RNA sequencing identified novel target genes for <i>Adansonia digitata</i> in breast and colon cancer cells

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Abstract

Adansonia digitata exhibits numerous beneficial effects. In the current study, we investigated the anti-cancer effects of four different extracts of A. digitata (polar and non-polar extracts of fruit powder and fibers) on the proliferation of human colon cancer (HCT116), human breast cancer (MCF-7), and human ovarian cancer (OVCAR-3 and OVCAR-4) cell lines. RNA sequencing revealed the influence of the effective A. digitata fraction on the gene expression profiles of responsive cells. The results indicated that only the polar extract of the A. digitata fibers exhibited anti-proliferative activities against HCT116 and MCF-7 cells, but not ovarian cancer cells. Moreover, the polar extract of the fibers resulted in the modulation of the expression of multiple genes in HCT116 and MCF-7 cells. We propose that casein kinase 2 alpha 3 ( CSNK2A3) is a novel casein kinase 2 ( CSNK2) isoform in HCT116 cells and report, for the first time, the potential involvement of FYVE, RhoGEF, and PH domain-containing 3 ( FGD3) in colon cancer. Together, these findings provide evidence supporting the anti-cancer potential of the polar extract of A. digitata fibers in this experimental model of breast and colon cancers.

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Fast gapped-read alignment with Bowtie 2.

Ben Langmead, Steven L Salzberg (2012)

As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.

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HISAT: a fast spliced aligner with low memory requirements.

Daehwan Kim, Ben Langmead, Steven L Salzberg (2019)

HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.

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Is Open Access

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

Bo Li, Colin Dewey (2011)

Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.

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Journal

Journal ID (nlm-ta): Sci Prog

Journal ID (iso-abbrev): Sci Prog

Journal ID (publisher-id): SCI

Journal ID (hwp): spsci

Title: Science Progress

Publisher: SAGE Publications (Sage UK: London, England )

ISSN (Print): 0036-8504

ISSN (Electronic): 2047-7163

Publication date (Electronic): 12 July 2021

Publication date Collection: Jul-Sep 2021

Volume: 104

Issue: 3

Electronic Location Identifier: 00368504211032084

Affiliations

[1 ]Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

[2 ]Department of Clinical Nutrition, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

[3 ]School of Computing, Engineering & Physical Sciences, University of the West of Scotland, Paisley, UK

[4 ]Marine Biodiscovery Centre, School of Natural & Computing Sciences, University of Aberdeen, Aberdeen, UK

Author notes

[*]Khaldoon Alsamman, Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, Rakkah, Dammam 31441, Saudi Arabia. Email: kmalsamman@ 123456iau.edu.sa

[*]Omar S. EL-Masry, Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, Rakkah, Dammam 31441, Saudi Arabia. Email: oselmasry@ 123456iau.edu.sa

Author information

Omar S. El-Masry https://orcid.org/0000-0002-0226-2923

Article

Publisher ID: 10.1177_00368504211032084

DOI: 10.1177/00368504211032084

PMC ID: 10450698

PubMed ID: 34251294

SO-VID: d6926768-250a-4e77-bef4-dd1d5843c538

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This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

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cover-date July-September 2021

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Keywords: adansonia digitata,colon cancer,rna sequencing,lc-hrms,cell proliferation,hct-116,mcf-7

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Keywords: adansonia digitata, colon cancer, rna sequencing, lc-hrms, cell proliferation, hct-116, mcf-7

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RNA sequencing identified novel target genes for Adansonia digitata in breast and colon cancer cells

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Most cited references 65

Fast gapped-read alignment with Bowtie 2.

HISAT: a fast spliced aligner with low memory requirements.

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

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