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  • Research Paper
  • Open Access

Characterization of the hypothalamic transcriptome in response to food deprivation reveals global changes in long noncoding RNA, and cell cycle response genes

Genes & NutritionStudying the relationship between genetics and nutrition in the improvement of human health201510:48

https://doi.org/10.1007/s12263-015-0496-9

  • Received: 2 June 2015
  • Accepted: 5 October 2015
  • Published:

Abstract

The hypothalamus integrates energy balance information from the periphery using different neuronal subtypes within each of the hypothalamic areas. However, the effects of prandial state on global mRNA, microRNA and long noncoding (lnc) RNA expression within the whole hypothalamus are largely unknown. In this study, mice were given either a 24-h fast, or ad libitum access to food. RNA samples were analyzed by microarray, and then a subset was confirmed using quantitative real-time PCR (QPCR). A total of 540 mRNAs were either up- or down-regulated with food deprivation. Since gene ontology enrichment analyses identified several categories of mRNAs related to cell cycle processes, ten cell-cycle-related genes were further analyzed using QPCR with six confirmed to be significantly up-regulated and one down-regulated in response to 24-h fasting. While 22 independent microRNAs were differentially expressed by microarray, secondary analysis by QPCR failed to confirm significant changes with fasting. There were 622 lncRNAs identified as differentially expressed, and of three tested by QPCR, two were confirmed. Overall, this is the first time that expression of hypothalamic lncRNAs has been shown to be responsive to food deprivation. In addition, this study is the first to identify a list of lncRNAs with high expression in RNA extracted from hypothalamus. Individual contributions from specific miRNA, lncRNA and mRNAs to the food deprivation response can now be further studied at the physiological and biochemical levels.

Keywords

  • microRNA
  • lncRNA
  • mRNA
  • Fasting
  • Gene ontology
  • Hypothalamus

Introduction

The hypothalamus integrates central nervous system control of energy balance including responses to both food deprivation and re-feeding. Within the hypothalamus, multiple neuronal subtypes respond with both cellular signaling (for example, leptin and insulin signals) and gene regulatory responses involving protein-coding genes (mRNA), microRNAs (miRNA) and long noncoding RNAs (lncRNA). According to the Mouse Genome Informatics: Gene Expression Database, there are 2630 mRNAs with postnatal expression in the midbrain (http://www.informatics.jax.org/, Smith et al. 2014). Of these, up to 588 are transcription factors (Good 2010). In addition, miRNAs also play a role in gene/protein regulation by repressing gene expression through an interaction with the mRNA transcript that results in either degradation or blocked translation of the mRNA. Finally, lncRNAs are emerging as key posttranscriptional regulators of mRNA, including regulation of transcription (both as scaffolds for chromatin modifying proteins and transcription factors, and as interfering moieties), splicing, mRNA decay and translation, as well as microRNA decoys (Rinn and Chang 2012; Yoon et al. 2013).

Differentially expressed hypothalamic mRNAs have been identified in selected rodent-based studies using the food deprivation/feeding paradigm. In a study by Poplawski et al. (2010), 48-h food deprivation in mice was used, in conjunction with a mouse expression array. This study found that global glucose metabolism was altered in response to this fast. In another study that uses laser microdissection in conjunction with rat whole-genome arrays, over 3000 differentially expressed mRNAs in the rat arcuate nucleus of the hypothalamus (Paulsen et al. 2009).

microRNAs have been reported to be differentially expressed in several brain disorders, including neuronal cancers, and neurodegeneration (Fassan et al. 2011; Roshan et al. 2009). There are also studies that demonstrate a role for microRNAs in more normal physiological processes, such as learning, synaptic plasticity and neuroadaptation (McNeill and Van Vactor 2012). A recent study using hypothalamic tissue from ob/ob mice, a genetically obese and leptin-deficient strain has identified changes in miR-200a, miR-200b and miR-429 (Crepin et al. 2014). Furthermore, within other tissue types, studies report an association of microRNA levels in adipose tissue, under high-fat diet or obesity, and in brown fat undergoing differentiation (Martinelli et al. 2010; Sun et al. 2011; Takanabe et al. 2008). Together, the evidence from these studies suggests that expression of hypothalamic microRNA may also change in response to normal changes in energy availability.

Long noncoding RNAs (lncRNA) are distinguished from microRNAs as they are more than 200 nucleotides in length (for reviews, see Kornfeld and Bruning 2014 and Kung et al. 2013). Similar to microRNAs, lncRNAs have been reported to be functionally associated with adipogenesis (Sun et al. 2013). In addition, studies also demonstrated that transcription of lncRNAs occurs in response to food supply and insulin/insulin-like growth factor levels (Ellis et al. 2014; Hellwig and Bass 2008). These indicate that lncRNAs might be involved in the energy balance regulation. In the brain, several papers have detailed the importance of lncRNA in neuronal differentiation and brain development (Aprea et al. 2013; Lin et al. 2014), but adult expression patterns under stress-type conditions such as fasting have not been reported.

The study reported herein utilized microarray platforms with the ability to capture exon-specific mRNA, microRNA and lncRNA data from the same samples. This allowed for full characterization of the whole hypothalamic transcriptome, including mRNA, microRNA and long noncoding RNA, in response to two prandial states, 24-h fasting and ad lib fed. As the global expression patterns have not been reported for either microRNA, nor for long noncoding RNAs in response to short-term fasting, these results characterize the simultaneous changes in all three subsets of the transcriptome, and help to further identify specific RNA targets in the fasting and fed response states.

Experimental procedures

Animals

The Institutional Animal Care and Use Committee at the Virginia Tech approved all studies. C57Bl/6 mice were purchased from The Jackson Laboratory as matched littermates, and only male mice were used in this experiment so that estrous cycles did not have to be taken into account during fasting. All mice were maintained under 12-h light/dark cycle with free access to food and water except as noted during experimentation. At the age of 8 weeks, mice were randomly separated to either a food-deprived (N = 3, microarray; N = 4–6, QPCR) or ad lib group (N = 3, microarray; N = 4–6, QPCR), and food was removed for deprived mice at 9 a.m. (Fig. 1a). After 24-h food deprivation, mice were euthanized and brains were collected. The hypothalamic region was isolated from the whole brain using a Zivic-Miller brain slicer, and taking a slice containing only the region between the optic chiasm and the mammillary bodies. That slice was further dissected to contain only the region between the cerebral peduncle on either side of the hypothalamus and to the top of where the third ventricle terminates, between the hypothalamus and thalamus. The slice was put into RNAlater ® solution (Ambion, USA) for RNA analysis. For a limited number of animals, gastrocnemius was collected to compare the sensitivity of our arrays for detecting tissue-specific microRNAs.
Fig. 1
Fig. 1

Experimental design and confirmation of food deprivation conditions. a Experimental design of the experiment, showing the timeline for the ad lib and food-deprived groups of animals. b Serum glucose levels, relative to ad lib fed. c Serum leptin levels, relative to ad lib fed **P ≤ 0.01

RNA isolation

The RNA was extracted using the mirVana™ PARIS™ Kit (Ambion, USA) according to the manufacturer’s procedure without modifications. Enrichment procedure for small RNAs was performed for microRNA study. RNA integrity was tested after extraction using Experion™ system (Bio-Rad, USA).

