to investigate genes associated with folic acid metabolism. Folic acid levels have been associated with different health indicators in the brain, prostate, and kidneys, but the mechanism by which folic acid influences these tissues is unclear. A computational analysis of genes in the tissue may help shed light on key gene networks in these processes. The authors take an informatic approach by investigating public NGS data from the NCBI GEO database. They specifically target mouse experiments involving folic acid for their data, and subsequently perform weighted gene coexpression network analysis (WGCNA), functional enrichment analysis, and protein-protein interaction network construction. After analysis, they found several novel genes have been connected to folic acid metabolism and renal development.

The general structure of the research and the paper is good. The authors explicitly state their objective to identify genes associated with folic acid metabolism. They do a good job of identifying previous studies correlating folic acid with biomarkers in different contexts. They then contextualize their study as an investigation towards the underlying molecular processes behind these associations. For the purpose of their exploratory analysis, the methods were suitable to address the research question. Their design flowed well in using the findings of one analysis in the one immediately after, so the rationale was clear altogether. The methods for the analyses also referenced literature sufficiently in justifying their use of statistical packages/software for a task. However, some parts of the methods were a little ambiguous. The biggest issue was in the figures. Some presentation decisions made the figures very messy and distracted from their significance. Furthermore, the captions could use more detail in explaining the value of various parts of a figure. Additionally, the writing throughout the paper was dotted with spacing issues, misspellings, and grammatical errors. In some cases, these created comprehension issues that were only cleared up after a full readthrough. Ultimately, the presented conclusions were supported by the results in the paper. Overall, I found that the quality of research is appropriate for publication in PLOS ONE, but its presentation in terms of writing and figures should be refined further.

Major:

• Some of the figures require further refinement. For Figure 1A, the numbers in the boxes are pixelated even in the high-resolution representation, and it is unclear how much the value they add in comparison to a separate supplemental table. Figure 4B is very messy overall. Shortening the names for the nodes or omitting some altogether would significantly improve legibility here. Figure S2 seems unclear for interpretation. Adding a regression line may help.

• For figure 2, it is unclear why the soft power of 11 was being highlighted in 2A and 2B when a soft power of 1 was ultimately chosen for the WGCNA analysis shown in 2C. For Figure 2C, more detail is required, as the figure doesn’t clearly connect to the authors’ claims that 40 modules were identified. Detailing the caption should help clarify this figure.

• I couldn’t find any explicit discussions of study limitations or potential future avenues for investigation, so acknowledging some issues could significantly strengthen the conclusion and help contextualize the research.

• The writing should be improved significantly to help the paper flow more smoothly.

Minor:

• In their introduction, the authors devote a lot of time to the central nervous system, a few lines to the kidneys, and very little to the prostate. While the current organization may reflect the state of folic acid research, the introduction should be rebalanced in the context of the paper’s findings. More focus on kidneys, a little more on prostate, and less on the CNS.

• Citing literature justification explicitly for the TPM cutoff and coefficient of variation would further strengthen the paper.

• The small amount of mice (<100 in total) could be increased to lend validity to the findings, especially since the mice are draw from 3 separate studies.

• Publishing code may help reduce or expose any concerns with study reproducibility.

• Conducting more parallel statistical analyses or performing experimental validation may help strengthen findings and research quality.

Reviewer #2: This manuscript describes the study for the identification of the genes related to folic acid metabolism and organ development by applying a bioinformatics pipeline based on public datasets. This study may be of interest to the readers, however, I have some recommendations for the authors.

Can the authors perform a sensitivity analysis or at least comment on how much the results are sensitive to the choice of parameter values or settings of the algorithms or packages that have been used in the bioinformatics pipeline?

The structure of the manuscript can be improved to make it easier to follow. For example, the flow of the bioinformatics pipeline can be given either in text format or by a figure. Also, Figure 4 can be redrawn since there is too much text in its current form.

Also, there are some typographical and spelling errors in the manuscript. Some examples are indicated below.

1. Material and Methods pp.8, line 212

“(fastq) downloaded from GSE116299 was processed using fastQ for filtering the low quality”

I suppose fastQ should be corrected ad fastQC and I think that the reference number 39 is not the the correct reference for fastQC

The correct citation can be

Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online]. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/

2. Correlationintra written as a single word

pp.3-line 70 analysis showed samples had high correlationintra-dataset (Figure 1A).

