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Effects of low and high maternal protein intake on fetal skeletal muscle miRNAome in sheep

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Abstract
Prenatal maternal feeding plays an important role in fetal development and has potential to cause long-lasting epigenetic modifications. MicroRNAs (miRNAs) are non-coding, single-stranded RNAs that serve as one epigenetic mechanism. Though miRNAs have crucial roles in fetal programming, growth, and development, there are limited data regarding maternal diet and sheep miRNA expression. Therefore, weanalyzed high and low maternal dietary protein for miRNA expression in sheep fetal longissimus dorsi. Pregnant ewes were fed an isoenergetic high-protein (HP, 160–270 g/day), low-protein (LP, 73–112 g/day), or standard protein diet (SP, 119–198 g/day) during pregnancy. miRNA expression profiles were evaluated using the Affymetrix GeneChip miRNA 4.0 Array. Twelve up-regulated, differentially expressed miRNAs (DE miRNAs) were identified targeting 65 genes. The oar-3957-5p miRNA was highly up-regulated in LP and SP compared to HP. Previous transcriptome analysis identified integrin and non-receptor protein tyrosine phosphatase genes targeted by miRNAs detected in the current experiment. A total of 28 GO terms and 10 pathway-based gene sets were significantly (padj < 0.05) enriched in target genes. Most genes targeted by identified miRNAs are involved in immune and muscle disease pathways. Our study shows dietary protein intake during pregnancy affects fetal skeletal muscle epigenetics via miRNA expression.
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Subject: Biology and Life Sciences  -   Animal Science, Veterinary Science and Zoology

1. Introduction

In all mammals, maternal nutrition in pregnancy plays a critical role in intrauterine development, fetal survival, growth, and organogenesis [1,2]. Therefore, maternal under- or over-nutrition during gestation may cause low offspring growth and performance [3] with significant influence on the economic viability of livestock industry [4,5].
Dietary nutrients, especially macronutrients, can influence the rate of gene transcription directly through interactions with the genomic regulatory elements or indirectly affecting and/or modulating important signaling pathways [6,7]. The understanding of how nutrition affects an individual’s phenotype has greatly benefited from developments in molecular biology and genomics, and this area is called nutrigenomics. The term “epigenetics” describes heritable phenotypic alterations that occur without a change in the DNA sequence [8]. A few examples of epigenetic mechanisms are those that modify the chromatin structure, such as DNA methylation and histone modifications, and as well as those that control the activity of proteins, like microRNAs (miRNAs). The term “Nutrimiromics” has been used to refer to the study of interactions between nutrients and food components with the organism’s genome via modification of gene expression due to epigenetic processes related to miRNAs [9].
Protein intake has a variety of nutritional and biological effects. Protein plays a vital role in the regulation of food intake, glucose and lipid metabolism, blood pressure, bone metabolism, and immunological function in addition to nutritional importance as a supply of amino acids for protein synthesis [10]. Proteins’ physiological functions are influenced by their physico-chemical characteristics, amino acid make-up, and bioactive peptides encoded in their amino acid sequences [10]. Furthermore, research has been done on how proteins affect fetal and post-natal development [11]. To date, more experiments have focused on protein restriction compared high protein about maternal nutritional effects on the fetus and infant [12,13]. Blumfield and Collins [13], stated that altered maternal protein intake during pregnancy may have detrimental effects to the offspring, and optimal macronutrient ratio is crucial for the pre- and post-natal environment. Additionally, protein content is one of the most expensive and limiting feed characteristics for livestock nutrition. As a feed ingredient, protein content is usually provided from human-edible ingredients like soybean, other oilseeds, and grains [14]. Thus, careful management of these limiting feedstuffs is critical for sustainable agriculture in the context of growing world population and increasing livestock product demand [15]. Furthermore, balanced protein consumption may help to control nitrogen pollution to aid both environmental and economical sustainability [16].
Previous research has examined extreme undernutrition or overnutrition conditions to test the effects of maternal nutrition on fetal development in livestock and model animals, including fetal programming of muscle and fat tissues [12,17,18]. However, few studies have examined potential effects of maternal protein intake on the overall miRNA expression of fetal skeletal muscle. We have previously reported that maternal diets differing in protein content alter fetal skeletal transcriptome, and maternal high protein compared standard protein triggered pathways related to the immune system and disease in sheep skeletal muscle [18]. On the other hand, the mechanisms by which these diets affect fetal miRNAome in sheep skeletal muscle are not known. The objective of this study was to investigate the impact of different maternal dietary protein levels during mid-to-late gestation on the total miRNA expression of fetal skeletal muscle in sheep.

2. Materials and Methods

2.1. Experimental Animals and Diets

Details of ewes and diets were explained detailed previously [18]. Briefly, three distinct protein diets (standard, high, and low protein) were used in the study to assess the effect of maternal dietary protein on the miRNAome of fetal skeletal muscle. Twelve Akkaraman ewes aged two were divided into three treatment groups at random, with four ewes in each group. Ewe specifications are presented in Sohel et al. [18]. After a 15-day adaptation period, the ewe’s daily dry matter (DM) feed consumption was assessed and compared with the US National Research Council (NRC) [19] recommendations for ewes. Three alternative feed regimens with variable protein content were created based on content of wheat straw, alfalfa, soybean meal, barley, and maize grain, alfalfa and soybean meal were the main protein sources in the diet, whereas barley and corn were the main sources of energy. Wheat straw and alfalfa were additional sources of cellulose and energy. The protein content of the diets was changed by substituting different amounts of soybean meal and alfalfa. As recommended by NRC [19], the ewes were fed from 0 to 30 days of gestation. Ewes were fed the following diets from day 31 to day 105 of gestation: diet 1 was a conventional diet with an NRC level of the nutritional content of feeds (daily DM feed intake ranged from 1252.3 to 1862.4 g, crude protein ranged from 119.4 to 198.6 g, and kcal ME/kg ranged from 2506.0 to 3909.3 g), diet 2: low-protein diet (1247.9–1726.4 g DM feed intake daily; 73.1–112.2 g crude protein; 2520.1–3649.0 kcal ME/kg); 3: high-protein diet (1252.4-629.8 g DM feed intake daily; 160.0–271.6 g crude protein; 2513.0–3880.9 kcal ME/kg), and diet 3: high-protein diet (daily, DM feed intake 1252.4 to 1829.8 g, crude protein 160.0 to 271.6 g and 2513.0 to 3880.9 kcal ME/kg). The diets included alfalfa, wheat straw, barley, corn, and soybean meal. The chemical composition of the materials for feed was ascertained in the feed analysis laboratory in accordance with A.O.A.C. (1990). Diets were made in an isoenergetic fashion. Specific information about diets was reported previously [18].