Microarray and statistical analysis

The microRNA analysis was done in triplicate (N = 3) using individual mouse samples and the Affymetrix GeneChip® microRNA 1.0 array. The Affymetrix microRNA array raw data were extracted using the Affymetrix microRNA QC tool. Global normalization was used to normalize the raw data. The log2 values of the expression levels for each microRNA were processed, and Student’s t test was performed between ad lib fed and food-deprived groups. To survey for candidate microRNA with differential expression, statistical criteria of P value ≤0.05 and fold change of ≥1.3 were used to identify differentially expressed microRNAs.

mRNA analysis was performed in triplicate with the same individual mouse samples group as were analyzed for the microRNAs. These were done using the Affymetrix GeneChip® Mouse Exon 1.0 ST array. Global normalization was used to normalize the raw data. The log2 values of the expression levels for each mRNA were processed, and Student’s t test was performed between ad lib fed and food-deprived groups. To survey for candidate mRNA with differential expression, statistical criteria used were a P value ≤0.05 and fold change of ≥1.3.

Exon array-based lncRNA analysis was performed with the Affymetrix GeneChip® Mouse Exon 1.0 ST array data using Noncoder (Gellert et al. 2013), a Web interface designed for lncRNA analysis with the Affymetrix GeneChip® Mouse Exon 1.0 ST array. The CEL files used for mRNA analysis were uploaded to Noncoder, and data were processed using RMA normalization. The log2 value of the expression level for each lncRNA was processed, and the Student’s t test was performed between ad lib fed and food-deprived groups. To survey for candidate lncRNA with differential expression, only lncRNAs with more than one probe set were used for further statistical comparisons, with cutoff of P value ≤0.05 and fold change of ≥1.3.

Quantitative PCR (QPCR) analysis

To perform independent confirmation of statistically significant changes in transcript abundance, RNA from 4 to 6 additional mice was obtained for all confirmatory analyses, with microRNA expression levels measured utilizing the Taqman microRNA assay (Applied Biosystems, Foster City, CA). For each microRNA tested, 5 ng of small RNA-enriched samples from N = 4–6 mice per assay was used. This number of individual mice used is based on experimental data and results from our laboratory and is sufficient to detect a 1.5-fold or better differences in expression (Vella et al. 2007). Reverse transcription and QPCR were performed according to the assay manual. The expression level of sno-202 RNA was used as the normalization control in microRNA analysis, as this NC RNA has previously been shown to be most effective in a normalization analysis (Brattelid et al. 2011). In our hands, we also found sno-202 to be consistently expressed between tissues and conditions. All QPCR results were compared between groups using Student’s t test with P value ≤0.05 for statistical significance.

The expression levels of mRNA and lncRNA were measured using designed primers and the iTaq™ SYBR® Green Supermix with Rox (Bio-Rad, Hercules, CA). New total hypothalamic RNA was isolated from N = 5–6 mice, and 2 μg of total RNA was treated with RQ1 RNase-Free DNase (Promega, Madison, Wisconsin) and then subjected to reverse transcription using M-MLV Reverse Transcriptase (Promega, Madison, Wisconsin). A 20 ng aliquot of cDNA was then used for QPCR analysis using the ABI 7900 system (Applied Biosystems, Foster City, CA). The expression level of β-actin was used as the normalization control. All QPCR results were compared between groups using Student’s t test with P value ≤0.05 for statistical significance. For lncRNA analysis, QPCR products were then sequenced to confirm that the sequence of the amplicon was unique to the lncRNA region.

Leptin and Glucose measurement

Whole blood was collected immediately after each mouse was euthanized. Blood glucose level was measured using FreeStyle Freedom Lite® Blood Glucose Monitoring System (Abbott Laboratories, USA). The serum was collected from the whole blood sample by centrifuging at 1000×g for 10 min at 4 °C. Serum leptin level was measured using the Mouse Leptin Quantikine ELISA kit (R&D System, USA) according to the manufacturer’s instructions and using manufacturer-provided standards to produce the standard curve. Blood glucose and leptin level were compared between groups using Student’s t test with P value ≥0.05 for statistical significance.

GO and STRING analysis

Gene ontology analysis was performed using GeneCodis3 (Carmona-Saez et al. 2007; Nogales-Cadenas et al. 2009; Tabas-Madrid et al. 2012). Genes passing the cutoff of microarray analysis were used for the GO Biological Process, GO Molecular Function, GO Cellular Component and KEGG pathways analysis. For the settings of statistical parameters, the minimum number of genes was set to two, and a hypergeometric statistical test and FDR P value correction were used. The results were listed with the multiple-testing-corrected, hypergeometric P value.

STRING version 10 was used in its online format to generate an interactive network mode using the all cell cycle genes tested for QPCR (Franceschini et al. 2013). The confidence view was used with one expansion of the protein interactions network.

Results

Leptin and glucose levels

Following 24-h food deprivation (Fig. 1a), serum leptin and blood glucose levels for each treatment group showed a significant reduction, as would be expected from fasting conditions (Fig. 1b, c).

Overall transcriptome changes

Twenty-four-hour food deprivation resulted in changes in the overall hypothalamic transcription of varying magnitude. Using DNA microarrays and the statistical selection criteria of a P value of ≤0.05 and fold change of ≥1.3, a subset of the candidate differentially expressed genes was identified. As shown in Table 1, the majority of detectable changes were found within the mRNA and lncRNA transcriptome, while the fewest changes were found within the microRNA transcription.
Table 1

Comparison of microRNA, lncRNA and mRNA array results

 

microRNA

lncRNA

mRNA

Total detected

536

12,521

16,294

Number of those significantly up-regulated by fasting

16

421

298

Number of those significantly down-regulated by fasting

6

201

242

Total detected RNA species, and those found to be significantly up- or down-regulated by food deprivation, compared to ad lib feeding, based on the criteria of at least 1.3-fold (either direction) different, and P ≤ 0.05

mRNA microarray analysis and QPCR validation

The data from the Affymetrix exon arrays (Supplemental Data File 1, excel list) were first used to identify differentially expressed hypothalamic mRNAs between ad lib fed and 24-h food-deprived animals. The log2 scatter plot comparison (Fig. 2a) suggested that there is no dramatic transcriptome adjustment in response to food deprivation, while volcano plot analysis (Fig. 2b) indicated that there were still some significant changes between the two conditions. In all, there were 540 candidate differentially expressed mRNAs identified, with 298 mRNAs found to be up-regulated and 242 mRNAs down-regulated with food deprivation (Table 1). The up- and down-regulated mRNAs identified using the array data are shown in Supplemental Table 1. The top ten mRNAs with the highest hypothalamic expression levels overall are shown in Supplemental Table 2. None of differentially expressed mRNAs were in the list of those with the highest hypothalamic expression. A subset of the differentially expressed candidate genes was selected for further analysis based on probable role in energy balance regulation, or brain function, and P value. As shown in Fig. 2c, d, most genes chosen using these criteria were confirmed to be significantly differentially expressed in independent samples isolated from ad lib and food-deprived animals. Of ten genes selected as differentially regulated with 24-h fasting, seven of these changes were confirmed by QPCR using an independent test set of animals.
Fig. 2
Fig. 2

mRNA array plots and QPCR analysis. a log2 scatter plot of hypothalamus mRNA array data from Affymetrix array. Data were filtered using a P value <0.05 and 1.3-fold change. b Volcano plot showing P value versus log2 expression level for data from Affymetrix array. Lines indicate where significance cutoff values were made. c QPCR analysis of selected mRNAs that were differentially expressed in food-deprived versus ad lib mice. d mRNAs were selected from cell cycle GO categories, and their expression levels tested by QPCR using N = 5–6 new samples for each group. All mRNA levels are reported as relative to levels in ad lib fed animals and normalized to the housekeeping gene β-actin. e String v10 was used to generate a network using the proteins tested in 7A as input. The network (interactive network mode) was expanded by one level. Only the names of the proteins input are shown. *P ≤ 0.05; **P ≤ 0.01