3. softPower=1 should be corrected as softPower=11

pp.3-line 82 when soft-thresholding power was 11 (Figure 2B). According to the parameters and criteria

83 of mentioned above (minModuleSize=30, dissimilarity=0.25, softPower=1 and

4. showedsiginificant written as a single word

pp.4-line 104 S1. Correlation analysis showed that the 97eigengenes in brown4 module showedsignificant

5. bisque4showed written as a single word

pp.4-line 106 bisque4showed low correlation (cor=0.40 and P=0.00012, Figure S2B). Then, the brown4

**********

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Reviewer #1: No

Reviewer #2: Yes: Volkan Atalay

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2020 Sep 11;15(9):e0238940. doi: 10.1371/journal.pone.0238940.r002

Author response to Decision Letter 0

29 Jun 2020

Dear editor,

Thank you for giving us the opportunity to revise the manuscript. We gladly accept the reviewers’ comments and revise them one by one. We hope that this revised manuscript will meet the publishing requirements.

The point by point response were listed below.

Thanks for the editor's work, and the reviewers for their careful review.

Kind regards,

Tingxi Hu

Reviewer #1: This study aims to investigate genes associated with folic acid metabolism. Folic acid levels have been associated with different health indicators in the brain, prostate, and kidneys, but the mechanism by which folic acid influences these tissues is unclear. A computational analysis of genes in the tissue may help shed light on key gene networks in these processes. The authors take an informatic approach by investigating public NGS data from the NCBI GEO database. They specifically target mouse experiments involving folic acid for their data, and subsequently perform weighted gene coexpression network analysis (WGCNA), functional enrichment analysis, and protein-protein interaction network construction. After analysis, they found several novel genes have been connected to folic acid metabolism and renal development.

The general structure of the research and the paper is good. The authors explicitly state their objective to identify genes associated with folic acid metabolism. They do a good job of identifying previous studies correlating folic acid with biomarkers in different contexts. They then contextualize their study as an investigation towards the underlying molecular processes behind these associations. For the purpose of their exploratory analysis, the methods were suitable to address the research question. Their design flowed well in using the findings of one analysis in the one immediately after, so the rationale was clear altogether. The methods for the analyses also referenced literature sufficiently in justifying their use of statistical packages/software for a task. However, some parts of the methods were a little ambiguous. The biggest issue was in the figures. Some presentation decisions made the figures very messy and distracted from their significance. Furthermore, the captions could use more detail in explaining the value of various parts of a figure. Additionally, the writing throughout the paper was dotted with spacing issues, misspellings, and grammatical errors. In some cases, these created comprehension issues that were only cleared up after a full readthrough. Ultimately, the presented conclusions were supported by the results in the paper. Overall, I found that the quality of research is appropriate for publication in PLOS ONE, but its presentation in terms of writing and figures should be refined further.

Response: Thanks for your review, we are willing to accept your suggestions and do our best to revise the article.

Major:

• Some of the figures require further refinement. For Figure 1A, the numbers in the boxes are pixelated even in the high-resolution representation, and it is unclear how much the value they add in comparison to a separate supplemental table. Figure 4B is very messy overall. Shortening the names for the nodes or omitting some altogether would significantly improve legibility here. Figure S2 seems unclear for interpretation. Adding a regression line may help.

Response: Thanks for your comment. We have revised all the figures to improve the quality and readability. Furthermore,more details were provided in the captions part.

• For figure 2, it is unclear why the soft power of 11 was being highlighted in 2A and 2B when a soft power of 1 was ultimately chosen for the WGCNA analysis shown in 2C. For Figure 2C, more detail is required, as the figure doesn’t clearly connect to the authors’ claims that 40 modules were identified. Detailing the caption should help clarify this figure.

Response: Thanks for your comment.We are sorry for incorrectly marking the value of soft power in the text. Figure 2C is analyzed under the parameters of 11, not 1. We have revised it in the manuscript.Figure 2C does indeed mislead the reader, so we added nodes statistical results for modules (Figure 2D). in addition, the captions were updated to clarify this figure.