2.2. Estrus Synchronization, Semen Preparation, and Artificial Insemination

During the annual mating season, estrus synchronization was achieved using progesterone-infused pessaries (EaziBreed CIDR; 1.38 mg progesterone; Zoetis, Turkey) for 12 days, 2 mL of PGF2 (5 mg/mL Dinoprost tromethamine; Dinolytic; Zoetis, Turkey) treatment on day 12, and 400 IU of eCG (Folligon, 1000 IU/vial PMSG, MSD Animal Health, Turkiye) at the time of pessary removal. Four 1- to 3-year-old rams were utilized in the investigation, and the semen was extracted using the artificial vaginal procedure while an estrus ewe was present. A phase-contrast microscope with a heated plate set at 37°C was used to measure the motility of the artificial vaginal method-obtained sperm. For the insemination of ewes, the ejaculates of four rams with a motility of 70% or higher were combined. To achieve homogeneity, the appropriate ejaculates were blended based on their spermatological motility. The density of mixed ejaculates was determined using the hemocytometry method. To create 0.5 ml of semen containing 150×106 motile spermatozoa, the semen was diluted with Trisma base, D-Fructose, Citric acid, and skimmed milk [20]. Then, a water bath set at 35 °C was used to keep the semen. Using diluted fresh semen, artificial insemination was carried out intravaginally 48 hours following sponge removal.

2.3. Necropsy, Sample Collection and Isolation of miRNA

On the 105th day of pregnancy, the dams had necropsies. A total of 14 fetuses were gathered from 12 dams. To avoid growth bias resulting from multiple births, twin birth fetuses were not included in the experiment. In the conventional protein group and the low protein group, respectively, there were two twin pregnancies. To make sure that each experimental group contained 3 fetuses, a fetus from the HP group was also removed. A sample of muscle tissue was taken from the fetal left longissimus dorsi muscles. Prior to miRNA extraction, all tissues were snap-frozen in liquid nitrogen and stored at a temperature of −80 °C. Thirty milligrams of each skeletal muscle tissue were homogenized in QIAzol Lysis reagent (Qiagen, Hilden, Germany) with liquid nitrogen using a homogenizer (Precellys Bertin, France). Using a miRNeasy mini kit (Qiagen, Hilden, Germany) in accordance with the manufacturer’s instructions, total RNA containing miRNA was isolated from tissue homogenates. The manufacturer’s recommendations were followed for the on-column DNA digestion stage using the RNase-Free DNase Set (Qiagen, Hilden, Germany). With the help of a BioSpec-nano micro-volume UV-Vis spectrophotometer (Shimadzu Co., Kyoto, Japan), the purity and concentration of the total RNA were assessed.

2.4. Microarray Hybridization and Data Analysis

The miRNA profile was determined from the Affymetrix Microarray system using the GeneChip miRNA 4.0 Array (Affymetrix, USA). The miRNA microarray contains 30,434 mature miRNAs from all organisms, miRBase set Release 20.0. Following the manufacturer’s instructions, 500 ng of total RNA was labeled using an Affymetrix FlashTag Biotin HSR RNA Labeling Kit. Following hybridization, the chip arrays were washed and stained using AGCC Fluidics Control Software on a Fluidics Station 450. After that, a high-resolution Affymetrix GeneChip Scanner 3000 was used to scan the array’s fluorescence. The Affymetrix GeneChip Command Console program produced probe cell intensity files (*CEL files), which were imported into probe-level summary files (*CHP files) for data extraction. Software called Expression Console and Transcriptome Analysis Console were used to analyze these *CHP files. After image analysis, the data were normalized. Data was compared using tests including the t test, analysis of variance (ANOVA), and significance analysis of microarray after log2 translation of the normalized signal intensities of arrays. The identification of miRNA-targeted genes was performed using TargetScan [21] (http://www.targetscan.org; accessed on 08 January 2024) with selection criteria of cumulative weighted value ≤ −0.75 for Targetscan. Genes targeted by multiple miRNAs were assembled from individual miRNA target lists. ConsensusPathDB [22] (http://cpdb.molgen.mpg.de/; accessed on 10 January 2024) was used to perform gene ontology (GO) annotation and enrichment analysis from three ontologies (molecular function, biological process, and cellular component) on the frequently identified target genes. The threshold for significance was set at p < 0.01 for both of these analyses. The predicted target gene lists were examined using the default collections of the KEGG, Reactome, and BioCarta (http://cgap.nci.nih.gov/Pathways/BioCarta_Pathways) databases.

3. Results

Microarray analysis was used to compare the expression profiles of miRNAs in the fetal longissimus dorsi muscle samples collected from ewes fed with different protein (standard-SP, high-HP, and low protein-LP) diets. As presented in Table 1, 12 different miRNAs were differentially expressed (>2-fold, p < 0.01, FDR <0.05) in the different diet groups as per the Affymetrix GeneChip miRNA 4.0 Array. Chromosomal locations and mature miRNA sequences of these 12 prominent miRNAs identified using Transcriptome Analysis Console commercial software are shown in Table 1.
We identified 2 significantly up-regulated miRNAs in the HP group compared to the SP group. (Table 1). The 2 miRNAs were oar-miR-3957-5p and oar-miR-329a-3p. The fold change of those miRNAs was 4.58, and 2.96, respectively. miRNA oar-miR-3957-5p was also upregulated in SP group compared to HP group with the fold change 4.25. One of the remarkable results of our miRNAome analysis was the identification of nine miRNAs on OAR18 in which they clustered together in a tight locus (Figure S1). The miRNA oar-miR-200c showed up-regulation in SP group compared to LP group. The differentially expressed miRNAs were grouped using a hierarchical clustering technique, and the outcomes demonstrated that the various diet groups could be readily distinguished by the hierarchical grouping of all covered ovine miRNAs (Figure S2).
Next, we analyzed the target genes that may be regulated by these 12 miRNAs in two layers. First, we identified genes targeted by multiple miRNAs (Table S1). In silico analyses identified that 32 genes found to be targeted by two miRNAs and no mRNA found to be targeted by three or more miRNAs (Table S1). In the second layer, a total of 65 genes were targeted by at least one of 12 miRNAs (Table S2). Hypergeometric distribution testing was used to examine the gene ontology (GO) of their anticipated target genes to investigate the roles of differentially regulated miRNAs among 3 distinct diet groups. GO terms for genes that targeted by 2 miRNAs were identified as being related to 19 biological processes and 6 molecular functions (Table 2). GO terms for genes that are targeted by single miRNA were identified as being related to 20 biological processes, involved in the creation of 5 molecular functions, and expected to take part in 3 cellular components (Table 3).
GO functional enrichment analysis and pathway enrichment analysis were performed on 32 genes targeted by multiple miRNAs and separately on 65 genes targeted by at least one miRNA (Table 2 and Table 3). GO terms showed that genes targeted by multiple miRNAs were mainly involved in biological processes (BP) lymphocyte chemotaxis, monocyte chemotaxis, regulation of nitrogen compound metabolic process, granulocyte chemotaxis and other categories that control immune system (Table 2). In terms of molecular function (MF), targeted mRNAs were involved in cytokine activity, cytokine receptor binding, protein kinase regulator activity (Table 2). We conducted a pathway enrichment study using public pathway databases to better understand the roles of these projected target genes, and 31 pathways were found (Table 3). Pathway analysis showed that targeted genes by multiple miRNAs were significantly enriched in the Chemokine receptors bind chemokines, Rheumatoid arthritis - Homo sapiens (human), Viral protein interaction with cytokine and cytokine receptor - Homo sapiens (human) and Toll-like receptor signaling pathway - Homo sapiens (human) (Table 3).
GO functional enrichment analysis for genes targeted by single mRNAs in terms of BP, the primary categories for the functions were cellular macromolecule biosynthetic process, macromolecule biosynthetic process, regulation of biosynthetic process and cellular nitrogen compound biosynthetic process cell (Table 4). In terms of MF, targeted mRNAs were involved in DNA binding, regulatory region nucleic acid binding and metal ion binding (Table 4). In terms of CC, targeted mRNAs were involved in nuclear lumen, nucleus, nucleoplasm (Table 4). Pathway analysis showed that targeted genes by single miRNAs were significantly enriched in the Rheumatoid arthritis - Homo sapiens (human), Transferrin endocytosis and recycling, Oncogene Induced Senescence and G1 to S cell cycle control (Table 5).