Gene ontology (GO) analysis of the 540 differentially regulated candidate genes between ad lib fed and food-deprived mice was performed, and the top five GO terms for each of the major component or process category are shown in Table 2. Close examination of the entire dataset of significant categories (P value ≤0.05) revealed that cell-cycle-related categories were found once in the top five list, and then multiple times in the remaining significant categories. These additional categories of cell-cycle-related terms are shown in Table 3. A subset of genes found in cell cycle categories was further analyzed using QPCR. Of eight cell cycle category genes analyzed, six were confirmed to be significantly differentially regulated in new independent hypothalamic mRNA samples from ad lib and food-deprived animals (Fig. 2e). Using STRING (Search Tool for Retrieval of Interacting Genes/proteins) (Szklarczyk et al. 2011) analysis, four of these comprise a network of protein products involved in cell cycle control, and all of the four, except rock 1, showed significantly different expression by QPCR (Fig. 2f) using the independent test set.
Table 2

GO analysis (mRNA arrays)

GO term

Gene count (significant)

Gene count (reference)

P value

Biological process

GO:0006810: transport

56

1620

8.73E−07

GO:0007049: cell cycle

26

533

3.41E−05

GO:0016310: phosphorylation

29

689

8.99E−05

GO:0048812: neuron projection morphogenesis

7

36

0.000192

GO:0006974: response to DNA damage stimulus

17

308

0.000447

Cellular component

GO:0016020: membrane

178

5723

4.76E−24

GO:0005886: plasma membrane

103

2782

1.23E−17

GO:0005634: nucleus

144

4867

7.00E−17

GO:0005737: cytoplasm

141

5026

1.33E−14

GO:0016021: integral to membrane

143

5398

6.95E−13

Molecular function

GO:0005515: protein binding

100

2999

3.92E−16

GO:0046872: metal ion binding

91

2802

5.08E−14

GO:0000166: nucleotide binding

64

1999

8.20E−10

GO:0005524: ATP binding

47

1421

6.59E−08

GO:0003677: DNA binding

51

1635

1.10E−07

KEGG pathways

KEGG:04670: leukocyte transendothelial migration

9

117

4.27E−05

KEGG:04724: glutamatergic synapse

9

123

6.32E−05

KEGG:04010: MAPK signaling pathway

10

263

0.004306

KEGG:04144: endocytosis

9

213

0.003344

KEGG:04062: chemokine signaling pathway

12

177

8.61E−06

All significantly differentially expressed mRNAs (529 total) were used in this analysis. The top five GO terms for each category are shown. The number of genes significant for that category (gene count, significant), the number of total genes in the reference category (gene count, reference) and P value for the GO term are shown

Table 3

Cell cycle category GO terms

GO term

Gene count (significant)

Gene count (reference)

P value

Cell cycle category

GO:0007049: cell cycle (BP)

26

533

3.41E−05

GO:0001938: positive regulation of endothelial cell proliferation (BP)

7

48

0.000652

GO:0001936: regulation of endothelial cell proliferation (BP)

2

4

0.031983

GO:0050768: negative regulation of neurogenesis (BP)

3

14

0.029825

GO:0043066: negative regulation of apoptotic process (BP)

14

340

0.017967

GO:0006915: apoptotic process (BP)

16

504

0.043501

List of all significant GO terms related to cell cycle categories is shown. The number of genes significant for that category (gene count, significant), the number of total genes in the reference category (gene count, reference) and P value for the GO term are shown

microRNA microarray analysis and QPCR validation

microRNA expression analysis was performed in triplicate on the Affymetrix microRNA microarray platforms (Supplemental Data File 2, excel list). Scatter plot analysis showed that the overall expression level differences between the 24-h fasting and ad lib treatment groups were similar and suggestive of few differentially regulated microRNAs, especially within the significance criteria (Fig. 3a, b). A total of 536 microRNAs were detected with the microarrays, but only 22 candidates for differential expression were identified using the relatively loose statistical criteria of P ≤ 0.05 and fold change ≥1.3, with 16 up-regulated and 6 down-regulated (Table 1 and Supplemental Table 3). A subset of these candidate microRNAs was chosen for further analysis based on p value and whether they had been found to be involved in any aspect of energy balance in previous studies, including those using other tissues than brain. These criteria identified six for further expression analysis using independent samples. However, QPCR analysis failed to confirm that any of these chosen microRNAs were significantly different in an independent analysis between ad lib and food-deprived treatment groups (Fig. 3c). Nevertheless, differences could be detected comparing skeletal muscle versus hypothalamic microRNA QPCR for three of these microRNAs (Fig. 3d), indicating that detection should have been possible with these probe sets had differences existed. For additional verification of the system, ad lib fed mice were used to compare hypothalamic and skeletal muscle (gastrocnemius) microRNAs (Supplemental Figure 1A), revealing the exploded comet shape suggestive of the expected significant differences in expression levels of specific microRNAs between the two tissue types. Specific microRNAs were then analyzed by QPCR using total RNA from both hypothalamus and skeletal muscle of both ad lib and fasted mice. As shown in Supplemental Fig. 1b, let-7d was highly expressed in both tissues, with similar expression levels under all conditions. Expression of the skeletal muscle-specific microRNA, miR-1, was significantly higher in skeletal muscle than in hypothalamus tissue, in both ad lib and fasted mice, as expected (Supplemental Figure 1C). Conversely, the nervous system-specific microRNA, miR-124a, was highly expressed in hypothalamic RNA isolated from both fed and fasted mice, when compared to its detection at a significantly lower level in gastrocnemius RNA isolated from either mice in either treatment group (Supplemental Figure 1D). The 10 highest expressing hypothalamic microRNAs in ad lib fed mice are shown in Supplemental Table 4. None of the microRNAs with the highest hypothalamic expression showed evidence of differential expression, but these results provide information on the hypothalamic microRNAome, overall, with highly expressed microRNAs warranting future exploration into their roles.
Fig. 3
Fig. 3

microRNA array plots and QPCR analysis. a log2 scatter plot of hypothalamus microRNA array data from Affymetrix array. Data were filtered using a P value <0.05 and 1.3-fold change. b Volcano plot showing P value versus log2 expression level for data from Affymetrix array. Lines indicate where significance cutoff values were made. c microRNA QPCR analysis in hypothalamic tissue of ad lib versus food-deprived mice. microRNAs were selected using the cutoff criteria, and expression level tested in N = 4–6 new samples for each group. All microRNA levels are reported relative to levels in ad lib fed animals and normalized to the sno-202 microRNA. d microRNA expression in skeletal muscle versus hypothalamus. *P ≤ 0.05; **P ≤ 0.01

