Skip to content
Open Access

A molecular view of the normal human thyroid structure and function reconstructed from its reference transcriptome map

BMC Genomics201718:739

https://doi.org/10.1186/s12864-017-4049-z

Received: 6 February 2017

Accepted: 10 August 2017

Published: 18 September 2017

Abstract

Background

The thyroid is the earliest endocrine structure to appear during human development, and thyroid hormones are necessary for proper organism development, in particular for the nervous system and heart, normal growth and skeletal maturation. To date a quantitative, validated transcriptional atlas of the whole normal human thyroid does not exist and the availability of a detailed expression map might be an excellent occasion to investigate the many features of the thyroid transcriptome.

Results

We present a view at the molecular level of the normal human thyroid histology and physiology obtained by a systematic meta-analysis of all the available gene expression profiles for the whole organ. A quantitative transcriptome reference map was generated by using the TRAM (Transcriptome Mapper) software able to combine, normalize and integrate a total of 35 suitable datasets from different sources thus providing a typical reference expression value for each of the 27,275 known, mapped transcripts obtained. The experimental in vitro validation of data was performed by “Real-Time” reverse transcription polymerase chain reaction showing an excellent correlation coefficient (r = 0.93) with data obtained in silico.

Conclusions

Our study provides a quantitative global reference portrait of gene expression in the normal human thyroid and highlights differential expression between normal human thyroid and a pool of non-thyroid tissues useful for modeling correlations between thyroidal gene expression and specific thyroid functions and diseases. The experimental in vitro validation supports the possible usefulness of the human thyroid transcriptome map as a reference for molecular studies of the physiology and pathology of this organ.

Keywords

Human thyroidGene expressionIntegrated transcriptome mapMeta-analysisHuman chromosome 21

Background

The thyroid is the earliest endocrine structure to appear during human development. Thyroid hormones are the first detected in the human fetal circulation at 11-13 gestation weeks [1] and are necessary for the proper organism development, in particular for the nervous system and heart, normal growth and skeletal maturation. Moreover, their importance in maintaining human health through their action on metabolism and cell growth is well known [2].

The structural and functional units of the thyroid are the follicles, cystic-like structures delimited by a single-layered epithelium of follicular cells or thyrocytes of endodermal origin. The follicles contain the colloid, consisting mainly of thyroglobulin (TG), the stored form of inactive thyroid hormones [3]. The thyroid is the only endocrine gland that produces and stores its initial product and then resumes and turns it into active hormones T4 and T3, tetraiodothyronine or thyroxine and triiodothyronine, respectively. The colloid contains an amount of stored iodothyroglobulin sufficient to regulate the metabolic activity of the body for up to three months [3]. Follicular cell activity level is regulated by the thyroid-stimulating hormone or thyrotropin (TSH) produced by the anterior pituitary gland and released in turn under the control of thyrotropin-releasing hormone produced in the hypothalamus and responding, with negative feedback, to serum levels of circulating thyroid hormones T3 and T4. In the stroma among the follicles are the parafollicular cells or C-cells that produce the calcitonin hormone which lower the blood calcium by inhibiting bone resorption and renal calcium recovery [3]. The number of C-cells is far lower than that of follicular cells and was estimated to be 8.70 × 105 in a standard human adult thyroid, while follicular cells were estimated to be 1.00 × 1010 [4, 5].

Several causes may lead to a decrease or an increase of thyroid function with the clinical pictures of hypo- or hyperthyroidism [6]. In particular, congenital hypothyroidism, the most frequent endocrine congenital disease, can occur either based on a thyroid hormone biosynthesis defect or predominantly due to thyroid dysgenesis [6]. In persons affected by Down syndrome (DS), in which the trisomy of chromosome 21 leads to an altered gene expression condition, the thyroid function is impaired in at least 15% of cases and usually it is a congenital hypothyroidism that is approximately 28 times more common in DS than in disomic subjects [7].

The thyroid gland is also the endocrine tissue most affected by cancer and many studies have been performed to link gene signatures with thyroid dysfunction or tumors [810]. Many studies typically concern the transcriptome of thyroid cancer [11], and in these cases the normal thyroid tissue often used as a control was derived from apparently healthy tissue of diseased gland (histologically normal thyroid tissue adjacent to thyroid tumor).

Transcriptome studies are able to provide a representation of gene expression at the molecular level, thus underlying pathways or genes with specific functions in a given tissue [12]. Alteration of the reference gene expression level could be then related to a particular pathologic process, with implications for diagnosis, prognosis and identification of molecular targets for therapy [13].

Human studies of gene expression profile in normal thyroid are limited and have been conducted on a variety of experimental platforms by different Authors over many years. Gene expression profiles of whole normal thyroid can be found in the public databases in the context of normal human gene expression studies.

The aim of this study was to perform a systematic meta-analysis of the gene expression profiling experiments on the whole normal human thyroid provided by the European Bioinformatics Institute (EBI) [14] and the National Center for Biotechnology Information (NCBI) [15] databases (named ArrayExpress and GEO (Gene Expression Omnibus), respectively) in order to generate a quantitative transcriptome reference map of the whole gland. RNA microarrays and RNA sequencing (RNA-Seq) are considered as the two main kinds of high-throughput technologies to study the gene expression profile [13], and it was demonstrated that comparison between microarray based quantification and RNA-Seq quantification showed concordance between the two technologies [16]. Due to the very small number of RNA-Seq studies available for the whole adult normal human thyroid (many experiments are performed on non-coding RNA expression, on fetal thyroid transcriptome or on cell lines), we have selected datasets obtained by expression microarrays published online from 2002 to 2013 for this study. Thirty-five suitable gene expression profile datasets found from different sources were combined and integrated using TRAM (Transcriptome Mapper) software [17], which is able to generate transcriptome maps from any source listing gene expression values for a given gene set, e.g. expression microarray, thus allowing a typical reference value of expression for each of the known and uncharacterized (unmapped) transcripts assayed by any of the experimental platforms used in this regard to be obtained (Fig. 1). In addition, our goal was to realize a graphical representation of gene expression profiles along the chromosomes to analyze transcription intensity in specific chromosomal regions (Fig. 1).
Fig. 1

Graphic representation of the TRAM software workflow (simplified) for the study of thyroid transcriptome (Pool A). Gene expression datasets obtained by any sample of interest in tab-delimited text format are imported, probe names are assigned to individual loci following conversion of all types of gene identifiers (IDs) into official gene symbols, raw gene expression values are intra-sample normalized as percentage of the sample mean value and inter-sample normalized by scaled quantile. The final reference value for each locus is the mean value of all available normalized values for that locus. The expression is finally also mapped along each chromosome and graphically displayed as expression intensity for each chromosomal segment, expressed as the mean of the expression values of the loci included in that segment. Over- and underexpressed regions are then determined following statistical analysis. If Pools A and B are compared, the values would represent the A/B ratios

Experimental validations has been performed to test if our map may provide a general reference quantitative model of the thyroid transcriptome. Finally, a quantitative gene expression map offers the possibility to highlight differential expression between normal human thyroid and a pool of non-thyroid tissues and we also tested if this may provide data useful to model correlations between thyroidal gene expression and specific thyroid functions and diseases.

Results

Database search and database building

We retrieved 35 samples from 10 microarray experiments performed on the whole normal thyroid excluding histologically normal samples recovered from tissue adjacent to thyroid cancer. Sample identifiers and main sample features are listed in the Additional file 1 (thyroid, male thyroid, female thyroid) and Additional file 2 (pool of non-thyroid tissues) (both datasets are available at: http://apollo11.isto.unibo.it/suppl).

The pool of non-thyroid tissue samples (624 samples from 12 microarray experiments) was already used in [12], except for the inclusion of brain samples and exclusion of thyroid samples in the comparison Pool B. Datasets were loaded into TRAM and analyzed obtaining five transcriptome maps: thyroid (Pool A); thyroid (Pool A) vs. pool of non-thyroid tissues (Pool B); male thyroid (Pool A.1); female thyroid (Pool A.2); male thyroid (Pool A.1) vs. female thyroid (Pool A.2).

Thyroid and thyroid vs. pool of non-thyroid tissues transcriptome map analysis

Each map provides data as previously described [18]. Briefly, a TRAM map provides a reference gene expression value for all human mapped loci following intra-sample normalization (the raw value is expressed as percentage of the mean value for that sample) and inter-sample normalization (the value is further normalized by the quantile method to provide the mean value among the expression values available for all samples and having the same rank when each profile is ordered by descending order of these values) [17]. To maximize the data that may be extracted from diverse experimental platforms in a cross-platform model, overcoming the typical limitation of the standard quantile method requiring each platform provides the same number of genes/values, we applied the scaled quantile method. This allowed the normalization of data derived from platforms with a highly different number of probes by adjusting the rank for each value in a sample in proportion to the sample having the maximum number of values, so effectively averaging highest/lowest values of a sample with the highest/lowest values of the other samples [17]. The final result is a reference expression value for a locus summarizing each available data point, allowing the comparison between two biological conditions when reference values for a given locus are present in both (A and B) sample pools considered, in the form of A/B ratio. In addition, the physical location based analysis highlights when the segment expression value (mean expression values of the genes contained in a 500 kb genomic segment) is found to be statistically significantly over−/underexpressed by hypergeometric distribution method in the comparison between the two sample sources [17]. Gene content of each over−/underexpressed genomic segment was further checked to exclude segments containing over−/underexpressed genes whose expression value resulted from less than five data points in at least one of the two compared pools [18]. A segment or a gene was considered to be statistically significantly over−/underexpressed for q < 0.05, where q is the p-value obtained by the method of hypergeometric distribution [17] and corrected for multiple comparisons. When the results were reported for the over−/underexpressed single genes, the segment was set to 12.5 kb and we considered only the genes associated with at least five data points [18].

Detailed results for each map are provided below and are also available at: http://apollo11.isto.unibo.it/suppl.

