Gene silencing in parasites: current status and future prospects

Harnessing model organism genomics to underpin the machine learning-based prediction of essential genes in eukaryotes - Biotechnological implications

The availability of high-quality genomes and advances in functional genomics have enabled large-scale studies of essential genes in model eukaryotes, including the 'elegant worm' (Caenorhabditis elegans; Nematoda) and the 'vinegar fly' (Drosophila melanogaster; Arthropoda). However, this is not the case for other, much less-studied organisms, such as socioeconomically important parasites, for which functional genomic platforms usually do not exist. Thus, there is a need to develop innovative techniques or approaches for the prediction, identification and investigation of essential genes. A key approach that could enable the prediction of such genes is machine learning (ML). Here, we undertake an historical review of experimental and computational approaches employed for the characterisation of essential genes in eukaryotes, with a particular focus on model ecdysozoans (C. elegans and D. melanogaster), and discuss the possible applicability of ML-approaches to organisms such as socioeconomically important parasites. We highlight some recent results showing that high-performance ML, combined with feature engineering, allows a reliable prediction of essential genes from extensive, publicly available 'omic data sets, with major potential to prioritise such genes (with statistical confidence) for subsequent functional genomic validation. These findings could 'open the door' to fundamental and applied research areas. Evidence of some commonality in the essential gene-complement between these two organisms indicates that an ML-engineering approach could find broader applicability to ecdysozoans such as parasitic nematodes or arthropods, provided that suitably large and informative data sets become/are available for proper feature engineering, and for the robust training and validation of algorithms. This area warrants detailed exploration to, for example, facilitate the identification and characterisation of essential molecules as novel targets for drugs and vaccines against parasitic diseases. This focus is particularly important, given the substantial impact that such diseases have worldwide, and the current challenges associated with their prevention and control and with drug resistance in parasite populations.

RNA interference in vivo in Schistosoma japonicum: Establishing and optimization of RNAi mediated suppression of gene expression by long dsRNA in the intra-mammalian life stages of worms

Schistosomes are parasitic platyhelminths that threaten over 600 million people globally. In recent years, RNA interference (RNAi) has been widely used as a molecular tool in research into the genomic function of parasites. We aim to develop effective protocols for application of RNAi technology in the intra-mammalian life stages of Schistosoma japonicum. In this work, the expression of the parasite gene encoding cathepsin B1 (SjCB1) was targeted by exposing the worms to 10 μg of long dsRNA dissolved in 0.1 ml of 0.7% NaCl injected into the tail vein of infected mice. This method was effective and specific for eliciting SjCB1 gene suppression in both male and female adult worms in vivo (>79.4% in male and >91.5% in female knockdown relative to control). In 60 cercaria infected mice, RNAi suppression of gene expression was best achieved by using 10 μg of target dsRNA for at least 4 days. The recommended procedure for interference producing long-term suppression was an injection of dsRNA on the first day of infection with booster injections administered every 4 days for up to 26 days. Long-term suppression of three published functional genes (peroxiredoxin-1, mago nashi, insulin receptor) in S. japonicum provided more information about the role of the expression of these genes in producing particular phenotypes. The protocols described here may be more convenient, economical and applicable, than currently available technology and have contributed to the observation of more phenotypes during worm development from schistosomula to adult. These approaches may promote and facilitate further studies into functional schistosome genomics.

RNAi in Arthropods: Insight into the Machinery and Applications for Understanding the Pathogen-Vector Interface

The availability of genome sequencing data in combination with knowledge of expressed genes via transcriptome and proteome data has greatly advanced our understanding of arthropod vectors of disease. Not only have we gained insight into vector biology, but also into their respective vector-pathogen interactions. By combining the strengths of postgenomic databases and reverse genetic approaches such as RNAi, the numbers of available drug and vaccine targets, as well as number of transgenes for subsequent transgenic or paratransgenic approaches, have expanded. These are now paving the way for in-field control strategies of vectors and their pathogens. Basic scientific questions, such as understanding the basic components of the vector RNAi machinery, is vital, as this allows for the transfer of basic RNAi machinery components into RNAi-deficient vectors, thereby expanding the genetic toolbox of these RNAi-deficient vectors and pathogens. In this review, we focus on the current knowledge of arthropod vector RNAi machinery and the impact of RNAi on understanding vector biology and vector-pathogen interactions for which vector genomic data is available on VectorBase.