• I couldn’t find any explicit discussions of study limitations or potential future avenues for investigation, so acknowledging some issues could significantly strengthen the conclusion and help contextualize the research.

Response: Thanks for your comment. We added both the limitations and potential future avenues in this manuscript. Our findings may remind us of the relationship between kidney disease and folic acid. Xu found that enalapril-folic acid therapy, compared with enalapril alone, can significantly delay the progression of chronic kidney disease (CKD) among patients with mild-to-moderate CKD. Also, Matsumoto et al found that treatment with folic acid in phase G3b and G4 may reduce renal disease progression by enhancing antioxidant defenses. These evidences further proved that folic acid was related to the kidney. The expression levels of these candidate genes may be regulated by folic acid, which in turn participates in kidney function. Although no further cellular or animal experiments have been carried out on candidate genes in this study, this present study can provide new insight for kidney development and kidney disease research.

• The writing should be improved significantly to help the paper flow more smoothly.

Response: Thanks for your comment.We improved the language by AJESCI language retouching service.

Minor:

• In their introduction, the authors devote a lot of time to the central nervous system, a few lines to the kidneys, and very little to the prostate. While the current organization may reflect the state of folic acid research, the introduction should be rebalanced in the context of the paper’s findings. More focus on kidneys, a little more on prostate, and less on the CNS.

Response: Thanks for your comment.We made major revisions to the introduction, reduced the description of AD. We more focus on kidneys and CDK.

• Citing literature justification explicitly for the TPM cutoff and coefficient of variation would further strengthen the paper.

Response: Thanks for your comment. The literatures were cited for the TPM cutoff and coefficient of variation.Kagale et al used a similar similarTPM cutoff parameter in their WGCNA analysis. Liao reported the method of the coefficient of variation range.

• The small amount of mice (<100 in total) could be increased to lend validity to the findings, especially since the mice are draw from 3 separate studies.

Response: Thank you for your suggestion. We agree with your comment very much. We consulted many literatures and removed some of them according to the research background, and found that not many data were available. So, the number of mice does not reach 100. In addition, the number of mice used in this article is in accordance with WGCNA requirements (https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/faq.html), so the results of this study are credible.

• Publishing code may help reduce or expose any concerns with study reproducibility.

Response: Thank you for your suggestion.We are very sorry that the code we used is an in-housepipeline, so it cannot be provided. But the manuscript already described all the parameters used.

• Conducting more parallel statistical analyses or performing experimental validation may help strengthen findings and research quality.

Response: Thank you for your suggestion.This study is mainly to reuse the published data by bioinformatic analysis, and then to screen the genes that related to folic acid treatment in kidney.The verification work is necessary, but it is also a pity that we are limited by factors such as animal materials.Therefore, we also discussed the limitations of this study in the discussion section.

Reviewer #2:This manuscript describes the study for the identification of the genes related to folic acid metabolism and organ development by applying a bioinformatics pipeline based on public datasets. This study may be of interest to the readers, however, I have some recommendations for the authors.

Can the authors perform a sensitivity analysis or at least comment on how much the results are sensitive to the choice of parameter values or settings of the algorithms or packages that have been used in the bioinformatics pipeline?

Response: Thank you for your comment. Part of parameters in our WGCNA analysis were choose base on the value that most papers published or the WGCNA official default setting (https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/index.html), so the result is robust and repeatable.Thearticles listed below used the similar parameters of WGCNA.

Young C D, Dammer E, Griffen T, et al. WGCNA identification of CXCL13 and associated genes involved in the Tumor Immune Microenvironment (TIME) of lung adenocarcinoma[J]. 2020.

Di Y, Chen D, Yu W, et al. Bladder cancer stage-associated hub genes revealed by WGCNA co-expression network analysis[J]. Hereditas, 2019, 156(1): 7.

Feltrin A S A, Tahira A C, Simoes S N, et al. Assessment of complementarity of WGCNA and NERI results for identification of modules associated to schizophrenia spectrum disorders[J]. PloS one, 2019, 14(1).