lncRNA microarray analysis and QPCR validation

The Affymetrix exon arrays were also used to identify hypothalamic lncRNAs which were differentially expressed between ad lib fed and 24-h fasted animals. Only lncRNAs with two or more probe sets were used in the analysis, giving a total of 12,521 lncRNAs that could be detected by Noncoder (Table 1) (Supplemental Data File 3, excel list) (Gellert et al. 2013). Scatter plot analysis showed that the overall expression level differences between food-deprived and ad lib treatment groups were similar. Employing the statistical criteria of P ≤ 0.05 and a fold change of ≥1.3, 421 candidate lncRNAs were found to be up-regulated by 24-h fasting compared to ad lib feeding, whereas 201 lncRNAs were down-regulated (Table 1; Fig. 4a, b). A list of the differentially expressed lncRNAs is shown in Supplemental Table 5. Three of the candidate lncRNAs with the highest fold change accompanied by highly significant p values between ad lib and food-deprived groups were selected for further analysis by QPCR. As shown in Fig. 4c, two lncRNAs, AK038506 and AK049914, were confirmed to be significantly differentially expressed, both on the array platform, and using QPCR in independent samples isolated from ad lib and food-deprived animals. The top ten lncRNAs with the highest hypothalamic expression levels overall are shown in Supplemental Table 6. Similar to the microRNAs, none of the lncRNAs with the highest hypothalamic expression showed evidence of differential expression, but provide information on the hypothalamic lncRNAome, and again those that might warrant further exploration.
Fig. 4
Fig. 4

lncRNA array plots and QPCR analysis. a log2 scatter plot of hypothalamus lncRNA array data. Data were filtered using a P value ≤0.05 and 1.3-fold change. b Volcano plot showing P value versus log2 expression level for data from lncRNA results. Lines indicate where significance cutoff values were made. c lncRNA QPCR analysis using hypothalamic tissue of ad lib versus food-deprived mice. All lncRNA levels are reported relative to levels in ad lib fed animals and normalized to the sno-microRNA **P ≤ 0.01

Discussion

The results of this study yield two major findings. First, after a 24-h fast, the hypothalamic microRNA response is minimal, when compared to the lncRNA or mRNA response. Statistically significant changes in microRNA expression levels could not be confirmed by QPCR in an independent test set, and to date, this is the only global hypothalamic analysis of the microRNA transcriptome in response to food deprivation. Second, and in contrast to the microRNA results, there are multiple lncRNAs and mRNAs differentially expressed following food deprivation. While the lncRNAs are not well characterized, and pathway analysis is not available for this subset of RNA, the mRNAs dataset revealed a high number of differentially expressed mRNAs within the cell cycle categories following gene ontology analysis. As noted, multiple pathways, including phosphorylation, stress response and specific enzyme categories, are also significantly changed when the mRNA datasets were compared between these conditions. This is the first study to fully characterize the transcriptome of the hypothalamus in 24-h fasting, compared to the ad lib fed animals.

Given the previous published studies showing differential regulation of microRNAs in brain, adipose and other tissues conditions such as cancer or obesity, it was surprising to find so few differentially expressed microRNAs via microarray analysis and none that could be confirmed by further QPCR analysis (Fassan et al. 2011; Martinelli et al. 2010; McNeill and Van Vactor 2012; Roshan et al. 2009; Sun et al. 2011; Takanabe et al. 2008). Although current database information suggests that there are only 105 microRNAs expressed in the midbrain above the detection threshold (http://www.microrna.org), we detected 536 microRNAs using the Affymetrix microarrays. Our data confirmed that the let-7 family, miR-124a, miR-125 family and miR-138 were highly represented in the hypothalamic microRNAome, and found several others such as miR-709 and miR-690 which have not previously been shown to be highly expressed in this region (Bak et al. 2008; Meister et al. 2013; Olsen et al. 2009). Moreover, we were able to demonstrate that the microarrays could detect many statistically significant differences between hypothalamus and skeletal muscle. However, only 22 candidate differentially expressed microRNAs were identified between the 24-h fasted and ad lib fed states in the hypothalamus, and none of the changes were confirmed to be statistically significant in an independent set of samples using QPCR. In a study by Sangiao-Alvarellos and colleagues which examined hypothalamic miRNAs in high-fat-fed, versus chronically restricted, low-fat-fed animals (fed at 65 % of control, but not completely food restricted), ten differentially expressed miRNAs were identified (Sangiao-Alvarellos et al. 2014). However, only one of these significant differences was repeatable in a later figure of the same paper with 48-h fast (Sangiao-Alvarellos et al. 2014). None of the microRNAs from Sangiao-Alvarellos and colleagues were differentially expressed in our samples, and thus, the findings by Sangiao-Alvarellos and colleagues are therefore consistent with ours, in few hypothalamic miRNAs changing in response to negative energy balance.

It is possible that statistically significant miRNA expression may have been detected at earlier time points but this beyond the scope of the current study. In addition, as this study examined the transcriptome of the whole hypothalamus, rather than region-specific transcriptomes, the possibility exists that region-specific changes were missed. For example, deletion of the dicer gene within hypothalamic POMC neurons was shown to lead to obesity in adulthood (Schneeberger et al. 2012). Given this finding and the dicer’s global role in processing microRNAs, it is likely that examining of nuclei-specific microRNAs may still reveal differential expression patterns with fasting or other conditions that change energy availability in the body.

Hypothalamic lncRNAs have not been previously implicated in any condition involving energy availability, making this study the first to both characterize the top differentially regulated lncRNAs and provide a list of highly expressed lncRNAs for this tissue. Noncoding RNAs, such as lncRNA, are of increasing interest, having predicted roles in transcriptional regulation during brain development and disease (Clark and Blackshaw 2014; Qureshi and Mehler 2013). This study identified more than 12,000 lncRNAs that are expressed within whole hypothalamus and is the first of its kind to do so for mouse hypothalamus undergoing ad lib feeding or fasting. To date, only one other study in canines has examined hypothalamic lncRNAs, and this was done in nonfasting male beagles (Roy et al. 2013). The study identified 57 lncRNAs in the canine hypothalamus, although none appear to have any overlap with those found in this study using our criteria. Other studies, not using hypothalamic tissue, have implicated lncRNAs in various nutritional states, including those found in the gut in germ free, versus microbiota (free-living) conditions, and those identified in diabetics (SNP association, without verification, Liang et al. 2015; Wessel et al. 2015). Additionally, lncRNAs have been shown to enhance brown and white adipocyte differentiation, participate in pancreatic beta-cell function and possibly protect against diet-induced obesity, as shown for SRA, the steroid receptor RNA activator (Kameswaran and Kaestner, 2014; Liu et al. 2014; Sun et al. 2011; Sun et al. 2013; Xu et al. 2015; You et al. 2015). The most relevant to our work would be SRA, as a whole body knockout of this lncRNA protected against diet-induced obesity (Liu et al. 2014). However, our results confirm theirs showing low expression in hypothalamus, with SRA not being found differentially expressed in our results.

Of the more than 12,000 identified, more than 600 lncRNAs were found differentially expressed by microarray analysis and can now be further studied by QPCR or other methods their possible roles in energy balance. Of the two lncRNAs confirmed by QPCR analysis to be differentially expressed with fasting, neither has been previously characterized for any tissue or condition. AK038506, which we have found to be significantly increased with fasting, was originally identified in adult male mouse hypothalamic cDNA during global cloning studies (Carninci et al. 2005). It is a 1933-nucleotide sequence with only minimal (less than 5 % overlap) to other GeneBank sequences by nucleotide BLAST (data not shown). AK049914, which is also significantly increased with fasting, is a 4061-nucleotide sequence, originally found in adult male mouse hippocampus cDNA, and sequence analysis indicates that this lncRNA appears to overlap within the third and fourth exons of the apolipoprotein D gene (Apod) (Carninci et al. 2005). Furthermore, there is a 289 bp sequence (NC_000082.6: 31308226 to 31308521) that has a very high similarity (97 % identities based on the BLAST search) to a sequence localized about 200 bp upstream to itself (NC_000082.6: 31309821 to 31310117), but still within the two exons of Apod. These together indicate a potential link of transcription activities between AK049914 and Apod. In fact, it has been reported that Apod is up-regulated by calorie restriction in whole brain, skeletal muscle and heart (Yan et al. 2012). A study from 1994 linked the apolipoprotein D gene to obesity and fasting insulin status (Vijayaraghavan et al. 1994). A more recent study has explored the finding that several Alzheimer’s disease-associated genes, including apolipoprotein E, have lncRNAs within their genes (Holden et al. 2013). These findings are consistent with the up-regulation of AK049914 during fasting and open the door for future studies on the role of lncRNAs in energy balance regulation, hypothalamus function and diseases such as Alzheimer’s disease.

