Control of Expression Level in Human Genes: Observations with Apoptosis Genes and Genes Involved in B cell Development

Article Information

Jay C. Brown

Department of Microbiology, Immunology and Cancer Biology, University of Virginia School of Medicine, Charlottesville, Virginia, 22908

*Corresponding author: Jay C. Brown, Department of Microbiology, Immunology and Cancer Biology, University of Virginia School of Medicine Box 800734, Charlottesville, Virginia  22908, USA

Received: 11 July 2022; Accepted: 20 July 2022; Published: 28 July 2022

Citation: Jay C. Brown. Control of Expression Level in Human Genes: Observations with Apoptosis Genes and Genes Involved in B cell Development. Journal of Bioinformatics and Systems Biology 5 (2022): 108-115.

View / Download Pdf Share at Facebook

Abstract

To understand the way a gene functions in development, one needs to know about the gene product’s functional capabilities, the tissues where it is located, and the level of its expression. It is now widely accepted that transcription factors can affect the level of gene expression, but the results emphasize the need for further clarification. The study described here was carried out to determine whether the amount of a transcription factor bound in the promoter region might be directly related to the level of the gene’s expression. The study was focused on a population of human genes involved in apoptosis, a pathway known to be affected by the transcription factor Ikaros (IKZF1 gene). For each apoptosis gene, information was accumulated about its expression level and about the level of IKZF1 binding in the promoter. The two measurements were then compared and interpreted to identify instances where the amount of IKZF1 binding is related to the level of gene expression. A similar analysis was carried out with genes involved in B cell development, also a gene population influenced by IKZF1. The results identified gene groups, each containing 3-8 genes, in which the expression level was related to IKZF1 binding in the promoter, a result that supports the idea that promoter bound IKZF1 can affect the level of gene expression. A further study was performed to examine the secondary, non-IKZF1 transcription factors bound in the promoters of apoptosis and B cell development genes. Prominent amounts of RBFOX2, ASH2L and TAF1 were observed in both populations suggesting IKZF1-rich promoters may resemble each other in their content of other transcription factor binding sites as well.

Keywords

gene expression; transcription factor; promoter; IKZF1; apoptosis; B cell development

gene expression articles, transcription factor articles, promoter, IKZF1 articles, apoptosis articles, B cell development articles