In the thyroid transcriptome map analysis, 933,461 data points corresponding to 27,275 mapped loci were included (Additional file 3). 11,620 data points of the map corresponded to 352 chromosome 21 (chr21) mapped loci. Results obtained by analysis included 31 significantly overexpressed segments, some of which are partially overlapped (see TRAM results file available at http://apollo11.isto.unibo.it/suppl). The genome segments having the highest statistically significant expression values are shown in Table 1. The first one is on chromosome 8 (8q24) and includes the known genes TG and NDRG1 (N-myc downstream regulated 1). The second overexpressed segment includes many members of the metallothionein gene family. There are no statistically significant underexpressed genomic segments.
Table 1

The first five genomic segments significantly over−/underexpressed in the Thyroid (Pool A) transcriptome map and in the Thyroid (Pool A) vs. Pool of non-thyroid tissues (Pool B) transcriptome map

Thyroid transcriptome map

Chra and Location

Segment Startb

Segment Endb

Value A

q-value

Genes in the segmentc

Overexpressed segments

 chr8

 8q24.22

134,000,001

134,500,000

1,040.20

0.0033

TG, Hs.654670, NDRG1

 chr16

 16q12.2

56,500,001

57,000,000

907.10

< 0.0001

MT2A, MT1E, MT1A, MT1F, MT1G, MT1H, MT1X, HERPUD1

 chr1

 1p21

103,750,001

104,250,000

768.87

0.0009

AMY2B, AMY2A, AMY1B

 chr11

 11q13.1

65,000,001

65,500,000

763.28

0.0001

NEAT1, Hs.736281, Hs.593027, MALAT1, Hs.712678, Hs.732685

 chr4

 4q13

70,500,001

71,000,000

689.99

0.0021

STATH, HTN3, HTN1

Thyroid vs. Pool of non-thyroid tissues transcriptome map

Chra and Location

Segment Startb

Segment Endb

Value A/B

q-value

Genes in the segmentc

Overexpressed segments

 chr8

 8q24

134,000,001

134,500,000

30.57

0.0024

TG, Hs.654670, ST3GAL1

 chr14

 14q13.2-q13.3d

36,500,001

37,000,000

13.47

< 0.0001

MBIP, SFTA3, Hs.715763, NKX2-1

 chr6

 6q25.1d

150,250,001

150,750,000

11.15

0.0005

Hs.15422, PPP1R14C, IYD

 chr2

 2q12-q14.1

113,750,001

114,250,000

6.76

0.0045

PAX8, Hs.656634, Hs.675465

 chr8

 8q23

110,500,001

111,000,000

5.17

< 0.0001

PKHD1L1, Hs.88651, EBAG9, Hs.632960

Underexpressed segments

 chr2

 2p12

79,000,001

79,500,000

0.17

0.0001

REG1B, REG1A, REG3A

 chr18

 18q12.1

28,500,001

29,000,000

0.27

0.0002

DSC3, Hs.557559, DSC1, DSG1

 chr1

 1q21-q22

153,000,001

153,500,000

0.29

< 0.0001

SPRR1B, SPRR2D, SPRR2A, LELP1, LOR, S100A, S100A7

 chr5

 5q32

147,250,001

147,750,000

0.30

< 0.0001

SCGB3A2, C5orf46, SPINK5, SPINK7

 chr10

 10q26.11

118,000,001

118,500,000

0.33

0.00087

PNLIP, PNLIPRP1, PNLIPRP2

Analysis was performed using default parameters (see Methods section). Overexpressed segments are sorted by decreasing expression values in the thyroid transcriptome map; underexpressed segments are sorted by increasing expression values. In the ‘Map’ mode, TRAM displays UniGene EST clusters (with the prefix “Hs.” in the case of H. sapiens) only if they have an expression value. For simplicity, some segments are not shown because they overlap with those highlighted in one of the listed regions. In bold the genes with a statistically significant over- or underexpression values in the segment. The complete results for these models are available as online supplementary material (see text). aChromosome. The segment location cytoband was derived from that of the first mapped gene within the segment. bSegment Start/End: chromosomal coordinates for each segment. cSignificantly over-/underexpressed genes as marked in the TRAM results. dCytoband not available in Gene was derived from the UCSC Genome Browser (http://genome-euro.ucsc.edu/cgi-bin/hgGateway)

At the single gene level the known gene presenting the highest expression value (8,662.34) is still TG, but the EST (expressed sequence tag) cluster Hs.732685 appears to have the highest absolute expression value (10,763.93). TG is followed by RPL41 (5,903.29) and TPT1 (5,743.83), encoding for the ribosomal protein L41 and for the tumor protein, translationally-controlled 1, respectively (Table 2). The lowest expression value belongs to the EST cluster Hs.734567 (2.95). The other genes with the highest and lowest expression values are shown in Table 2 and Additional file 3. Among the known genes of chromosome 21, TFF3, encoding for trefoil factor 3, has the highest expression value (2,726.93) (Table 2).
Table 2

List of the five most over- and underexpressed genes (all significantly, with q < 0.05) in the Thyroid (Pool A) transcriptome map

Gene name

Value A

Location

Data points A

SD as % of expression A

Overexpressed genes

 Hs.732685a

10,763.93

11q13.1

11

23.12

 TG

8,662.34

8q24

33

33.14

 RPL41

5,903.29

12q13

32

51.15

 TPT1

5,743.83

13q14

101

42.11

 EEF1A1

5,537.45

6q14.1

106

59.81

Overexpressed chr21 genes

 TFF3

2,726.93

21q22.3

36

78.82

 Hs.709790

1,358.90

21q22.11

18

56.76

 CSTB

1,271.11

21q22.3

33

123.06

 SOD1

1,156.80

21q22.11

37

59.00

 PCP4

848.72

21q22.2

33

70.73

Underexpressed genes

 Hs.734567b

2.95

12p13.2

11

50.79

 HTR3E

3.08

3q27.1

8

28.12

 Hs.591590b

3.32

2p12

11

60.32

 LOC645739

3.40

13q12.11

9

27.64

 ZNF852

3.45

3p21.31

11

56.87

Underexpressed chr21 genes

 Hs.434909b

4.32

21q22.12

11

48.95

 Hs.555591b

4.65

21q21.2

11

49.55

 Hs.27261b

4.86

21q22.13

11

89.74

 KRTAP19-4

5.21

21q22.1

11

48.26

 LOC339622

5.29

21q21.1-q21.2

11

102.47

‘Value’: mean gene expression value normalized across all the pool samples; ‘Data points’: number of spots associated to an expression value for the locus; ‘SD’: standard deviation for the expression value expressed as a percentage of the mean. Overexpressed genes are sorted by decreasing gene expression value, underexpressed genes are sorted by increasing gene expression value. Full results are available as supplementary material (see text). aAccording to the criteria detailed in the Methods section, the segment window (12,500 bp) contains more than one over−/underexpressed gene, but the significance is assumed to remain because the expression value of this over−/underexpressed gene prevails over the others. bCytoband not available in Gene was derived from the UCSC Genome Browser (http://genome-euro.ucsc.edu/cgi-bin/hgGateway)

In the analysis of the thyroid vs. pool of non-thyroid tissues transcriptome map, regional differential expression of Pool A (35 thyroid samples) versus Pool B (624 non-thyroid tissues samples) for a total of 26,750 analyzed loci with values for both Pools A and B, was investigated. Results obtained by analysis include 34 significantly over−/underexpressed segments: 22 of them are overexpressed and 12 are underexpressed. The genome segment that has the highest statistically significant expression ratio is on chromosome 8 (8q24) (Table 1) including again the known gene TG and also ST3GAL1 gene encoding for ST3 beta-galactoside alpha-2,3-sialyltransferase 1 and the EST cluster Hs.654670. The following overexpressed segments contain genes having a key role in thyroid functioning as NKX2-1 (NK2 homeobox 1), IYD (iodotyrosine deiodinase) and PAX8 (paired box 8) (Table 1). The genome segment that has the lowest statistically significant expression ratio is on chromosome 2 (2p12), including the known genes REG1B, REG1A and REG3A, encoding for regenerating family member 1 beta, 1 alpha and 3 alpha, respectively (Table 1).

At the single gene level, an increase of more than 10 times of the gene expression ratio was observed in all the first 51 loci when ordered by decreasing values of thyroid/non-thyroid expression ratio, mostly including known genes with thyroid function. Notably, the first two overexpressed genes in thyroid in comparison with non-thyroid tissues are TG (ratio = 188.22) and TPO (thyroid peroxidase) (ratio = 151.17) followed by SLC26A7 (solute carrier family 26 member 7) (ratio = 66.94), IYD (ratio = 66.25) and TSHR (thyroid stimulating hormone receptor) (ratio = 49.36) (Table 3 and Additional file 4). We also observed that in this range of expression ratios, one chr21 known gene is included: TFF3 (ratio = 16.14). Among the genes with the lowest A/B expression ratio, a 100-fold decrease (ratio = 0.01) was observed for the gene LCE5A, encoding for the late cornified envelope 5A (Table 3).
Table 3

List of the five (or more) most over- or underexpressed genes (all significantly, with q < 0.05, except genes with a note) in the Thyroid (Pool A) vs. Pool of non-thyroid tissues samples (Pool B) transcriptome map

Gene name

Value A

Value B

Ratio A/B

Location

Data points A

Data points B

SD as % of expression A

SD as % of expression B

Overexpressed genes

    

 TG

8,662.34

46.02

188.22

8q24

33

566

33.14

221.41

 TPO

2,245.11

14.85

151.17

2p25

35

560

54.57

118.35

 Hs.654670ab

1,727.93

12.30

140.54

8q24

18

413

86.71

108.60

 SLC26A7

1,107.90

16.55

66.94

8q23

53

1,198

132.82

226.70

 IYD

890.07

13.44

66.25

6q25.1

24

456

65.51

137.47

 Hs.656634ab

1,982.24

39.54

50.14

2q13

18

413

80.05

101.09

 TSHR

487.64

9.88

49.36

14q31

92

1,907

91.14

130.07

Overexpressed chr21 genes

 TFF3

2,726.93

168.96

16.14

21q22.3

36

758

78.82

210.27

 Hs.129583b

273.20

24.62

11.10

21q22.13

11

296

51.98

69.50

 DNMT3L

101.54

13.76

7.38

21q22.3

31

449

357.89

107.64

 IFNAR1

272.52

45.93

5.93

21q22.11

65

1,405

491.61

75.21

 Hs.709790ab

1,358.90

231.48

5.87

21q22.11

18

418

56.76

78.65

Under-expressed genes

    