4. Discussion

Proteins are vital for the structure, function, and regulation of the body’s tissues and organs in all living organisms; in addition, they are the most expensive and often most limiting ingredients in livestock feed formulation [23]. Excess fed protein can contribute to excreted nitrogen with undesirable nitrate leaching to groundwater and atmospheric ammonia emissions [24]. Despite the importance of protein for all living organisms, ruminants use dietary protein for production rather inefficiently (as defined by g N in product/g N intake) [25]. This creates opportunity to utilize technologies such as dynamically alter nutrient intakes via mid-infrared spectroscopy of milk composition [24,26]. Additionally, feedomics may assist in modifying nutrition for monetary and long-term gains. The key components of the feedomics study are nutrigenomics, nutrigenetics, and nutritional epigenetics, which aid in establishing a link between genetic variation and nutrient-driven epigenetic changes that are proposed as the main barrier to meeting nutritional demands [27]. Maternal nutrition is a major intrauterine environmental factor that alters expression of the fetal genome in ways that may have lifelong consequences. Such fetal programming has been shown to be regulated by epigenetic modifications, including DNA methylation, histone modifications, and as well as miRNAs [12].
In addition, we have identified that miR-3957-5p was upregulated in both LP and SP groups vs HP group (Table 1). Pokharel et al. [28] detected miR-3957-5p in the tissue biopsies of corpus luteum and endometrium of pregnant ewes. Prior work demonstrated an abundance of miRNAs in packed form as extracellular vesicles to maintain feto-maternal communication in multiple mammals [29,30,31]. In the current study, together with miR-3957-5p, other miRNAs revealed a large cluster of miRNAs on chromosome 18 (Table 1) which may co-regulate different biological processes [32]. This is consistent with Pokharel et al. [28] who also detected similar miRNAs in the same clustered locus. This miRNA cluster is highly conserved among placental mammals and known to be regulated by a maternally imprinted DLK1-DIO3 region, which has been associated with severe muscle disease caused by impaired expression of dystrophin called Duchenne muscular dystrophy [33,34,35]. Additionally, two growth QTL associated with sheep body weight [36] and average daily gain [37] were found coinciding with the miRNA cluster on OAR18. In the same experimental design subjected to current study, Sohel et al. [18] showed that a high-protein diet consumed by the mother predominantly changed the expression of mRNAs involved in myogenesis and immunity in the developing fetus’s skeletal muscle. Both Shandilya et al. [38] and Sharma et al. [39,40] showed that sheep exposed to bacterial lipopolysaccharide challenge exhibited differentially regulated miRNAs such as miR-3957-5p, miR-329a-3p, miR-379-5p (Table 1) which regulate biological processes such as stress and immune responses.
A critical layer of analysis involves miRNA targeting the same genes (Table S2). This is an established part of miRNA biology and a useful way to highlight important genes and pathways [41,42]. Multiple miRNAs regulating the same target gene are naturally cooperative since miRNAs act to inhibit targets; this heterotypic regulation is believed to offer extra regulatory specificity [43]. And specially, there is ongoing debate over whether clustered miRNAs with different seed sequences co-evolved due to shared functions [43]. In the current study, two members of miRNA cluster that are oar-miR-3957-5p and oar-miR-432 (Table S2; Figure S1) are cooperatively targeting Calcium/calmodulin dependent protein kinase IG (CAMK1G) and Ras protein specific guanine nucleotide releasing factor 1 (RASGRF1) genes. Calcium/calmodulin-dependent protein kinases are known to be key regulators of calcium signaling in health and disease [44] and it has been observed that T cells and B cells produce less IFN-γ when CaMKIV is inhibited or silent [45]. Futhermore experiments showed that in myoblasts and myotubes treated with IFN-γ, calcium/calmodulin-dependent protein kinase IV specifically suppresses the production of co-stimulatory molecules and pro-inflammatory cytokines/chemokines [46]. In terms of RASGRF1, it is an intrinsic key mediator for brain-derived neurotrophic factor (BDNF)-induced R-Ras activation and R-Ras-mediated axonal morphological regulation [47]. One of the nerve growth factor family’s members, BDNF, is primarily produced by the brain and its main role involves synaptic modulation, neurogenesis, neuron survival, immune regulation, myocardial contraction, and angiogenesis in the brain [48]. On the other hand, previous experiments had shown that BDNF has a significant regulatory role in myogenic differentiation in skeletal muscle which acts as a retrograde survival factor for motor neurons in the neuromuscular system [49,50]. Additionally, on the same miRNA cluster, oar-miR-379-5p and oar-miR-134-5p were found to be cooperatively targeting Glutamyl-TRNA Amidotransferase Subunit C (GATC) (Table S2). GATC was associated with mitochondrial respiratory dysfunction which cause skeletal muscle atrophy, is a state of uncontrolled inflammation and oxidative stress [51]. Multiple target analysis showed a complex network of mutual interactions between let-7c, miRNAs on various chromosomes and mRNAs (Table S2). In literature role of Let-7c and other Let-7 family members’s in myogenesis [52] and ovine fetal skeletal muscle development related to maternal obesity had been shown [53].
A second outcome involves immune genes. Chemokine ligands (CCLs), CCL3, CCL3L1, and CCL3L3 are all targeted by both oar-miR-432 and let-7c (Table S2). CCL3 is critical for trophoblast invasion and maintenance of pregnancy [54,55]. While others have studied dietary impacts on sheep, to our knowledge, this is the first demonstration that maternal diet protein intakes in the context of isoenergetic dietary formulations could impact CCL3 and maintenance of pregnancy. In addition, IL7 is targeted by both oar-miR-432 and oar-miR-200c (Table S2). This is particularly striking because IL7 influences development of the offspring’s T cell immunity [56,57]. These examples highlight the importance of maternal protein intake on multiple critical outcomes for fetal health and lifetime wellbeing.
Rheumatoid arthritis - Homo sapiens (human) pathways were identified in different protein intake groups among both layers of target prediction (Table 3 and Table 5). Rheumatoid arthritis (RA) is a chronic, inflammatory, systemic autoimmune disease that causes skeletal muscle loss and weakness in RA individuals [58,59]. Similarly, transferrin endocytosis and recycling pathway is associated with iron homeostasis, is a key regulator of innate and adaptive immunity and plays a decisive role in inflammatory processes [60]. These findings were consistent with the findings of transcriptome analysis [18], which revealed that many detected genes are involved in pathways related to the immune system and diseases.