gene expression articles gene expression Research articles gene expression review articles gene expression PubMed articles gene expression PubMed Central articles gene expression 2023 articles gene expression 2024 articles gene expression Scopus articles gene expression impact factor journals gene expression Scopus journals gene expression PubMed journals gene expression medical journals gene expression free journals gene expression best journals gene expression top journals gene expression free medical journals gene expression famous journals gene expression Google Scholar indexed journals transcription factor articles transcription factor Research articles transcription factor review articles transcription factor PubMed articles transcription factor PubMed Central articles transcription factor 2023 articles transcription factor 2024 articles transcription factor Scopus articles transcription factor impact factor journals transcription factor Scopus journals transcription factor PubMed journals transcription factor medical journals transcription factor free journals transcription factor best journals transcription factor top journals transcription factor free medical journals transcription factor famous journals transcription factor Google Scholar indexed journals promoter articles promoter Research articles promoter review articles promoter PubMed articles promoter PubMed Central articles promoter 2023 articles promoter 2024 articles promoter Scopus articles promoter impact factor journals promoter Scopus journals promoter PubMed journals promoter medical journals promoter free journals promoter best journals promoter top journals promoter free medical journals promoter famous journals promoter Google Scholar indexed journals IKZF1 articles IKZF1 Research articles IKZF1 review articles IKZF1 PubMed articles IKZF1 PubMed Central articles IKZF1 2023 articles IKZF1 2024 articles IKZF1 Scopus articles IKZF1 impact factor journals IKZF1 Scopus journals IKZF1 PubMed journals IKZF1 medical journals IKZF1 free journals IKZF1 best journals IKZF1 top journals IKZF1 free medical journals IKZF1 famous journals IKZF1 Google Scholar indexed journals apoptosis articles apoptosis Research articles apoptosis review articles apoptosis PubMed articles apoptosis PubMed Central articles apoptosis 2023 articles apoptosis 2024 articles apoptosis Scopus articles apoptosis impact factor journals apoptosis Scopus journals apoptosis PubMed journals apoptosis medical journals apoptosis free journals apoptosis best journals apoptosis top journals apoptosis free medical journals apoptosis famous journals apoptosis Google Scholar indexed journals B cell development articles B cell development Research articles B cell development review articles B cell development PubMed articles B cell development PubMed Central articles B cell development 2023 articles B cell development 2024 articles B cell development Scopus articles B cell development impact factor journals B cell development Scopus journals B cell development PubMed journals B cell development medical journals B cell development free journals B cell development best journals B cell development top journals B cell development free medical journals B cell development famous journals B cell development Google Scholar indexed journals ENCODE articles ENCODE Research articles ENCODE review articles ENCODE PubMed articles ENCODE PubMed Central articles ENCODE 2023 articles ENCODE 2024 articles ENCODE Scopus articles ENCODE impact factor journals ENCODE Scopus journals ENCODE PubMed journals ENCODE medical journals ENCODE free journals ENCODE best journals ENCODE top journals ENCODE free medical journals ENCODE famous journals ENCODE Google Scholar indexed journals RNA-seq articles RNA-seq Research articles RNA-seq review articles RNA-seq PubMed articles RNA-seq PubMed Central articles RNA-seq 2023 articles RNA-seq 2024 articles RNA-seq Scopus articles RNA-seq impact factor journals RNA-seq Scopus journals RNA-seq PubMed journals RNA-seq medical journals RNA-seq free journals RNA-seq best journals RNA-seq top journals RNA-seq free medical journals RNA-seq famous journals RNA-seq Google Scholar indexed journals chromosome articles chromosome Research articles chromosome review articles chromosome PubMed articles chromosome PubMed Central articles chromosome 2023 articles chromosome 2024 articles chromosome Scopus articles chromosome impact factor journals chromosome Scopus journals chromosome PubMed journals chromosome medical journals chromosome free journals chromosome best journals chromosome top journals chromosome free medical journals chromosome famous journals chromosome Google Scholar indexed journals

Article Details

Graphical Abstract

fortune-biomass-feedstock

Graphical Abstrac

1. Introduction

Control of gene expression is one of the most actively studied areas of molecular biology today, and this has been the case for more than half a century. The effort has been highly productive. As a result, we now have an advanced understanding of regulatory control as it occurs in a wide variety of organisms including humans. Relevant features uncovered include the role of transcription factors, enhancers, epigenetic modifications, and the way chromatin architecture can affect gene expression [1-4]. A neglected area, however, has been an identification of factors that affect the quantitative level of expression. Studies have addressed whether a gene is on or off but avoided the issue of whether expression is high, medium, or low. It is expected that the same factors that affect expression will also affect the level of expression, but one would like to have additional information about how such functions operate.

The study described here was designed to confront the issue of expression level directly. The goal was to test the role of a promoter bound transcription factor with the level of gene expression. Analysis was focused on a single transcription factor, Ikaros (IKZF1) and a population of 56 human genes involved in apoptosis, a system whose function is known to be affected by IKZF1 [5]. For each apoptosis gene, two values were identified, (1) the level of expression and (2) the amount of IKZF1 bound at the promoter. The values were then plotted, and the plots were interpreted to indicate whether IKZF1 was affecting expression of a gene and if so then whether the effect was to potentiate or repress the level of expression. A similar analysis was carried out with 64 human genes involved in B cell development, a function also known to be regulated by IKZF1 [6-8]. The results identify several groups of genes in which promoter bound IKZF1 is correlated with the level of gene expression.