Kagale S, Nixon J, Khedikar Y, Pasha A, Provart NJ, Clarke WE, et al. The developmental transcriptome atlas of the biofuel crop Camelina sativa. The Plant Journal. 2016; 88(5): 879-894. https://doi.org/10.1111/tpj.13302.

Liao E. Challenges in High-throughput Data Analysis: Proteomic Data Pre-processing and Network Methods for Integrating Multiple Data Types: UCLA; 2012

The structure of the manuscript can be improved to make it easier to follow. For example, the flow of the bioinformatics pipeline can be given either in text format or by a figure. Also, Figure 4 can be redrawn since there is too much text in its current form.

Response: Thank you for your comment.We have restructured the article structure according to the author's instructions. The flow of the bioinformatics pipeline is shown in Figure S1. In addition, Figure 4 was redrawn. We rename the nodes to gene symbles.

Also, there are some typographical and spelling errors in the manuscript. Some examples are indicated below.

1. Material and Methods pp.8, line 212

“(fastq) downloaded from GSE116299 was processed using fastQ for filtering the low quality”

I suppose fastQ should be corrected ad fastQC and I think that the reference number 39 is not the the correct reference for fastQC

The correct citation can be

Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online]. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/

Response: Thank you for your comment. We revised it according to your comment.

2. Correlationintra written as a single word

pp.3-line 70 analysis showed samples had high correlationintra-dataset (Figure 1A).

Response: Thank you for your comment. We revised it according to your comment.

3. softPower=1 should be corrected as softPower=11

pp.3-line 82 when soft-thresholding power was 11 (Figure 2B). According to the parameters and criteria

83 of mentioned above (minModuleSize=30, dissimilarity=0.25, softPower=1 and

Response: Thank you for your comment. the parameter is 11, not 1. Revised it as required.

4. showedsiginificant written as a single word

pp.4-line 104 S1. Correlation analysis showed that the 97eigengenes in brown4 module showedsignificant

Response: Thank you for your comment. Revised it as required.

5. bisque4showed written as a single word

pp.4-line 106 bisque4showed low correlation (cor=0.40 and P=0.00012, Figure S2B). Then, the brown4

Response: Thank you for your comment. Revised it as required.

Attachment

Submitted filename: response to reviewers.docx

Click here for additional data file. (21.3KB, docx)

Decision Letter 1

Jishnu Das

21 Jul 2020

PONE-D-20-10567R1

Novel genes associate with folic acid-mediated metabolism in mouse: a bioinformatics study

PLOS ONE

Dear Dr. Hu,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

ACADEMIC EDITOR:

The reviewers raised a few additional concerns that should be addressed. The manuscript would also improve from further proofreading and language editing.

==============================

Please submit your revised manuscript by Sep 04 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Jishnu Das, Ph.D.

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors did a good job of addressing overarching concerns as well as specific prior issues raised by modifying the paper and directly describing the changes in response to the comments. Overall, I have no other significant concerns, but I did notice a few final typos to fix.

Final revisions:

• Line 34: “HubgenesHub genes” should be changed to just “Hubgenes” or “Hub genes”

• Line 84: “10and 14” should be fixed to “10 and 14”

• Line 162: “wasnoted” should be broken up into “was noted”

• 172: “97eigengenes” and “showedsignificant” should be split into “97 eigengenes” and “showed significant”

• 195: “Hubgeneswith” should be split up

• 223: “B12can” should be split up

• Figure 2D should be retitled “Number of nodes in modules” or something along those lines. Numbers is currently misspelled as numbers.

Reviewer #2: The authors addressed my comments. However, there are still some typographical errors.

These errors are all two words written together and they can be corrected easily by using a spellchecker.

pp1 line 34 HubgenesHub genes

pp6 line 195 hubgeneswith

pp6 line 200 weresimultaneously

pp6 line 202 wereidentified

pp7 line 241 promotestumor

pp8 line 273 mayhave

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2020 Sep 11;15(9):e0238940. doi: 10.1371/journal.pone.0238940.r004

Author response to Decision Letter 1

27 Jul 2020

Dear editor,

Thank you for giving us the opportunity to revise the manuscript. We gladly accept the reviewers’ comments and revise them one by one. We hope that this revised manuscript will meet the publishing requirements.

The point by point response were listed below.