5. Conclusions

In summary, muscle biology is crucial for economic and nutritional value of sheep, and long-term optimization of sheep production in requires enhanced understanding of the molecular mechanisms that control muscle growth and metabolism. To the best of our knowledge, this is the first study that investigates the nutrimiromics of sheep fetal skeletal muscle based on differing maternal protein intake. In addition, experiments that investigate effect of nutrients and bioactive compounds that regulate miRNAs in the fetal period are very limited. The findings of our research show that maternal diet in terms of different protein levels may affect fetal miRNAome that epigenetically affect the skeletal muscle transcriptome in vivo [18]. The discovery of multiple miRNAs that target mRNAs is a significant development that may enable us to further comprehend the network that connects miRNAs and their targets. Since the maternal high-protein diet specifically changed the expression of miRNAs involved in number of pathways and biological processes that are unique to disease and immunity, these results may have provided insight into the reversible nature of epigenetic modifications, which hold promise for the use of miRNAs in the diagnosis, prevention, and treatment of health conditions in the offspring.

Supplementary Materials

The following supporting information can be downloaded at the website of this paper posted on Preprints.org, Figure S1: miRNA cluster on OAR18; Figure S2: Heat map of the microarray miRNA expression profile in fetal skelatal muscle derived from high, low, and standard protein groups; Table S1: List of targeted mRNAs by single miRNA; Table S2: List of targeted mRNAs by multiple miRNA.

Author Contributions

MUC, MMHS, and BA conceived and designed the study. KG and MA contributed insemination and necropsy. KG, MA, KA, and MK performed the sample collection. YK and MK carried out animal nutrition experiments. MUC, MMHS, and BA contributed genetic experiments. MUC and MMHS performed the research and analyzed the data. SNW contributed to analysis. MUC and BA drafted the paper. All authors have read and approved the manuscript.

Funding

This research was funded by Erciyes University, Scientific Research Projects Coordination Unit under the grant number: FOA-2015-5611. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Institutional Review Board Statement

All experimental protocols in the study were approved and conducted under the supervision of Animal Experiments Local Ethics Committee of Erciyes University (Approval Number: 12 June 2013-#13/96), Kayseri, Türkiye, and the methods were also carried out in accordance with the ARRIVE guidelines.

Data Availability Statement

All data generated and analyzed in this study are presented in the figures/tables and supplementary information documents.