 LCE5A

24.09

1,639.23

0.01

1q21.3

11

30

30.42

39.05

 MSMB

9.44

527.57

0.02

10q11.2

53

1,152

124.26

347.24

 PRM2 a

6.51

309.26

0.02

16p13.2

44

553

49.00

530.15

 KRTDAP

11.24

480.77

0.02

19q13.12

16

454

39.24

354.39

 ORM2

6.19

255.66

0.02

9q32

21

529

38.82

477.48

Underexpressed chr21 genes

 S100B

15.70

259.61

0.06

21q22.3

44

865

111.12

324.61

 TFF1

22.66

184.51

0.12

21q22.3

33

652

108.08

280.42

 KRTAP10-10

14.16

91.19

0.16

21q22.3

16

30

46.04

92.21

 OLIG1

12.59

76.13

0.17

21q22.11

24

481

59.64

350.79

 TPTE

11.07

53.19

0.21

21p11

21

559

76.74

465.37

‘Value’: mean gene expression value normalized across all the pool samples; ‘Data points’: number of spots associated to an expression value for the locus; ‘SD’: standard deviation for the expression value expressed as a percentage of the mean. Overexpressed genes are sorted by decreasing gene expression ratio, underexpressed genes are sorted by increasing gene expression ratio. Full results are available as supplementary material (see text). aThis gene is one of the five most over−/underexpressed ones, but the value is not statistically significant. This is either because this gene was present with other genes even in the 12,500 bp segment and its expression value does not prevail over the others, or because the segment containing the gene does not fulfil criteria for local over−/underexpression bCytoband not available in Gene was derived from the UCSC Genome Browser (http://genome-euro.ucsc.edu/cgi-bin/hgGateway)

When there are more than five genes listed, it is because we believe it is important to highlight them

The availability of complete TRAM datasets allows to manually check for single loci of interest if the high standard deviation (e.g., SD > 100% of the mean expression value) calculated by TRAM for each locus might arise from: physiological inter-individual (inter-sample) variation; imprecision in the measurement while approaching the lowest range of detection (i.e. expression values < 50); artifacts due to the fact that in some experimental platforms different probes located on the array have been assigned to the same locus but they actually detect different transcripts with systematically consistent highly different measures for each of the probes.

Male thyroid vs. female thyroid transcriptome map analysis

The thyroid samples found as described above and for which the sex of the sample donor was available were grouped into two additional datasets: Pool A.1 including 6 male samples and Pool A.2 including 12 female samples (Additional file 1), and the corresponding transcriptome maps were generated and compared.

The gene expression value for each of the 25,954 loci of the male vs. female thyroid differential transcriptome map is available providing for each of them the respective gene expression ratio (Additional file 5, supplementary full results for transcriptome maps are available at: http://apollo11.isto.unibo.it/suppl/).

At the single gene level, a more than 10-fold increase of the gene expression ratio was observed only for the first locus KDM5D on chromosome Y encoding for the lysine demethylase 5D. In particular, a more than 8-fold increase was observed for the known genes DDX3Y (8.74), USP9Y (8.50), both on Y, and TAS2R38 (8.25), encoding respectively for DEAD-box helicase 3, ubiquitin specific peptidase 9, Y-linked, and for taste 2 receptor member 38 on chr7 (Additional file 5). Among the genes with the lowest A.1/A.2 expression ratio, the XIST gene (X inactive specific transcript, on chrX) was the fifth with a 26.71-fold decrease (ratio = 0.04) (Additional file 5).

Bivariate analysis of the statistical correlation between the gene expression values of the thyroid transcriptome map in male and in female cells was performed (Fig. 2). The results showed a significant statistical correlation of data (r = 0.92, p-value < 0.0001), confirming the large overlapping of results between the two transcriptome maps, with the exception of single genes with a well-known sex-biased expression pattern.
Fig. 2

Bivariate analysis between the gene expression values of the thyroid transcriptome map in male and in female cells. The fit line is shown; Pearson correlation coefficient is 0.92 and p-value < 0.0001

When sorting by Pool A.1 (male thyroid) expression values, the most expressed genes have a similar expression pattern in both sexes with differential ratios close to 1 (Additional file 6).

Housekeeping gene search

In the thyroid transcriptome map, the search for housekeeping genes with the described criteria (Methods section) retrieved 45 loci, including BLOC1S2 (biogenesis of lysosomal organelles complex 1 subunit 2) which is the known gene that has the lowest standard deviation (SD = 13.39 expressed as percentage of the mean value), and the known genes ACTG1 and RPLP0, encoding for actin gamma 1 and ribosomal protein lateral stalk subunit P0, respectively, having the highest number of data points (n = 180 and n = 76) (Table 4) and an excellent ratio between the Pools A and B (1.04 and 0.99).
Table 4

Predicted housekeeping genes from the thyroid transcriptome map (explanation in the text); (n = 45)

Gene symbol

Chromosome

Location

Expression value

Data points

SD

Hs.670607

chr4

N/A

232.70

18

11.95

BLOC1S2

chr10

10q24.31

181.44

22

13.39

Hs.706927

chr6

N/A

357.37

18

13.82

TMEM256

chr17

17p13.1

153.91

22

18.35

POM121C

chr7

7q11.2

347.50

20

22.49

CUEDC2

chr10

10q24.32

210.93

31

22.83

RPLP0*

chr12

12q24.2

3,340.82

76

23.40

PJA1

chrX

Xq13.1

234.54

31

23.83

Hs.713747

chr1

N/A

169.93

18

23.92

RAB25

chr1

1q22

199.48

31

24.00

CIB1

chr15

15q25.3-q26

314.61

33

25.01

MRPL18

chr6

6q25.3

182.80

23

25.09

SCAMP3

chr1

1q21

152.98

33

25.11

Hs.633993

chr8

N/A

2,050.15

18

25.21

Hs.714416

chr3

N/A

100.67

18

25.31

RBM23

chr14

14q11.2

257.95

31

25.67

Hs.117688

chr8

N/A

160.32

22

25.73

Hs.546523

chr2

N/A

178.40

18

25.96

DCXR

chr17

17q25.3

210.53

31

25.98

Hs.193784

chr3

N/A

491.90

22

26.02

ABRACL

chr6

6q24.1

116.71

23

26.15

MAP1LC3B2

chr12

12q24.22

157.45

18

26.29

DGCR6

chr22

22q11

209.18

20

26.31

GTPBP6

chrX; chrY

Xp22.33; Yp11.32

120.83

23

26.61

AIMP2

chr7

7p22

181.19

43

26.84

CSNK2B

chr6

6p21.3

337.74

25

26.89

TM2D2

chr8

8p11.22

256.54

28

27.09

CLIC1

chr6

6p21.3

530.89

25

27.21

Hs.715132

chrX

N/A

196.12

22

27.42

Hs.732277

chr19

N/A

218.68

22

28.49

Hs.705664

chr7

N/A

1,167.55

18

28.62

NMRAL1

chr16

16p13.3

110.71

45

28.70

Hs.713819

chr5

N/A

241.81

18

28.93

RPP21

chr6

6p22.1

111.91

23

28.94

PNMA1

chr14

14q24.3

148.75

23

28.94

ACTG1*

chr17

17q25

3,583.46

180

29.06

AKR1B1

chr7

7q35

258.94

25

29.09

APOA1BP

chr1

1q21

231.69

32

29.35

MRPL24

chr1

1q23.1

183.95

31

29.44

ABCF1

chr6

6p21.33

123.95

25

29.56

ZNF627

chr19

19p13.2

112.37

24

29.70

METRNL

chr17

17q25.3

175.03

22

29.78

OCIAD2

chr4

4p11

286.18

20

29.92

EIF2B2

chr14

14q24.3

248.05

33

29.94

DIABLO

chr12

12q24.31

211.60

31

29.98

Genes are sorted in ascending order of SD as percentage of the mean value. *In bold, the two best genes at behaving like a housekeeping gene due to a combination of a low SD, a high expression value and a high number of data points, following checking in the “Values A” TRAM table that the 180 (ACTG1) and 76 (RPLP0) data points are derived from 33 and 22 samples, respectively, out of the 35 thyroid samples analyzed. N/A: not available in the Gene database (http://www.ncbi.nlm.nih.gov/gene) when the analysis was performed

Gene expression level in the thyroid and association to thyroid mutant phenotypes

We chose to analyze the first 50 known genes of the thyroid vs. non-thyroid tissue differential transcriptome map, sorted in decreasing order of the A/B ratio, in order to find an association to thyroid phenotypes registered in OMIM (Online Mendelian Inheritance in Man). We compared the set of the first 25 vs. the second set of 25 known genes through Fisher’s exact test and we found a statistically significant greater number of genes associated with thyroid phenotype in the first set of 25 known genes, 32% of the genes (8 out of 25, with A/B ratio from 13.91 (FOXE1, forkhead box E1) to 188.22 (TG)) rather than 4% of the second group (one out of 25 (DUOX2, dual oxidase 2, ratio A/B = 7.26)) (p = 0.023) (Table 5).
Table 5

Association of the first 50 known genes more expressed in thyroid respect to non-thyroid pool tissues (ratio between Pools A and B) to OMIM phenotypes in the case of gene mutation

# Gene

A/B Value

OMIM-Phenotype

OMIM-Phenotype ID

A)

1

TG

188.22

Thyroid Dyshormonogenesis 3 (AR)

#274700

2

TPO

151.17

Thyroid Dyshormonogenesis 2A (AR)

#274500

3

SLC26A7

66.94

-

N/A

4

IYD

66.25

Thyroid Dyshormonogenesis 4 (AR)

#274800

5

TSHR

49.36

Hyperthyroidism, familial gestational

#603373

   

Hyperthyroidism, nonautoimmune (AD)

#609152

   

Hypothyroidism, congenital, nongoitrous, 1 (AR)

#275200

   

Thyroid adenoma, hyperfunctioning, somatic

N/A

   

Thyroid carcinoma with thyrotoxicosis, somatic

N/A

6

SLC26A4

44.46

Pendred syndrome (AR)

#274600

7

SNORA26

40.26

-

N/A

8

PRH2

37.53

-

N/A

9

SLC26A4-AS1

36.09

not in OMIM

 

10

SFTA3

34.92

not in OMIM

 

11

HIST2H4B

32.52

not in OMIM

 