In this study, animals were food-deprived for 24 h, in order to analyze the effects of food deprivation on the whole transcriptome. The mRNAs were studied as part of the whole transcriptome, with interesting findings detected in cell-cycle-related gene expression, through both GO and STRING network analyses. In particular, p21/Cdkn1a was one of the most significant food deprivation up-regulated genes, and this finding was confirmed by QPCR. p21/Cdkn1a controls progression of cells through the cell cycle, by blocking cyclin-dependent kinase activity, with two published reports linking p21/Cdkn1a activity, obesity and adipose tissue (Inoue et al. 2008; Nakatsuka et al. 2012). Interestingly, the link between fasting-induced up-regulation of p21/Cdkn1a has previously been made for hypothalamic tissue as well. In a 2010 study of C57Bl/6 mice undergoing a 48-h food deprivation, p21/Cdkn1a expression was significantly up-regulated (Poplawski et al. 2010). More recently, in a 2013 paper, liver and hypothalamus of mice fasted 24 and 48 h showed significant induction of a p21/Cdkn1a promoter construct (Tinkum et al. 2013). Further examination of our dataset revealed that there were many genes with GO categories related to cell cycle processes. Based on this, we looked into whether differential regulation of other cell-cycle-related genes could be confirmed by QPCR and were able to confirm six out of eight of these genes in follow-up studies on new tissue samples. The apparent relationship between fasting and cell cycle regulation by this dataset is an interesting one, given that studies suggest that the onset of hypothalamic adult neurogenesis is related to high levels of leptin or ciliary neurotrophic factor (Kokoeva et al. 2005). However, up-regulation of p21/Cdkn1a by food deprivation, which lowers serum leptin levels, is consistent with a blockage in cell cycle and reduced proliferation and the opposite of what is seen with leptin and/or CNTF exposure (Cheng 2013). At this time, more studies would be needed to determine whether differential regulation of the p21/Cdkn1a and other cell cycle regulation-associated genes result in apoptosis, cell cycle arrest or some other event following food deprivation.

It is known, through work using several different models, that calorie restriction can increase individual longevity, and at least one study has examined hypothalamic gene expression in response to a 60 % restricted diet (Fu et al. 2006). In that study, nine genes were differentially regulated by calorie restriction in aging mice, but none overlapped with those in our study. Calorie restriction can reduce levels of oxidative stress and protect proteins, lipids and DNA form oxidative damage, thereby possibly contributing to cellular protection against diabetes, cardiovascular disease, cancers and neurodegenerative diseases (Mattson 2005; Mattson and Wan 2005; Sohal and Weindruch 1996). While an acute fast, such as 24-h food deprivation, is a different treatment than long-term calorie restriction, biological processes category such as “response to DNA damage stimulus” (GO:0006974), “oxidation–reduction process” (GO:0055114) and “lipid biosynthetic process” (GO:0008610) was found among the top groups of GO terms. Likewise, stress-related mRNAs such as Sult1a1, Cirbp and Tsc22d3 were also confirmed to be differentially regulated by QPCR (De Leeuw et al. 2007; Maglich et al. 2004; Szklarczyk et al. 2012). Our findings present new directions to study the potential relationship between the acute and chronic effects of calorie restriction.

In summary, there is little evidence of a global microRNA response following food deprivation of 24 h in mouse hypothalamic tissue, even though microRNAs were detectable in hypothalamus and those tested were differentially expressed when compared in skeletal muscle. It is possible that physiological stressors other than food deprivation may result in differential microRNA expression, or the use of whole hypothalamus in our analysis was not appropriate to detect the select, nuclei-specific microRNAs that were differentially expressed following food deprivation. The study, however, was designed to fully characterize the whole transcriptome, including lncRNAs, of which over 600 found to be differentially expressed. In addition, close to 50 % of all of the mRNA species represented on the array were also detectable in hypothalamic tissue, and over 500 of them were differentially regulated in response to food deprivation. Our confirmation of a relationship between food deprivation and the differential regulation of mRNAs within the cell cycle control gene ontology categories now provides new insight into hypothalamic plasticity and adult hypothalamic neurogenesis (Kokoeva et al. 2005). These data also suggest new directions of research aimed at clarifying role of lncRNA in the fields of obesity, caloric restriction and nutrigenomics, specifically in identifying possible hypothalamic gene regulatory pathways activated by food deprivation signals.

Declarations

Acknowledgments

This work was supported by a Grant from the National Institutes of Health (DK086655, DJG, RH, and RVJ, coPIs). We thank Jinhua Zhang for excellent technical assistance, and members of the Animal Care Staff in the Integrated Life Sciences Building for providing exceptional animal care.

Compliance with ethical standards

Conflict of interest

Hao Jiang, Thero Modise, Richard Helm, Roderick V. Jensen and Deborah J. Good declare that they have no conflict of interest.

Ethical standard

All institutional and national guidelines for the care and use of laboratory animals were followed.

Authors’ Affiliations

(1)
Department of Human Nutrition Foods and Exercise, Virginia Tech, 1981 Kraft Drive (0913), Blacksburg, VA 24061, USA
(2)
Department of Biochemistry, Virginia Tech, Blacksburg, VA 24061, USA
(3)
Department of Biological Sciences, Virginia Tech, Blacksburg, VA 24061, USA
(4)
Program in Genomics, Bioinformatics and Computational Biology, Virginia Tech, Blacksburg, VA 24061, USA
(5)
Present address: Department of Neurology, Washington University in St Louis, St Louis, MO 63110, USA

