Epigenetic Regulations, Motif and Pathway Identification of Myelofibrosis Chip Sequences

doi:10.21203/rs.3.rs-1429895/v1

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Epigenetic Regulations, Motif and Pathway Identification of Myelofibrosis Chip Sequences

https://doi.org/10.21203/rs.3.rs-1429895/v1

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Next generation sequence technique that is Chromatin immunoprecipitation (ChIP) on the ChIP–Seq of Myelofibrosis. Myelofibrosis is a type of blood cancer which is also considered as a form of chronic leukemia. It is two types primary myelofibrosis and secondary myelofibrosis its leads to complication of an autoimmune disease, additional disease features include hepatosplenomegaly, extramedullary hematopoiesis (EMH) etc. In this work, its chip-Seq is retrieved from SRA database, the PCR duplicates are removed and finally the genome enriched region is identified using the MAC2 call peak tool. This annotated peak table was used to identify the motifs present in the chip-Seq of Myelofibrosis. Peak table was annotated to identify the genes and the corresponding pathways was identified from the KEGG pathway.

Chromatin immunoprecipitation (ChIP)

Myelofibrosis

Next Generation sequencing

PCR duplicates

MACS2 Callpeak

Motif

KEGG pathway

ChIP sequencing (ChIP-Seq) is a powerful method for identifying genome-wide DNA binding sites for transcription factors and other proteins. “Next generation” genome sequences technology can provide one – two orders of magnitude increase in the amount of sequence that will be cost effective and new technology ChIP – Seq directly provide whole genome of mammalian Protein – DNA interactions [1, 2]. It starts with crosslinking of DNA-protein complexes and then fragmented, they are treated with an exonuclease to cut unbound oligonucleotides. Protein-specific antibodies are used to immunoprecipitation the DNA-protein complex. DNA is extracted and sequenced, giving high-resolution sequences of the protein-binding sites. ChIP Seq has many advantages such as, it captures DNA targets for transcription factors or histone modifications across the entire genome of any organism and also it defines transcription factor binding sites [3]. ChIP–Seq could be the way for genome-wide profiling of DNA-binding proteins, or nucleosomes and attributable to the tremendous progress in next-generation sequencing technology [4].

Myelofibrosis is a type of blood cancer which is also considered as a form of chronic leukemia[5, 6]. In this disease, the bone marrow is replaced by fibrous scar tissue. It is two types primary myelofibrosis (occurs on its own) and secondary myelofibrosis (occurs as the result of separate disease) its leads to complication of an autoimmune disease. The bone marrow develops three types of cells white blood cells, red blood cells and platelets [7]. When mutation occurs in a single cell of DNA, it passes on to new cells and leading to defective of cell divides.Characteristic of myelofibrosis like megakaryocytes (over production of giant cells) [8], decreases production of red blood cells – may occur anemia and thrombocytopenia (deficiency of platelet production). Overall world in about 12% cases, primary myelofibrosis will progress to acute myeloid leukemia fastly growing of blood cancer [9, 10].

The primary aim of the current research is to provide a detailed overview of epigenetic regulations of Myelofibrosis and throw a light on the pathways involved [11]. Galaxy tutorial by Lauren Mills, Analyzing ChIP-Seq Data in Galaxy is used to analyze Mus musculus Myelofibrosis fastq sequences having SRA accession number DRR311742 and DRR311743 were retrived from SRA database. We mapped the reads using Bowtie2 [12]. Using Collect Alignment Summary Metrics tool we take the summary of our alignment. Next using RmDup we remove PCR duplicates [13] and Collect Alignment Summary Metrics tool was re-run. Finally, using MACS2 Callpeak we identify peaks from alignment results [14, 15]. Using Peak calling we identify areas in our genome that have been enriched with our aligned reads, these areas are those where protein interacts with DNA.

Next, we annotate our peaks table to take top 100 most significant peaks and identify the genes overlapping with these peaks and Genes were selected model using Swiss prot with Ramachandran plot [16, 17, 18]was done. We identify the motifs using SeqPos motif analysis tool. Biological sequence motifs are short conserved sequence pattern associated with distinct functions that usually represents important structural or functional features [19]. Finally, the pathways of these genes were identified from KEGG pathways [20, 21].

3.1 FASTQC quality reports

FASTQC quality reports was given quality control to Mus musculus chip-sequences with SRA accession number DRR311742 and DRR311743. FASTQC results were given in Fig. 1(a) for DRR311742 and in Fig. 1(b) for DRR311743.

3.2 Bowtie2

Mus musculus chip-sequences with SRA accession number DRR311742 and DRR311743 were mapped using Mus musculus ref Seq using Bowtie2 (Fig. 2).