12

HSP90AA2

19.71

-

N/A

13

NKX2-1

18.20

Choreoathetosis, hypothyroidism, and neonatal respiratory distress (AD)

#610978

   

Thyroid cancer, monmedullary, 1 (AD)

#188550

14

LIPG

17.93

-

N/A

15

C16orf89

17.27

not in OMIM

 

16

DIO1

15.19

Hyperthyroxinemia (AD)

+147,892

17

FOXE1

13.91

Athyroidal hypothyroidism, with spiky hair and cleft palate

#241850

   

Bamforth-Lazarus syndrome) (AR)

 
   

Thyroid cancer, nonmedullary, 4 (AD)

#616534

18

TNFRFS11B

13.52

-

N/A

19

RMST

13.14

-

N/A

20

HHEX

12.93

-

N/A

21

SNORD22

12.16

-

N/A

22

INPP5J

11.14

-

N/A

23

CLIC3

9.99

-

N/A

24

OTOS

9.19

-

N/A

25

SLC25A29

9.09

-

N/A

B)

26

SGK223

8.99

not in OMIM

 

27

ID4

8.97

-

N/A

28

PDE8B

8.84

-

N/A

29

LRP2

8.42

-

N/A

30

ZBED2

8.33

-

N/A

31

ST6GAL2

8.14

-

N/A

32

DIO2

8.00

-

N/A

33

CLDN3

7.39

-

N/A

34

DUOX2

7.26

Thryoid dyshormonogenesis 6 (AR)

#607200

35

MGAT4C

7.04

-

N/A

36

IPCEF1

6.93

not in OMIM

 

37

COL23A1

6.88

-

N/A

38

CRABP1

6.78

-

N/A

39

CYS1

6.51

not in OMIM

 

40

SHISA2

6.46

not in OMIM

 

41

PKHD1L1

6.36

-

N/A

42

WDR72

6.35

-

N/A

43

TCERG1L

6.19

not in OMIM

 

44

MPPED2

6.18

-

N/A

45

IGFBPL1

6.13

-

N/A

46

NEUROG2

6.13

-

N/A

47

GABRA6

6.08

-

N/A

48

ID3

5.75

-

N/A

49

HIRA

5.66

-

N/A

50

MT1F

5.49

-

N/A

Gene symbols related to thyroid phenotypes are in bold, as well as the related phenotypes. A) the first 25 genes with the highest A/B ratio (>9 folds); B) the second group of 25 more differentially expressed genes with A/B ratio ranging from 8.99 to 5.49 folds. Genes with data points < 5 and SD as % of the mean value >200 were excluded from the analysis. AR: autosomal recessive; AD: autosomal dominant

Validation of thyroid transcriptome map results through Real-Time RT-PCR (reverse transcription-polymerase chain reaction)

In order to confirm the results of our meta-analysis study, we validated the transcriptome map of whole thyroid performing some Real-Time RT-PCR assays.

Using criteria as described in the Methods section, we selected 17 genes from the thyroid transcriptome map: TG, RPL41, B2M, ACTB, TPO, IYD, SDC2, TSHR, SERPINF1, MYH9, BACE2, REEP3, YKT6, NTNG1, RIPPLY3, NPTX1, TBX18 (Table 6). The SDC2 gene was chosen as the reference gene in that its predicted intermediate level of expression to avoid artifacts more likely to happen while approaching extremely low or high values. The primer pairs used to validate results are listed in the Additional file 7. The in vitro observed gene expression ratios between each target gene and the reference gene are provided in Table 6. The correlation between the observed and expected gene expression ratios as calculated by bivariate analysis was statistically highly significant with the Pearson correlation coefficient being 0.93 and p-value < 0.0001 (Fig. 3).
Table 6

Selected genes to validate the thyroid transcriptome map in vitro through Real-Time RT-PCR

Gene symbol

Gene name

EEV

SD

ER

Ct mean

OR

TG

Thyroglobulin

8,662.34

33.14

11.51

17.55

7.41

RPL41

Ribosomal protein L41

5,903.29

51.15

7.85

17.11

10.056

B2M

Beta-2-microglobulin

2,988.28

64.22

3.97

18.22

4.66

ACTB

Actin, beta

2,338.95

63.50

3.11

19.38

2.085

TPO

Thyroid peroxidase

2,245.11

54.57

2.98

19.38

2.08

IYD

Iodotyrosine deiodinase

890.07

65.51

1.18

21.16

0.61

SDC2

Syndecan 2

752.49

59.44

1

20.44

1

TSHR

Thyroid stimulating hormone receptor

487.64

91.14

0.65

21.34

0.54

SERPINF1

Serpin family F member 1

320.48

65.03

0.43

23.63

0.11

MYH9

Myosin, heavy chain 9, non-muscle

315.95

104.13

0.42

24.19

0.074

BACE2

Beta-site APP-cleaving enzyme 2

105.67

47.06

0.14

24.75

0.050

REEP3

Receptor accessory protein 3

85.88

108.64

0.11

N/A

N/A

YKT6

YKT6 v-SNARE homolog (S. cerevisiae)

29.63

96.23

0.04

26.08

0.020

NTNG1

Netrin G1

17.89

83.44

0.024

32.51

2.33 × 10−4

RIPPLY3

Ripply transcriptional repressor 3

16.88

97.44

0.022

33.10

1.55 × 10−4

NPTX1

Neuronal pentraxin 1

16.77

79.37

0.022

33.37

1.28 × 10−4

TBX18

T-box 18

11.52

100.73

0.015

28.63

0.0034

In bold the gene chosen as reference. From left to right: official gene symbol of the selected gene; official full gene name; expected expression value (EEV), i.e. mean linear expression value for the gene as provided by TRAM software; standard deviation (SD) as percentage of the mean; expected ratio (ER) between target and reference gene expression value; threshold cycle (Ct) mean provided by BioRad CFX Manager Software 2.1 manually positioning the threshold line; observed ratio (OR) determined between each target gene and the reference gene using the 2∆Ct method, according to the formula: 2ΔCt = 2 Ct ref. - Ct target

Fig. 3

Bivariate analysis between observed (Real-Time RT-PCR) and expected (TRAM) expression ratios of 16 selected genes (listed in Table 6; REEP3 resulted to be undetectable) in the thyroid transcriptome map. The fit line is shown; Pearson correlation coefficient is 0.93 and p-value < 0.0001

Discussion

Whole normal human thyroid transcriptome map

The human thyroid is an extremely differentiated organ performing exclusive functions related in particular to the production of hormones with a variety of actions indispensable for normal development, growth and function of the organism. This particularity is well reflected in classic dissertations about the thyroid in the fields of biochemistry [19], histology [20], anatomy [3] and physiology [21], where typical proteins originating from the thyroid are considered a marker of the organ. Modern technologies both in molecular and computational biology now make possible a quantitative, global representation of the whole gene transcription profile in a cell type which is not biased by previous knowledge, but allows systematic reconstruction of the critical aspects of gene expression at a genomic level.

We present here a quantitative transcriptome reference map of the whole normal human thyroid, i.e. a reference typical value of expression for each of the 27,275 known, mapped and 13,002 uncharacterized (unmapped) transcripts. Available gene expression data from a relevant number of samples and different sources were integrated through the TRAM software [17], resulting in a gene expression atlas of the whole gland useful as a “generally applicable” transcriptome model of normal thyroid. Actually, the TRAM approach has been very recently used with success in comparing normal vs. pathological conditions in colon cancer [22] and Parkinson’s disease [23], therefore possible conceivable applications of the atlas we provided here include quantitative, systematic comparisons of this profile with the one obtained from thyroid normal-near-tumor samples as well as tumor samples in further works on the subjects.

TRAM software analysis provides a segment representation of the genome that could help to identify chromatin domains whose genes are encoding for proteins interacting with each other or belonging to the same cell pathway [17].

The transcriptome maps showed an excellent portrait of thyroid physiology, in fact the first overexpressed segment is on chr8 (Table 1) and includes the known genes TG and NDRG1. The first gene encodes for the form of thyroid hormone storage, the major product of thyroid gland, accumulated in large quantities in the lumen of thyroid follicles. The expression of the second gene is increased in thyroid carcinomas with respect to normal and benign thyroid lesions and is correlated with more advanced tumor stages [24]. NDRG1 is a member of the N-myc downregulated gene family (alpha/beta hydrolase superfamily) and encodes for a cytoplasmic protein involved in stress responses, hormone responses, cell growth, and differentiation, necessary for p53-mediated caspase activation and apoptosis (see NCBI Gene entry https://www.ncbi.nlm.nih.gov/gene/10397). The same segment also includes the EST cluster Hs.654670 already found to be highly expressed in the thyroid, according to its UniGene EST profile, and whose transcript is increased ~ 141 fold in thyroid compared to non-thyroid tissues according to our map. This segment could represent a previously unknown cluster of genes converging on specialized thyroid functions and where physical proximity could suggest a coregulation of expression.

The second segment overexpressed is on chr16 (Table 1) and includes the gene cluster of metallothioneins: MT2A, MT1E, MT1A, MT1F, MT1G, MT1H and MT1X. Metallothioneins are cytoprotective proteins acting as scavengers of toxic metal ions or reactive oxygen species. They are upregulated in follicular thyroid carcinoma and are regarded as a marker of thyroid stress in Graves’ disease [25]. The Authors supposed that TSH represents a key regulator of metallothionein expression in thyrocytes. In fact, TSHR stimulation induced expression of metallothionein isoform 1X (MT1X) in human follicular carcinoma cells [25]. MT1G (metallothionein 1G) expression was frequently silenced or downregulated in thyroid cancer cell lines and was also significantly decreased in primary thyroid cancer tissues compared with non-malignant thyroid tissues [26]. The segment representation of the transcriptome map could be used to find those chromatin domains including genes less characterized in relation to the thyroid tissue (see Table 1 and TRAM results file available at: http://apollo11.isto.unibo.it/suppl).