2. Materials and Methods

2.1 Gene databases

The study was performed beginning with two human gene populations, one of genes involved in apoptosis (Supplementary Table 1) and the other of B cell development genes (Supplementary Table 2). Apoptosis and B cell development genes were derived beginning with those reported by Jourdan et al. [5] and Calonga-Solis et al. [9], respectively. Transcription levels were derived from the GTEx Portal of RNA-seq results as reported in the UCSC Genome Browser (version hg38; https://genome.ucsc.edu/ ). ChIP-seq results for IKZF1 and control transcription factor binding to the gene promoter were obtained for GM12878 cells by way of the Integrated Genome Browser (https://igv.org/app/). Secondary, non-IKZF1 transcription factor binding sites were retrieved from the ENCODE Transcription Factor ChIP Clusters data base by way of the UCSC Genome Browser. For each gene reported, the list of TF binding sites from ENCODE was examined visually, and the number of binding sites was counted. The counts were used to identify the three most prevalent binding sites which were reported as TF rank 1-TF rank 3. Gene functions were retrieved from GeneCards (https://www.genecards.org/).

2.2 Data analysis

Data were recorded with RStudio and Excel. Results were analyzed and rendered graphically with SigmaPlot 14.5.

3. Results

3.1 Experimental strategy

The goal of the project described here was to test the idea that binding of a transcription factor in the promoter region of a gene might affect the quantitative level of the gene’s expression. It was expected this might be the case as transcription factors (TF) are known to affect whether a gene is expressed or not. It is reasonable therefore to consider that the same factors that affect on vs. off might also affect the degree of on. Also, the resources needed to carry out the test are readily available. The results of RNA-seq studies yield the required information about gene expression levels and ChIP-seq results provide promoter binding information. After accumulating the above information, the study involved plotting the level of gene expression (RNA-seq results) against the amount of promoter bound TF (ChIP-seq results) to determine if the plot indicates a relationship between the two values.

Fig. 1 shows a graphic representation of the expected results as described above. The gene expression value was plotted on the y axis and promoter bound IKZF1 on the x. Data points along the green line of text correspond to genes where IKZF1 activates gene expression while points along the red text indicate repression. Both activating and repressive effects are expected since, like many transcription factors, IKZF1 can enhance or suppress transcription depending on its context of other variables [10, 11]. Data points close to the x or y axes correspond to genes where IKZF1 has little or no effect on expression (black text).

fortune-biomass-feedstock

Figure 1: Graph showing the expected result when gene transcription level (y axis) is plotted against the amount of transcription factor bound in the promoter (x axis). Points with a negative slope (red text) indicate the transcription factor is acting to repress transcription. A positive slope (green text) is expected if the transcription factor is acting to potentiate expression. Data points near the axes (black text) are expected if the transcription factor has little effect on transcription.

3.2 Apoptosis results

A plot of the apoptosis data yielded the expected result (Fig. 2a). The plot showed that the range examined was well-populated with points corresponding to individual apoptosis genes. For instance, of 56 apoptosis genes in the database, 43 are present in the plot. Red lines suggest the identify of genes that might be related to each other because their expression level is linearly related to IKZF1 level in the promoter. The genes between BCL2A1 and CASP8 are an example. Expression of these genes is inversely related to the level of IKZF1 bound to the promoter indicating that IKZF1 is acting repressively with the genes. For further analysis, a name was assigned to each of the four gene groups identified (Groups 1-4; see Fig. 2a). Only repressive relations are noted (red lines), but other groups including activating groups can be observed and are considered viable interpretations like the ones suggested.

fortune-biomass-feedstock

Figure 2: Graph showing the level of apoptosis gene expression plotted against the level of promoter bound IKZF1 (a) and REST (b). Note that most genes are in the dynamic range of the IKZF1 plot indicating their expression is affected by the presence of IKZF1. In contrast, most genes in the REST plot are in the range expected if the transcription factor has little effect on gene expression.