Acknowledgments

The authors are indebted to Erciyes University Agricultural Research-Application Farm (ERÜTAM) for providing animal facilities and Betül Ziya Eren Genome and Stem Cell Research Centre (GENKÖK) for providing laboratory space and equipment.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Ji, Y.; Wu, Z.; Dai, Z.; Wang, X.; Li, J.; Wang, B.; Wu, G. Fetal and neonatal programming of postnatal growth and feed efficiency in swine. J. Anim. Sci. Biotechnol. 2017, 8, 42. [Google Scholar] [CrossRef] [PubMed]
  2. Lin, Y.-J.; Huang, L.-T.; Tsai, C.-C.; Sheen, J.-M.; Tiao, M.-M.; Yu, H.-R.; Lin, I.-C.; Tain, Y.-L. Maternal high-fat diet sex-specifically alters placental morphology and transcriptome in rats: Assessment by next-generation sequencing. Placenta 2019, 78, 44–53. [Google Scholar] [CrossRef] [PubMed]
  3. Hoffman, M.L.; Reed, S.A.; Pillai, S.M.; Jones, A.K.; McFadden, K.K.; Zinn, S.A.; Govoni, K.E. Physiology and Endocrinology Symposium: The Effects of Poor Maternal Nutrition During Gestation on Offspring Postnatal Growth and Metabolism. J. Anim. Sci. 2017, 95, 2222–2232. [Google Scholar] [CrossRef] [PubMed]
  4. Du, M.; Tong, J.; Zhao, J.; Underwood, K.R.; Zhu, M.; Ford, S.P.; Nathanielsz, P.W. Fetal programming of skeletal muscle development in ruminant animals1. J. Anim. Sci. 2010, 88, E51–E60. [Google Scholar] [CrossRef] [PubMed]
  5. Du, M.; Wang, B.; Fu, X.; Yang, Q.; Zhu, M.-J. Fetal programming in meat production. Meat Sci. 2015, 109, 40–47. [Google Scholar] [CrossRef]
  6. Sohel, M.H. Macronutrient modulation of mRNA and microRNA function in animals: A review. Anim. Nutr. 2020, 6, 258–268. [Google Scholar] [CrossRef] [PubMed]
  7. Sohel, M.H.; Konca, Y.; Cinar, M.U. Impacts of Macronutrients on Gene Expression: Recent Evidence to Understand Productive and Reproductive Performance of Livestock. Turk Tarim Gida Bilim Teknol. Derg. 2018, 6, 203–212. [Google Scholar] [CrossRef]
  8. Ho, D.H.; Burggren, W.W. Epigenetics and transgenerational transfer: a physiological perspective. J. Exp. Biol. 2010, 213, 3–16. [Google Scholar] [CrossRef] [PubMed]
  9. Quintanilha, B.J.; Reis, B.Z.; Duarte, G.B.S.; Cozzolino, S.M.F.; Rogero, M.M. Nutrimiromics: Role of microRNAs and Nutrition in Modulating Inflammation and Chronic Diseases. Nutrients 2017, 9, 1168. [Google Scholar] [CrossRef]
  10. Jahan-Mihan, A.; Luhovyy, B.L.; El Khoury, D.; Anderson, G.H. Dietary Proteins as Determinants of Metabolic and Physiologic Functions of the Gastrointestinal Tract. Nutrients 2011, 3, 574–603. [Google Scholar] [CrossRef]
  11. Jahan-Mihan, A.; Rodriguez, J.; Christie, C.; Sadeghi, M.; Zerbe, T. The Role of Maternal Dietary Proteins in Development of Metabolic Syndrome in Offspring. Nutrients 2015, 7, 9185–9217. [Google Scholar] [CrossRef] [PubMed]
  12. Meruvu, S.; Schutz, L.F.; Choudhury, M. Nutritional Influence on miRNA Epigenetic Regulation: Effect of Maternal Diet and miRNAs on the Fetal Metabolic Programming. In Molecular Nutrition: Mother and Infant, 1st ed.; Vinciguerra, M., Cordero Sanchez, P., Eds.; Academic Press: Cambridge, Massachusetts, 2021; pp. 401–420. [Google Scholar]
  13. Blumfield, M.L.; E Collins, C. High-protein diets during pregnancy: healthful or harmful for offspring? Am. J. Clin. Nutr. 2014, 100, 993–995. [Google Scholar] [CrossRef] [PubMed]
  14. Kim, S.W.; Less, J.F.; Wang, L.; Yan, T.; Kiron, V.; Kaushik, S.J.; Lei, X.G. Meeting Global Feed Protein Demand: Challenge, Opportunity, and Strategy. Annu. Rev. Anim. Biosci. 2019, 7, 221–243. [Google Scholar] [CrossRef] [PubMed]
  15. Rauw, W.M.; Rydhmer, L.; Kyriazakis, I.; Øverland, M.; Gilbert, H.; Dekkers, J.C.; Hermesch, S.; Bouquet, A.; Izquierdo, E.G.; Louveau, I.; et al. Prospects for sustainability of pig production in relation to climate change and novel feed resources. J. Sci. Food Agric. 2020, 100, 3575–3586. [Google Scholar] [CrossRef] [PubMed]
  16. Almaraz, M.; Kuempel, C.D.; Salter, A.M.; Halpern, B.S. The impact of excessive protein consumption on human wastewater nitrogen loading of US waters. Front. Ecol. Environ. 2022, 20, 452–458. [Google Scholar] [CrossRef]
  17. Kizilaslan, M.; Arzik, Y.; Cinar, M.U.; Konca, Y. Genome-Wise Engineering of Ruminant Nutrition–Nutrigenomics: Applications, Challenges, and Future Perspectives–A Review. Ann. Anim. Sci. 2021, 22, 511–521. [Google Scholar] [CrossRef]
  18. Sohel, M.H.; Akyuz, B.; Konca, Y.; Arslan, K.; Gurbulak, K.; Abay, M.; Kaliber, M.; Cinar, M.U. Differential protein input in the maternal diet alters the skeletal muscle transcriptome in fetal sheep. Mamm. Genome 2020, 31, 309–324. [Google Scholar] [CrossRef]
  19. NRC: Nutrient Requirements of Small Ruminants. 2007, National Academy Press.
  20. Paulenz, H.; Ådnøy, T.; Fossen, O.H.; Söderquist, L.; Berg, K.A. Effect of deposition site and sperm number on the fertility of sheep inseminated with liquid semen. Veter- Rec. 2002, 150, 299–302. [Google Scholar] [CrossRef] [PubMed]
  21. McGeary, S.E.; Lin, K.S.; Shi, C.Y.; Pham, T.M.; Bisaria, N.; Kelley, G.M.; Bartel, D.P. The biochemical basis of microRNA targeting efficacy. Science 2019, 366, 1470. [Google Scholar] [CrossRef]
  22. Herwig, R.; Hardt, C.; Lienhard, M.; Kamburov, A. Analyzing and interpreting genome data at the network level with ConsensusPathDB. Nat. Protoc. 2016, 11, 1889–1907. [Google Scholar] [CrossRef]
  23. Parisi, G.; Tulli, F.; Fortina, R.; Marino, R.; Bani, P.; Zotte, A.D.; De Angelis, A.; Piccolo, G.; Pinotti, L.; Schiavone, A.; et al. Protein hunger of the feed sector: the alternatives offered by the plant world. Ital. J. Anim. Sci. 2020, 19, 1204–1225. [Google Scholar] [CrossRef]
  24. Moorby, J.; Fraser, M.D. Review: New feeds and new feeding systems in intensive and semi-intensive forage-fed ruminant livestock systems. Animal 2021, 15, 100297. [Google Scholar] [CrossRef] [PubMed]
  25. Calsamiglia, S.; Ferret, A.; Reynolds, C.K.; Kristensen, N.B.; van Vuuren, A.M. Strategies for optimizing nitrogen use by ruminants. Animal 2010, 4, 1184–1196. [Google Scholar] [CrossRef] [PubMed]