At single gene level, the analysis of the most overexpressed genes shows loci which might have a typical thyroid function. These include well known genes and uncharacterized loci whose investigation should be furthered (Table 2). The known gene most expressed is again TG (expression value = 8,662.34) (Table 2), however, the maximum value of the expression is presented by the EST cluster Hs.732685 (expression value = 10,763.93) mapping within the known locus MALAT1 (metastasis associated lung adenocarcinoma transcript 1 (non-protein-coding)) on chr11, as shown by BLASTN analysis. We found that the platform probe (1558678_s_at from GPL570 platform) refers to MALAT1, but performing a bioinformatic analysis, we noted that this cluster is mainly characterized by antisense sequences with respect to MALAT1 orientation. This datum could explain the different expression between the EST cluster Hs.732685 and MALAT1 (expression values 10,763.92 and 2,180.86, respectively). Due to widespread expression at a high level in human tissues, it is possible that this uncharacterized transcript is a housekeeping gene. This cluster was previously found as overexpressed in hippocampus transcriptome map [18].

The main thyroid function is to produce thyroid hormones in large amounts and this may be reflected by the fact that among the 5 most expressed genes are included RPL41 (expression value = 5903.29), encoding for ribosomal protein L41, and EEF1A1 (expression value = 5537.45) encoding for eukaryotic translation elongation factor 1 alpha 1, which is responsible for the enzymatic delivery of aminoacyl tRNAs to the ribosome [27]. Their overexpression could be considered a sign of active protein synthesis. Analysis of EST database for EEF1A1 showed a very high expression through a variety of human tissues and in agreement with its basic housekeeping function, a very high expression level in cells actively producing proteins in the process of translation is not surprising.

Another highly expressed known gene is TPT1 (expression value = 5743.83) encoding for the tumor protein, translationally-controlled 1 (TCTP) which is a highly-conserved, cyto-protective protein implicated in many physiological and disease processes, in particular cancer, where it is associated with unfavorable prognosis [28].

Due to the fact that DS, in which the trisomy of chromosome 21 leads to an altered gene expression condition, is characterized by a high prevalence of thyroid dysfunction, in particular congenital hypothyroidism [29], particular attention was paid to the expression of chr21 genes which were already overexpressed in transcriptome maps generated by TRAM when comparing diploid and trisomic blood cells [30]. In our thyroid transcriptome map the most expressed chr21 gene is TFF3 (expression value = 2726.93). Recently TFF3 has been described as a gene that could function as an oncogene or oncosuppressor in dependence on the cellular context. In fact, in many types of solid tumors an increase of TFF3 expression level was observed, while in all thyroid cancers of follicular cell origin its expression was decreased when in normal thyroid it was highly expressed [31]. Other known overexpressed genes on chr21 are CSTB (cystatin B) and SOD1 (superoxide dismutase 1, soluble) (Table 2) encoding, respectively, for a protease inhibitor that has been reported to protect neurons from oxidative stress and for a free superoxide radical destroyer that controls the redox state of the cells. Recent results support the hypothesis of a direct interaction between the two proteins that may be relevant to render cells more responsive to oxidative stress conditions [32]. The Authors suggest a common activation pathway of their transcription. The expression ratio, obtained from TRAM analysis, between the 2 genes is 1.098, consistent with a stoichiometric molecular ratio of 1:1 between proteins that work associated in a molecular complex. It would be interesting to deepen the functions of the overexpressed chr21 genes, to study them in relation to the pathogenesis of DS, and to search for new genes on chr21 (where there are only 273 known genes [33]) in light of the fact that a very small region apparently intergenic appears to be conserved in all subjects diagnosed with DS [34]. The study of pathogenesis of congenital hypothyrodism as well as of other alterations associated to DS will also take advantage of the just released transcriptomic atlas obtained by RNA-Seq method in 15 normal human embryo tissues and organs, among which is included the thyroid [35].

Differential transcriptome map: Thyroid vs. pool of non-thyroid tissues

To highlight the thyroid-specific gene expression profile, a comparison pool composed of 624 samples derived from 53 different human tissues or organs has been accurately assembled. The differential transcriptome map was obtained by the comparison of the whole thyroid vs. the pool of non-thyroid tissues. The last allowed us to obtain an average value for each gene from non-thyroid tissues.

The differential transcriptome map showed the overexpression of chromatin domains including genes known to be involved in the thyroid function. For example, the first segment on chr8 again includes the known gene TG, followed by a segment on chr14 including NKX2-1. We also found IYD in the third segment on chr6 and PAX8 in the fourth segment on chr2 (Table 1). All these genes, encoding for thyroid specific transcriptional factors or enzymes, are fundamental in thyroid hormone synthesis [36].

Detailed analysis of the results can be extended to all over- and underexpressed segments (see TRAM results file available at: http://apollo11.isto.unibo.it/suppl). The results proved that, among the overexpressed segments, those including genes with a typical thyroid function prevail, unlike what can be observed among the underexpressed segments as expected by a differential transcriptome map of the thyroid vs. all non-thyroid tissues (Table 1).

Analysis at the single gene level shows a molecular atlas of the thyroid functions and histology structure. Simply observing the list of the first 100 genes (out of a total of 26,750 loci; Additional file 4), ordered according to the decreasing differential expression values, we found genes involved in all phases of the thyroid hormone synthesis. Once again the most overexpressed gene is TG encoding for the scaffold for the iodination of tyrosine and the thyroid hormone storage form, followed by TPO whose product performs iodination of tyrosine residues in TG and phenoxy-ester formation between pairs of iodinated tyrosines to generate the thyroid hormones. They were followed by the known coding genes SLC26A7, IYD, TSHR and SLC26A4 (solute carrier family 26 member 4) (Table 3 and Additional file 4). IYD catalyzes the deiodination of mono- and diiodotyrosine and is necessary for iodide salvage [37] and further production of hormones. The presence of abundant TSHRs on thyroid cells is crucial for their response to the main thyroid stimulating factor TSH. SLC26A7 and SLC26A4 belong to an anion exchangers family [38] and in particular, SLC26A4 is known to exchange iodine ions. Also presenting high differential expression values (between 18.20 and 12.54-fold increase respect to non-thyroid tissues), we noted NKX2-1, PAX8, FOXE1 and HHEX (hematopoietically expressed homeobox) genes that encode for transcription factors essentially for thyroid development and growth as for the adult hormonogenesis through the expression regulation of genes involved in this process. These transcription factors are expressed also in other tissues, but are coexpressed only in the thyroid [39]. Other high differentially expressed genes (differential expression ratio > 7) with known thyroid functions are DIO1 and DIO2, encoding for deiodinases catalyzing the conversion of T4 in T3, the more active form of thyroid hormone, and DUOX2 encoding for an enzyme included in a peroxide generating system that is part of the protein complex catalyzing thyroid hormone synthesis together with TPO [40, 41].

We may also consider that the functional importance of some genes is not always linked to their level of expression. It is a classical axiom that thyroid produces thyroid hormones and calcitonin, but how much is the calcitonin gene expressed? In our map, the CALCA (calcitonin related polypeptide alpha) gene, encoding the principal form of calcitonin produced by the thyroid, showed an absolute expression value in the thyroid of 218.27 vs. the TG expression value of 8,662.34 (ratio = 39.69), when the CALCA thyroid value was compared with the mean value obtained from the pool of non-thyroid tissue the differential expression ratio was 3.65-fold increase (TG showed a differential expression ratio = 188.22) with a difference between the two genes of 51.57 folds (Additional file 4). It should also be considered that the calcitonin-producing cells are quantitatively much less of the follicular cells (see Background section) and that in any case a differential expression of 3.5 times is significant.

Among chr21 genes, an expression ratio higher than 10 was observed for TFF3 gene (expression ratio = 16.14), which is again the most overexpressed chr21 gene (Table 3) probably because of its typical function in normal thyroid. It is followed by the uncharacterized EST cluster Hs.129583 (expression ratio = 11.10) (Table 3).

It should be noted here that, when dealing with the lowest gene expression values in our map, they are descriptive of very low or null activity of the gene, but they do not permit the drawing of conclusions about biological information related to specific gene repression in the considered tissue, because the use of a pool of various non-thyroid tissues in our general model might obscure whether a gene is specifically down-regulated in thyroid or is very highly expressed tissue-specifically elsewhere. However, the availability of the data for both thyroid alone (absolute value) and thyroid vs. non-thyroid pool (ratio value) may help in analyzing single situations of interest.

This quantitative representation of gene expression in the human thyroid, obtained without any a priori specific assumption and fully coherent with established biological knowledge about thyroid structure/function, seems to show at the molecular level the classical description of the basic histology of thyroid tissue. This also suggests that other genes with less characterized roles in the thyroid, but with a relevant specific expression in this organ, may have critical functions for thyroid molecular physiopathology. For instance, SLC26A7, presently known for its role in the kidney (NCBI Gene entry 115111), turns out here to be the known gene most overexpressed in thyroid in comparison to non-thyroid tissues (67-fold) while simultaneously having to date no thyroid-related function or phenotype described for it (Tables 3 and 5). Interestingly, a syndrome characterized by deafness and thyroid goiter (Pendred syndrome) has been associated to mutations of the related gene SLC26A4, also belonging to the same anion transporter gene family and overexpressed in thyroid (Table 5). Uncharacterized EST clusters highly and specifically expressed in thyroid (Table 3) might be associated in the same way to presently unknown functional loci with a relevant role in this organ.

Gene expression level in the thyroid and association to thyroid mutant phenotypes

The interesting finding of a very high differential expression level in the thyroid respect to non-thyroid tissues for a number of thyroid specific genes encoding for proteins fundamental in thyroid hormone production, led us to verify if there was a correlation between the high differential expression level and the occurrence of a pathological phenotype when these genes are mutated. Even considering only the first 50 most expressed known genes in the thyroid, a statistically significant association (p = 0.023) between the entity of the gene differential expression profile (A/B ratio values) in the thyroid vs. non-thyroid tissues and the description of pathological thyroid phenotypes in OMIM when these genes are mutated may be observed (Table 5).

Remarkably, this is a demonstration that reference quantitative expression values for a gene in a tissue could be systematically used as clues to associate a mutation in that gene, or an abnormal quantitative variation in its expression, to an increased probability that the alteration might affect the function of that given human tissue. The map described here could function as a forecasting map providing the possibility to discover new genes with key roles in the thyroid and hence supporting further research for a thyroid disease gene. However, it should be noted that also mutations of genes expressed at low level, as those for some transcription factors or regulative RNAs, could have a strong impact on the phenotype.

