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O'Meara MJ, Rapala JR, Nichols CB, Alexandre AC, Billmyre RB, Steenwyk JL, Alspaugh JA, O'Meara TR. CryptoCEN: A Co-Expression Network for Cryptococcus neoformans reveals novel proteins involved in DNA damage repair. PLoS Genet 2024; 20:e1011158. [PMID: 38359090 PMCID: PMC10901339 DOI: 10.1371/journal.pgen.1011158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 02/28/2024] [Accepted: 01/30/2024] [Indexed: 02/17/2024] Open
Abstract
Elucidating gene function is a major goal in biology, especially among non-model organisms. However, doing so is complicated by the fact that molecular conservation does not always mirror functional conservation, and that complex relationships among genes are responsible for encoding pathways and higher-order biological processes. Co-expression, a promising approach for predicting gene function, relies on the general principal that genes with similar expression patterns across multiple conditions will likely be involved in the same biological process. For Cryptococcus neoformans, a prevalent human fungal pathogen greatly diverged from model yeasts, approximately 60% of the predicted genes in the genome lack functional annotations. Here, we leveraged a large amount of publicly available transcriptomic data to generate a C. neoformans Co-Expression Network (CryptoCEN), successfully recapitulating known protein networks, predicting gene function, and enabling insights into the principles influencing co-expression. With 100% predictive accuracy, we used CryptoCEN to identify 13 new DNA damage response genes, underscoring the utility of guilt-by-association for determining gene function. Overall, co-expression is a powerful tool for uncovering gene function, and decreases the experimental tests needed to identify functions for currently under-annotated genes.
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Affiliation(s)
- Matthew J O'Meara
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Jackson R Rapala
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Connie B Nichols
- Departments of Medicine and Molecular Genetics/Microbiology; and Cell Biology, Duke University School of Medicine, Durham, North Carolina, United States of America
| | - A Christina Alexandre
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - R Blake Billmyre
- Departments of Pharmaceutical and Biomedical Sciences/Infectious Disease, College of Pharmacy/College of Veterinary Medicine, University of Georgia, Athens, Georgia, United States of America
| | - Jacob L Steenwyk
- Howard Hughes Medical Institute and the Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, United States of America
| | - J Andrew Alspaugh
- Departments of Medicine and Molecular Genetics/Microbiology; and Cell Biology, Duke University School of Medicine, Durham, North Carolina, United States of America
| | - Teresa R O'Meara
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, United States of America
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Wang J, Yang L, Cheng A, Tham CY, Tan W, Darmawan J, de Sessions PF, Wan Y. Direct RNA sequencing coupled with adaptive sampling enriches RNAs of interest in the transcriptome. Nat Commun 2024; 15:481. [PMID: 38212309 PMCID: PMC10784512 DOI: 10.1038/s41467-023-44656-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 12/22/2023] [Indexed: 01/13/2024] Open
Abstract
Abundant cellular transcripts occupy most of the sequencing reads in the transcriptome, making it challenging to assay for low-abundant transcripts. Here, we utilize the adaptive sampling function of Oxford Nanopore sequencing to selectively deplete and enrich RNAs of interest without biochemical manipulation before sequencing. Adaptive sampling performed on a pool of in vitro transcribed RNAs resulted in a net increase of 22-30% in the proportion of transcripts of interest in the population. Enriching and depleting different proportions of the Candida albicans transcriptome also resulted in a 11-13.5% increase in the number of reads on target transcripts, with longer and more abundant transcripts being more efficiently depleted. Depleting all currently annotated Candida albicans transcripts did not result in an absolute enrichment of remaining transcripts, although we identified 26 previously unknown transcripts and isoforms, 17 of which are antisense to existing transcripts. Further improvements in the adaptive sampling of RNAs will allow the technology to be widely applied to study RNAs of interest in diverse transcriptomes.