  26. Gengler, N.; Soyeurt, H.; Dehareng, F.; Bastin, C.; Colinet, F.; Hammami, H.; Vanrobays, M.-L.; Lainé, A.; Vanderick, S.; Grelet, C.; et al. Capitalizing on fine milk composition for breeding and management of dairy cows. J. Dairy Sci. 2016, 99, 4071–4079. [Google Scholar] [CrossRef] [PubMed]
  27. Preethi, K.A.; Sekar, D. Dietary microRNAs: Current status and perspective in food science. J. Food Biochem. 2021, 45, e13827. [Google Scholar] [CrossRef] [PubMed]
  28. Pokharel, K.; Peippo, J.; Li, M.; Kantanen, J. Identification and characterization of miRNAs during early pregnancy in domestic sheep. Anim. Genet. 2020, 51, 833–836. [Google Scholar] [CrossRef] [PubMed]
  29. Kaczmarek, M.M.; Najmula, J.; Guzewska, M.M.; Przygrodzka, E. MiRNAs in the Peri-Implantation Period: Contribution to Embryo–Maternal Communication in Pigs. Int. J. Mol. Sci. 2020, 21, 2229. [Google Scholar] [CrossRef]
  30. Morales-Prieto, D.M.; Favaro, R.R.; Markert, U.R. Placental miRNAs in feto-maternal communication mediated by extracellular vesicles. Placenta 2020, 102, 27–33. [Google Scholar] [CrossRef] [PubMed]
  31. Salilew-Wondim, D.; Gebremedhn, S.; Hoelker, M.; Tholen, E.; Hailay, T.; Tesfaye, D. The Role of MicroRNAs in Mammalian Fertility: From Gametogenesis to Embryo Implantation. Int. J. Mol. Sci. 2020, 21, 585. [Google Scholar] [CrossRef]
  32. Kabekkodu, S.P.; Shukla, V.; Varghese, V.K.; Souza, J.D.; Chakrabarty, S.; Satyamoorthy, K. Clustered miRNAs and their role in biological functions and diseases. Biol. Rev. 2018, 93, 1955–1986. [Google Scholar] [CrossRef]
  33. Hong, A.V.; Bourg, N.; Sanatine, P.; Poupiot, J.; Charton, K.; Gicquel, E.; Massourides, E.; Spinazzi, M.; Richard, I.; Israeli, D. Dlk1-Dio3 cluster miRNAs regulate mitochondrial functions in the dystrophic muscle in Duchenne muscular dystrophy. Life Sci. Alliance 2022, 6, e202201506. [Google Scholar] [CrossRef] [PubMed]
  34. Sanson, M.; Hong, A.V.; Massourides, E.; Bourg, N.; Suel, L.; Amor, F.; Corre, G.; Bénit, P.; Barthélémy, I.; Blot, S.; et al. miR-379 links glucocorticoid treatment with mitochondrial response in Duchenne muscular dystrophy. Sci. Rep. 2020, 10, 1–17. [Google Scholar] [CrossRef] [PubMed]
  35. Amor, F.; Hong, A.V.; Corre, G.; Sanson, M.; Suel, L.; Blaie, S.; Servais, L.; Voit, T.; Richard, I.; Israeli, D. Cholesterol metabolism is a potential therapeutic target in Duchenne muscular dystrophy. J. Cachex- Sarcopenia Muscle 2021, 12, 677–693. [Google Scholar] [CrossRef]
  36. Walling, G.A.; Visscher, P.M.; Wilson, A.D.; McTeir, B.L.; Simm, G.; Bishop, S.C. Mapping of quantitative trait loci for growth and carcass traits in commercial sheep populations1. J. Anim. Sci. 2004, 82, 2234–2245. [Google Scholar] [CrossRef] [PubMed]
  37. Zhang, L.; Liu, J.; Zhao, F.; Ren, H.; Xu, L.; Lu, J.; Zhang, S.; Zhang, X.; Wei, C.; Lu, G.; et al. Genome-Wide Association Studies for Growth and Meat Production Traits in Sheep. PLoS ONE 2013, 8, e66569. [Google Scholar] [CrossRef] [PubMed]
  38. Shandilya, U.K.; Sharma, A.; Naylor, D.; Canovas, A.; Mallard, B.; Karrow, N.A. Expression Profile of miRNA from High, Middle, and Low Stress-Responding Sheep during Bacterial Endotoxin Challenge. Animals 2023, 13, 508. [Google Scholar] [CrossRef]
  39. Sharma, A.; Shandilya, U.K.; Sullivan, T.; Naylor, D.; Canovas, A.; Mallard, B.A.; Karrow, N.A. Identification of Ovine Serum miRNAs Following Bacterial Lipopolysaccharide Challenge. Int. J. Mol. Sci. 2020, 21, 7920. [Google Scholar] [CrossRef] [PubMed]
  40. Sharma, A.; Shandilya, U.K.; Sullivan, T.; Naylor, D.; Cánovas, A.; Mallard, B.; Karrow, N. PSXIV-20 Ovine circulatory markers regulating the acute-phase response of variable stress responding sheep to LPS challenge. J. Anim. Sci. 2021, 99, 494–495. [Google Scholar] [CrossRef]
  41. E Peter, M. Targeting of mRNAs by multiple miRNAs: the next step. Oncogene 2010, 29, 2161–2164. [Google Scholar] [CrossRef]
  42. Wu, S.; Huang, S.; Ding, J.; Zhao, Y.; Liang, L.; Liu, T.; He, X. Multiple microRNAs Modulate p21Cip1/Waf1 Expression by Directly Targeting Its 3′ Untranslated Region. Oncogene. 2010, 29, 2302–2308. [Google Scholar] [CrossRef]
  43. DeVeale, B.; Swindlehurst-Chan, J.; Blelloch, R. The roles of microRNAs in mouse development. Nat. Rev. Genet. 2021, 22, 307–323. [Google Scholar] [CrossRef] [PubMed]
  44. Junho, C.V.C.; Caio-Silva, W.; Trentin-Sonoda, M.; Carneiro-Ramos, M.S. An Overview of the Role of Calcium/Calmodulin-Dependent Protein Kinase in Cardiorenal Syndrome. Front. Physiol. 2020, 11, 735. [Google Scholar] [CrossRef] [PubMed]
  45. Ichinose, K.; Juang, Y.; Crispín, J.C.; Kis-Toth, K.; Tsokos, G.C. Suppression of autoimmunity and organ pathology in lupus-prone mice upon inhibition of calcium/calmodulin-dependent protein kinase type IV. Arthritis Rheum. 2010, 63, 523–529. [Google Scholar] [CrossRef] [PubMed]
  46. Gu, R.; Ding, M.; Shi, D.; Huang, T.; Guo, M.; Yu, L.; Hu, J.; Huang, W.; Liao, H. Calcium/Calmodulin-Dependent Protein Kinase IV Mediates IFN-γ-Induced Immune Behaviors in Skeletal Muscle Cells. Cell. Physiol. Biochem. 2018, 46, 351–364. [Google Scholar] [CrossRef] [PubMed]
  47. Umeda, K.; Negishi, M.; Katoh, H. RasGRF1 mediates brain-derived neurotrophic factor-induced axonal growth in primary cultured cortical neurons. Biochem. Biophys. Rep. 2018, 17, 56–64. [Google Scholar] [CrossRef] [PubMed]
  48. Rentería, I.; García-Suárez, P.C.; Fry, A.C.; Moncada-Jiménez, J.; Machado-Parra, J.P.; Antunes, B.M.; Jiménez-Maldonado, A. The Molecular Effects of BDNF Synthesis on Skeletal Muscle: A Mini-Review. Front. Physiol. 2022, 13, 934714. [Google Scholar] [CrossRef]
  49. Mousavi, K.; Jasmin, B.J. BDNF is Expressed in Skeletal Muscle Satellite Cells and Inhibits Myogenic Differentiation. J. Neurosci. 2006, 26, 5739–49. [Google Scholar] [CrossRef]
  50. Mousavi, K.; Parry, D.J.; Jasmin, B.J. BDNF rescues myosin heavy chain IIB muscle fibers after neonatal nerve injury. Am. J. Physiol. Physiol. 2004, 287, C22–C29. [Google Scholar] [CrossRef]
  51. Chen, X.; Ji, Y.; Liu, R.; Zhu, X.; Wang, K.; Yang, X.; Liu, B.; Gao, Z.; Huang, Y.; Shen, Y.; et al. Mitochondrial dysfunction: roles in skeletal muscle atrophy. J. Transl. Med. 2023, 21, 1–24. [Google Scholar] [CrossRef]