Sex-biased gene expression in the human thyroid

We performed a study of sex-biased gene expression in the whole thyroid gland to investigate whether gender has a significant influence on the thyroid gene expression profile observed in our analysis. For this purpose an additional differential transcriptome map to investigate specifically sex-biased gene expression patterns was obtained.

Interestingly, when sorting by Pool A.1 (male thyroid) expression values, we noted that the more expressed genes have a similar expression pattern in both sexes with differential ratios close to 1 (Additional file 6). These results are an additional confirmation of the reliability of the data produced by our map and support a high differential expression mainly for a small percentage of genes that are known to have a different expression between males and females, most of them sex-linked, while genes important for the thyroid function remain similarly expressed in both sexes (Fig. 2). Melé et al. [16] recently demonstrated that the variation in gene expression is greater among tissues (47%) than among individuals (4%).

Experimental validation of the thyroid transcriptome map

To prove the reliability of the thyroid transcriptome map generated by TRAM software, we performed an experimental validation of the obtained data. The excellent correlation between the expected and observed data (r = 0.93, p-value < 0.0001) (Fig. 3) allows us to consider all other intervening values among the points we selected as bona fide values, thus for the first time providing a reliable and quantitative representation of the reference expression values for 27,275 mapped transcripts and 13,002 uncharacterized (unmapped) transcripts of the whole normal human thyroid. These data suggest that our systematic transcriptome map, while highlighting interesting aspects of the structure and function of the normal human thyroid, may be a useful tool as a quantitative reference for gene expression studies related to this gland, as a whole, in a physiologically normal condition.

The quantitative results typically provided by the TRAM approach compare favorably with the ones provided by other available tools able to extract information about gene expression levels from published datasets, in that the advanced probe-to-locus assignment process maximizes the number of transcripts for which an expression value is obtained, while the powerful algorithm of data integration from diverse experimental platforms attenuates systematic biases associated to individual platforms. We have previously shown, in the case of brain transcriptome map, that TRAM map offers specific advantages over a specific brain gene expression atlas [12]. To simply but explicatively compare the data presented here for the human thyroid with the ones provided by the EBI Gene Expression Atlas [42] (https://www.ebi.ac.uk/gxa/home), we have extracted from this browser numerical values for the expression level of the 16 genes we have successfully measured by RT-PCR (Additional file 8) by selecting “Homo sapiens” as organism and “Thyroid gland” as tissue, then considering the first two proposed datasets, “GTEx” [43] and “Illumina Body Map” (both RNA-Seq based and normalized via RPKM (Reads Per Kilobase Million) method). Correlation coefficient with the in vitro validated values was 0.70 and 0.59, respectively, compared to 0.93 observed with TRAM results (Additional file 8). This fits better at the cost of additional manual curation in the initial steps of the set-up of a TRAM analysis, not needed for the atlases automatically maintained in the web environment; analogous results were obtained by searching BioGPS [44] (data not shown). In the case of TRAM, values are less correlated to the experimentally determined measure while approaching the lower limit of detection, as expected due to the hybridization-based nature of the microarray platforms.

It is worthwhile to finally note that this effective and validated quantitative approach in determining reference expression values for the loci expressed in a transcriptome extends to the ability of TRAM to provide a chromosomal region-based analysis by searching for statistically significant over−/underexpression of genomic segments, not based on the simple enrichment in the segment of over−/underexpressed genes contained in the segment, but on the actual determination of an expression value for the whole genomic segment derived by summarizing the quantitative expression values of the genes contained in it.

Search for reference genes to study gene expression in the human thyroid

The availability of the systematic and detailed expression map presented here for the thyroid represented an excellent occasion to investigate, among many features of the transcriptome, the suitability of individual genes as the best stable reference genes for data normalization in gene expression studies. This is because they fulfill criteria including a widely diffused, constant and high expression [45].

The best gene at behaving like a housekeeping gene in the thyroid is ACTG1, encoding for actin gamma 1, a cytoplasmic actin found in non-muscle cells as a cytoskeletal component (Table 4). The other form of non-muscle cytoskeletal actin is ACTB (actin beta). Weber et al. showed that, between 6 typical reference genes, ACTB was the most stable and suitable as housekeeping in normal human thyroid and goiter tissues [46]. However, in that study ACTG1 was not investigated. The gene for the ribosomal protein RPLP0 also presents positively fulfilling the combination of high expression level, low SD among samples and high number of measured data points across multiple samples (Table 4).

Conclusions

While our quantitative global reference portrait of gene expression in the normal human thyroid shows very high expression of genes known to exert basilar actions for the thyroid functions, in particular when comparing thyroid and non-thyroid tissues, the identification of many other genes or uncharacterized transcripts differentially expressed in the thyroid opens the way for the identification of genetic determinants related to the thyroid physiology.

The experimental in vitro validation as well as the remarkable correlation between the level of transcription of a gene and its probability to be associated, when mutated, to a genetic thyroid disease further supports the possible usefulness of the human thyroid transcriptome map as a reference for molecular studies of the physiology and pathology of this organ. For example, the analysis of chr21 gene quantitative expression levels could be used for successive studies of differential expression between normal thyroid tissue vs. trisomy 21 thyroid tissue, inferring gene behavior in an affected condition.

Methods

Database search and selection

A systematic search in gene expression data repositories for any single sample available listing gene expression values for the human thyroid and non-thyroid tissues from subjects explicitly referred to as “healthy” or “normal” was conducted exactly as previously described [47], except than the search term “thyroid” was used instead of “heart”. The criteria for inclusion or exclusion in the analysis of each retrieved dataset also were as previously described [47].

TRAM analysis

TRAM software [17] allows the import of gene expression data recorded in the GEO and ArrayExpress databases or in a custom source in tab-delimited text format whether the data are referred to microarry, RNA-Seq or proteomic platforms for the gene product expression studies. It also allows the integration of all data by decoding probe set identifiers to gene symbols via UniGene data parsing [48], normalizing data from multiple platforms using intra-sample and inter-sample normalization (scaled quantile normalization) [49], creating a graphical representation of gene expression profiles along the chromosomes and determining the statistical significance of differential expression of chromosomal segments in comparison with the other segments in the biological condition studied and in comparison with values calculated for the corresponding segments in the second biological condition when two conditions A and B are compared. The statistical analysis used by TRAM to this aim is hypergeometric distribution, a recognised algorithm able to test the probability ‘p’ that colocalization of over−/underexpressed genes within the same chromosomal segment may be due to chance [17, 50]. A graphical representation of the overall TRAM software workflow is provided in Fig. 1.

The value for each locus, in each biological condition, is the mean value of all available values for that locus. The genome wide gene expression median value was used in order to determine percentiles of expression for each gene.

Using the “Map” mode graphical representation we searched for over−/underexpressed genome segments which have a window size of 500,000 bp (base pairs) and a shift of 250,000 bp. The expression value for each genomic segment is the mean of the expression values of the loci included in that segment. A segment is defined over−/underexpressed if it has an expression value which is significantly different between two conditions analyzed, and contains at least three individually over−/underexpressed genes, e.g. genes which have expression values within the highest or the lowest 2.5th percentile.

We created two directories (folders) for each source which contain all sample datasets selected for the study. To compare whole thyroid samples with a pool of non-thyroid tissues, we collected the first in a folder named Pool A and the second in a folder named Pool B. In addition, the thyroid samples for which the sex of the sample donor was available were used to provide a differential transcriptome map between male and female thyroids: Male thyroid (Pool A.1) and Female thyroid (Pool A.2), not including samples deriving from mixed male/female tissues (Additional file 1). The comparisons allowed us to analyze differential maps using the ratio of the mean expression values for each locus in addition to the maps related to each single type of sample.

Significance of the over−/underexpression for single genes was determined by running TRAM in “Map” mode and lowering the segment window to 12,500 bp and the minimum number of over−/underexpressed genes in that window to 1. This window size is lower than 20% of the 67-kb mean size of a human protein-coding gene (as determined by searching the recent GeneBase database [51]: mean of gene size from 17,958 “reviewed” or “validated” entries in the NCBI Gene April 2015 annotation release), so the significant over−/underexpression of a segment almost always corresponds to that of a gene. When the segment window contains more than one gene, the significance is maintained if the expression value of the over−/underexpressed gene prevails over the others.

Sample expression values equal to or lower than “0” (≤ 0) will be thresholded by TRAM [47] to 95% of the minimum positive value present in that sample, in order to obtain meaningful numbers when dividing “Samples Pool A” values by “Sample Pool B” values. Assuming that in these cases an expression level is too low to be detected under the used experimental conditions, this transformation still allows a ratio between values in the Pool A and values in the Pool B to be obtained, which is useful to highlight differential gene expression.

Housekeeping gene search

We determined the genes that behave like housekeeping genes, in that they are mainly involved in fundamental cellular functions and tend to be constantly expressed at a high level, typically, but not necessarily, in a consistent way across many different tissues/organs [52, 53]. A search of housekeeping genes best suitable for the study of human thyroid was performed in the transcriptome map of human thyroid created as described above (Pool A) using the following parameters in combination: expression value > 100 in order to select genes expressed above the mean value (that is posed equal to 100) and so at an appreciable level; data points number (where a data point is a measure of the expression value for a gene obtained from a single spot on the array) ≥ of half the number of samples of the map in order to select commonly expressed genes (≥ 18); SD, expressed as a percentage of the mean value, ≤ 30 in order to identify genes with a low expression variation among different thyroid samples.

Gene expression level in the thyroid and association to thyroid phenotypes

The availability of a measured ratio of the expression level of a gene in the thyroid vs. non-thyroid tissues provides means to formally test the hypothesis that the mutation in a gene with a typical high expression level in the thyroid, in comparison with non-thyroid tissues, is most likely that it will result in a pathologic thyroid-related phenotype. For this purpose, for each of the first 50 known genes most expressed in thyroid respect to the mean of non-thyroid tissues (ratio A/B ≥ 5.49), the certain association to a pathological thyroid phenotype was recorded by manual searching of the OMIM database [54]. Following sorting of the genes by their differential expression value in descending order, Fisher’s exact test was used to compare the first 25 genes against the following 25 genes in thyroid vs. non-thyroid tissues differential transcriptome map to test the significance of the association of a high ratio expression value to the OMIM description of a pathological thyroid phenotype when they are mutated. In this analysis we have considered only genes with the following parameters: data points number ≥ 5 both in Pool A and Pool B; SD, expressed as a percentage of the mean value, < 200% when expression value was > 50 (high SD is tolerated in the low range of values due to a tendency of imprecision when approaching the low limit of detection).