A control experiment was performed in which ChIP-seq results from the transcription factor REST were substituted for IKZF1 (Fig. 2b). REST was considered to be an appropriate control transcription factor as it has not been implicated in regulation of apoptosis genes. Results demonstrated that few apoptosis genes are found in the same range of the plot observed for IKZF1 genes. Two are CASP7 and BBC3 (see Fig. 2b). The results are interpreted to indicate that other transcription factors do not have the same influence on apoptosis gene expression as IKZF1. Together the experimental and control studies suggest the identification of several apoptosis gene groups (Fig 2a) in which promoter bound IKZF1 is related to the level of gene expression

3.3 B cell development genes

A similar analysis was carried out with genes in the B cell development database. Fig. 3a shows a plot of gene expression against IKZF1 promoter binding for 59 of the 64 B cell development genes. As in the case of apoptosis genes, groups are suggested in which gene expression is related to IKZF1 binding (Groups 1-5). Among the B cell development genes, two groups contain genes in which IKZF1 is interpreted to exert a repressive effect on transcription level (Groups 2 and 4) while in the other three groups IKZF1 is activating (Groups 1, 3 and 5). A control study was performed in which the transcription factor SMC3 was substituted for IKZF1, and the results were plotted against gene expression. The results demonstrated little evidence of genes with the same expression/transcription factor values observed with IKZF1 (Fig. 3b). As with the apoptosis genes, the results with B cell development genes are interpreted to suggest the existence of specific gene groups in which expression is related to IKZF1 binding in the promoter.

fortune-biomass-feedstock

Figure 3: Graph showing the level of B cell development gene expression plotted against the level of promoter bound IKZF1 (a) and SMC3 (b). Note that most genes are in the dynamic range of the IKZF1 plot indicating their expression is affected by the presence of IKZF1. In contrast, most genes in the SMC3 plot are in the range expected if the transcription factor has little effect on gene expression.

Transcription factor binding sites in the promoters of expression group member genes Identification of apoptosis genes related by their response to IKZF1 suggested group members might be related in other ways that would be revealing about the control of their expression. Additional information about gene group members was therefore accumulated and compared. Features examined were gene chromosome, gene length, gene function, and the presence of non-IKZF1 binding sites in the promoter. The latter measure involved rank ordering TFs according to their binding site abundance in the promoter. TFs with higher rank were those that have greater abundance. Information accumulated about apoptosis group genes was also accumulated for B cell development genes.

3.4 Apoptosis gene groups

The results with apoptosis group genes show little similarity in chromosome or gene length (Table 1). For instance, no two genes are on the same chromosome in groups 2, 3 and 4. This result was expected and suggests much of the information relevant to control of gene expression is present in the local area of the gene. Some evidence of grouping by gene function was evident (Table 1). For instance, four of the six genes in group 1 encode caspases (i.e. CASP1, CASP4, CASP6 and CASP10). Three of the 5 genes in group 3 encode proteins involved in tumor necrosis factor function.

Table 1: Properties of apoptosis genes in proposed regulatory groups

Gene group

   

Gene

     

Gene

Gene

Chr

Length (kb)

TF rank 1

TF rank 2

TF rank 3

function

1

CASP1

11

9.7

IKZF1

TAF1

ZBTB33

apoptosis effector

 

BIRC7

20

4.6

RBM39

ASH2L

SP1

inhibits apoptosis

 

CASP4

11

25.7

ASH2L

EP300

REST

apoptosis effector

 

CASP6

4

14.8

RBFOX2

AGO1

TAF1

apoptosis effector

 

CASP10

2

38.5

CHD4

KDM1A

HDAC1

apoptosis effector

 

BCL2L10

15

3.5

YY1

HDAC6

ZFX

regulates apoptosis

 

2

BCL2A1

15

10.3

IKZF1

MTA3

MLLT1

regulates apoptosis

 

TNFRSF10B

8

48.9

IKZF1

RBFOX2

HDAC1

TNF receptor

 

AIFM1

23

38.4

IKZF1

RBFOX2

RNF2

induces apoptosis

 

CASP7

10

51.7

CTBP1

DPF2

IKZF1

apoptosis effector

 