3.3 Collect alignment before:

We use the tool Collect Alignment Summary Metrics tool take the summary of our mapping done above. Table 1 contains the alignment summary for DRR311742 and Table 2 for DRR311743.

Next, we removed the PCR duplicates using RmDup. Table 3 & 4 contains the Alignment summary for DRR311742 and DRR311743 and removing PCR duplicates.

3.4 MACS2 Callpeak :

As per the alignment summary (Table 3, 4), we see that the reads are less post RmDup which implies that the duplicate reads are removed. Next, we use MAC2 call peak tool to identify areas in the genome that are enriched with the aligned reads. Model-based Analysis of ChIP-Seq (MACS) is a commonly used tool for identifying transcription factor binding sites. The algorithm confines the influence of genome complexity to evaluate the significance of enriched ChIP regions. This tool improves the spatial resolution of binding sites by combining the information of both sequencing tag position and orientation. Here, MACS is used along with a control sample (DRR311742) which increases specificity of the peak calls (Fig. 3). MACS2 models the distance between the paired forward and reverse strand peaks and uses 1000 enriched regions to model the distance between the forward and reverse strand peaks (Fig. 4).

3.5 Motif analysis:

We identify the motifs present in our Myelofibrosis genome ChIP-Seq. We used SeqPos motif analysis tool. The file, top 100 most significant peaks in bed format was selected for motif identification (Table 5).

3.6 SWISS PORT

Genes were selected from the Myelofibrosis genome, model using Swiss prot with Ramachandran plot.

The receptor model and corresponding Ramachandran plot results are given in Fig.5. Accession number used for modeling is given in table 6.

Finally, from the annotation results our peaks table by taking top 100 most significant peaks, we identify the genes overlapping with these peaks which are given in Table 7. The pathways of these genes were identified from KEGG pathways names in Table: 7

4.1 Tight Junctions (TJs) Pathway information is provided in Fig. 6.

4.2 RAP1 signaling Pathway information is provided in Fig. 7.

4.3 cGMP - PKG signaling Pathway information is provided in Fig. 8.

4.4 Thermogenesis Pathway information is provided in Fig. 9.

4.5 Tight Junction (TJs) Pathway information is provided in Fig. 10.

A high-quality immunoprecipitation ChIP peak is detected in our results which imply high concentration of DNA binding proteins. Further, motifs were detected from the ChIP peak. Further, top DNA binding protein genes were identified and corresponding pathways was identified from KEGG pathway. Tight junctions (TJs) pathway, RAP1 signaling pathway, cGMP - PKG signaling pathway, Thermogenesis pathway and Tight junctions (TJs).

1. Ethical Approval and Consent to participate:

Not applicable

2. Human Ethics and Animal Ethics statements:

Not applicable

3. Consent for publication:

Not applicable

4. Availability of supporting data:

a. SRA sequences

SRA ID	Source
DRR311742	https://www.ncbi.nlm.nih.gov/sra/?term=DRR311742
DRR311743	https://www.ncbi.nlm.nih.gov/sra/?term=DRR311743

5. Competing interests:

None

6. Funding:

No funding

7. Authors' contributions:

All authors have equal contribution

8. Acknowledgements:

We acknowledge our institution for providing us lab

9. Authors' information

Preenon Bagchi1,2, Sravani Sane1 and Shylesh Murthy IA2

1Padmashree Institute of Management and Sciences, Bengaluru, India.

2Vasishth Academy of Advanced Studies and Research, Bengaluru, India. Bengaluru, India.

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Tables are available in the Supplementary Files section.

No competing interests reported.

t1.png
Table: 1 Alignment summary for DRR311742
t2.png
Table: 2 Alignment summary for DRR311743
t3.png

Table: 3 Alignment summary for DRR311742 post RmDup
t4.png
Table: 4 Alignment summary for DRR311743 post RmDup
t5.png
Table – 5 Motif Analysis:
t6.png
Table: 6 Genes are involved in Myelofibrosis with Accession number:
t7.png
Table: 7 Identified Genes and pathways

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Version 1

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Epigenetic Regulations, Motif and Pathway Identification of Myelofibrosis Chip Sequences

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials And Methods

3. Results And Discussion

3.1 FASTQC quality reports

3.2 Bowtie2

3.3 Collect alignment before:

3.4 MACS2 Callpeak :

3.5 Motif analysis:

3.6 SWISS PORT

4. Kegg Pathway Analysis:

5. Conclusion

Declarations

1. Ethical Approval and Consent to participate:

2. Human Ethics and Animal Ethics statements:

3. Consent for publication:

4. Availability of supporting data:

5. Competing interests:

6. Funding:

7. Authors' contributions:

8. Acknowledgements:

9. Authors' information

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1