Thanks for the editor's work, and the reviewers for their careful review.

Kind regards,

Tingxi Hu

Reviewer #1: The authors did a good job of addressing overarching concerns as well as specific prior issues raised by modifying the paper and directly describing the changes in response to the comments. Overall, I have no other significant concerns, but I did notice a few final typos to fix.

Final revisions:

• Line 34: “HubgenesHub genes” should be changed to just “Hubgenes” or “Hub genes”

• Line 84: “10and 14” should be fixed to “10 and 14”

• Line 162: “wasnoted” should be broken up into “was noted”

• 172: “97eigengenes” and “showedsignificant” should be split into “97 eigengenes” and “showed significant”

• 195: “Hubgeneswith” should be split up

• 223: “B12can” should be split up

Response: Thank you for your comments. We have corrected all the typographical errors you mentioned and other errors in this article.

• Figure 2D should be retitled “Number of nodes in modules” or something along those lines. Numbers is currently misspelled as numbers.

Response: Thanks for your comment. We're sorry that the title is misspelled. The title is now adjusted to “Number of nodes in modules”.

Reviewer #2: The authors addressed my comments. However, there are still some typographical errors.

These errors are all two words written together and they can be corrected easily by using a spellchecker.

pp1 line 34 HubgenesHub genes

pp6 line 195 hubgeneswith

pp6 line 200 weresimultaneously

pp6 line 202 wereidentified

pp7 line 241 promotestumor

pp8 line 273 mayhave

Response: Thank you for your comments. We have corrected all the typographical errors you mentioned and other errors in this article.

Attachment

Submitted filename: response to reviewers.docx

Click here for additional data file. (16.7KB, docx)

Decision Letter 2

Jishnu Das

3 Aug 2020

PONE-D-20-10567R2

Novel genes associate with folic acid-mediated metabolism in mouse: a bioinformatics study

PLOS ONE

Dear Dr. Hu,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

ACADEMIC EDITOR:

While the scientific content of the manuscript is now suitable, there are still many basic grammatical and language errors throughout the manuscript. If published in its current form, it would reflect poorly on the authors and the journal. The manuscript needs significant language editing to make it suitable for publication (perhaps working with a professional language editing service could help).

==============================

Please submit your revised manuscript by Sep 17 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

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  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Jishnu Das, Ph.D.

Academic Editor

PLOS ONE

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2020 Sep 11;15(9):e0238940. doi: 10.1371/journal.pone.0238940.r006

Author response to Decision Letter 2

24 Aug 2020

Dear Editor,

Thank you for your comments.

We have polished the manuscript as required. We have selected a third-party organization to polish the manuscript, and the polishing report has been uploaded.

Best regards

Hu

Attachment

Submitted filename: Response to Reviewers.docx

Click here for additional data file. (12.1KB, docx)

Decision Letter 3

Jishnu Das

27 Aug 2020

Novel genes associated with folic acid-mediated metabolism in mouse: A bioinformatics study

PONE-D-20-10567R3

Dear Dr. Hu,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Jishnu Das, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Jishnu Das

2 Sep 2020

PONE-D-20-10567R3

Novel genes associated with folic acid-mediated metabolism in mouse: A bioinformatics study

Dear Dr. Hu:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Jishnu Das

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. The GO functional enrichment analysis of the eigengenes in the brown4 module.

    (XLSX)

    Click here for additional data file. (22KB, xlsx)
    S1 Fig. Heatmap of the expression of eigengenes in the bisque4 (A) and brown4 (B) module, respectively.

    (TIF)

    Click here for additional data file. (872.8KB, tif)
    S2 Fig. Gene significance of eigengenes in the brown4 (A) and bisque4 (B) module, respectively.

    (TIF)

    Click here for additional data file. (1.3MB, tif)
    Attachment

    Submitted filename: response to reviewers.docx

    Click here for additional data file. (21.3KB, docx)
    Attachment

    Submitted filename: response to reviewers.docx

    Click here for additional data file. (16.7KB, docx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    Click here for additional data file. (12.1KB, docx)

    Data Availability Statement

    All relevant data are within the manuscript and its Supporting Information files.

    Articles from PLoS ONE are provided here courtesy of PLOS

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