CASP8

2

53.1

IKZF1

ASH2L

RBFOX2

apoptosis effector

               

3

BCL2L2

14

9.4

IKZF1

PHF8

ASH2L

regulates apoptosis

 

BCL2L1

20

59.5

IKZF1

RBFOX2

ASH2L

regulates apoptosis

 

TNFRSF1A

3

17.9

IKZF1

MAX

REST

TNF receptor

 

TNFSF1A

12

13.3

IKZF1

RBFOX2

HDAC1

TNF family cytokine

 

TNFRSF11B

8

28.3

IKZF1

KDM4A

EZH2

TNF receptor

 

4

BAX

19

6.9

RBFOX2

HDAC1

IKZF1

regulates apoptosis

 

FAS

10

23.7

TAF1

IKZF1

EZH2

TNF receptor

 

FADD

11

4.1

RBFOX2

RNF2

MYC

regulates apoptosis

The role of TFs other than IKZF1 was probed by examining the abundance of their binding sites in apoptosis gene promoters. Binding site abundance was rank ordered beginning with data from ENCODE as described in Materials and Methods. The top three TFs are reported for each apoptosis gene (Table 1). As expected, IKZF1 had the highest abundance among rank 1 TFs with 10 of the 19 genes in the aggregate of the four apoptosis groups. A greater diversity was observed among rank 2 and 3 TFs. Among 15 rank 2 TFs, for instance, only two were represented more than once.

3.5 B cell development gene groups

As in the case of the apoptosis genes, genes in B cell development groups show little evidence of similarity in chromosome or gene length (Table 2). B cell development genes are enriched, however, in gene functions including transcription control and DNA repair. For example, 3 of 8 group 1 genes encode aspects of DNA repair (Table 2). Three of five group 5 genes are TFs.

Table 2: Properties of B cell development genes in proposed regulatory groups

Gene group

Gene

Chr

Gene Length (kb)

TF rank 1

TF rank 2

TF rank 3

Gene function

 
 

1

BACH2

6

370.4

EZH2

RBFOX2

EP300

control transcription

 
 

RUNX2

6

222.8

TAF1

FOS

TAF7

transcription factor

 
 

PTPRC

1

118.4

MTA3

ATF7

MLLT1

protein phosphatase

 
 

MRE11

11

78.6

ZFX

SMAD5

HDAC1

DNA repair

 
 

NHEJ1

2

91.5

KDM1A

EP300

HDAC2

DNA repair

 
 

GAB1

4

137.8

ASH2L

RBFOX2

RBBP5

adaptor protein

 
 

CREBBP

16

155.7

RBFOX2

DPF2

TAF1

histone acetylation

 
 

RPA2

1

23.1

RBFOX2

HNRNPLL

L3MBTL2

DNA repair

 
                 

2

TLR1

4

8.5

IKZF1

MLLT1

MEF2B

pathogen recognition

 
 

POU2F2

19

46.5

EZH2

SMC3

PCBP1

transcription factor

 
 

TRAF1

9

26.8

IKZF1

RBFOX2

ASH2L

TNF receptor subunit

 
 

JAK1

1

234.5

RBFOX2

HDAC2

RBBP5

protein tyr kinase

 
 

PRKACA

19

26.1

RBFOX2

ZBTB7A

TAF1

protein kinase A

 
                 

3

IL4

5

8.7

IKZF1

GATAD2B

DPF2

cytokine

 
 

BATF

14

24.5

IKZF1

RCOR1

ZNF217

transcription factor

 
 

TRAF1

9

26.8

IKZF1

RBFOX2

ASH2L

TNF receptor subunit

 
 

ELF1

13

87.4

ASH2L

HDAC1

CHD1

transcription factor

 
                 

4

DCLRE1C

10

49.5

RBFOX2

DPF2

IKZF1

DNA repair

 
 

CD86

3

65.8

IKZF1

DPF2

CBX5

T cell activation

 
 