References

  1. Aprea J, Prenninger S, Dori M, Ghosh T, Monasor LS, Wessendorf E, Zocher S, Massalini S, Alexopoulou D, Lesche M, Dahl A, Groszer M, Hiller M, Calegari F (2013) Transcriptome sequencing during mouse brain development identifies long non-coding RNAs functionally involved in neurogenic commitment. EMBO J 32(24):3145–3160. doi:10.1038/emboj.2013.245 PubMed CentralView ArticlePubMedGoogle Scholar
  2. Bak M, Silahtaroglu A, Moller M, Christensen M, Rath MF, Skryabin B, Tommerup N, Kauppinen S (2008) MicroRNA expression in the adult mouse central nervous system. RNA. doi:10.1261/rna.783108 PubMed CentralPubMedGoogle Scholar
  3. Brattelid T, Aarnes EK, Helgeland E, Guvaag S, Eichele H, Jonassen AK (2011) Normalization strategy is critical for the outcome of miRNA expression analyses in the rat heart. Physiol Genom. doi:10.1152/physiolgenomics.00131.2010 Google Scholar
  4. Carmona-Saez P, Chagoyen M, Tirado F, Carazo JM, Pascual-Montano A (2007) GENECODIS: a web-based tool for finding significant concurrent annotations in gene lists. Genome Biol. doi:10.1186/gb-2007-8-1-r3 PubMed CentralPubMedGoogle Scholar
  5. Carninci P, Kasukawa T, Katayama S, Gough J, Frith MC, Maeda N, Oyama R, Ravasi T, Lenhard B, Wells C, Kodzius R, Shimokawa K, Bajic VB, Brenner SE, Batalov S, Forrest AR, Zavolan M, Davis MJ, Wilming LG, Aidinis V, Allen JE, Ambesi-Impiombato A, Apweiler R, Aturaliya RN, Bailey TL, Bansal M, Baxter L, Beisel KW, Bersano T, Bono H, Chalk AM, Chiu KP, Choudhary V, Christoffels A, Clutterbuck DR, Crowe ML, Dalla E, Dalrymple BP, de Bono B, Della Gatta G, di Bernardo D, Down T, Engstrom P, Fagiolini M, Faulkner G, Fletcher CF, Fukushima T, Furuno M, Futaki S, Gariboldi M, Georgii-Hemming P, Gingeras TR, Gojobori T, Green RE, Gustincich S, Harbers M, Hayashi Y, Hensch TK, Hirokawa N, Hill D, Huminiecki L, Iacono M, Ikeo K, Iwama A, Ishikawa T, Jakt M, Kanapin A, Katoh M, Kawasawa Y, Kelso J, Kitamura H, Kitano H, Kollias G, Krishnan SP, Kruger A, Kummerfeld SK, Kurochkin IV, Lareau LF, Lazarevic D, Lipovich L, Liu J, Liuni S, McWilliam S, Madan Babu M, Madera M, Marchionni L, Matsuda H, Matsuzawa S, Miki H, Mignone F, Miyake S, Morris K, Mottagui-Tabar S, Mulder N, Nakano N, Nakauchi H, Ng P, Nilsson R, Nishiguchi S, Nishikawa S, Nori F, Ohara O, Okazaki Y, Orlando V, Pang KC, Pavan WJ, Pavesi G, Pesole G, Petrovsky N, Piazza S, Reed J, Reid JF, Ring BZ, Ringwald M, Rost B, Ruan Y, Salzberg SL, Sandelin A, Schneider C, Schonbach C, Sekiguchi K, Semple CA, Seno S, Sessa L, Sheng Y, Shibata Y, Shimada H, Shimada K, Silva D, Sinclair B, Sperling S, Stupka E, Sugiura K, Sultana R, Takenaka Y, Taki K, Tammoja K, Tan SL, Tang S, Taylor MS, Tegner J, Teichmann SA, Ueda HR, van Nimwegen E, Verardo R, Wei CL, Yagi K, Yamanishi H, Zabarovsky E, Zhu S, Zimmer A, Hide W, Bult C, Grimmond SM, Teasdale RD, Liu ET, Brusic V, Quackenbush J, Wahlestedt C, Mattick JS, Hume DA, Kai C, Sasaki D, Tomaru Y, Fukuda S, Kanamori-Katayama M, Suzuki M, Aoki J, Arakawa T, Iida J, Imamura K, Itoh M, Kato T, Kawaji H, Kawagashira N, Kawashima T, Kojima M, Kondo S, Konno H, Nakano K, Ninomiya N, Nishio T, Okada M, Plessy C, Shibata K, Shiraki T, Suzuki S, Tagami M, Waki K, Watahiki A, Okamura-Oho Y, Suzuki H, Kawai J, Hayashizaki Y, Consortium F, Group RGER and Genome Science G (2005) The transcriptional landscape of the mammalian genome. Science. doi:10.1126/science.1112014
  6. Cheng MF (2013) Hypothalamic neurogenesis in the adult brain. Front Neuroendocrinol. doi:10.1016/j.yfrne.2013.05.001 PubMedGoogle Scholar
  7. Clark BS, Blackshaw S (2014) Long non-coding RNA-dependent transcriptional regulation in neuronal development and disease. Front Genet. doi:10.3389/fgene.2014.00164 PubMed CentralPubMedGoogle Scholar
  8. Crepin D, Benomar Y, Riffault L, Amine H, Gertler A, Taouis M (2014) The over-expression of miR-200a in the hypothalamus of ob/ob mice is linked to leptin and insulin signaling impairment. Mol Cell Endocrinol. doi:10.1016/j.mce.2013.12.016 PubMedGoogle Scholar
  9. De Leeuw F, Zhang T, Wauquier C, Huez G, Kruys V, Gueydan C (2007) The cold-inducible RNA-binding protein migrates from the nucleus to cytoplasmic stress granules by a methylation-dependent mechanism and acts as a translational repressor. Exp Cell Res. doi:10.1016/j.yexcr.2007.09.017 PubMedGoogle Scholar
  10. Ellis BC, Graham LD, Molloy PL (2014) CRNDE, a long non-coding RNA responsive to insulin/IGF signaling, regulates genes involved in central metabolism. Biochim Biophys Acta 1843(2):372–386. doi:10.1016/j.bbamcr.2013.10.016 View ArticlePubMedGoogle Scholar
  11. Fassan M, Sachsenmeir K, Rugge M, Baffa R (2011) Role of miRNA in distinguishing primary brain tumors from secondary tumors metastatic to the brain. Frontiers Biosci 3:970–979View ArticleGoogle Scholar
  12. Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C, Jensen LJ (2013) STRING v91: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. doi:10.1093/nar/gks1094 Google Scholar
  13. Fu C, Xi L, McCarter R, Hickey M, Han ES (2006) Early hypothalamic response to age-dependent gene expression by calorie restriction. Neurobiol Aging. doi:10.1016/j.neurobiolaging.2005.06.006 PubMedGoogle Scholar
  14. Gellert P, Ponomareva Y, Braun T, Uchida S (2013) Noncoder: a web interface for exon array-based detection of long non-coding RNAs. Nucleic Acids Res. doi:10.1093/nar/gks877 PubMed CentralPubMedGoogle Scholar
  15. Good DJ (2010) Transcriptional regulation of sensed energy availability within hypothalamic neurons. Open Neuroendocrinol J 3:38–44Google Scholar
  16. Hellwig S, Bass BL (2008) A starvation-induced noncoding RNA modulates expression of Dicer-regulated genes. Proc Natl Acad Sci USA 105(35):12897–12902. doi:10.1073/pnas.0805118105 PubMed CentralView ArticlePubMedGoogle Scholar
  17. Holden T, Nguyen A, Lin E, Cheung E, Dehipawala S, Ye J, Tremberger G Jr, Lieberman D, Cheung T (2013) Exploratory bioinformatics study of lncRNAs in Alzheimer’s disease mRNA sequences with application to drug development. Comput Math Methods Med. doi:10.1155/2013/579136 PubMed CentralPubMedGoogle Scholar
