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Decoding the Genomic Evolution of Pathogenic Eukaryotes Through Integrated Multi-Omics...
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Decoding the Genomic Evolution of Pathogenic Eukaryotes Through Integrated Multi-Omics Approaches

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Microbial Genetics and Genomics".

Deadline for manuscript submissions: 20 August 2024 | Viewed by 599

Special Issue Editors

Dr. Dinah Qutob

E-Mail Website
Guest Editor
Department of Biological Sciences, Kent State University at Stark, 6000 Frank Ave NW, North Canton, OH 44720, USA
Interests: microbiology; molecular biology, genetics; epigenetics, pathology; cell biology
Prof. Dr. Adam C. Underwood

E-Mail Website
Guest Editor
Division of Mathematics and Science, Walsh University, North Canton, OH 44720, USA
Interests: molecular biology; cell physiology; genome evolution; transcription factors

Special Issue Information

Dear Colleagues,

Global initiatives, such as The Earth BioGenome Project (EBP), are working to expand and accelerate the development of genomic resources encompassing all extant eukaryotes. Advances in high-throughput sequencing technologies, coupled with robust computational and machine learning approaches, are revolutionizing our understanding of molecular mechanisms that drive the diversification of life.

At the core of EBP's mission lies a key objective: enhancing our understanding of eukaryotic pathogen biology and mechanisms that underlie acquisition of virulence. Comparative genomics allows us to pinpoint critical moments and events in evolutionary trajectories revealing the adaptive strategies used by eukaryotic pathogens in response to interactions with host organisms and the environment.

In this Special Issue, we invite scholarly contributions, including review articles and original research, that focus on exploring the evolution of eukaryotic pathogens. Emphasis should be placed on harnessing the potential of multi-omic technologies and innovative tools capable of capturing changes in the genomic architecture, as well as precisely tracking fluctuations in transcriptomic, proteomic, epigenomic, and metabolomic perturbations. Undoubtedly, the insights derived from these technologies will significantly enrich our understanding of infectious diseases and open new avenues for solutions in public health and agriculture.

Dr. Dinah Qutob
Prof. Dr. Adam C. Underwood
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • coevolution
  • phylogenomics
  • effectors/secretome
  • epigenetics
  • non-vertical transmission
  • evolutionary drivers
  • host–pathogen dynamics
  • drug resistance
  • antigenic variation

Published Papers (1 paper)

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Research

30 pages, 17809 KiB  
Article
Enhancing Gene Co-expression Network Inference for the Malaria Parasite Plasmodium falciparum
by Qi Li, Katrina A. Button-Simons, Mackenzie A. C. Sievert, Elias Chahoud, Gabriel F. Foster, Kaitlynn Meis, Michael T. Ferdig and Tijana Milenković
Genes 2024, 15(6), 685; https://doi.org/10.3390/genes15060685 - 25 May 2024
Viewed by 312
Abstract
Background: Malaria results in more than 550,000 deaths each year due to drug resistance in the most lethal Plasmodium (P.) species P. falciparum. A full P. falciparum genome was published in 2002, yet 44.6% of its genes have unknown functions. [...] Read more.
Background: Malaria results in more than 550,000 deaths each year due to drug resistance in the most lethal Plasmodium (P.) species P. falciparum. A full P. falciparum genome was published in 2002, yet 44.6% of its genes have unknown functions. Improving the functional annotation of genes is important for identifying drug targets and understanding the evolution of drug resistance. Results: Genes function by interacting with one another. So, analyzing gene co-expression networks can enhance functional annotations and prioritize genes for wet lab validation. Earlier efforts to build gene co-expression networks in P. falciparum have been limited to a single network inference method or gaining biological understanding for only a single gene and its interacting partners. Here, we explore multiple inference methods and aim to systematically predict functional annotations for all P. falciparum genes. We evaluate each inferred network based on how well it predicts existing gene–Gene Ontology (GO) term annotations using network clustering and leave-one-out crossvalidation. We assess overlaps of the different networks’ edges (gene co-expression relationships), as well as predicted functional knowledge. The networks’ edges are overall complementary: 47–85% of all edges are unique to each network. In terms of the accuracy of predicting gene functional annotations, all networks yielded relatively high precision (as high as 87% for the network inferred using mutual information), but the highest recall reached was below 15%. All networks having low recall means that none of them capture a large amount of all existing gene–GO term annotations. In fact, their annotation predictions are highly complementary, with the largest pairwise overlap of only 27%. We provide ranked lists of inferred gene–gene interactions and predicted gene–GO term annotations for future use and wet lab validation by the malaria community. Conclusions: The different networks seem to capture different aspects of the P. falciparum biology in terms of both inferred interactions and predicted gene functional annotations. Thus, relying on a single network inference method should be avoided when possible. Supplementary data: Attached. Full article
(This article belongs to the Special Issue Decoding the Genomic Evolution of Pathogenic Eukaryotes Through Integrated Multi-Omics Approaches)
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