Umeå University's logo

umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
ProkSeq for complete analysis of RNA-Seq data from prokaryotes
Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine). Umeå University, Faculty of Medicine, Molecular Infection Medicine Sweden (MIMS). Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR). (Maria Fallman)
Swedish University of Agricultural Sciences, Umeå, Sweden.ORCID iD: 0000-0002-3053-0796
Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR). Umeå University, Faculty of Medicine, Molecular Infection Medicine Sweden (MIMS). Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine).ORCID iD: 0000-0001-6874-6384
2021 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 37, no 1, p. 126-128Article in journal (Refereed) Published
Abstract [en]

Summary: Since its introduction, RNA-Seq technology has been used extensively in studies of pathogenic bacteria to identify and quantify differences in gene expression across multiple samples from bacteria exposed to different conditions. With some exceptions, tools for studying gene expression, determination of differential gene expression, downstream pathway analysis and normalization of data collected in extreme biological conditions is still lacking. Here, we describe ProkSeq, a user-friendly, fully automated RNA-Seq data analysis pipeline designed for prokaryotes. ProkSeq provides a wide variety of options for analysing differential expression, normalizing expression data and visualizing data and results.

Availability and implementation: ProkSeq is implemented in Python and is published under the MIT source license. The pipeline is available as a Docker container https://hub.docker.com/repository/docker/snandids/prokseq-v2.0, or can be used through Anaconda: https://anaconda.org/snandiDS/prokseq. The code is available on Github: https://github.com/snandiDS/prokseq and a detailed user documentation, including a manual and tutorial can be found at https://prokseqV20.readthedocs.io.

Place, publisher, year, edition, pages
UK: Oxford University Press, 2021. Vol. 37, no 1, p. 126-128
National Category
Microbiology
Research subject
biology
Identifiers
URN: urn:nbn:se:umu:diva-178930DOI: 10.1093/bioinformatics/btaa1063ISI: 000649437800019PubMedID: 33367516Scopus ID: 2-s2.0-85134379041OAI: oai:DiVA.org:umu-178930DiVA, id: diva2:1520695
Funder
Knut and Alice Wallenberg Foundation, 2016.0063Swedish Research Council, 2018-02855Available from: 2021-01-21 Created: 2021-01-21 Last updated: 2024-07-02Bibliographically approved
In thesis
1. Molecular mechanisms of Yersinia pseudotuberculosis for adaptation and establishment of infection in host tissue
Open this publication in new window or tab >>Molecular mechanisms of Yersinia pseudotuberculosis for adaptation and establishment of infection in host tissue
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Bacterial pathogens can evade the host’s immune defence to adapt and establish an infection within the host. Some even slip into a quiescent state to establish themselves without acutely harming the host. Phylogenetically unrelated bacteria can share similar strategies for the establishment of infection and for persistence. Our lab previously showed that Yersinia pseudotuberculosis underwent a dramatic reprogramming from a virulent phenotype expressing virulence genes, including T3SS and Yop effectors during early infection, to an adapted phenotype capable of persisting in tissue. The overall aim of my PhD study was to dissect the mechanisms behind bacterial adaptation and maintenance of infection within host tissue using Y. pseudotuberculosis as a model pathogen. The ultimate goal is to identify key players of critical importance for the ability of the bacterium to maintain and establish infection in host tissue. In my studies, I mainly focused on bacterial biofilm and the role of the alternative sigma factor RpoN. Much of my studies involve RNA-Seq analyses, encouraging me to develop a convenient, time-efficient, and all-purpose RNA-Seq data analysis package especially designed for prokaryotic organisms. The package is available online as a free tool and can be used by any biologist with minimal computational knowledge. We systematically examined biofilm formation of Y. pseudotuberculosis under different stress conditions and found that biofilm development involved a series of adaptive responses against various stressors, including bile, pH, amino acid deprivation, and temperature and oxygen-level changes. Analyses of transcription profiles of bacteria forming biofilm in different conditions revealed a set of core genes that were similarly regulated in biofilm bacteria independently of induced environment. The transcriptional regulator RpoN, commonly known as sigma 54, was found to be important for biofilm formation, and a ∆rpoN mutant strain was severely attenuated in virulence. To understand the regulatory mechanisms involved, we investigated gene expressions in wild-type (WT) and the isogenic ∆rpoN mutant strain and also chromatin immunoprecipitation followed by sequencing. We have identified RpoN binding sites in the Y. pseudotuberculosis genome and revealed a complex regulation by RpoN involving both activation and repression effects. We also investigated the role of RpoN in regulation of the Type III secretion system (T3SS) and found that RpoN was required for a functional T3SS, which is essential for bacterial virulence properties in host tissue. Our work indicates that Yersinia modulates itself in multiple ways to create niches favourable to growth and survival in the host environment. We have identified some key regulators and genes that will be explored further for their potential as novel targets for the development of new antibiotics.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2021. p. 80
Series
Umeå University medical dissertations, ISSN 0346-6612
Keywords
RpoN, T3SS, RNA-Seq, Biofilm, Transcription, Yersinia, Data analysis, ChIP-Seq
National Category
Biological Sciences
Research subject
biology
Identifiers
urn:nbn:se:umu:diva-181852 (URN)978-91-7855-488-1 (ISBN)
Public defence
2021-04-23, Hall Betula, NUS, Building 6M, Umeå, 09:00 (English)
Opponent
Supervisors
Funder
Knut and Alice Wallenberg Foundation, 2016.0063Swedish Research Council, 2018-02855
Note

Uppgift om ISBN för tryckt format saknas i publikationen

Uppgift om serienummer saknas i publikationen 

Available from: 2021-04-01 Created: 2021-03-28 Last updated: 2024-07-02Bibliographically approved

Open Access in DiVA

fulltext(223 kB)209 downloads
File information
File name FULLTEXT02.pdfFile size 223 kBChecksum SHA-512
175145c9ca4d4caecaa37d22bdc4718ef3ea79cf69d26aae308ef7b0fc3a476f2bef3fd172543ca06702a806c3b86ed90bcaba4b51de41ae1d1550c484a3fa40
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMedScopus

Authority records

Mahmud, A. K. M. FirojDelhomme, NicolasFällman, Maria

Search in DiVA

By author/editor
Mahmud, A. K. M. FirojDelhomme, NicolasFällman, Maria
By organisation
Department of Molecular Biology (Faculty of Medicine)Molecular Infection Medicine Sweden (MIMS)Umeå Centre for Microbial Research (UCMR)
In the same journal
Bioinformatics
Microbiology

Search outside of DiVA

GoogleGoogle Scholar
Total: 236 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 554 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf