Loss of centromere function drives karyotype evolution in closely related Malassezia species
Abstract
Genomic rearrangements associated with speciation often result in chromosome number variation among closely related species. Malassezia species show variable karyotypes ranging between 6 and 9 chromosomes. Here, we experimentally identified all 8 centromeres in M. sympodialis as 3 to 5 kb long kinetochore-bound regions spanning an AT-rich core and depleted of the canonical histone H3. Centromeres of similar sequence features were identified as CENP-A-rich regions in Malassezia furfur with 7 chromosomes, and histone H3 depleted regions in Malassezia slooffiae and Malassezia globosa with 9 chromosomes each. Analysis of synteny conservation across centromeres with newly generated chromosome-level genome assemblies suggests two distinct mechanisms of chromosome number reduction from an inferred 9-chromosome ancestral state: (a) chromosome breakage followed by loss of centromere DNA and (b) centromere inactivation accompanied by changes in DNA sequence following chromosome-chromosome fusion. We propose AT-rich centromeres drive karyotype diversity in the Malassezia species complex through breakage and inactivation.
Data availability
The Mtw1 ChIP sequencing reads reported in this paper have been deposited under NCBI BioProject (Accession number PRJNA509412). The genome sequence assemblies of M. globosa, M. slooffiae, and M. furfur have been deposited in GenBank with accession numbers SAMN10720087, SAMN10720088, and SAMN13341476 respectively.
-
Genome assembly of Malassezia slooffiaeGenBank, SAMN10720088.
-
Genome assembly of Malassezia globosaGenBank, SAMN10720087.
-
Genome assembly of Malassezia furfurGenBank, SAMN13341476.
-
Malassezia restricta CBS 7877 genome, complete sequenceNCBI BioSample, SAMN09377640.
-
Genome sequencing of Malassezia nana JCM 12085NCBI BioProject, PRJDB3735.
-
Genome sequencing of Malassezia dermatis JCM 11348NCBI BioProject, PRJDB3732.
-
Genome sequencing of Malassezia japonica JCM 11963NCBI BioProject, PRJDB3733.
Article and author information
Author details
Funding
Tata Innovation Fellowship (BT/HRT/35/01/03/2017)
- Kaustuv Sanyal
Department of Biotechnology , Ministry of Science and Technology (BT/INF/22/SP27679/2018)
- Kaustuv Sanyal
National Institutes of Health (R37 award-AI39115-21; R01 award-AI50113-15)
- Joseph Heitman
Agency for Science, Technology and Research (H18/01a0/016)
- Thomas L Dawson
Jawaharlal Nehru Centre for Advanced Scientific Research (Graduate student fellowship)
- Sundar Ram Sankaranarayanan
Science and Engineering Research Board (PDF/2016/002858)
- Md Hashim Reza
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Wolf-Dietrich Heyer, University of California, Davis, United States
Version history
- Received: November 26, 2019
- Accepted: January 20, 2020
- Accepted Manuscript published: January 20, 2020 (version 1)
- Version of Record published: February 17, 2020 (version 2)
Copyright
© 2020, Sankaranarayanan et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 3,227
- views
-
- 451
- downloads
-
- 33
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Genetics and Genomics
- Neuroscience
Genome-wide association studies have revealed >270 loci associated with schizophrenia risk, yet these genetic factors do not seem to be sufficient to fully explain the molecular determinants behind this psychiatric condition. Epigenetic marks such as post-translational histone modifications remain largely plastic during development and adulthood, allowing a dynamic impact of environmental factors, including antipsychotic medications, on access to genes and regulatory elements. However, few studies so far have profiled cell-specific genome-wide histone modifications in postmortem brain samples from schizophrenia subjects, or the effect of antipsychotic treatment on such epigenetic marks. Here, we conducted ChIP-seq analyses focusing on histone marks indicative of active enhancers (H3K27ac) and active promoters (H3K4me3), alongside RNA-seq, using frontal cortex samples from antipsychotic-free (AF) and antipsychotic-treated (AT) individuals with schizophrenia, as well as individually matched controls (n=58). Schizophrenia subjects exhibited thousands of neuronal and non-neuronal epigenetic differences at regions that included several susceptibility genetic loci, such as NRG1, DISC1, and DRD3. By analyzing the AF and AT cohorts separately, we identified schizophrenia-associated alterations in specific transcription factors, their regulatees, and epigenomic and transcriptomic features that were reversed by antipsychotic treatment; as well as those that represented a consequence of antipsychotic medication rather than a hallmark of schizophrenia in postmortem human brain samples. Notably, we also found that the effect of age on epigenomic landscapes was more pronounced in frontal cortex of AT-schizophrenics, as compared to AF-schizophrenics and controls. Together, these data provide important evidence of epigenetic alterations in the frontal cortex of individuals with schizophrenia, and remark for the first time on the impact of age and antipsychotic treatment on chromatin organization.
-
- Computational and Systems Biology
- Genetics and Genomics
We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS–trait associations with a significance of p < 5 × 10−8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway–trait associations and 153 tissue–trait associations with strong biological interpretability, including ‘circadian pathway-chronotype’ and ‘arachidonic acid-intelligence’. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1–39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.