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Affiliation(s)
- Jiaxu Wang
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, 138672, Singapore
| | - Lin Yang
- Oxford Nanopore Technologies, Singapore, 138667, Singapore
| | - Anthony Cheng
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, 138672, Singapore
| | | | - Wenting Tan
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, 138672, Singapore
| | - Jefferson Darmawan
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, 138672, Singapore
| | | | - Yue Wan
- Stem Cell and Regenerative Biology, Genome Institute of Singapore, A*STAR, Singapore, 138672, Singapore.
- Department of Biochemistry, National University of Singapore, Singapore, 117596, Singapore.
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O’Meara MJ, Rapala JR, Nichols CB, Alexandre C, Billmyre RB, Steenwyk JL, Alspaugh JA, O’Meara TR. CryptoCEN: A Co-Expression Network for Cryptococcus neoformans reveals novel proteins involved in DNA damage repair. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.17.553567. [PMID: 37645941 PMCID: PMC10462067 DOI: 10.1101/2023.08.17.553567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Elucidating gene function is a major goal in biology, especially among non-model organisms. However, doing so is complicated by the fact that molecular conservation does not always mirror functional conservation, and that complex relationships among genes are responsible for encoding pathways and higher-order biological processes. Co-expression, a promising approach for predicting gene function, relies on the general principal that genes with similar expression patterns across multiple conditions will likely be involved in the same biological process. For Cryptococcus neoformans, a prevalent human fungal pathogen greatly diverged from model yeasts, approximately 60% of the predicted genes in the genome lack functional annotations. Here, we leveraged a large amount of publicly available transcriptomic data to generate a C. neoformans Co-Expression Network (CryptoCEN), successfully recapitulating known protein networks, predicting gene function, and enabling insights into the principles influencing co-expression. With 100% predictive accuracy, we used CryptoCEN to identify 13 new DNA damage response genes, underscoring the utility of guilt-by-association for determining gene function. Overall, co-expression is a powerful tool for uncovering gene function, and decreases the experimental tests needed to identify functions for currently under-annotated genes.
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Affiliation(s)
- Matthew J. O’Meara
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Jackson R. Rapala
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
| | - Connie B. Nichols
- Departments of Medicine and Molecular Genetics/Microbiology; and Cell Biology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Christina Alexandre
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
| | - R. Blake Billmyre
- Departments of Pharmaceutical and Biomedical Sciences/Infectious Disease, College of Pharmacy/College of Veterinary Medicine, University of Georgia, Athens, Georgia, USA
| | - Jacob L Steenwyk
- Howards Hughes Medical Institute and the Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
| | - J. Andrew Alspaugh
- Departments of Medicine and Molecular Genetics/Microbiology; and Cell Biology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Teresa R. O’Meara
- Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
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Hu X, Zhang Y, Du M, Yang E. Efficient and specific DNA oligonucleotide rRNA probe-based rRNA removal in Talaromyces marneffei. Mycology 2022; 13:106-118. [PMID: 35711330 PMCID: PMC9196791 DOI: 10.1080/21501203.2021.2017045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
Emerging evidence showed that lncRNAs play important roles in a wide range of biological processes of fungi such as Saccharomyces cerevisiae. However, systemic identification of lncRNAs in non-model fungi is a challenging task as the efficiency of rRNA removal has been proved to be affected by mismatches of universal rRNA-targeting probes of commercial kits, which forces deeper sequencing depth and increases costs. Here, we developed a low-cost and simple rRNA depletion method (rProbe) that could efficiently remove more than 99% rRNA in both yeast and mycelium samples of Talaromyces marneffei. The efficiency and robustness of rProbe were demonstrated to outperform the Illumina Ribo-Zero kit. Using rProbe RNA-seq, we identified 115 differentially expressed lncRNAs and constructed lncRNA-mRNA co-expression network related to dimorphic switch of T. marneffei. Our rRNA removal method has the potential to be a useful tool to explore non-coding transcriptomes of non-model fungi by adjusting rRNA probe sequences species specifically.
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Affiliation(s)
- Xueyan Hu
- Department of Medical Bioinformatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Yun Zhang
- Department of Medical Bioinformatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Minghao Du
- Department of Microbiology & Infectious Disease Center, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Ence Yang
- Department of Medical Bioinformatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
- Department of Microbiology & Infectious Disease Center, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
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