In vitro validation of the thyroid transcriptome map

In order to obtain experimental confirmation of the meta-analysis derived map, we randomly selected a group of genes with expression values covering the whole range of the expression magnitude order as calculated by TRAM. In the validation set, thyroid-specific genes (TPO, IYD, TSHR) and genes which are not commonly considered to have a specific thyroid function (RPL41, B2M, SERPINF1, BACE2, NTNG1, NPTX1, TBX18) were included. The genes are known and characterized, also verifying that they are not included among the genes with a known incomplete determination of their 5′ coding sequence [55].

Complementary DNA (cDNA) templates were obtained from reverse transcription of commercial total RNA extracted from human normal thyroids pooled from 64 male/female Caucasians, aged between 15 and 61 years with sudden death as the reported cause of death while no further RNA source information is available from the provider (Clontech, Mountain View, CA). The reverse transcription was performed according to [47].

Primer pairs were designed with “Amplify 3” software [56] following standard criteria [57]. They are designed on different exons, except for the ACTB gene, to specifically recognize expressed sequences and to bind to regions common to all isoforms of the same gene since microarray probe sequences are complementary to sequences common of the known isoforms of the same gene. These constraints caused the amplicon length variation between 99 and 247 bp (Additional file 7). Primers for ACTB gene are designed on the same exon to avoid unspecific products and to amplify the same trait recognized by the probes used in microarray platforms. A control PCR reaction was performed using the commercial RNA directly as a template to verify a potential contaminating DNA amplification by the commercial RNA sample.

Real-Time PCR assays were performed in triplicate, using the CFX96 instrument (Bio-Rad Laboratories, Hercules, CA). The reactions were performed in a total volume of 20 μL using Sybr Select Master Mix 2× for CFX (Applied Biosystem, by Life Technologies) according to manifacturer instructions. Cycling parameters were: 2 min at 50 °C (UDG activation), 2 min at 95 °C (AmpliTaq Fast DNA Polymerase UP activation), 40 cycles of 15 s at 95 °C (denature) and of 1 min at 60 °C (anneal and extend). A melting step needs to be performed to assay amplification specificity. This step consisted of an increase in temperature of 0.5 °C/s from 65 °C to 95 °C.

For each gene we used the primer pair that gave between 90 and 110% efficiency. We used the 2∆Ct (delta cycle threshold) method, a variation of the Livak method [58], that uses the difference between reference and target gene Ct values for each sample to do a relative quantification normalized to a reference gene (Observed Ratio(reference/target) = 2Ct(reference) – Ct(target)). We set the gene with an intermediate expression value and a low standard deviation (SD, expressed as percentage of the mean value) in TRAM analysis as reference gene. The ratio among the transcriptome map expression values was calculated by dividing each expression value of the target gene by the expression value of the reference (expected ratio). Then we compared this value with the observed ratio and we examined the relationship between these two variables through bivariate statistical analysis using JMP 5.1 software (SA Institute, Campus Drive Cary, NC).

Abbreviations

cDNA: 

complementary DNA

DS: 

Down syndrome

EBI: 

European bioinformatics institute

EST: 

expressed sequence tag

GEO: 

Gene expression omnibus

NCBI: 

National center for biotechnology information

OMIM: 

Online mendelian inheritance in man

RNA-Seq: 

RNA sequencing

RT-PCR: 

reverse transcription - polymerase chain reaction

SD: 

standard deviation

TRAM: 

Transcriptome mapper (software)

Declarations

Acknowledgments

We wish to sincerely thank the Fondazione Umano Progresso, Milano, Italy for their fundamental support of our research on trisomy 21 and this study.

We thank all the other people that have very kindly contributed by individual donations to support part of the fellowships as well as hardware, software and reagents for the experiments. In particular, we are profoundly grateful to Matteo and Elisa Mele, to the Costa family, “Gruppo Arzdore”, Parrocchia di Dozza and Associazione Turistica Pro Loco di Dozza (Dozza, Bologna, Italy), as well as to Vittoria Aiello and Massimiliano Albanese and their friends.

We are grateful to Kirsten Welter for her kind and expert revision of the manuscript and to Gabriella Mattei for providing original microphotographs used in Fig. 1.

Funding

This work was supported by donations from Fondazione Umano Progresso and from other donors acknowledged above which supported the purchase of the hardware and software that were necessary to conduct the research.

MCP’s fellowship has been co-funded by a donation from Fondazione Umano Progresso and by donations following the international fundraising initiative by Vittoria Aiello and Massimiliano Albanese (donors listed at the site: http://www.massimilianoalbanese.net/ds-research/?lang=en). The fellowship for AP has been mainly funded by the Fondazione Umano Progresso. MC’s fellowship has been co-funded by donations from Fondazione Umano Progresso, Milano, Italy and by a grant from Fondazione del Monte di Bologna e Ravenna, Bologna, Italy. The fellowship for FA has been mainly funded by donations from ‘Gruppo Arzdore’, Dozza, (BO), Italy and by the Natali family, Petriolo (MC), Italy, in memory of Leonardo Natali.

Availability of data and materials

The datasets generated and/or analyzed during the current study are available in our public, institutional site for supplements: http://apollo11.isto.unibo.it/suppl.

Authors’ contributions

LV performed the search and analysis of data and was a major contributor in writing the manuscript. AP, FA and MCP contributed to the data analysis and to the writing of the manuscript. MCP reviewed the data analysis. PS contributed to the search and analysis of data and to the writing of the manuscript. MC contributed to the search and analysis of data and to the writing of the manuscript, performed the experimental validation of data and coordinated the work. All authors read, critically discussed and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Experimental, Diagnostic and Specialty Medicine, (DIMES), Unit of Histology, Embryology and Applied Biology, University of Bologna, Bologna, Italy