TRAF1

9

26.8

IKZF1

RBFOX2

ASH2L

TNF receptor subunit

 
 

TCF3

19

43.3

RBFOX2

KDM4A

BRD4

transcription factor

 
                 

5

RUNX1

21

261.5

IKZF1

MTA3

ZBED1

transcription factor

 
 

RUNX3

1

65.5

MLLT1

IKZF1

DPF2

transcription factor

 
 

NFKB1

4

115.9

TAF1

RBFOX2

IKZF1

transcription factor

 
 

NBN

8

51.3

IKZF1

ATF2

EP300

DNA repair

 
 

RELA

11

9.3

IKZF1

ASH2L

RBFOX2

NFKB subunit

 

3.6 Comparison of TFs in apoptosis and B cell development gene promoters

The availability of rank ordered TF binding sites in apoptosis and B cell development promoters made it possible to compare binding sites in the two gene populations. While both populations were found to be enriched in IKZF1 binding sites in the promoter, one could now ask whether there was a similarity in less abundant TF binding sites as well. An analysis was performed beginning with all 57 binding sites recorded in grouped apoptosis genes (i.e., ranks 1-3), and the same binding sites (78) for B cell development genes. For each binding site present, the number was counted, expressed as a proportion of the total and the proportion plotted.

Results for the 8 most abundant TFs are shown in Fig. 4. They demonstrate a similarity between apoptosis and B cell development in 5 of the 8 TFs in the plot (i.e., IKZF1, RBFOX2, ASH2L TAF1 and EZH2). B cell development gene promoters are enriched in DPF2 and EP300 while apoptosis genes are enriched in HDAC1. The results are interpreted to emphasize the similarities in the apoptosis and B cell development gene promoters. The similarity in IKZF1 (Fig. 4) was expected as the gene populations were selected because of their response to IKZF1. The other four similarities are novel and suggest that high abundance of one of the five TFs in the promoter is correlated with high abundance of the others.

Figure 4: Plot comparing the transcription factor binding sites in apoptosis and B cell development genes. The plot includes all TF binding sites in the two gene populations, 57 for apoptosis genes and 78 for B cell development. Note the similarity observed in the proportion of IKZF1, RBFOX2, ASH2L, TAF1 and EZH2 in the two gene populations.

4. Discussion

A reliable strategy was employed here for exploring how the level of gene expression is controlled. Plot the level of gene expression against the level of promoter bound TF, and if a relationship exists, then the plot should reveal it. In the study described here, the chances of success were increased by the use of gene populations, apoptosis genes and B cell development genes, whose function was known to be influenced by IKZF1 [5-7]. The study also benefitted from the availability of information about the level of gene expression and about the extent of transcription factor binding at the promoter.

The results yielded a gratifying number of genes in which expression was related to IKZF1 promoter binding, 43 of 56 in the case of apoptosis genes and 59 of 64 for B cell development. This finding supports the view that IKZF1 has an important role in controlling the genes of the two systems examined. Also, the method proved very good in discriminating regulation due to IKZF1 from that of the control transcription factors, REST in the case of apoptosis genes and SMC3 for B cell development. A significantly greater number of responsive genes were identified with IKZF1 compared to the control TFs (see Figs. 2 and 3). The method therefore suggests itself for a role in future studies aiming to distinguish active from inactive TFs for a specific gene population.

It was expected that this study would reveal the observed abundance of IKZF1 binding sites in the promoters of apoptosis and B cell development genes. The two gene populations were chosen for study because of their dependence on Ikaros. Not expected, however, was the observed similarity in non-IKZF1 TF binding sites such as RBFOX, ASH2L and TAF1 (Fig. 4). The result suggests that similar gene regulatory elements may be found in genes in the same functional system [12]. Use of such similar control mechanisms may be an asset in integrating the elements of a functional pathway during evolutionary adaptation.