  18. Inoue N, Yahagi N, Yamamoto T, Ishikawa M, Watanabe K, Matsuzaka T, Nakagawa Y, Takeuchi Y, Kobayashi K, Takahashi A, Suzuki H, Hasty AH, Toyoshima H, Yamada N, Shimano H (2008) Cyclin-dependent kinase inhibitor, p21WAF1/CIP1, is involved in adipocyte differentiation and hypertrophy, linking to obesity, and insulin resistance. J Biol Chem. doi:10.1074/jbc.M801824200 Google Scholar
  19. Kameswaran V, Kaestner KH (2014) The Missing lnc(RNA) between the pancreatic beta-cell and diabetes. Front Genet. doi:10.3389/fgene.2014.00200 PubMed CentralPubMedGoogle Scholar
  20. Kokoeva MV, Yin H, Flier JS (2005) Neurogenesis in the hypothalamus of adult mice: potential role in energy balance. Science. doi:10.1126/science.1115360 PubMedGoogle Scholar
  21. Kornfeld JW, Bruning JC (2014) Regulation of metabolism by long, non-coding RNAs. Front Genet 5:57. doi:10.3389/fgene.2014.00057 PubMed CentralView ArticlePubMedGoogle Scholar
  22. Kung JT, Colognori D, Lee JT (2013) Long noncoding RNAs: past, present, and future. Genetics 193(3):651–669. doi:10.1534/genetics.112.146704 PubMed CentralView ArticlePubMedGoogle Scholar
  23. Liang L, Ai L, Qian J, Fang JY, Xu J (2015) Long noncoding RNA expression profiles in gut tissues constitute molecular signatures that reflect the types of microbes. Sci Rep. doi:10.1038/srep11763 Google Scholar
  24. Lin N, Chang KY, Li Z, Gates K, Rana ZA, Dang J, Zhang D, Han T, Yang CS, Cunningham TJ, Head SR, Duester G, Dong PD, Rana TM (2014) An evolutionarily conserved long noncoding RNA TUNA controls pluripotency and neural lineage commitment. Mol Cell 53(6):1005–1019. doi:10.1016/j.molcel.2014.01.021 PubMed CentralView ArticlePubMedGoogle Scholar
  25. Liu S, Sheng L, Miao H, Saunders TL, MacDougald OA, Koenig RJ, Xu B (2014) SRA gene knockout protects against diet-induced obesity and improves glucose tolerance. J Biol Chem. doi:10.1074/jbc.M114.564658 Google Scholar
  26. Maglich JM, Watson J, McMillen PJ, Goodwin B, Willson TM, Moore JT (2004) The nuclear receptor CAR is a regulator of thyroid hormone metabolism during caloric restriction. J Biol Chem. doi:10.1074/jbc.M313601200 PubMedGoogle Scholar
  27. Martinelli R, Nardelli C, Pilone V, Buonomo T, Liguori R, Castano I, Buono P, Masone S, Persico G, Forestieri P, Pastore L, Sacchetti L (2010) miR-519d overexpression is associated with human obesity. Obesity. doi:10.1038/oby.2009.474 PubMedGoogle Scholar
  28. Mattson MP (2005) Energy intake, meal frequency, and health: a neurobiological perspective. Annu Rev Nutr. doi:10.1146/annurev.nutr.25.050304.092526 PubMedGoogle Scholar
  29. Mattson MP, Wan R (2005) Beneficial effects of intermittent fasting and caloric restriction on the cardiovascular and cerebrovascular systems. J Nutr Biochem. doi:10.1016/j.jnutbio.2004.12.007 PubMedGoogle Scholar
  30. McNeill E, Van Vactor D (2012) MicroRNAs shape the neuronal landscape. Neuron. doi:10.1016/j.neuron.2012.07.005 PubMed CentralPubMedGoogle Scholar
  31. Meister B, Herzer S, Silahtaroglu A (2013) MicroRNAs in the Hypothalamus. Neuroendocrinology. doi:10.1159/000355619 PubMedGoogle Scholar
  32. Nakatsuka A, Wada J, Hida K, Hida A, Eguchi J, Teshigawara S, Murakami K, Kanzaki M, Inoue K, Terami T, Katayama A, Ogawa D, Kagechika H, Makino H (2012) RXR antagonism induces G0/G1 cell cycle arrest and ameliorates obesity by up-regulating the p53-p21(Cip1) pathway in adipocytes. J Pathol. doi:10.1002/path.3001 PubMedGoogle Scholar
  33. Nogales-Cadenas R, Carmona-Saez P, Vazquez M, Vicente C, Yang X, Tirado F, Carazo JM, Pascual-Montano A (2009) GeneCodis: interpreting gene lists through enrichment analysis and integration of diverse biological information. Nucleic Acids Res. doi:10.1093/nar/gkp416 Google Scholar
  34. Olsen L, Klausen M, Helboe L, Nielsen FC, Werge T (2009) MicroRNAs show mutually exclusive expression patterns in the brain of adult male rats. PLoS ONE. doi:10.1371/journal.pone.0007225 Google Scholar
  35. Paulsen SJ, Larsen LK, Jelsing J, Janssen U, Gerstmayer B, Vrang N (2009) Gene expression profiling of individual hypothalamic nuclei from single animals using laser capture microdissection and microarrays. J Neurosci Methods. doi:10.1016/j.jneumeth.2008.09.024 PubMedGoogle Scholar
  36. Poplawski MM, Mastaitis JW, Yang XJ, Mobbs CV (2010) Hypothalamic responses to fasting indicate metabolic reprogramming away from glycolysis toward lipid oxidation. Endocrinology. doi:10.1210/en.2010-0702 PubMed CentralPubMedGoogle Scholar
  37. Qureshi IA, Mehler MF (2013) Long non-coding RNAs: novel targets for nervous system disease diagnosis and therapy. Neurotherapeutics. doi:10.1007/s13311-013-0199-0 PubMed CentralPubMedGoogle Scholar
  38. Rinn JL, Chang HY (2012) Genome regulation by long noncoding RNAs. Annu Rev Biochem. doi:10.1146/annurev-biochem-051410-092902 PubMed CentralPubMedGoogle Scholar
  39. Roshan R, Ghosh T, Scaria V, Pillai B (2009) MicroRNAs: novel therapeutic targets in neurodegenerative diseases. Drug Discov Today. doi:10.1016/j.drudis.2009.09.009 PubMedGoogle Scholar
  40. Roy M, Kim N, Kim K, Chung WH, Achawanantakun R, Sun Y, Wayne R (2013) Analysis of the canine brain transcriptome with an emphasis on the hypothalamus and cerebral cortex. Mamm Genome. doi:10.1007/s00335-013-9480-0 PubMedGoogle Scholar
  41. Sangiao-Alvarellos S, Pena-Bello L, Manfredi-Lozano M, Tena-Sempere M, Cordido F (2014) Perturbation of hypothalamic microRNA expression patterns in male rats after metabolic distress: impact of obesity and conditions of negative energy balance. Endocrinology. doi:10.1210/en.2013-1770 PubMedGoogle Scholar
  42. Schneeberger M, Altirriba J, Garcia A, Esteban Y, Castano C, Garcia-Lavandeira M, Alvarez CV, Gomis R, Claret M (2012) Deletion of miRNA processing enzyme Dicer in POMC-expressing cells leads to pituitary dysfunction, neurodegeneration and development of obesity. Mol Metab. doi:10.1016/j.molmet.2012.10.001 PubMed CentralPubMedGoogle Scholar
