Dr David Chyou

Research

I have a PhD degree in mathematics. My research interests include bioinformatics, biostatistics and genomics, particularly on the analyses of microbial and fungal genomes. I develop and use software to answer fundamental questions about the biology of these organisms. I also have an ongoing research interest in analysing medical big-data.

My current research projects are around the following research themes:

The CRISPR Big-data

CRISPR-Cas system in archaea and bacteria acts as a prokaryotic immune system that incorporates sections of foreign nucleic acid sequences, including phage genomes, as spacers into CRISPR arrays in hosts genomes. In an invasion event, the CRISPR-Cas system uses the spacers to target and degrade foreign nucleic acid sequences that were encountered by the host. The biology of the CRISPR-Cas system makes CRISPR arrays a natural database of invaders including viruses of pathogenic bacteria, that provides valuable information to the healthcare sector and primary industries. My recent research in this direction is around the development of an application that predicts hosts of bacteriophages based on CRISPR biology, CRISPRHost. The algorithm of CRISPRHost has contributed to a Nature publication (Rollie et al., 2020) that investigates 'CRISPR-Cas auto-immunity in bacteria'. Currently, CRISPRHost is used in an agricultural research project that aims to mitigate global warming by reducing ruminal methane production in livestock. Putative methanogen phages that invade the methane-producing microbes were identified by CRISPRHost.

I have also been designing an algorithm to predict guide-crRNA genes (i.e. tracrRNAs), which is an important member of the type-II CRISPR-Cas system (Chyou and Brown, 2019), and is also an important reagent in CRISPR-based genome-editing technologies.

New ways of terminating bacterial gene expression

In bacteria, there are two established transcription termination (TT) mechanisms, rho-dependent termination (RDT) and intrinsic termination (IT). However, experimental findings suggest that a significant proportion of transcription events terminate efficiently in vivo, but do not fit the two established TT mechanisms. This project aims to investigate the hypothetical uncharacterized 3rd-class TT mechanism, which we named it C3T. The project started with the development of a machine-learning (ML) based algorithm, to predict RDT and IT in gene-operon termini. Then, gene-operon termini with neither a RDT nor an IT prediction are putative C3T and will be studied experimentally. In late 2019, Assoc. Prof. Chris Brown, Dr Joe Wade (New York) and I secured a Marsden grant to further develop our ML algorithm for TT classification and to discover the unconventional C3T mechanisms in bacteria. The ML algorithm can now scan for RDT and IT terminators in the whole genome.

This study opens a new field of study in microbial gene-expression, and aids to the discovery of novel targets for antibiotics.

Comparative genomics in fungi

Fungi have a variety of morphologies, but roughly in 2 classes: Mushroom-like fungi have a stipe (stalk) and a pilus (cap), whereas ruffle-like fungi have a fruit body without a stalk, or with only a short stalk. Fungal morphology has evolved quite rapidly, so that mushroom-like fungi and truffle-like fungi can be in the same genus. The Cortinarius genus is a very good example. Also in late 2019, Assoc. Prof. David Orlovich (Botany), Assoc. Prof. Chris Brown, Assoc. Prof. Tina Summerfield (Botany), Dr Johnathan Plett (Sydney) and I secured a Marsden grant to compare genomes and transcriptomes of mushroom-like and truffle-like fungi statistically to identify genes that contribute to the morphological differences in these organisms, and then characterize them experimentally. We hypothesized that such morphological switches, as they have occurred with such frequency in this phylum, are governed by alterations to a common set of genetic and cellular mechanisms.

Recently (2021), this project spinned into a side project that is about the development of a computational pipeline to annotate non-coding RNAs (ncRNA) in fungal genomes. Especially in genomes of edible mushrooms, ncRNA annotations are often missing or incomplete in fungal genomes, and this project aims to address this shortcoming. A complete annotation of coding and non-coding genes aids to the development of better crops and benefits the agricultural sector.

Funding

Our projects are generously funded by NZ Royal Society Marsden Fund, University of Otago Research Grant, and the Otago Biochemistry Department.