  52. Papaioannou, G.; Inloes, J.B.; Nakamura, Y.; Paltrinieri, E.; Kobayashi, T. let-7 and miR-140 microRNAs coordinately regulate skeletal development. Proc. Natl. Acad. Sci. 2013, 110, E3291–E3300. [Google Scholar] [CrossRef]
  53. Yan, X.; Huang, Y.; Zhao, J.-X.; Rogers, C.J.; Zhu, M.-J.; Ford, S.P.; Nathanielsz, P.W.; Du, M. Maternal obesity downregulates microRNA let-7g expression, a possible mechanism for enhanced adipogenesis during ovine fetal skeletal muscle development. Int. J. Obes. 2012, 37, 568–575. [Google Scholar] [CrossRef] [PubMed]
  54. Jones, A.K.; Hoffman, M.L.; Pillai, S.M.; McFadden, K.K.; E Govoni, K.; A Zinn, S.; A Reed, S. Gestational restricted- and over-feeding promote maternal and offspring inflammatory responses that are distinct and dependent on diet in sheep†. Biol. Reprod. 2017, 98, 184–196. [Google Scholar] [CrossRef]
  55. Zhang, S.; Ding, J.; Zhang, Y.; Liu, S.; Yang, J.; Yin, T. Regulation and Function of Chemokines at the Maternal–Fetal Interface. Front. Cell Dev. Biol. 2022, 10, 826053. [Google Scholar] [CrossRef]
  56. Ngom, P.T.; Collinson, A.C.; Pido-Lopez, J.; Henson, S.M.; Prentice, A.M.; Aspinall, R. Improved thymic function in exclusively breastfed infants is associated with higher interleukin 7 concentrations in their mothers' breast milk. Am. J. Clin. Nutr. 2004, 80, 722–728. [Google Scholar] [CrossRef] [PubMed]
  57. Aspinall, R.; Prentice, A.M.; Ngom, P.T. Interleukin 7 from Maternal Milk Crosses the Intestinal Barrier and Modulates T-Cell Development in Offspring. PLOS ONE 2011, 6, e20812. [Google Scholar] [CrossRef] [PubMed]
  58. Bennett, J.L.; Pratt, A.G.; Dodds, R.; Sayer, A.A.; Isaacs, J.D. Rheumatoid sarcopenia: loss of skeletal muscle strength and mass in rheumatoid arthritis. Nat. Rev. Rheumatol. 2023, 19, 239–251. [Google Scholar] [CrossRef]
  59. Steinz, M.M.; Santos-Alves, E.; Lanner, J.T. Skeletal muscle redox signaling in rheumatoid arthritis. Clin. Sci. 2020, 134, 2835–2850. [Google Scholar] [CrossRef]
  60. Rossatti, P.; Redpath, G.M.I.; Ziegler, L.; Samson, G.P.B.; Clamagirand, C.D.; Legler, D.F.; Rossy, J. Rapid increase in transferrin receptor recycling promotes adhesion during T cell activation. BMC Biol. 2022, 20, 1–21. [Google Scholar] [CrossRef]
Table 1. The list of differentially expressed miRNAs with a fold change ≥ 2 and FDR = 0 in fetal skeletal muscles derived from high (HP), low (LP), and standard protein (SP) group.
Table 1. The list of differentially expressed miRNAs with a fold change ≥ 2 and FDR = 0 in fetal skeletal muscles derived from high (HP), low (LP), and standard protein (SP) group.
Transcript ID (Array Design) miRBase ID Fold change p-value Style Mature sequence OAR
LP vs. HP
oar-miR-3957-5p MIMAT0019325 4.58 0.04 up cucggagaguggagcugugggugu 18
oar-miR-329a-3p MIMAT0019266 2.96 0.04 up aacacaccugguuaaccuuuuu 18
SP vs. HP
oar-miR-3957-5p MIMAT0019325 4.25 0.005 up cucggagaguggagcugugggugu 18
oar-miR-432 MIMAT0001416 2.47 0.007 up ucuuggaguaggucauugggugg 18
oar-miR-200c MIMAT0030044 2.24 0.009 up uaauacugccggguaaugaugg 3
oar-miR-362 MIMAT0030060 2.24 0.01 up aauccuuggaaccuaggugugagu X
oar-miR-409-3p MIMAT0019328 2.2 0.01 up cgaauguugcucggugaaccccu 18
oar-let-7c MIMAT0014964 2.18 0.01 up ugagguaguagguuguaugguu 1
oar-miR-493-3p MIMAT0019238 2.17 0.01 up ugaaggucuacugugugccagg 18
oar-miR-181a MIMAT0014973 2.12 0.02 up aacauucaacgcugucggugagu 12
oar-miR-379-5p MIMAT0019247 2.11 0.02 up ugguagacuauggaacguaggc 18
oar-miR-134-5p MIMAT0019308 2.1 0.03 up ugugacugguugaccagaggg 18
oar-miR-127 MIMAT0001415 2.04 0.04 up aucggauccgucugagcuuggcu 18
SP vs. LP
oar-miR-200c MIMAT0030044 2.45 0.01 up uaauacugccggguaaugaugg 3
Table 2. GO terms of 32 genes targeted by multiple miRNAs with ConsensusPathDB (category level 4) (adjusted p<0.01).
Table 2. GO terms of 32 genes targeted by multiple miRNAs with ConsensusPathDB (category level 4) (adjusted p<0.01).
Category Gene ontology term Set size Candidates contained p-value q-value
Biological process GO:0048247 lymphocyte chemotaxis 65 3 (4.6%) 9.74e-05 0.00792
GO:0002548 monocyte chemotaxis 71 3 (4.2%) 0.000127 0.00792
GO:0051171 regulation of nitrogen compound metabolic process 6033 17 (0.3%) 0.000574 0.0199
GO:0071621 granulocyte chemotaxis 124 3 (2.4%) 0.000655 0.0199
GO:0080090 regulation of primary metabolic process 6227 17 (0.3%) 0.000863 0.0199
GO:0031323 regulation of cellular metabolic process 6297 17 (0.3%) 0.000996 0.0199
GO:0097530 granulocyte migration 149 3 (2.0%) 0.00111 0.0199
GO:0006796 phosphate-containing compound metabolic process 3342 11 (0.3%) 0.0032 0.0419
GO:0044271 cellular nitrogen compound biosynthetic process 5019 14 (0.3%) 0.0032 0.0419
GO:0009059 macromolecule biosynthetic process 5040 14 (0.3%) 0.00335 0.0419
GO:0060255 regulation of macromolecule metabolic process 6339 16 (0.3%) 0.0037 0.042
GO:0044267 cellular protein metabolic process 5282 14 (0.3%) 0.00531 0.0553
GO:0034976 response to endoplasmic reticulum stress 298 3 (1.0%) 0.00779 0.0602
GO:0034654 nucleobase-containing compound biosynthetic process 4307 12 (0.3%) 0.00786 0.0602
GO:0060326 cell chemotaxis 305 3 (1.0%) 0.00837 0.0602
GO:0048638 regulation of developmental growth 306 3 (1.0%) 0.00845 0.0602
GO:0018130 heterocycle biosynthetic process 4372 12 (0.3%) 0.00889 0.0602
GO:0019438 aromatic compound biosynthetic process 4383 12 (0.3%) 0.00908 0.0602
GO:0034645 cellular macromolecule biosynthetic process 4976 13 (0.3%) 0.00916 0.0602
Molecular
function 		GO:0005125 cytokine activity 237 4 (1.7%) 0.000299 0.00459
GO:0005126 cytokine receptor binding 273 4 (1.5%) 0.00051 0.00459
GO:0019887 protein kinase regulator activity 190 3 (1.6%) 0.00224 0.0134
GO:0048018 receptor ligand activity 493 4 (0.8%) 0.00445 0.0164
GO:0019210 kinase inhibitor activity 73 2 (2.7%) 0.00456 0.0134
GO:0001664 G protein-coupled receptor binding 294 3 (1.0%) 0.00757 0.0227
Table 3. Pathway analysis of 32 genes targeted by multiple miRNAs with ConsensusPathDB (adjusted p<0.01).
Table 3. Pathway analysis of 32 genes targeted by multiple miRNAs with ConsensusPathDB (adjusted p<0.01).
Pathway name Set size Candidates contained p-value q-value Pathway source
Chemokine receptors bind chemokines 62 3 (4.8%) 0.000117 0.00397 Reactome