It was also unexpected to note the relative homogeneity in the most abundant TF in both the apoptosis and B cell development gene populations (10 of 19 genes in the case of apoptosis genes and 10 of 26 in B cell development). The result suggests there may be a special significance attached to the most abundant TF in the genes examined. Possibilities include specificity for co-factor binding or interaction with enhancers. The observation is an intriguing one that justifies further investigation.

Competing interests

The authors declare that there are no conflicts of interest.

References:

  1. Lambert SA, Jolma A, Campitelli LF, Das PK, Yin Y, Albu M, et al. The Human Transcription Factors. Cell 172 (2018): 650-65.
  2. Levine M, Cattoglio C, Tjian R. Looping back to leap forward: transcription enters a new era. Cell 157 (2014): 13-25.
  3. Morgan MAJ, Shilatifard A. Reevaluating the roles of histone-modifying enzymes and their associated chromatin modifications in transcriptional regulation. Nat Genet 52 (2020): 1271-81.
  4. Beagan JA, Phillips-Cremins JE. On the existence and functionality of topologically associating domains. Nat Genet 52 (2020): 8-16.
  5. Jourdan M, Reme T, Goldschmidt H, Fiol G, Pantesco V, De Vos J, et al. Gene expression of anti- and pro-apoptotic proteins in malignant and normal plasma cells. Br J Haematol 145 (2009): 45-58.
  6. Georgopoulos K, Bigby M, Wang JH, Molnar A, Wu P, Winandy S, et al. The Ikaros gene is required for the development of all lymphoid lineages. Cell 79 (1994): 143-56.
  7. Oliveira VC, Lacerda MP, Moraes BBM, Gomes CP, Maricato JT, Souza OF, et al. Deregulation of Ikaros expression in B-1 cells: New insights in the malignant transformation to chronic lymphocytic leukemia. J Leukoc Biol 106 (2019): 581-94.
  8. Sellars M, Kastner P, Chan S. Ikaros in B cell development and function. World J Biol Chem 2 (2011): 132-9.
  9. Calonga-Solis V, Amorim LM, Farias TDJ, Petzl-Erler ML, Malheiros D, Augusto DG. Variation in genes implicated in B-cell development and antibody production affects susceptibility to pemphigus. Immunology 162 (2021): 58-67.
  10. Dijon M, Bardin F, Murati A, Batoz M, Chabannon C, Tonnelle C. The role of Ikaros in human erythroid differentiation. Blood 111 (2008): 1138-46.
  11. Pulte D, Lopez RA, Baker ST, Ward M, Ritchie E, Richardson CA, et al. Ikaros increases normal apoptosis in adult erythroid cells. Am J Hematol 81 (2006): 12-8.
  12. Reiter F, Wienerroither S, Stark A. Combinatorial function of transcription factors and cofactors. Curr Opin Genet Dev 43 (2017): 73-81.