References

  1. Stathatos N. Thyroid physiology. Med Clin North Am. 2012;96:165–73.View ArticlePubMedGoogle Scholar
  2. Ganong W. The thyroid gland. In: Ganong W, editor. Review of medical physiology. 15th ed. East Norwalk: Appleton & Lange; 1991. p. 296–311.Google Scholar
  3. Standring S. Neck and upper neurodigestive tract. In: Standring S, editor. Gray's anatomy: the anatomical basis of clinical practice. 39th ed. Philadelphia: Churchill Livingstone; 2005. p. 560–4.Google Scholar
  4. Bianconi E, Piovesan A, Facchin F, Beraudi A, Casadei R, Frabetti F, Vitale L, Pelleri MC, Tassani S, Piva F, et al. An estimation of the number of cells in the human body. Ann Hum Biol. 2013;40:463–71.View ArticlePubMedGoogle Scholar
  5. Gibson WG, Peng TC, Croker BP. Age-associated C-cell hyperplasia in the human thyroid. Am J Pathol. 1982;106:388–93.PubMedPubMed CentralGoogle Scholar
  6. Krude H, Kuhnen P, Biebermann H. Treatment of congenital thyroid dysfunction: achievements and challenges. Best Pract Res Clin Endocrinol Metab. 2015;29:399–413.View ArticlePubMedGoogle Scholar
  7. Purdy IB, Singh N, Brown WL, Vangala S, Devaskar UP. Revisiting early hypothyroidism screening in infants with down syndrome. J Perinatol. 2014;34:936–40.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Ria R, Simeon V, Melaccio A, Di Meo G, Trino S, Mazzoccoli C, Saltarella I, Lamanuzzi A, Morano A, Gurrado A, et al. Gene expression profiling of normal thyroid tissue from patients with thyroid carcinoma. Oncotarget. 2016;7:29677–88.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Valderrabano P, Zota VE, McIver B, Coppola D, Leon ME. Molecular assays in Cytopathology for thyroid cancer. Cancer Control. 2015;22:152–7.View ArticlePubMedGoogle Scholar
  10. Yu J, Mai W, Cui Y, Kong L. Key genes and pathways predicted in papillary thyroid carcinoma based on bioinformatics analysis. J Endocrinol Investig. 2016;39:1285–93.View ArticleGoogle Scholar
  11. Swierniak M, Wojcicka A, Czetwertynska M, Stachlewska E, Maciag M, Wiechno W, Gornicka B, Bogdanska M, Koperski L, de la Chapelle A, Jazdzewski K. In-depth characterization of the microRNA transcriptome in normal thyroid and papillary thyroid carcinoma. J Clin Endocrinol Metab. 2013;98:E1401–9.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Caracausi M, Vitale L, Pelleri MC, Piovesan A, Bruno S, Strippoli P. A quantitative transcriptome reference map of the normal human brain. Neurogenetics. 2014;15:267–87.View ArticlePubMedGoogle Scholar
  13. Costa Ade F, Franco OL. Insights into RNA transcriptome profiling of cardiac tissue in obesity and hypertension conditions. J Cell Physiol. 2015;230:959–68.View ArticlePubMedGoogle Scholar
  14. Brooksbank C, Bergman MT, Apweiler R, Birney E, Thornton J. The European bioinformatics Institute's data resources 2014. Nucleic Acids Res. 2014;42:D18–25.View ArticlePubMedGoogle Scholar
  15. Barrett T, Edgar R. Gene expression omnibus: microarray data storage, submission, retrieval, and analysis. Methods Enzymol. 2006;411:352–69.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Melé M, Ferreira PG, Reverter F, DeLuca DS, Monlong J, Sammeth M, Young TR, Goldmann JM, Pervouchine DD, Sullivan TJ, et al. Human genomics. The human transcriptome across tissues and individuals. Science. 2015;348:660–5.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Lenzi L, Facchin F, Piva F, Giulietti M, Pelleri MC, Frabetti F, Vitale L, Casadei R, Canaider S, Bortoluzzi S, et al. TRAM (Transcriptome Mapper): database-driven creation and analysis of transcriptome maps from multiple sources. BMC Genomics. 2011;12:121.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Caracausi M, Rigon V, Piovesan A, Strippoli P, Vitale L, Pelleri MC. A quantitative transcriptome reference map of the normal human hippocampus. Hippocampus. 2016;26:13–26.View ArticlePubMedGoogle Scholar
  19. Lehninger AL. Biochemistry: the molecular basis of cell structure and function. second ed. New York: Worth Publishers; 1975.Google Scholar
  20. Weiss L, Greep RO. Histology. fourth ed. New York: McGraw-Hill; 1977.Google Scholar
  21. Mountcastle VB. Medical physiology. fourteenth ed. C. V. Mosby Company: St. Louis; 1980.Google Scholar
  22. Rodia MT, Ugolini G, Mattei G, Montroni I, Zattoni D, Ghignone F, Veronese G, Marisi G, Lauriola M, Strippoli P, Solmi R. Systematic large-scale meta-analysis identifies a panel of two mRNAs as blood biomarkers for colorectal cancer detection. Oncotarget. 2016;7:30295–306.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Mariani E, Frabetti F, Tarozzi A, Pelleri MC, Pizzetti F, Casadei R. Meta-analysis of Parkinson's disease Transcriptome data using TRAM software: whole Substantia Nigra tissue and single dopamine neuron differential gene expression. PLoS One. 2016;11:e0161567.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Gerhard R, Nonogaki S, Fregnani JH, Soares FA, Nagai MA. NDRG1 protein overexpression in malignant thyroid neoplasms. Clinics (Sao Paulo). 2010;65:757–62.Google Scholar
  25. Back CM, Stohr S, Schafer EA, Biebermann H, Boekhoff I, Breit A, Gudermann T, Buch TR. TSH induces metallothionein 1 in thyrocytes via Gq/11- and PKC-dependent signaling. J Mol Endocrinol. 2013;51:79–90.View ArticlePubMedGoogle Scholar
  26. Fu J, Lv H, Guan H, Ma X, Ji M, He N, Shi B, Hou P. Metallothionein 1G functions as a tumor suppressor in thyroid cancer through modulating the PI3K/Akt signaling pathway. BMC Cancer. 2013;13:462.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Abbas W, Kumar A, Herbein G. The eEF1A proteins: at the crossroads of Oncogenesis, apoptosis, and viral infections. Front Oncol. 2015;5:75.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Bommer UA, Iadevaia V, Chen J, Knoch B, Engel M, Proud CG. Growth-factor dependent expression of the translationally controlled tumour protein TCTP is regulated through the PI3-K/Akt/mTORC1 signalling pathway. Cell Signal. 2015;27:1557–68.View ArticlePubMedGoogle Scholar
  29. Kariyawasam D, Carre A, Luton D, Polak M. Down syndrome and nonautoimmune hypothyroidisms in neonates and infants. Horm Res Paediatr. 2015;83:126–31.View ArticlePubMedGoogle Scholar
  30. Pelleri MC, Piovesan A, Caracausi M, Berardi AC, Vitale L, Strippoli P. Integrated differential transcriptome maps of acute Megakaryoblastic leukemia (AMKL) in children with or without down syndrome (DS). BMC Med Genet. 2014;7:63.Google Scholar
  31. Abols A, Ducena K, Andrejeva D, Sadovska L, Zandberga E, Vilmanis J, Narbuts Z, Tars J, Eglitis J, Pirags V, Line A. Trefoil factor 3 is required for differentiation of thyroid follicular cells and acts as a context-dependent tumor suppressor. Neoplasma. 2015;62:914–24.View ArticlePubMedGoogle Scholar
  32. Ulbrich L, Cozzolino M, Marini ES, Amori I, De Jaco A, Carri MT, Augusti-Tocco G. Cystatin B and SOD1: protein-protein interaction and possible relation to neurodegeneration. Cell Mol Neurobiol. 2014;34:205–13.View ArticlePubMedGoogle Scholar
  33. Piovesan A, Caracausi M, Antonaros F, Pelleri MC, Vitale L. GeneBase 1.1: a tool to summarise data from NCBI Gene datasets and its application to an update of human gene statistics. Database (Oxford). 2016.Google Scholar
  34. Pelleri MC, Cicchini E, Locatelli C, Vitale L, Caracausi M, Piovesan A, Rocca A, Poletti G, Seri M, Strippoli P, Cocchi G. Systematic reanalysis of partial trisomy 21 cases with or without down syndrome suggests a small region on 21q22.13 as critical to the phenotype. Hum Mol Genet. 2016;25:2525–38.PubMedPubMed CentralGoogle Scholar
  35. Gerrard DT, Berry AA, Jennings RE, Piper Hanley K, Bobola N, Hanley NA. An integrative transcriptomic atlas of organogenesis in human embryos. elife. 2016;5Google Scholar
  36. Szinnai G. Clinical genetics of congenital hypothyroidism. Endocr Dev. 2014;26:60–78.View ArticlePubMedGoogle Scholar
  37. Bobyk KD, Ballou DP, Rokita SE. Rapid kinetics of dehalogenation promoted by iodotyrosine deiodinase from human thyroid. Biochemistry. 2015;54:4487–94.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Alper SL, Sharma AK. The SLC26 gene family of anion transporters and channels. Mol Asp Med. 2013;34:494–515.View ArticleGoogle Scholar
  39. Afink GB, Veenboer G, de Randamie J, Keijser R, Meischl C, Niessen H, Ris-Stalpers C. Initial characterization of C16orf89, a novel thyroid-specific gene. Thyroid. 2010;20:811–21.View ArticlePubMedGoogle Scholar
  40. Grasberger H. Defects of thyroidal hydrogen peroxide generation in congenital hypothyroidism. Mol Cell Endocrinol. 2010;322:99–106.View ArticlePubMedGoogle Scholar
  41. Song Y, Ruf J, Lothaire P, Dequanter D, Andry G, Willemse E, Dumont JE, Van Sande J, De Deken X. Association of duoxes with thyroid peroxidase and its regulation in thyrocytes. J Clin Endocrinol Metab. 2010;95:375–82.View ArticlePubMedGoogle Scholar
  42. Petryszak R, Keays M, Tang YA, Fonseca NA, Barrera E, Burdett T, Fullgrabe A, Fuentes AM, Jupp S, Koskinen S, et al. Expression atlas update--an integrated database of gene and protein expression in humans, animals and plants. Nucleic Acids Res. 2016;44:D746–52.View ArticlePubMedGoogle Scholar
  43. Consortium G. The genotype-tissue expression (GTEx) project. Nat Genet. 2013;45:580–5.View ArticleGoogle Scholar
  44. Wu C, Jin X, Tsueng G, Afrasiabi C, Su AI. BioGPS: building your own mash-up of gene annotations and expression profiles. Nucleic Acids Res. 2016;44:D313–6.View ArticlePubMedGoogle Scholar
  45. Casadei R, Pelleri MC, Vitale L, Facchin F, Lenzi L, Canaider S, Strippoli P, Frabetti F. Identification of housekeeping genes suitable for gene expression analysis in the zebrafish. Gene Expr Patterns. 2011;11:271–6.View ArticlePubMedGoogle Scholar
  46. Weber R, Bertoni AP, Bessestil LW, Brasil BM, Brum LS, Furlanetto TW. Validation of reference genes for normalization gene expression in reverse transcription quantitative PCR in human normal thyroid and goiter tissue. Biomed Res Int. 2014;2014:198582.PubMedPubMed CentralGoogle Scholar
  47. Caracausi M, Piovesan A, Vitale L, Pelleri MC. Integrated Transcriptome map highlights structural and functional aspects of the normal human heart. J Cell Physiol. 2017;232:759–70.View ArticlePubMedGoogle Scholar
  48. Lenzi L, Frabetti F, Facchin F, Casadei R, Vitale L, Canaider S, Carinci P, Zannotti M, Strippoli P. UniGene tabulator: a full parser for the UniGene format. Bioinformatics. 2006;22:2570–1.View ArticlePubMedGoogle Scholar
  49. Piovesan A, Vitale L, Pelleri MC, Strippoli P. Universal tight correlation of codon bias and pool of RNA codons (codonome): the genome is optimized to allow any distribution of gene expression values in the transcriptome from bacteria to humans. Genomics. 2013;101:282–9.View ArticlePubMedGoogle Scholar
  50. Coppe A, Danieli GA, Bortoluzzi S. REEF: searching REgionally enriched features in genomes. BMC Bioinformatics. 2006;7:453.View ArticlePubMedPubMed CentralGoogle Scholar
  51. Piovesan A, Caracausi M, Ricci M, Strippoli P, Vitale L, Pelleri MC. Identification of minimal eukaryotic introns through GeneBase, a user-friendly tool for parsing the NCBI gene databank. DNA Res. 2015;22:495–503.View ArticlePubMedPubMed CentralGoogle Scholar
  52. Butte AJ, Dzau VJ, Glueck SB. Further defining housekeeping, or "maintenance," genes focus on "a compendium of gene expression in normal human tissues". Physiol Genomics. 2001;7:95–6.PubMedGoogle Scholar
  53. Tu Z, Wang L, Xu M, Zhou X, Chen T, Sun F. Further understanding human disease genes by comparing with housekeeping genes and other genes. BMC Genomics. 2006;7:31.View ArticlePubMedPubMed CentralGoogle Scholar
  54. Amberger JS, Bocchini CA, Schiettecatte F, Scott AF, Hamosh A. OMIM.Org: online Mendelian inheritance in man (OMIM(R)), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 2015;43:D789–98.View ArticlePubMedGoogle Scholar
  55. Casadei R, Piovesan A, Vitale L, Facchin F, Pelleri MC, Canaider S, Bianconi E, Frabetti F, Strippoli P. Genome-scale analysis of human mRNA 5′ coding sequences based on expressed sequence tag (EST) database. Genomics. 2012;100:125–30.View ArticlePubMedGoogle Scholar
  56. Engels WR. Contributing software to the internet: the amplify program. Trends Biochem Sci. 1993;18:448–50.View ArticlePubMedGoogle Scholar
  57. Sharrocks A. The design of primer for PCR. In: Griffin HG, Griffin AM, editors. PCR technology—current innovations. Boca Raton: CRC Press; 1994. p. 5–11.Google Scholar
  58. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(−Delta Delta C(T)) method. Methods. 2001;25:402–8.View ArticlePubMedGoogle Scholar

Copyright

© The Author(s). 2017

Advertisement