  43. Smith CM, Finger JH, Kadin JA, Richardson JE, Ringwald M (2014) The gene expression database for mouse development (GXD): putting developmental expression information at your fingertips. Dev Dyn. doi:10.1002/dvdy.24155 Google Scholar
  44. Sohal RS, Weindruch R (1996) Oxidative stress, caloric restriction, and aging. Science 273(5271):59–63PubMed CentralView ArticlePubMedGoogle Scholar
  45. Sun L, Xie H, Mori MA, Alexander R, Yuan B, Hattangadi SM, Liu Q, Kahn CR, Lodish HF (2011) Mir193b-365 is essential for brown fat differentiation. Nat Cell Biol. doi:10.1038/ncb2286 Google Scholar
  46. Sun L, Goff LA, Trapnell C, Alexander R, Lo KA, Hacisuleyman E, Sauvageau M, Tazon-Vega B, Kelley DR, Hendrickson DG, Yuan B, Kellis M, Lodish HF, Rinn JL (2013) Long noncoding RNAs regulate adipogenesis. Proc Natl Acad Sci USA. doi:10.1073/pnas.1222643110 Google Scholar
  47. Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P, Jensen LJ, von Mering C (2011) The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 39(Database issue):D561–D568. doi:10.1093/nar/gkq973 PubMed CentralView ArticlePubMedGoogle Scholar
  48. Szklarczyk K, Korostynski M, Golda S, Solecki W, Przewlocki R (2012) Genotype-dependent consequences of traumatic stress in four inbred mouse strains. Genes Brain Behav. doi:10.1111/j.1601-183X.2012.00850.x PubMedGoogle Scholar
  49. Tabas-Madrid D, Nogales-Cadenas R, Pascual-Montano A (2012) GeneCodis3: a non-redundant and modular enrichment analysis tool for functional genomics. Nucleic Acids Res. doi:10.1093/nar/gks402 PubMed CentralPubMedGoogle Scholar
  50. Takanabe R, Ono K, Abe Y, Takaya T, Horie T, Wada H, Kita T, Satoh N, Shimatsu A, Hasegawa K (2008) Up-regulated expression of microRNA-143 in association with obesity in adipose tissue of mice fed high-fat diet. Biochem Biophys Res Commun. doi:10.1016/j.bbrc.2008.09.050 PubMedGoogle Scholar
  51. Tinkum KL, White LS, Marpegan L, Herzog E, Piwnica-Worms D, Piwnica-Worms H (2013) Forkhead box O1 (FOXO1) protein, but not p53, contributes to robust induction of p21 expression in fasted mice. J Biol Chem. doi:10.1074/jbc.M113.494328 PubMed CentralPubMedGoogle Scholar
  52. Vella KR, Burnside AS, Brennan KM, Good DJ (2007) Expression of the hypothalamic transcription factor Nhlh2 is dependent on energy availability. J Neuroendocrinol. doi:10.1111/j.1365-2826.2007.01556.x PubMed CentralPubMedGoogle Scholar
  53. Vijayaraghavan S, Hitman GA, Kopelman PG (1994) Apolipoprotein-D polymorphism: a genetic marker for obesity and hyperinsulinemia. J Clin Endocrinol Metab. doi:10.1210/jcem.79.2.7913935 PubMedGoogle Scholar
  54. Wessel J, Chu AY, Willems SM, Wang S, Yaghootkar H, Brody JA, Dauriz M, Hivert MF, Raghavan S, Lipovich L, Hidalgo B, Fox K, Huffman JE, An P, Lu Y, Rasmussen-Torvik LJ, Grarup N, Ehm MG, Li L, Baldridge AS, Stancakova A, Abrol R, Besse C, Boland A, Bork-Jensen J, Fornage M, Freitag DF, Garcia ME, Guo X, Hara K, Isaacs A, Jakobsdottir J, Lange LA, Layton JC, Li M, Hua Zhao J, Meidtner K, Morrison AC, Nalls MA, Peters MJ, Sabater-Lleal M, Schurmann C, Silveira A, Smith AV, Southam L, Stoiber MH, Strawbridge RJ, Taylor KD, Varga TV, Allin KH, Amin N, Aponte JL, Aung T, Barbieri C, Bihlmeyer NA, Boehnke M, Bombieri C, Bowden DW, Burns SM, Chen Y, Chen YD, Cheng CY, Correa A, Czajkowski J, Dehghan A, Ehret GB, Eiriksdottir G, Escher SA, Farmaki AE, Franberg M, Gambaro G, Giulianini F, Goddard WA, 3rd, Goel A, Gottesman O, Grove ML, Gustafsson S, Hai Y, Hallmans G, Heo J, Hoffmann P, Ikram MK, Jensen RA, Jorgensen ME, Jorgensen T, Karaleftheri M, Khor CC, Kirkpatrick A, Kraja AT, Kuusisto J, Lange EM, Lee IT, Lee WJ, Leong A, Liao J, Liu C, Liu Y, Lindgren CM, Linneberg A, Malerba G, Mamakou V, Marouli E, Maruthur NM, Matchan A, McKean-Cowdin R, McLeod O, Metcalf GA, Mohlke KL, Muzny DM, Ntalla I, Palmer ND, Pasko D, Peter A, Rayner NW, Renstrom F, Rice K, Sala CF, Sennblad B, Serafetinidis I, Smith JA, Soranzo N, Speliotes EK, Stahl EA, Stirrups K, Tentolouris N, Thanopoulou A, Torres M, Traglia M, Tsafantakis E, Javad S, Yanek LR, Zengini E, Becker DM, Bis JC, Brown JB, Cupples LA, Hansen T, Ingelsson E, Karter AJ, Lorenzo C, Mathias RA, Norris JM, Peloso GM, Sheu WH, Toniolo D, Vaidya D, Varma R, Wagenknecht LE, Boeing H, Bottinger EP, Dedoussis G, Deloukas P, Ferrannini E, Franco OH, Franks PW, Gibbs RA, Gudnason V, Hamsten A, Harris TB, Hattersley AT, Hayward C, Hofman A, Jansson JH, Langenberg C, Launer LJ, Levy D, Oostra BA, O’Donnell CJ, O’Rahilly S, Padmanabhan S, Pankow JS, Polasek O, Province MA, Rich SS, Ridker PM, Rudan I, Schulze MB, Smith BH, Uitterlinden AG, Walker M, Watkins H, Wong TY, Zeggini E, Consortium EP-I, Laakso M, Borecki IB, Chasman DI, Pedersen O, Psaty BM, Tai ES, van Duijn CM, Wareham NJ, Waterworth DM, Boerwinkle E, Kao WH, Florez JC, Loos RJ, Wilson JG, Frayling TM, Siscovick DS, Dupuis J, Rotter JI, Meigs JB, Scott RA and Goodarzi MO (2015) Low-frequency and rare exome chip variants associate with fasting glucose and type 2 diabetes susceptibility. Nat Commun. doi:10.1038/ncomms6897
  55. Xu S, Chen P, Sun L (2015) Regulatory networks of non-coding RNAs in brown/beige adipogenesis. Biosci Rep. doi:10.1042/BSR20150155 Google Scholar
  56. Yan L, Park JY, Dillinger JG, De Lorenzo MS, Yuan C, Lai L, Wang C, Ho D, Tian B, Stanley WC, Auwerx J, Vatner DE, Vatner SF (2012) Common mechanisms for calorie restriction and adenylyl cyclase type 5 knockout models of longevity. Aging Cell. doi:10.1111/acel.12013 PubMed CentralGoogle Scholar
  57. Yoon JH, Abdelmohsen K, Gorospe M (2013) Posttranscriptional gene regulation by long noncoding RNA. J Mol Biol. doi:10.1016/j.jmb.2012.11.024 PubMed CentralPubMedGoogle Scholar
  58. You LH, Zhu LJ, Yang L, Shi CM, Pang LX, Zhang J, Cui XW, Ji CB, Guo XR (2015) Transcriptome analysis reveals the potential contribution of long noncoding RNAs to brown adipocyte differentiation. Mol Genet Genomics. doi:10.1007/s00438-015-1026-6 Google Scholar

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