COVID-19 adverse outcome pathway 15 2 (13.3%) 0.000241 0.00397 Wikipathways
Rheumatoid arthritis - Homo sapiens (human) 93 3 (3.3%) 0.000378 0.00397 KEGG
Viral protein interaction with cytokine and cytokine receptor - Homo sapiens (human) 100 3 (3.0%) 0.000483 0.00397 KEGG
Chagas disease - Homo sapiens (human) 102 3 (2.9%) 0.000512 0.00397 KEGG
Toll-like receptor signaling pathway - Homo sapiens (human) 104 3 (2.9%) 0.000542 0.00397 KEGG
IL-7 signaling pathway 25 2 (8.0%) 0.000683 0.00429 Wikipathways
Cytokine-cytokine receptor interaction - Homo sapiens (human) 295 4 (1.4%) 0.00102 0.00558 KEGG
Cyclin D associated events in G1 44 2 (4.5%) 0.00212 0.00931 Reactome
G1 Phase 44 2 (4.5%) 0.00212 0.00931 Reactome
Chemokine signaling pathway - Homo sapiens (human) 192 3 (1.6%) 0.00316 0.0126 KEGG
Peptide ligand-binding receptors 207 3 (1.4%) 0.00391 0.0143 Reactome
Lipid and atherosclerosis - Homo sapiens (human) 215 3 (1.4%) 0.00435 0.0146 KEGG
Human cytomegalovirus infection - Homo sapiens (human) 225 3 (1.3%) 0.00493 0.0146 KEGG
DNA damage response 68 2 (2.9%) 0.00498 0.0146 Wikipathways
E2F transcription factor network 75 2 (2.7%) 0.00603 0.0166 PID
Table 4. Gene ontology terms of 65 genes with ConsensusPathDB (category level 4) (adjusted p<0.01).
Table 4. Gene ontology terms of 65 genes with ConsensusPathDB (category level 4) (adjusted p<0.01).
Category level GO term Set size Candidates contained p-value q-value
Biological process GO:0034645 cellular macromolecule biosynthetic process 4976 30 (0.6%) 6.54e-06 0.000634
GO:0009059 macromolecule biosynthetic process 5040 30 (0.6%) 8.62e-06 0.000634
GO:0009889 regulation of biosynthetic process 4310 27 (0.6%) 1.38e-05 0.000676
GO:0044271 cellular nitrogen compound biosynthetic process 5019 29 (0.6%) 2.59e-05 0.000881
GO:0010467 gene expression 5644 31 (0.5%) 2.99e-05 0.000881
GO:0051171 regulation of nitrogen compound metabolic process 6033 32 (0.5%) 4.06e-05 0.000996
GO:0080090 regulation of primary metabolic process 6227 32 (0.5%) 8.1e-05 0.0017
GO:0031323 regulation of cellular metabolic process 6297 32 (0.5%) 0.000103 0.00189
GO:0060255 regulation of macromolecule metabolic process 6339 32 (0.5%) 0.000119 0.00195
GO:0018130 heterocycle biosynthetic process 4372 25 (0.6%) 0.000176 0.00259
GO:0090304 nucleic acid metabolic process 5270 28 (0.5%) 0.0002 0.00267
GO:1901362 organic cyclic compound biosynthetic process 4529 25 (0.6%) 0.000316 0.00387
GO:0034654 nucleobase-containing compound biosynthetic process 4307 24 (0.6%) 0.000393 0.00445
GO:0019438 aromatic compound biosynthetic process 4383 24 (0.5%) 0.000516 0.00542
GO:0009892 negative regulation of metabolic process 3076 18 (0.6%) 0.00172 0.0169
GO:0050779 RNA destabilization 36 2 (5.6%) 0.00478 0.0421
GO:0009893 positive regulation of metabolic process 3654 19 (0.5%) 0.00487 0.0421
GO:0051101 regulation of DNA binding 124 3 (2.4%) 0.00543 0.0439
GO:0072175 epithelial tube formation 126 3 (2.4%) 0.00568 0.0439
GO:0016331 morphogenesis of embryonic epithelium 141 3 (2.1%) 0.00774 0.0569
Molecular
function		 GO:0003677 DNA binding 2515 22 (0.9%) 6.1e-07 7.63e-06
GO:0001067 regulatory region nucleic acid binding 1535 17 (1.1%) 7.63e-07 7.63e-06
GO:0046872 metal ion binding 4259 23 (0.5%) 0.000869 0.00579
GO:0001228 DNA-binding transcription activator activity, RNA polymerase II-specific 444 6 (1.4%) 0.00164 0.00822
GO:0004879 nuclear receptor activity 52 2 (3.8%) 0.00978 0.0391
Cellular
component		 GO:0031981 nuclear lumen 4461 24 (0.5%) 0.000678 0.0183
GO:0005634 nucleus 7626 33 (0.4%) 0.00198 0.0183
GO:0005654 nucleoplasm 4103 21 (0.5%) 0.00341 0.0307
Table 5. Pathway analysis of 65 genes with ConsensusPathDB (adjusted p<0.01).
Table 5. Pathway analysis of 65 genes with ConsensusPathDB (adjusted p<0.01).
Pathway name Set size Candidates contained p-value q-value Pathway source
Rheumatoid arthritis - Homo sapiens (human) 93 8 (8.7%) 1.69e-05 0.00776 KEGG
Transferrin endocytosis and recycling 31 4 (12.9%) 0.000533 0.0765 Reactome
Oncogene Induced Senescence 32 4 (12.5%) 0.000603 0.0765 Reactome
Small cell lung cancer - Homo sapiens (human) 92 6 (6.5%) 0.000935 0.0765 KEGG
G1 to S cell cycle control 64 5 (7.8%) 0.00112 0.0765 Wikipathways
Small cell lung cancer 96 6 (6.2%) 0.00117 0.0765 Wikipathways
Adenosine ribonucleotides de novo biosynthesis 38 4 (10.5%) 0.00117 0.0765 HumanCyc
RND1 GTPase cycle 42 4 (9.5%) 0.00171 0.0776 Reactome
Human cytomegalovirus infection - Homo sapiens (human) 225 9 (4.0%) 0.00185 0.0776 KEGG
RND2 GTPase cycle 43 4 (9.3%) 0.00186 0.0776 Reactome
Cyclin D associated events in G1 44 4 (9.1%) 0.00203 0.0776 Reactome
G1 Phase 44 4 (9.1%) 0.00203 0.0776 Reactome
Cyclins and cell cycle regulation 23 3 (13.0%) 0.00269 0.0948 BioCarta
Spinal Cord Injury 117 6 (5.1%) 0.00319 0.104 Wikipathways
Cell cycle: g1/s check point 25 3 (12.0%) 0.00343 0.105 BioCarta
Insulin receptor recycling 26 3 (11.5%) 0.00384 0.109 Reactome
Collecting duct acid secretion - Homo sapiens (human) 27 3 (11.1%) 0.00429 0.109 KEGG
Constitutive Signaling by AKT1 E17K in Cancer 27 3 (11.1%) 0.00429 0.109 Reactome
Lipid and atherosclerosis - Homo sapiens (human) 215 8 (3.7%) 0.00508 0.119 KEGG
Iron uptake and transport 57 4 (7.0%) 0.00523 0.119 Reactome
Superpathway of purine nucleotide salvage 59 4 (6.8%) 0.00591 0.119 HumanCyc
Purine nucleotides de novo biosynthesis 59 4 (6.8%) 0.00591 0.119 HumanCyc
Oxidative phosphorylation - Homo sapiens (human) 133 6 (4.5%) 0.00597 0.119 KEGG
Prion disease - Homo sapiens (human) 273 9 (3.3%) 0.00661 0.126 KEGG
Chemokine receptors bind chemokines 62 4 (6.5%) 0.00704 0.129 Reactome
Vitamin D Receptor Pathway 184 7 (3.8%) 0.00765 0.13 Wikipathways
Viral protein interaction with cytokine and cytokine receptor - Homo sapiens (human) 100 5 (5.0%) 0.00776 0.13 KEGG
Ectoderm Differentiation 142 6 (4.2%) 0.00814 0.13 Wikipathways
Amino acids regulate mTORC1 34 3 (8.8%) 0.00824 0.13 Reactome
ROS and RNS production in phagocytes 35 3 (8.6%) 0.00893 0.135 Reactome
Toll-like receptor signaling pathway - Homo sapiens (human) 104 5 (4.8%) 0.00912 0.135 KEGG
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