Identifying Cattle Breed-Specific Partner Choice of Transcription Factors during the African Trypanosomiasis Disease Progression Using Bioinformatics Analysis
Abstract
:1. Introduction
1.1. Conserved Functions of Transcription Factors across Mammals
1.2. Transcription Factors, Potential Targets for Vaccine Development
2. Materials and Methods
2.1. Microarray Dataset
2.2. Identification of Monotonically Expressed Genes
2.3. Identification of Transcription Factor Cooperation
- Promoter sequences: The promoter sequence (covering the −500 to 100 bp regions relative to a transcription start site) of each significant monotonically expressed gene (MEG) is extracted from the UCSC genome browser [43].
- Creation of the PWM library and TFBS detection: For the detection of TFBSs in the promoters of MEGs, we obtained PWMs from the TRANSFAC database (release 2018.1) [44].Until now, based on the functional analysis and comprehensive performance evaluation strategies, different studies have shown that the computational TFBS detection methods using PWMs are well established and highly applied. However, their prediction performance is prone to high rates of false positive predictions [30,45,46]. In order to eliminate the false predictions to some extent in our analysis, we manually created a specific PWM library following our previous study [47]. For this purpose, we first obtained all available cattle TFs from AnimalTFDB 2.0 [48] and identified the expression values (TPM values) of their corresponding TF genes in the gene expression dataset, under study. Second, the TFs were excluded from further analysis if the TPM values of their TF genes were zero. After that, the corresponding PWMs of the remaining TFs were obtained from the TRANSFAC database [44]. Finally, based on the Pearson correlation between these PWMs, we applied hierarchical clustering and used only the PWMs with the highest information content from each cluster as representative to create our final non-redundant vertebrate PWM library (see Supplementary Table S1).In addition, we applied the Match program [49] using these PWMs and their TRANSFAC specific profile parameter minSum to minimize the sum of false positive and false negative rate for the detection of putative TFBSs in promoter sequences.
- Pre-defined distances: For the identification of cooperative TFs based on the co-occurrence of their TFBSs, the PC-TraFF algorithm requires pre-defined minimum and maximum distance thresholds. In this study, the recommended distance values of and were used for the minimum and maximum distance, respectively.
3. Results and Discussion
3.1. Data Processing
3.2. Identification of MEGs
3.3. Identification of Cooperative TFs
3.4. Cooperative TFs in Liver Tissue
3.5. Cooperative TFs in Spleen Tissue
3.6. Cooperative TFs in Lymph Node Tissue
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Boran | N’Dama | |||
---|---|---|---|---|
Ascending | Descending | Ascending | Descending | |
Liver | 741 | 308 | 757 | 124 |
Spleen | 669 | 126 | 13 | 139 |
Lymph node | 87 | 5 | 119 | 114 |
Cooperative Transcription Factor Pairs | |||
---|---|---|---|
Breed | Liver | Spleen | Lymph Node |
Boran | PPARG–RFX5 HAND1E47–THAP1 HAND1E47–TFAP2A THAP1–E2F1 HOXA6–BATF TTF1–RFX5 DBP–PAX8 E2F1–PPARA E2F1–E2F1 | EMX2–BATF FOXM1–JUND HOXA4–HOXB7 PPARA–E2F1 E2F1–E2F1 E2F1–TFAP2A | JUND–BATF HOXA6–MAFF SIX3–MAFF HAND1E47–E2F1 HOXA4–BATF FOXM1–HNF4G DBP–FOXM1 SIX3–RFX5 HAND1E47–FOSL1 FOXA1–MAFF HOXA4–MAFF SIX3–BATF SIX3–HNF4G HOXA4–SIX3 THAP1–PPARA E2F1–PPARA EMX2–BATF THAP1–THAP1 |
N’Dama | PPARG–SIX5 FOXM1–DLX3 HAND1E47–E2F1 HAND1E47–USF2 SIX5–PPARA SIX3–THAP1 THAP1–THAP1 PPARA–DBP PPARA–TFAP2A E2F1–E2F1 | HMBOX–BATF HOXB7–BATF SIX5–THAP1 HOXA4–HMBOX1 HAND1E47–DBP HOXA6–BATF SIX5–E2F1 HMBOX1–RFX5 E2F1–TFAP2A | SMAD4–E2F1 HOXA6–BATF HOXA4–HOXB7 TCF4–HNF4G E2F1–TFAP2A THAP1–HNF4G DBP–TFAP2A HAND1E47–RFX5 THAP1–PPARA E2F1–PPARA EMX2–BATF HOXA4–SIX3 THAP1–THAP1 |
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Rajavel, A.; Heinrich, F.; Schmitt, A.O.; Gültas, M. Identifying Cattle Breed-Specific Partner Choice of Transcription Factors during the African Trypanosomiasis Disease Progression Using Bioinformatics Analysis. Vaccines 2020, 8, 246. https://doi.org/10.3390/vaccines8020246
Rajavel A, Heinrich F, Schmitt AO, Gültas M. Identifying Cattle Breed-Specific Partner Choice of Transcription Factors during the African Trypanosomiasis Disease Progression Using Bioinformatics Analysis. Vaccines. 2020; 8(2):246. https://doi.org/10.3390/vaccines8020246
Chicago/Turabian StyleRajavel, Abirami, Felix Heinrich, Armin Otto Schmitt, and Mehmet Gültas. 2020. "Identifying Cattle Breed-Specific Partner Choice of Transcription Factors during the African Trypanosomiasis Disease Progression Using Bioinformatics Analysis" Vaccines 8, no. 2: 246. https://doi.org/10.3390/vaccines8020246