Supplementary Table 1: All apoptosis genes used in this study: 56 genes

Gene

Gene expressiona

IKZF1 ChIP-seq signalb

REST ChIP-seq signalc

AIFM1

38.2

96

4

APAF1

8.9

189

5

BAD

66.7

427

3

BAK1

35.9

42

4

BAX

80

123

3

BBC3

16.5

144

77

BCL2

23.8

173

7

BCL2A1

74.5

66

3

BCL2L1

180.4

61

4

BCL2L10

9.7

47

3

BCL2L11

17.9

66

7

BCL2L12

20.9

22

4

BCL2L13

19.7

73

3

BCL2L14

4.6

19

2

BCL2L2

158.1

70

2

BID

37.7

82

4

BIK

23.9

26

3

BIRC2

46.5

808

3

BIRC3

43.6

175

4

BIRC5

13.4

75

3

BIRC6

17.8

68

4

BIRC7

55.5

11

2

BMF

14.3

172

4

BNIP3

73

429

4

BNIP3L

122.8

56

3

BOK

70.2

5

4

CASP1

53.6

10

3

CASP10

13.9

46

5

CASP2

3

325

3

CASP3

21.2

63

9

CASP4

45.6

23

4

CASP5

5.5

5

3

CASP6

25.8

38

3

CASP7

24.3

110

33

CASP8

14.9

114

3

CASP9

28.3

13

7

CFLAR

68

277

4

CYCS

93.9

94

4

DIABLO

18.1

22

185

ENDOG

15.1

75

5

FADD

9.4

133

3

FAS

42.3

127

4

FASLG

5.8

41

2

HRK

4.6

23

248

HTRA2

44

254

3

NAIP

1

2

3

PMAIP1

25.3

184

13

TNF

9

95

4

TNFRSF10A

12.6

172

3

TNFRSF10B

68

78

4

TNFRSF10C

2.6

51

2

TNFRSF10D

17.7

101

3

TNFRSF11B

135.6

109

3

TNFRSF1A

157.1

93

3

TNFSF10

144.6

75

3

XIAP

15.7

34

5

a TPM; NIH Genotype-Tissue Expression Project, Version 8, August 2019.

b ChIP-sep signal p-value; GM12878 cells; downloaded from IGV, experiment

ENCSR874AFU, accession ENCFF678BHT.

c ChIP-seq signal p-value; GM12878 cells; downloaded from IGV, experiment

ENCSR000BGF, accession ENCFF898SKK.

Supplementary Table 2: All B cell development genes used in this study: 64 genes

Gene

Gene expressiona

IKZF1 ChIP-seq signalb

SMC3 ChIP-seq signalc

BACH2

3.7

36

40

BATF

6.6

140

4

CD40

42.7

533

6

CD86

11.9

187

46

CHEK2

8.8

75

5

CREBBP

38.7

123

11

CUX1

30.6

191

68

DCLRE1C

5.1

207

10

E2F3

12.1

83

18

E2F6

9.8

259

32

ELF1

54.8

204

20

ERCC1

51.7

639

49

ETS1

93.5

168

11

GAB1

24.1

92

33

IKBKB

42.9

508

46

IKZF2

10.7

84

43

IL15

4.9

99

25

IL4

0.5

132

5

INPP5D

27.2

332

8

JAK1

107.3

169

21

JAK3

16

93

2

MAP3K14

19.3

108

85

MDC1

39.6

106

3

MRE11

16.4

67

31

NBN

37.1

277

56

NFKB1

29

276

8

NHEJ1

18.7

30

50

PARP1

59.2

112

6

PIK3R1

59.7

140

41

PMS2

14

108

28

POU2F1

11

1095

48

POU2F2

6.4

172

93

PRKACA

108.8

130

23

PRKDC

25.9

116

4

PTPRC

9.9

52

16

RAD50

16.8

129

17

RELA

96.2

370

3

RELB

29.6

104

18

RPA1

34.9

88

29

RPA2

41.7

138

5

RPA3

20.5

118

48

RUNX1

8.5

247

32

RUNX2

6.3

46

21

RUNX3

6.3

242

18

SMAD3

30.1

267

18

SMAD7

47.7

81

5

SP1

45.3

26

21

SPI1

23.3

57

55

SUPT5H

105.4

135

20

SWAP70

42.8

88

33

TCF3

30.5

150

31

TGFB1

127.3

153

26

TGFBR1

21.6

142

65

TLR1

6.4

166

11

TLR5

6.3

107

50

TLR6

2.6

112

39

TLR9

3.1

60

71

TNFRSF8

3.9

526

60

TNFSF13

45.7

76

21

TNFSF13B

6

110

5

TRAF1

23.8

165

3

TRAF2

15.5

123

11

TRAF3

15.1

217

8

XRCC4

52

77

4

a TPM; NIH Genotype-Tissue Expression Project, Version 8, August 2019.

b ChIP-seq signal p-value; GM12878 cells; downloaded from IGV, experiment

ENCSR874AFU, accession ENCFF678BHT.

c ChIP-seq signal p-value; GM12878 cells; downloaded from IGV, experiment

ENCSR000DZP, accession ENCFF179PKD.

© 2016-2024, Copyrights Fortune Journals. All Rights Reserved